In recent years great progress has been made in identification of structural variants (SV) in the human genome. However, the interpretation of SVs, especially located in non-coding DNA, remains challenging. One of the reasons stems in the lack of tools exclusively designed for clinical SVs evaluation acknowledging the 3D chromatin architecture. Therefore, we present TADeus2 a web server dedicated for a quick investigation of chromatin conformation changes, providing a visual framework for the interpretation of SVs affecting topologically associating domains (TADs). This tool provides a convenient visual inspection of SVs, both in a continuous genome view as well as from a rearrangement's breakpoint perspective. Additionally, TADeus2 allows the user to assess the influence of analyzed SVs within flaking coding/non-coding regions based on the Hi-C matrix. Importantly, the SVs pathogenicity is quantified and ranked using TADA, ClassifyCNV tools and sampling-based P-value. TADeus2 is publicly available at https://tadeus2.mimuw.edu.pl.

Correct identification and effective visualization of interactions in biomolecular structures facilitate understanding of their functions and molecular design. In response to the practical needs of structure-based analysis, we have created a Mapiya web server. The Mapiya integrates four main functionalities: (i) generation of contact maps - intramolecular and intermolecular-for proteins, nucleic acids, and their complexes; (ii) characterization of the interactions physicochemical nature, (iii) interactive visualization of biomolecular conformations with automatic zoom on selected contacts using Molstar and (iv) additional sequence- and structure-based analyses performed with third-party software and in-house algorithms combined into an easy-to-use interface. Thus, Mapiya offers a highly customized analysis of the molecular interactions' in various biological systems. The web server is available at: http://mapiya.lcbio.pl/.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Multi-CSAR is a web server that can efficiently and more accurately order and orient the contigs in the assembly of a target genome into larger scaffolds based on multiple reference genomes. Given a target genome and multiple reference genomes, Multi-CSAR first identifies sequence markers shared between the target genome and each reference genome, then utilizes these sequence markers to compute a scaffold for the target genome based on each single reference genome, and finally combines all the single reference-derived scaffolds into a multiple reference-derived scaffold. To run Multi-CSAR, the users need to upload a target genome to be scaffolded and one or more reference genomes in multi-FASTA format. The users can also choose to use the 'weighting scheme of reference genomes' for Multi-CSAR to automatically calculate different weights for the reference genomes and choose either 'NUCmer on nucleotides' or 'PROmer on translated amino acids' for Multi-CSAR to identify sequence markers. In the output page, Multi-CSAR displays its multiple reference-derived scaffold in two graphical representations (i.e. Circos plot and dotplot) for the users to visually validate the correctness of scaffolded contigs and in a tabular representation to further validate the scaffold in detail. Multi-CSAR is available online at http://genome.cs.nthu.edu.tw/Multi-CSAR/.

Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.

Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming-especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has been extensively filtered and is no longer only based on confirmed binders but also includes non-binding substances to reduce false positives. Using these methods, accuracy for the ATC prediction could be increased by almost 5% to 80.5% compared to the previous version, and additionally the scoring function now offers values which are easily assessable at first glance. SuperPred 3.0 is publicly available without the need for registration at: https://prediction.charite.de/index.php.

Mitochondria are subcellular organelles present in almost all eukaryotic cells, which play a central role in cellular metabolism. Different tissues, health and age conditions are characterized by a difference in mitochondrial structure and composition. The visual data mining platform mitoXplorer 1.0 was developed to explore the expression dynamics of genes associated with mitochondrial functions that could help explain these differences. It, however, lacked functions aimed at integrating mitochondria in the cellular context and thus identifying regulators that help mitochondria adapt to cellular needs. To fill this gap, we upgraded the mitoXplorer platform to version 2.0 (mitoXplorer 2.0). In this upgrade, we implemented two novel integrative functions, network analysis and transcription factor enrichment, to specifically help identify signalling or transcriptional regulators of mitochondrial processes. In addition, we implemented several other novel functions to allow the platform to go beyond simple data visualization, such as an enrichment function for mitochondrial processes, a function to explore time-series data, the possibility to compare datasets across species and an IDconverter to help facilitate data upload. We demonstrate the usefulness of these functions in three specific use cases. mitoXplorer 2.0 is freely available without login at http://mitoxplorer2.ibdm.univ-mrs.fr.

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.

Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients' phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Symptom Annotation Made Simple), we want to bring sign-based phenotyping to routine clinical care, to hospital patients as well as to outpatients. Our web-based application provides access to three widely used annotation systems: HPO, OMIM, Orphanet. Whilst data can be stored in our database, phenotypes can also be imported and exported as Global Alliance for Genomics and Health (GA4GH) Phenopackets without using the database. The web interface can easily be integrated into local databases, e.g. clinical information systems. SAMS offers users to share their data with others, empowering patients to record their own signs and symptoms (or those of their children) and thus provide their doctors with additional information. We think that our approach will lead to better characterised patients which is not only helpful for finding disease mutations but also to better understand the pathophysiology of diseases and to recruit patients for studies and clinical trials. SAMS is freely available at https://www.genecascade.org/SAMS/.

VRprofile2 is an updated pipeline that rapidly identifies diverse mobile genetic elements in bacterial genome sequences. Compared with the previous version, three major improvements were made. First, the user-friendly visualization could aid users in investigating the antibiotic resistance gene cassettes in conjunction with various mobile elements in the multiple resistance region with mosaic structure. VRprofile2 could compare the predicted mobile elements to the collected known mobile elements with similar architecture. A new mobilome indicator was proposed to give an overall estimation of the mobilome size in individual bacterial genomes. Second, the relationship between antibiotic resistance genes, mobile elements, and host strains would be efficiently examined with the aid of predicted strain's sequence typing, the incompatibility group and the transferability of plasmids. Finally, the updated back-end database, MobilomeDB2, now collected nearly a thousand active mobile elements retrieved from literature or based on prediction. The pre-computed results of the antibiotic resistance gene-carrying mobile elements of >5500 ESKAPEE genomes were also provided. We expect that VRprofile2 will provide better support for researchers interested in bacterial mobile elements and the dissemination of antibiotic resistance. VRprofile2 is freely available to all users without any login requirement at https://tool2-mml.sjtu.edu.cn/VRprofile.

Large-scale phenome-wide association studies performed using densely-phenotyped cohorts such as the UK Biobank (UKB), reveal many statistically robust gene-phenotype relationships for both clinical and continuous traits. Here, we present Gene-SCOUT, a tool used to identify genes with similar continuous trait fingerprints to a gene of interest. A fingerprint reflects the continuous traits identified to be statistically associated with a gene of interest based on multiple underlying rare variant genetic architectures. Similarities between genes are evaluated by the cosine similarity measure, to capture concordant effect directionality, elucidating clusters of genes in a high dimensional space. The underlying gene-biomarker population-scale association statistics were obtained from a gene-level rare variant collapsing analysis performed on over 1500 continuous traits using 394 692 UKB participant exomes, with additional metabolomic trait associations provided through Nightingale Health's recent study of 121 394 of these participants. We demonstrate that gene similarity estimates from Gene-SCOUT provide stronger enrichments for clinical traits compared to existing methods. Furthermore, we provide a fully interactive web-resource (http://genescout.public.cgr.astrazeneca.com) to explore the pre-calculated exome-wide similarities. This resource enables a user to examine the biological relevance of the most similar genes for Gene Ontology (GO) enrichment and UKB clinical trait enrichment statistics, as well as a detailed breakdown of the traits underpinning a given fingerprint.

Histone H3.3 is an H3 variant which differs from the canonical H3.1/2 at four residues, including a serine residue at position 31 which is evolutionarily conserved. The H3.3 S31 residue is phosphorylated (H3.3 S31Ph) at heterochromatin regions including telomeres and pericentric repeats. However, the role of H3.3 S31Ph in these regions remains unknown. In this study, we find that H3.3 S31Ph regulates heterochromatin accessibility at telomeres during replication through regulation of H3K9/K36 histone demethylase KDM4B. In mouse embryonic stem (ES) cells, substitution of S31 with an alanine residue (H3.3 A31 -phosphorylation null mutant) results in increased KDM4B activity that removes H3K9me3 from telomeres. In contrast, substitution with a glutamic acid (H3.3 E31, mimics S31 phosphorylation) inhibits KDM4B, leading to increased H3K9me3 and DNA damage at telomeres. H3.3 E31 expression also increases damage at other heterochromatin regions including the pericentric heterochromatin and Y chromosome-specific satellite DNA repeats. We propose that H3.3 S31Ph regulation of KDM4B is required to control heterochromatin accessibility of repetitive DNA and preserve chromatin integrity.

Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.

Holliday junction is the key homologous recombination intermediate, resolved by structure-selective endonucleases (SSEs). SLX1 is the most promiscuous SSE of the GIY-YIG nuclease superfamily. In fungi and animals, SLX1 nuclease activity relies on a non-enzymatic partner, SLX4, but no SLX1-SLX4 like complex has ever been characterized in plants. Plants exhibit specialized DNA repair and recombination machinery. Based on sequence similarity with the GIY-YIG nuclease domain of SLX1 proteins from fungi and animals, At-HIGLE was identified to be a possible SLX1 like nuclease from plants. Here, we elucidated the crystal structure of the At-HIGLE nuclease domain from Arabidopsis thaliana, establishing it as a member of the SLX1-lineage of the GIY-YIG superfamily with structural changes in DNA interacting regions. We show that At-HIGLE can process branched-DNA molecules without an SLX4 like protein. Unlike fungal SLX1, At-HIGLE exists as a catalytically active homodimer capable of generating two coordinated nicks during HJ resolution. Truncating the extended C-terminal region of At-HIGLE increases its catalytic activity, changes the nicking pattern, and monomerizes At-HIGLE. Overall, we elucidated the first structure of a plant SLX1-lineage protein, showed its HJ resolving activity independent of any regulatory protein, and identified an in-built novel regulatory mechanism engaging its C-terminal region.

Successful meiotic recombination, and thus fertility, depends on conserved axis proteins that organize chromosomes into arrays of anchored chromatin loops and provide a protected environment for DNA exchange. Here, we show that the stereotypic chromosomal distribution of axis proteins in Saccharomyces cerevisiae is the additive result of two independent pathways: a cohesin-dependent pathway, which was previously identified and mediates focal enrichment of axis proteins at gene ends, and a parallel cohesin-independent pathway that recruits axis proteins to broad genomic islands with high gene density. These islands exhibit elevated markers of crossover recombination as well as increased nucleosome density, which we show is a direct consequence of the underlying DNA sequence. A predicted PHD domain in the center of the axis factor Hop1 specifically mediates cohesin-independent axis recruitment. Intriguingly, other chromosome organizers, including cohesin, condensin, and topoisomerases, are differentially depleted from the same regions even in non-meiotic cells, indicating that these DNA sequence-defined chromatin islands exert a general influence on the patterning of chromosome structure.

Contact-dependent growth inhibition is a mechanism of interbacterial competition mediated by delivery of the C-terminal toxin domain of CdiA protein (CdiA-CT) into neighboring bacteria. The CdiA-CT of enterohemorrhagic Escherichia coli EC869 (CdiA-CTEC869) cleaves the 3'-acceptor regions of specific tRNAs in a reaction that requires the translation factors Tu/Ts and GTP. Here, we show that CdiA-CTEC869 has an intrinsic ability to recognize a specific sequence in substrate tRNAs, and Tu:Ts complex promotes tRNA cleavage by CdiA-CTEC869. Uncharged and aminoacylated tRNAs (aa-tRNAs) were cleaved by CdiA-CTEC869 to the same extent in the presence of Tu/Ts, and the CdiA-CTEC869:Tu:Ts:tRNA(aa-tRNA) complex formed in the presence of GTP. CdiA-CTEC869 interacts with domain II of Tu, thereby preventing the 3'-moiety of tRNA to bind to Tu as in canonical Tu:GTP:aa-tRNA complexes. Superimposition of the Tu:GTP:aa-tRNA structure onto the CdiA-CTEC869:Tu structure suggests that the 3'-portion of tRNA relocates into the CdiA-CTEC869 active site, located on the opposite side to the CdiA-CTEC869 :Tu interface, for tRNA cleavage. Thus, CdiA-CTEC869 is recruited to Tu:GTP:Ts, and CdiA-CT:Tu:GTP:Ts recognizes substrate tRNAs and cleaves them. Tu:GTP:Ts serves as a reaction scaffold that increases the affinity of CdiA-CTEC869 for substrate tRNAs and induces a structural change of tRNAs for efficient cleavage by CdiA-CTEC869.

Mammalian early epiblasts at different phases are characterized by naïve, formative, and primed pluripotency states, involving extensive transcriptome changes. Here, we report that deadenylase Cnot8 of Ccr4-Not complex plays essential roles during the transition from naïve to formative state. Knock out (KO) Cnot8 resulted in early embryonic lethality in mice, but Cnot8 KO embryonic stem cells (ESCs) could be established. Compared with the cells differentiated from normal ESCs, Cnot8 KO cells highly expressed a great many genes during their differentiation into the formative state, including several hundred naïve-like genes enriched in lipid metabolic process and gene expression regulation that may form the naïve regulation networks. Knockdown expression of the selected genes of naïve regulation networks partially rescued the differentiation defects of Cnot8 KO ESCs. Cnot8 depletion led to the deadenylation defects of its targets, increasing their poly(A) tail lengths and half-life, eventually elevating their expression levels. We further found that Cnot8 was involved in the clearance of targets through its deadenylase activity and the binding of Ccr4-Not complex, as well as the interacting with Tob1 and Pabpc1. Our results suggest that Cnot8 eliminates naïve regulation networks through mRNA clearance, and is essential for naïve-to-formative pluripotency transition.

Gene-editing technologies, including the widespread usage of CRISPR endonucleases, have the potential for clinical treatments of various human diseases. Due to the rapid mutations of SARS-CoV-2, specific and effective prevention and treatment by CRISPR toolkits for coronavirus disease 2019 (COVID-19) are urgently needed to control the current pandemic spread. Here, we designed Type III CRISPR endonuclease antivirals for coronaviruses (TEAR-CoV) as a therapeutic to combat SARS-CoV-2 infection. We provided a proof of principle demonstration that TEAR-CoV-based RNA engineering approach leads to RNA-guided transcript degradation both in vitro and in eukaryotic cells, which could be used to broadly target RNA viruses. We report that TEAR-CoV not only cleaves SARS-CoV-2 genome and mRNA transcripts, but also degrades live influenza A virus (IAV), impeding viral replication in cells and in mice. Moreover, bioinformatics screening of gRNAs along RNA sequences reveals that a group of five gRNAs (hCoV-gRNAs) could potentially target 99.98% of human coronaviruses. TEAR-CoV also exerted specific targeting and cleavage of common human coronaviruses. The fast design and broad targeting of TEAR-CoV may represent a versatile antiviral approach for SARS-CoV-2 or potentially other emerging human coronaviruses.

Erratum in Nucleic Acids Res. 2022 Mar 21;50(5):2987-2989.

Here, we introduce scMAGIC (Single Cell annotation using MArker Genes Identification and two rounds of reference-based Classification [RBC]), a novel method that uses well-annotated single-cell RNA sequencing (scRNA-seq) data as the reference to assist in the classification of query scRNA-seq data. A key innovation in scMAGIC is the introduction of a second-round RBC in which those query cells whose cell identities are confidently validated in the first round are used as a new reference to again classify query cells, therefore eliminating the batch effects between the reference and the query data. scMAGIC significantly outperforms 13 competing RBC methods with their optimal parameter settings across 86 benchmark tests, especially when the cell types in the query dataset are not completely covered by the reference dataset and when there exist significant batch effects between the reference and the query datasets. Moreover, when no reference dataset is available, scMAGIC can annotate query cells with reasonably high accuracy by using an atlas dataset as the reference.

The accurate definition of an epitranscriptome is endangered by artefacts resulting from RNA degradation after cell death, a ubiquitous yet little investigated process. By tracing RNA marker modifications through tissue preparation protocols, we identified a major blind spot from daily lab routine, that has massive impact on modification analysis in small RNAs. In particular, m6,6A and Am as co-varying rRNA marker modifications, appeared in small RNA fractions following rRNA degradation in vitro and in cellulo. Analysing mouse tissue at different time points post mortem, we tracked the progress of intracellular RNA degradation after cell death, and found it reflected in RNA modification patterns. Differences were dramatic between liver, where RNA degradation commenced immediately after death, and brain, yielding essentially undamaged RNA. RNA integrity correlated with low amounts of co-varying rRNA markers. Thus validated RNA preparations featured differentially modified tRNA populations whose information content allowed a distinction even among the related brain tissues cortex, cerebellum and hippocampus. Inversely, advanced cell death correlated with high rRNA marker content, and correspondingly little with the naïve state of living tissue. Therefore, unless RNA and tissue preparations are executed with utmost care, interpretation of modification patterns in tRNA and small RNA are prone to artefacts.

While several microRNAs (miRNAs) have been proposed to act as tumor suppressors, a consensual definition of tumor suppressing miRNAs is still missing. Similarly to coding genes, we propose that tumor suppressor miRNAs must show evidence of genetic or epigenetic inactivation in cancers, and exhibit an anti-tumorigenic (e.g., anti-proliferative) activity under endogenous expression levels. Here we observe that this definition excludes the most extensively studied tumor suppressor candidate miRNA, miR-34a. In analyzable cancer types, miR-34a does not appear to be down-regulated in primary tumors relatively to normal adjacent tissues. Deletion of miR-34a is occasionally found in human cancers, but it does not seem to be driven by an anti-tumorigenic activity of the miRNA, since it is not observed upon smaller, miR-34a-specific alterations. Its anti-proliferative action was observed upon large, supra-physiological transfection of synthetic miR-34a in cultured cells, and our data indicates that endogenous miR-34a levels do not have such an effect. Our results therefore argue against a general tumor suppressive function for miR-34a, providing an explanation to the lack of efficiency of synthetic miR-34a administration against solid tumors.

Site-specific incorporation of distinct non-canonical amino acids into proteins via genetic code expansion requires mutually orthogonal aminoacyl-tRNA synthetase/tRNA pairs. Pyrrolysyl-tRNA synthetase (PylRS)/tRNAPyl pairs are ideal for genetic code expansion and have been extensively engineered for developing mutually orthogonal pairs. Here, we identify two novel wild-type PylRS/tRNAPyl pairs simultaneously present in the deep-rooted extremely halophilic euryarchaeal methanogen Candidatus Methanohalarchaeum thermophilum HMET1, and show that both pairs are functional in the model halophilic archaeon Haloferax volcanii. These pairs consist of two different PylRS enzymes and two distinct tRNAs with dissimilar discriminator bases. Surprisingly, these two PylRS/tRNAPyl pairs display mutual orthogonality enabled by two unique features, the A73 discriminator base of tRNAPyl2 and a shorter motif 2 loop in PylRS2. In vivo translation experiments show that tRNAPyl2 charging by PylRS2 is defined by the enzyme's shortened motif 2 loop. Finally, we demonstrate that the two HMET1 PylRS/tRNAPyl pairs can simultaneously decode UAG and UAA codons for incorporation of two distinct noncanonical amino acids into protein. This example of a single base change in a tRNA leading to additional coding capacity suggests that the growth of the genetic code is not yet limited by the number of identity elements fitting into the tRNA structure.

What makes a mouse a mouse, and not a hamster? Differences in gene regulation between the two organisms play a critical role. Comparative analysis of gene coexpression networks provides a general framework for investigating the evolution of gene regulation across species. Here, we compare coexpression networks from 37 species and quantify the conservation of gene activity 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. We find that ancient genes are expressed in multiple cell types and have well conserved coexpression patterns, however they are expressed at different levels across cell types. Thus, differential regulation of ancient gene programs contributes to transcriptional cell identity. We propose that this differential regulation may play a role in cell diversification in both the animal and plant kingdoms.

Regulatory RNA molecules have been widely investigated as components for synthetic gene circuits, complementing the use of protein-based transcription factors. Among the potential advantages of RNA-based gene regulators are their comparatively simple design, sequence-programmability, orthogonality, and their relatively low metabolic burden. In this work, we developed a set of riboswitch-inspired riboregulators in Escherichia coli that combine the concept of toehold-mediated strand displacement (TMSD) with the switching principles of naturally occurring transcriptional and translational riboswitches. Specifically, for translational activation and repression, we sequestered anti-anti-RBS or anti-RBS sequences, respectively, inside the loop of a stable hairpin domain, which is equipped with a single-stranded toehold region at its 5' end and is followed by regulated sequences on its 3' side. A trigger RNA binding to the toehold region can invade the hairpin, inducing a structural rearrangement that results in translational activation or deactivation. We also demonstrate that TMSD can be applied in the context of transcriptional regulation by switching RNA secondary structure involved in Rho-dependent termination. Our designs expand the repertoire of available synthetic riboregulators by a set of RNA switches with no sequence limitation, which should prove useful for the development of robust genetic sensors and circuits.

It is important to control CRISPR/Cas9 when sufficient editing is obtained. In the current study, rational engineering of guide RNAs (gRNAs) is performed to develop small-molecule-responsive CRISPR/Cas9. For our purpose, the sequence of gRNAs are modified to introduce ligand binding sites based on the rational design of ligand-RNA pairs. Using short target sequences, we demonstrate that the engineered RNA provides an excellent scaffold for binding small molecule ligands. Although the 'stem-loop 1' variants of gRNA induced variable cleavage activity for different target sequences, all 'stem-loop 3' variants are well tolerated for CRISPR/Cas9. We further demonstrate that this specific ligand-RNA interaction can be utilized for functional control of CRISPR/Cas9 in vitro and in human cells. Moreover, chemogenetic control of gene editing in human cells transfected with all-in-one plasmids encoding Cas9 and designer gRNAs is demonstrated. The strategy may become a general approach for generating switchable RNA or DNA for controlling other biological processes.

The poly(A)-tail appended to the 3'-end of most eukaryotic transcripts plays a key role in their stability, nuclear transport, and translation. These roles are largely mediated by Poly(A) Binding Proteins (PABPs) that coat poly(A)-tails and interact with various proteins involved in the biogenesis and function of RNA. While it is well-established that the nuclear PABP (PABPN) binds newly synthesized poly(A)-tails and is replaced by the cytoplasmic PABP (PABPC) on transcripts exported to the cytoplasm, the distribution of transcripts for different genes or isoforms of the same gene on these PABPs has not been investigated on a genome-wide scale. Here, we analyzed the identity, splicing status, poly(A)-tail size, and translation status of RNAs co-immunoprecipitated with endogenous PABPN or PABPC in human cells. At steady state, many protein-coding and non-coding RNAs exhibit strong bias for association with PABPN or PABPC. While PABPN-enriched transcripts more often were incompletely spliced and harbored longer poly(A)-tails and PABPC-enriched RNAs had longer half-lives and higher translation efficiency, there are curious outliers. Overall, our study reveals the landscape of RNAs bound by PABPN and PABPC, providing new details that support and advance the current understanding of the roles these proteins play in poly(A)-tail synthesis, maintenance, and function.

SUMOylation is critical for numerous cellular signalling pathways, including the maintenance of genome integrity via the repair of DNA double-strand breaks (DSBs). If misrepaired, DSBs can lead to cancer, neurodegeneration, immunodeficiency and premature ageing. Using systematic human proteome microarray screening combined with widely applicable carbene footprinting, genetic code expansion and high-resolution structural profiling, we define two non-conventional and topology-selective SUMO2-binding regions on XRCC4, a DNA repair protein important for DSB repair by non-homologous end-joining (NHEJ). Mechanistically, the interaction of SUMO2 and XRCC4 is incompatible with XRCC4 binding to three other proteins important for NHEJ-mediated DSB repair. These findings are consistent with SUMO2 forming a redundant NHEJ layer with the potential to regulate different NHEJ complexes at distinct levels including, but not limited to, XRCC4 interactions with XLF, LIG4 and IFFO1. Regulation of NHEJ is not only relevant for carcinogenesis, but also for the design of precision anti-cancer medicines and the optimisation of CRISPR/Cas9-based gene editing. In addition to providing molecular insights into NHEJ, this work uncovers a conserved SUMO-binding module and provides a rich resource on direct SUMO binders exploitable towards uncovering SUMOylation pathways in a wide array of cellular processes.

Ligand-Induced duplex-quadruplex transition within the c-MYC promoter region is one of the most studied and advanced ideas for c-MYC regulation. Despite its importance, there is a lack of methods for monitoring such process in cells, hindering a better understanding of the essence of c-MYC G-quadruplex as a drug target. Here we developed a new fluorescent probe ISCH-MYC for specific c-MYC G-quadruplex recognition based on GTFH (G-quadruplex-Triggered Fluorogenic Hybridization) strategy. We validated that ISCH-MYC displayed distinct fluorescence enhancement upon binding to c-MYC G-quadruplex, which allowed the duplex-quadruplex transition detection of c-MYC G-rich DNA in cells. Using ISCH-MYC, we successfully characterized the induction of duplex to G-quadruplex transition in the presence of G-quadruplex stabilizing ligand PDS and further monitored and evaluated the altered interactions of relevant transcription factors Sp1 and CNBP with c-MYC G-rich DNA. Thus, our study provides a visualization strategy to explore the mechanism of G-quadruplex stabilizing ligand action on c-MYC G-rich DNA and relevant proteins, thereby empowering future drug discovery efforts targeting G-quadruplexes.

Mediator activates RNA polymerase II (Pol II) function during transcription, but it remains unclear whether Mediator is able to travel with Pol II and regulate Pol II transcription beyond the initiation and early elongation steps. By using in vitro and in vivo transcription recycling assays, we find that human Mediator 1 (MED1), when phosphorylated at the mammal-specific threonine 1032 by cyclin-dependent kinase 9 (CDK9), dynamically moves along with Pol II throughout the transcribed genes to drive Pol II recycling after the initial round of transcription. Mechanistically, MED31 mediates the recycling of phosphorylated MED1 and Pol II, enhancing mRNA output during the transcription recycling process. Importantly, MED1 phosphorylation increases during prostate cancer progression to the lethal phase, and pharmacological inhibition of CDK9 decreases prostate tumor growth by decreasing MED1 phosphorylation and Pol II recycling. Our results reveal a novel role of MED1 in Pol II transcription and identify phosphorylated MED1 as a targetable driver of dysregulated Pol II recycling in cancer.

A key role of chromatin kinases is to phosphorylate histone tails during mitosis to spatiotemporally regulate cell division. Vaccinia-related kinase 1 (VRK1) is a serine-threonine kinase that phosphorylates histone H3 threonine 3 (H3T3) along with other chromatin-based targets. While structural studies have defined how several classes of histone-modifying enzymes bind to and function on nucleosomes, the mechanism of chromatin engagement by kinases is largely unclear. Here, we paired cryo-electron microscopy with biochemical and cellular assays to demonstrate that VRK1 interacts with both linker DNA and the nucleosome acidic patch to phosphorylate H3T3. Acidic patch binding by VRK1 is mediated by an arginine-rich flexible C-terminal tail. Homozygous missense and nonsense mutations of this acidic patch recognition motif in VRK1 are causative in rare adult-onset distal spinal muscular atrophy. We show that these VRK1 mutations interfere with nucleosome acidic patch binding, leading to mislocalization of VRK1 during mitosis, thus providing a potential new molecular mechanism for pathogenesis.

The steady state levels of RNAs, often referred to as expression levels, result from a well-balanced combination of RNA transcription and decay. Alterations in RNA levels will therefore result from tight regulation of transcription rates, decay rates or both. Here, we explore the role of RNA stability in achieving balanced gene expression and present genome-wide RNA stabilities in Drosophila melanogaster male and female cells as well as male cells depleted of proteins essential for dosage compensation. We identify two distinct RNA-stability mediated responses involved in regulation of gene expression. The first of these responds to acute and global changes in transcription and thus counteracts potentially harmful gene mis-expression by shifting the RNA stability in the direction opposite to the transcriptional change. The second response enhances inter-individual differential gene expression by adjusting the RNA stability in the same direction as a transcriptional change. Both mechanisms are global, act on housekeeping as well as non-housekeeping genes and were observed in both flies and mammals. Additionally, we show that, in contrast to mammals, modulation of RNA stability does not detectably contribute to dosage compensation of the sex-chromosomes in D. melanogaster.

Many prokaryotes encode CRISPR-Cas systems as immune protection against mobile genetic elements (MGEs), yet a number of MGEs also harbor CRISPR-Cas components. With a few exceptions, CRISPR-Cas loci encoded on MGEs are uncharted and a comprehensive analysis of their distribution, prevalence, diversity, and function is lacking. Here, we systematically investigated CRISPR-Cas loci across the largest curated collection of natural bacterial and archaeal plasmids. CRISPR-Cas loci are widely but heterogeneously distributed across plasmids and, in comparison to host chromosomes, their mean prevalence per Mbp is higher and their distribution is distinct. Furthermore, the spacer content of plasmid CRISPRs exhibits a strong targeting bias towards other plasmids, while chromosomal arrays are enriched with virus-targeting spacers. These contrasting targeting preferences highlight the genetic independence of plasmids and suggest a major role for mediating plasmid-plasmid conflicts. Altogether, CRISPR-Cas are frequent accessory components of many plasmids, which is an overlooked phenomenon that possibly facilitates their dissemination across microbiomes.

Imbalance in the finely orchestrated system of chromatin-modifying enzymes is a hallmark of many pathologies such as cancers, since causing the affection of the epigenome and transcriptional reprogramming. Here, we demonstrate that a loss-of-function mutation (LOF) of the major histone lysine methyltransferase SETDB1 possessing oncogenic activity in lung cancer cells leads to broad changes in the overall architecture and mechanical properties of the nucleus through genome-wide redistribution of heterochromatin, which perturbs chromatin spatial compartmentalization. Together with the enforced activation of the epithelial expression program, cytoskeleton remodeling, reduced proliferation rate and restricted cellular migration, this leads to the reversed oncogenic potential of lung adenocarcinoma cells. These results emphasize an essential role of chromatin architecture in the determination of oncogenic programs and illustrate a relationship between gene expression, epigenome, 3D genome and nuclear mechanics.

RNA structure and function are intimately tied to RNA binding protein recognition and regulation. Posttranslational modifications are chemical modifications which can control protein biology. The role of PTMs in the regulation RBPs is not well understood, in part due to a lacking analysis of PTM deposition on RBPs. Herein, we present an analysis of posttranslational modifications (PTMs) on RNA binding proteins (RBPs; a PTM RBP Atlas). We curate published datasets and primary literature to understand the landscape of PTMs and use protein-protein interaction data to understand and potentially provide a framework for understanding which enzymes are controlling PTM deposition and removal on the RBP landscape. Intersection of our data with The Cancer Genome Atlas also provides researchers understanding of mutations that would alter PTM deposition. Additional characterization of the RNA-protein interface provided from in-cell UV crosslinking experiments provides a framework for hypotheses about which PTMs could be regulating RNA binding and thus RBP function. Finally, we provide an online database for our data that is easy to use for the community. It is our hope our efforts will provide researchers will an invaluable tool to test the function of PTMs controlling RBP function and thus RNA biology.

Lineage-determining transcription factors (LD-TFs) drive the differentiation of progenitor cells into a specific lineage. In CD4+ T cells, T-bet dictates differentiation of the TH1 lineage, whereas GATA3 drives differentiation of the alternative TH2 lineage. However, LD-TFs, including T-bet and GATA3, are frequently co-expressed but how this affects LD-TF function is not known. By expressing T-bet and GATA3 separately or together in mouse T cells, we show that T-bet sequesters GATA3 at its target sites, thereby removing GATA3 from TH2 genes. This redistribution of GATA3 is independent of GATA3 DNA binding activity and is instead mediated by the T-bet DNA binding domain, which interacts with the GATA3 DNA binding domain and changes GATA3's sequence binding preference. This mechanism allows T-bet to drive the TH1 gene expression program in the presence of GATA3. We propose that redistribution of one LD-TF by another may be a common mechanism that could explain how specific cell fate choices can be made even in the presence of other transcription factors driving alternative differentiation pathways.

DNA supercoiling is a key regulator of all DNA metabolic processes including replication, transcription, and recombination, yet a reliable genomic assay for supercoiling is lacking. Here, we present a robust and flexible method (Psora-seq) to measure whole-genome supercoiling at high resolution. Using this tool in Escherichia coli, we observe a supercoiling landscape that is well correlated to transcription. Supercoiling twin-domains generated by RNA polymerase complexes span 25 kb in each direction - an order of magnitude farther than previous measurements in any organism. Thus, ribosomal and many other highly expressed genes strongly affect the topology of about 40 neighboring genes each, creating highly integrated gene circuits. Genomic patterns of supercoiling revealed by Psora-seq could be aptly predicted from modeling based on gene expression levels alone, indicating that transcription is the major determinant of chromosome supercoiling. Large-scale supercoiling patterns were highly symmetrical between left and right chromosome arms (replichores), indicating that DNA replication also strongly influences supercoiling. Skew in the axis of symmetry from the natural ori-ter axis supports previous indications that the rightward replication fork is delayed several minutes after initiation. Implications of supercoiling on DNA replication and chromosome domain structure are discussed.

RNA methyltransferases (MTases) are ubiquitous enzymes whose hitherto low profile in medicinal chemistry, contrasts with the surging interest in RNA methylation, the arguably most important aspect of the new field of epitranscriptomics. As MTases become validated as drug targets in all major fields of biomedicine, the development of small molecule compounds as tools and inhibitors is picking up considerable momentum, in academia as well as in biotech. Here we discuss the development of small molecules for two related aspects of chemical biology. Firstly, derivates of the ubiquitous cofactor S-adenosyl-l-methionine (SAM) are being developed as bioconjugation tools for targeted transfer of functional groups and labels to increasingly visible targets. Secondly, SAM-derived compounds are being investigated for their ability to act as inhibitors of RNA MTases. Drug development is moving from derivatives of cosubstrates towards higher generation compounds that may address allosteric sites in addition to the catalytic centre. Progress in assay development and screening techniques from medicinal chemistry have led to recent breakthroughs, e.g. in addressing human enzymes targeted for their role in cancer. Spurred by the current pandemic, new inhibitors against coronaviral MTases have emerged at a spectacular rate, including a repurposed drug which is now in clinical trial.

Protein function often requires remodeling of protein structure. In the well-studied iteron-containing plasmids, the initiator of replication has a dimerization interface that undergoes chaperone-mediated remodeling. This remodeling reduces dimerization and promotes DNA replication, since only monomers bind origin DNA. A structurally homologs interface exists in RctB, the replication initiator of Vibrio cholerae chromosome 2 (Chr2). Chaperones also promote Chr2 replication, although both monomers and dimers of RctB bind to origin, and chaperones increase the binding of both. Here we report how five changes in the dimerization interface of RctB affect the protein. The mutants are variously defective in dimerization, more active as initiator, and except in one case, unresponsive to chaperone (DnaJ). The results indicate that chaperones also reduce RctB dimerization and support the proposal that the paradoxical chaperone-promoted dimer binding likely represents sequential binding of monomers on DNA. RctB is also activated for replication initiation upon binding to a DNA site, crtS, and three of the mutants are also unresponsive to crtS. This suggests that crtS, like chaperones, reduces dimerization, but additional evidence suggests that the remodelling activities function independently. Involvement of two remodelers in reducing dimerization signifies the importance of dimerization in limiting Chr2 replication.

Monocot DICER-LIKE3 (DCL3) and DCL5 produce distinct 24-nt small interfering RNAs (siRNAs), heterochromatic siRNAs (hc-siRNAs) and phased secondary siRNAs (phasiRNAs), respectively. The former small RNAs are linked to silencing of transposable elements and heterochromatic repeats, and the latter to reproductive processes. It is assumed that these DCLs evolved from an ancient 'eudicot-type' DCL3 ancestor, which may have produced both types of siRNAs. However, how functional differentiation was achieved after gene duplication remains elusive. Here, we find that monocot DCL3 and DCL5 exhibit biochemically distinct preferences for 5' phosphates and 3' overhangs, consistent with the structural properties of their in vivo double-stranded RNA substrates. Importantly, these distinct substrate specificities are determined by the PAZ domains of DCL3 and DCL5, which have accumulated mutations during the course of evolution. These data explain the mechanism by which these DCLs cleave their cognate substrates from a fixed end, ensuring the production of functional siRNAs. Our study also indicates how plants have diversified and optimized RNA silencing mechanisms during evolution.

The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose-response study designs used in safety assessments. To benchmark DGEA methods for dose-response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Our Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose-response study designs. Collectively, our benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.

Methionyl-tRNA synthetase (MetRS) charges tRNAMet with l-methionine (L-Met) to decode the ATG codon for protein translation, making it indispensable for all cellular lives. Many gram-positive bacteria use a type 1 MetRS (MetRS1), which is considered a promising antimicrobial drug target due to its low sequence identity with human cytosolic MetRS (HcMetRS, which belongs to MetRS2). Here, we report crystal structures of a representative MetRS1 from Staphylococcus aureus (SaMetRS) in its apo and substrate-binding forms. The connecting peptide (CP) domain of SaMetRS differs from HcMetRS in structural organization and dynamic movement. We screened 1049 chemical fragments against SaMetRS preincubated with or without substrate ATP, and ten hits were identified. Four cocrystal structures revealed that the fragments bound to either the L-Met binding site or an auxiliary pocket near the tRNA CCA end binding site of SaMetRS. Interestingly, fragment binding was enhanced by ATP in most cases, suggesting a potential ATP-assisted ligand binding mechanism in MetRS1. Moreover, co-binding with ATP was also observed in our cocrystal structure of SaMetRS with a class of newly reported inhibitors that simultaneously occupied the auxiliary pocket, tRNA site and L-Met site. Our findings will inspire the development of new MetRS1 inhibitors for fighting microbial infections.

How type 2 Topoisomerase (TopoII) proteins relax and simplify the topology of DNA molecules is one of the most intriguing open questions in genome and DNA biophysics. Most of the existing models neglect the dynamics of TopoII which is expected of proteins searching their targets via facilitated diffusion. Here, we show that dynamic binding of TopoII speeds up the topological relaxation of knotted substrates by enhancing the search of the knotted arc. Intriguingly, this in turn implies that the timescale of topological relaxation is virtually independent of the substrate length. We then discover that considering binding biases due to facilitated diffusion on looped substrates steers the sampling of the topological space closer to the boundaries between different topoisomers yielding an optimally fast topological relaxation. We discuss our findings in the context of topological simplification in vitro and in vivo.

Eukaryotic genomes are pervasively transcribed by RNA polymerase II (RNAPII), and transcription of long non-coding RNAs often overlaps with coding gene promoters. This might lead to coding gene repression in a process named Transcription Interference (TI). In Saccharomyces cerevisiae, TI is mainly driven by antisense non-coding transcription and occurs through re-shaping of promoter Nucleosome-Depleted Regions (NDRs). In this study, we developed a genetic screen to identify new players involved in Antisense-Mediated Transcription Interference (AMTI). Among the candidates, we found the HIR histone chaperone complex known to be involved in de novo histone deposition. Using genome-wide approaches, we reveal that HIR-dependent histone deposition represses the promoters of SAGA-dependent genes via antisense non-coding transcription. However, while antisense transcription is enriched at promoters of SAGA-dependent genes, this feature is not sufficient to define the mode of gene regulation. We further show that the balance between HIR-dependent nucleosome incorporation and transcription factor binding at promoters directs transcription into a SAGA- or TFIID-dependent regulation. This study sheds light on a new connection between antisense non-coding transcription and the nature of coding transcription initiation.

The transport of mRNAs to distal subcellular compartments is an important component of spatial gene expression control in neurons. However, the mechanisms that control mRNA localization in neurons are not completely understood. Here, we identify the abundant base modification, m6A, as a novel regulator of this process. Transcriptome-wide analysis following genetic loss of m6A reveals hundreds of transcripts that exhibit altered subcellular localization in hippocampal neurons. Additionally, using a reporter system, we show that mutation of specific m6A sites in select neuronal transcripts diminishes their localization to neurites. Single molecule fluorescent in situ hybridization experiments further confirm our findings and identify the m6A reader proteins YTHDF2 and YTHDF3 as mediators of this effect. Our findings reveal a novel function for m6A in controlling mRNA localization in neurons and enable a better understanding of the mechanisms through which m6A influences gene expression in the brain.

Vibrio cholerae biofilm formation/maintenance is controlled by myriad factors; chief among these are the regulator VpsR and cyclic di-guanosine monophosphate (c-di-GMP). VpsR has strong sequence similarity to enhancer binding proteins (EBPs) that activate RNA polymerase containing sigma factor σ54. However, we have previously shown that transcription from promoters within the biofilm biogenesis/maintenance pathways uses VpsR, c-di-GMP and RNA polymerase containing the primary sigma factor (σ70). Previous work suggested that phosphorylation of VpsR at a highly conserved aspartate, which is phosphorylated in other EBPs, might also contribute to activation. Using the biofilm biogenesis promoter PvpsL, we show that in the presence of c-di-GMP, either wild type or the phospho-mimic VpsR D59E activates PvpsL transcription, while the phospho-defective D59A variant does not. Furthermore, when c-di-GMP levels are low, acetyl phosphate (Ac∼P) is required for significant VpsR activity in vivo and in vitro. Although these findings argue that VpsR phosphorylation is needed for activation, we show that VpsR is not phosphorylated or acetylated by Ac∼P and either sodium phosphate or potassium phosphate, which are not phosphate donors, fully substitutes for Ac∼P. We conclude that VpsR is an unusual regulator that senses phosphate directly, rather than through phosphorylation, to aid in the decision to form/maintain biofilm.

DNA ligases, critical enzymes for in vivo genome maintenance and modern molecular biology, catalyze the joining of adjacent 3'-OH and 5'-phosphorylated ends in DNA. To determine whether DNA annealing equilibria or properties intrinsic to the DNA ligase enzyme impact end-joining ligation outcomes, we used a highly multiplexed, sequencing-based assay to profile mismatch discrimination and sequence bias for several ligases capable of efficient end-joining. Our data reveal a spectrum of fidelity and bias, influenced by both the strength of overhang annealing as well as sequence preferences and mismatch tolerances that vary both in degree and kind between ligases. For example, while T7 DNA ligase shows a strong preference for ligating high GC sequences, other ligases show little GC-dependent bias, with human DNA Ligase 3 showing almost none. Similarly, mismatch tolerance varies widely among ligases, and while all ligases tested were most permissive of G:T mismatches, some ligases also tolerated bulkier purine:purine mismatches. These comprehensive fidelity and bias profiles provide insight into the biology of end-joining reactions and highlight the importance of ligase choice in application design.

We have identified seven putative guanine quadruplexes (G4) in the RNA genome of tick-borne encephalitis virus (TBEV), a flavivirus causing thousands of human infections and numerous deaths every year. The formation of G4s was confirmed by biophysical methods on synthetic oligonucleotides derived from the predicted TBEV sequences. TBEV-5, located at the NS4b/NS5 boundary and conserved among all known flaviviruses, was tested along with its mutated variants for interactions with a panel of known G4 ligands, for the ability to affect RNA synthesis by the flaviviral RNA-dependent RNA polymerase (RdRp) and for effects on TBEV replication fitness in cells. G4-stabilizing TBEV-5 mutations strongly inhibited RdRp RNA synthesis and exhibited substantially reduced replication fitness, different plaque morphology and increased sensitivity to G4-binding ligands in cell-based systems. In contrast, strongly destabilizing TBEV-5 G4 mutations caused rapid reversion to the wild-type genotype. Our results suggest that there is a threshold of stability for G4 sequences in the TBEV genome, with any deviation resulting in either dramatic changes in viral phenotype or a rapid return to this optimal level of G4 stability. The data indicate that G4s are critical elements for efficient TBEV replication and are suitable targets to tackle TBEV infection.

Prokaryotic Argonautes (pAgos) use small nucleic acids as specificity guides to cleave single-stranded DNA at complementary sequences. DNA targeting function of pAgos creates attractive opportunities for DNA manipulations that require programmable DNA cleavage. Currently, the use of mesophilic pAgos as programmable endonucleases is hampered by their limited action on double-stranded DNA (dsDNA). We demonstrate here that efficient cleavage of linear dsDNA by mesophilic Argonaute CbAgo from Clostridium butyricum can be activated in vitro via the DNA strand unwinding activity of nuclease deficient mutant of RecBC DNA helicase from Escherichia coli (referred to as RecBexo-C). Properties of CbAgo and characteristics of simultaneous cleavage of DNA strands in concurrence with DNA strand unwinding by RecBexo-C were thoroughly explored using 0.03-25 kb dsDNAs. When combined with RecBexo-C, CbAgo could cleave targets located 11-12.5 kb from the ends of linear dsDNA at 37°C. Our study demonstrates that CbAgo with RecBexo-C can be programmed to generate DNA fragments with custom-designed single-stranded overhangs suitable for ligation with compatible DNA fragments. The combination of CbAgo and RecBexo-C represents the most efficient mesophilic DNA-guided DNA-cleaving programmable endonuclease for in vitro use in diagnostic and synthetic biology methods that require sequence-specific nicking/cleavage of linear dsDNA at any desired location.

The link between genomic structure and biological function is yet to be consolidated, it is, however, clear that physical manipulation of the genome, driven by the activity of a variety of proteins, is a crucial step. To understand the consequences of the physical forces underlying genome organization, we build a coarse-grained polymer model of the genome, featuring three fundamentally distinct classes of interactions: lengthwise compaction, i.e., compaction of chromosomes along its contour, self-adhesion among epigenetically similar genomic segments, and adhesion of chromosome segments to the nuclear envelope or lamina. We postulate that these three types of interactions sufficiently represent the concerted action of the different proteins organizing the genome architecture and show that an interplay among these interactions can recapitulate the architectural variants observed across the tree of life. The model elucidates how an interplay of forces arising from the three classes of genomic interactions can drive drastic, yet predictable, changes in the global genome architecture, and makes testable predictions. We posit that precise control over these interactions in vivo is key to the regulation of genome architecture.

Omics-based biomedical learning frequently relies on data of high-dimensions (up to thousands) and low-sample sizes (dozens to hundreds), which challenges efficient deep learning (DL) algorithms, particularly for low-sample omics investigations. Here, an unsupervised novel feature aggregation tool AggMap was developed to Aggregate and Map omics features into multi-channel 2D spatial-correlated image-like feature maps (Fmaps) based on their intrinsic correlations. AggMap exhibits strong feature reconstruction capabilities on a randomized benchmark dataset, outperforming existing methods. With AggMap multi-channel Fmaps as inputs, newly-developed multi-channel DL AggMapNet models outperformed the state-of-the-art machine learning models on 18 low-sample omics benchmark tasks. AggMapNet exhibited better robustness in learning noisy data and disease classification. The AggMapNet explainable module Simply-explainer identified key metabolites and proteins for COVID-19 detections and severity predictions. The unsupervised AggMap algorithm of good feature restructuring abilities combined with supervised explainable AggMapNet architecture establish a pipeline for enhanced learning and interpretability of low-sample omics data.

Increasing evidence shows that promoters and enhancers could be related to 3D chromatin structure, thus affecting cellular functions. Except for their roles in forming canonical chromatin loops, promoters and enhancers have not been well studied regarding the maintenance of broad chromatin organization. Here, we focused on the active promoters/enhancers predicted to form many 3D contacts with other active promoters/enhancers (referred to as hotspots) and identified dozens of loci essential for cell growth and survival through CRISPR screening. We found that the deletion of an essential hotspot could lead to changes in broad chromatin organization and the expression of distal genes. We showed that the essentiality of hotspots does not result from their association with individual genes that are essential for cell viability but rather from their association with multiple dysregulated non-essential genes to synergistically impact cell fitness.

Many pathological processes are driven by RNA-protein interactions, making such interactions promising targets for molecular interventions. HIV-1 assembly is one such process, in which the viral genomic RNA interacts with the viral Gag protein and serves as a scaffold to drive Gag multimerization that ultimately leads to formation of a virus particle. Here, we develop self-assembled RNA nanostructures that can inhibit HIV-1 virus assembly, achieved through hybridization of multiple artificial small RNAs with a stem-loop structure (STL) that we identify as a prominent ligand of Gag that can inhibit virus particle production via STL-Gag interactions. The resulting STL-decorated nanostructures (double and triple stem-loop structures denoted as Dumbbell and Tribell, respectively) can elicit more pronounced viral blockade than their building blocks, with the inhibition arising as a result of nanostructures interfering with Gag multimerization. These findings could open up new avenues for RNA-based therapy.

Recent advances in the field of high throughput (meta-)transcriptomics and proteomics call for easy and rapid methods enabling to explore not only single genes or proteins but also extended biological systems. Gene set enrichment analysis is commonly used to find relations in a set of genes and helps to uncover the biological meaning in results derived from high-throughput data. The basis for gene set enrichment analysis is a solid functional classification of genes. Here, we describe a comprehensive database containing multiple functional classifications of genes of all (>55 000) publicly available complete bacterial genomes. In addition to the most common functional classes such as COG and GO, also KEGG, InterPro, PFAM, eggnog and operon classes are supported. As classification data for features is often not available, we offer fast annotation and classification of proteins in any newly sequenced bacterial genome. The web server FUNAGE-Pro enables fast functional analysis on single gene sets, multiple experiments, time series data, clusters, and gene network modules for any prokaryote species or strain. FUNAGE-Pro is freely available at http://funagepro.molgenrug.nl.

The Annotation Query (AnnoQ) (http://annoq.org/) is designed to provide comprehensive and up-to-date functional annotations for human genetic variants. The system is supported by an annotation database with ∼39 million human variants from the Haplotype Reference Consortium (HRC) pre-annotated with sequence feature annotations by WGSA and functional annotations to Gene Ontology (GO) and pathways in PANTHER. The database operates on an optimized Elasticsearch framework to support real-time complex searches. This implementation enables users to annotate data with the most up-to-date functional annotations via simple queries instead of setting up individual tools. A web interface allows users to interactively browse the annotations, annotate variants and search variant data. Its easy-to-use interface and search capabilities are well-suited for scientists with fewer bioinformatics skills such as bench scientists and statisticians. AnnoQ also has an API for users to access and annotate the data programmatically. Packages for programming languages, such as the R package, are available for users to embed the annotation queries in their scripts. AnnoQ serves researchers with a wide range of backgrounds and research interests as an integrated annotation platform.

Proteins in food and personal care products can pose a risk for an immediate immunoglobulin E (IgE)-mediated allergic response. Bioinformatic tools can assist to predict and investigate the allergenic potential of proteins. Here we present AllerCatPro 2.0, a web server that can be used to predict protein allergenicity potential with better accuracy than other computational methods and new features that help assessors making informed decisions. AllerCatPro 2.0 predicts the similarity between input proteins using both their amino acid sequences and predicted 3D structures towards the most comprehensive datasets of reliable proteins associated with allergenicity. These datasets currently include 4979 protein allergens, 162 low allergenic proteins, and 165 autoimmune allergens with manual expert curation from the databases of WHO/International Union of Immunological Societies (IUIS), Comprehensive Protein Allergen Resource (COMPARE), Food Allergy Research and Resource Program (FARRP), UniProtKB and Allergome. Various examples of profilins, autoimmune allergens, low allergenic proteins, very large proteins, and nucleotide input sequences showcase the utility of AllerCatPro 2.0 for predicting protein allergenicity potential. The AllerCatPro 2.0 web server is freely accessible at https://allercatpro.bii.a-star.edu.sg.

Genome engineering-induced cleavage sites can be resolved by non-homologous end joining (NHEJ) or homology-directed repair (HDR). Identifying genetically modified clones at the target locus remains an intensive and laborious task. Different workflows and software that rely on deep sequencing data have been developed to detect and quantify targeted mutagenesis. Nevertheless, these pipelines require high-quality reads generated by Next Generation Sequencing (NGS) platforms. Here, we have developed a robust, versatile, and easy-to-use computational webserver named CRISPRnano (www.CRISPRnano.de) that enables the analysis of low-quality reads generated by affordable and portable sequencers including Oxford Nanopore Technologies (ONT) devices. CRISPRnano allows fast and accurate identification, quantification, and visualization of genetically modified cell lines, it is compatible with NGS and ONT sequencing reads, and it can be used without an internet connection.

Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.

Most complex disease-associated loci mapped by genome-wide association studies (GWAS) are located in non-coding regions. It remains elusive which genes the associated loci regulate and in which tissues/cell types the regulation occurs. Here, we present PCGA (https://pmglab.top/pcga), a comprehensive web server for jointly estimating both associated tissues/cell types and susceptibility genes for complex phenotypes by GWAS summary statistics. The web server is built on our published method, DESE, which represents an effective method to mutually estimate driver tissues and genes by integrating GWAS summary statistics and transcriptome data. By collecting and processing extensive bulk and single-cell RNA sequencing datasets, PCGA has included expression profiles of 54 human tissues, 2,214 human cell types and 4,384 mouse cell types, which provide the basis for estimating associated tissues/cell types and genes for complex phenotypes. We develop a framework to sequentially estimate associated tissues and cell types of a complex phenotype according to their hierarchical relationships we curated. Meanwhile, we construct a phenotype-cell-gene association landscape by estimating the associated tissues/cell types and genes of 1,871 public GWASs. The association landscape is generally consistent with biological knowledge and can be searched and browsed at the PCGA website.

While great advances in predicting the effects of coding variants have been made, the assessment of non-coding variants remains challenging. This is especially problematic for variants within promoter regions which can lead to over-expression of a gene or reduce or even abolish its expression. The binding of transcription factors to the DNA can be predicted using position weight matrices (PWMs). More recently, transcription factor flexible models (TFFMs) have been introduced and shown to be more accurate than PWMs. TFFMs are based on hidden Markov models and can account for complex positional dependencies. Our new web-based application FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants affect the binding of 1387 different human transcription factors. For each variant and transcription factor, the software combines the results of different models for a final prediction of the resulting binding-affinity change. The software is written in C++ for speed but variants can be entered through a web interface. Alternatively, a VCF file can be uploaded to assess variants identified by high-throughput sequencing. The search can be restricted to variants in the vicinity of candidate genes. FABIAN-variant is available freely at https://www.genecascade.org/fabian/.

In this study we show that protein language models can encode structural and functional information of GPCR sequences that can be used to predict their signaling and functional repertoire. We used the ESM1b protein embeddings as features and the binding information known from publicly available studies to develop PRECOGx, a machine learning predictor to explore GPCR interactions with G protein and β-arrestin, which we made available through a new webserver (https://precogx.bioinfolab.sns.it/). PRECOGx outperformed its predecessor (e.g. PRECOG) in predicting GPCR-transducer couplings, being also able to consider all GPCR classes. The webserver also provides new functionalities, such as the projection of input sequences on a low-dimensional space describing essential features of the human GPCRome, which is used as a reference to track GPCR variants. Additionally, it allows inspection of the sequence and structural determinants responsible for coupling via the analysis of the most important attention maps used by the models as well as through predicted intramolecular contacts. We demonstrate applications of PRECOGx by predicting the impact of disease variants (ClinVar) and alternative splice forms from healthy tissues (GTEX) of human GPCRs, revealing the power to dissect system biasing mechanisms in both health and disease.

We present BioExcel Building Blocks Workflows, a web-based graphical user interface (GUI) offering access to a collection of transversal pre-configured biomolecular simulation workflows assembled with the BioExcel Building Blocks library. Available workflows include Molecular Dynamics setup, protein-ligand docking, trajectory analyses and small molecule parameterization. Workflows can be launched in the platform or downloaded to be run in the users' own premises. Remote launching of long executions to user's available High-Performance computers is possible, only requiring configuration of the appropriate access credentials. The web-based graphical user interface offers a high level of interactivity, with integration with the NGL viewer to visualize and check 3D structures, MDsrv to visualize trajectories, and Plotly to explore 2D plots. The server requires no login but is recommended to store the users' projects and manage sensitive information such as remote credentials. Private projects can be made public and shared with colleagues with a simple URL. The tool will help biomolecular simulation users with the most common and repetitive processes by means of a very intuitive and interactive graphical user interface. The server is accessible at https://mmb.irbbarcelona.org/biobb-wfs.

Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of 'omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new 'omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography-mass spectrometry (LC-MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

Molecular dynamics simulation is a proven technique for computing and visualizing the time-resolved motion of macromolecules at atomic resolution. The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers without requiring advanced skills, facilitating interactive exploration and collaborative visual analysis. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories and improve their online viewing and analysis. With the new instance, the MDsrv simplifies the creation of sessions, which allows the exchange of MD trajectories with preset representations and perspectives. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets, which greatly expands its applicability and use, as the data no longer needs to be accessible on a local server. In addition, initial analyses such as sequence or structure alignments, distance measurements, or RMSD calculations have been implemented, which optionally support visual analysis. Finally, based on Mol*, MDsrv now provides faster and more efficient visualization of even large trajectories compared to its predecessor tool NGL.

Synteny conservation analysis is a well-established methodology to investigate the potential functional role of unknown prokaryotic genes. However, bioinformatic tools to reconstruct and visualise genomic contexts usually depend on slow computations, are restricted to narrow taxonomic ranges, and/or do not allow for the functional and interactive exploration of neighbouring genes across different species. Here, we present GeCoViz, an online resource built upon 12 221 reference prokaryotic genomes that provides fast and interactive visualisation of custom genomic regions anchored by any target gene, which can be sought by either name, orthologous group (KEGGs, eggNOGs), protein domain (PFAM) or sequence. To facilitate functional and evolutionary interpretation, GeCoViz allows to customise the taxonomic scope of each analysis and provides comprehensive annotations of the neighbouring genes. Interactive visualisation options include, among others, the scaled representations of gene lengths and genomic distances, and on the fly calculation of synteny conservation of neighbouring genes, which can be highlighted based on custom thresholds. The resulting plots can be downloaded as high-quality images for publishing purposes. Overall, GeCoViz offers an easy-to-use, comprehensive, fast and interactive web-based tool for investigating the genomic context of prokaryotic genes, and is freely available at https://gecoviz.cgmlab.org.

Advances in genetic manipulation and genome engineering techniques have enabled on-demand targeted deletion, insertion, and substitution of DNA sequences. One important step in these techniques is the design of editing sequences (e.g. primers, homologous arms) to precisely target and manipulate DNA sequences of interest. Experimental biologists can employ multiple tools in a stepwise manner to assist editing sequence design (ESD), but this requires various software involving non-standardized data exchange and input/output formats. Moreover, necessary quality control steps might be overlooked by non-expert users. This approach is low-throughput and can be error-prone, which illustrates the need for an automated ESD system. In this paper, we introduce AutoESD (https://autoesd.biodesign.ac.cn/), which designs editing sequences for all steps of genetic manipulation of many common homologous-recombination techniques based on screening-markers. Notably, multiple types of manipulations for different targets (CDS or intergenic region) can be processed in one submission. Moreover, AutoESD has an entirely cloud-based serverless architecture, offering high reliability, robustness and scalability which is capable of parallelly processing hundreds of design tasks each having thousands of targets in minutes. To our knowledge, AutoESD is the first cloud platform enabling precise, automated, and high-throughput ESD across species, at any genomic locus for all manipulation types.

Most bacteria and archaea possess multiple antiviral defence systems that protect against infection by phages, archaeal viruses and mobile genetic elements. Our understanding of the diversity of defence systems has increased greatly in the last few years, and many more systems likely await discovery. To identify defence-related genes, we recently developed the Prokaryotic Antiviral Defence LOCator (PADLOC) bioinformatics tool. To increase the accessibility of PADLOC, we describe here the PADLOC web server (freely available at https://padloc.otago.ac.nz), allowing users to analyse whole genomes, metagenomic contigs, plasmids, phages and archaeal viruses. The web server includes a more than 5-fold increase in defence system types detected (since the first release) and expanded functionality enabling detection of CRISPR arrays and retron ncRNAs. Here, we provide user information such as input options, description of the multiple outputs, limitations and considerations for interpretation of the results, and guidance for subsequent analyses. The PADLOC web server also houses a precomputed database of the defence systems in > 230,000 RefSeq genomes. These data reveal two taxa, Campylobacterota and Spriochaetota, with unusual defence system diversity and abundance. Overall, the PADLOC web server provides a convenient and accessible resource for the detection of antiviral defence systems.

Long distance enhancers can physically interact with promoters to regulate gene expression through formation of enhancer-promoter (E-P) interactions. Identification of E-P interactions is also important for profound understanding of normal developmental and disease-associated risk variants. Although the state-of-art predictive computation methods facilitate the identification of E-P interactions to a certain extent, currently there is no efficient method that can meet various requirements of usage. Here we developed EPIXplorer, a user-friendly web server for efficient prediction, analysis and visualization of E-P interactions. EPIXplorer integrates 9 robust predictive algorithms, supports multiple types of 3D contact data and multi-omics data as input. The output from EPIXplorer is scored, fully annotated by regulatory elements and risk single-nucleotide polymorphisms (SNPs). In addition, the Visualization and Downstream module provide further functional analysis, all the output files and high-quality images are available for download. Together, EPIXplorer provides a user-friendly interface to predict the E-P interactions in an acceptable time, as well as understand how the genome-wide association study (GWAS) variants influence disease pathology by altering DNA looping between enhancers and the target gene promoters. EPIXplorer is available at https://www.csuligroup.com/EPIXplorer.

Modeling 3D genome organisation has been booming in the last years thanks to the availability of experimental datasets of genomic contacts. However, the field is currently missing the standardisation of methods and metrics to compare predictions and experiments. We present 3DGenBench, a web server available at https://inc-cost.eu/benchmarking/, that allows benchmarking computational models of 3D Genomics. The benchmark is performed using a manually curated dataset of 39 capture Hi-C profiles in wild type and genome-edited mouse cells, and five genome-wide Hi-C profiles in human, mouse, and Drosophila cells. 3DGenBench performs two kinds of analysis, each supplied with a specific scoring module that compares predictions of a computational method to experimental data using several metrics. With 3DGenBench, the user obtains model performance scores, allowing an unbiased comparison with other models. 3DGenBench aims to become a reference web server to test new 3D genomics models and is conceived as an evolving platform where new types of analysis will be implemented in the future.

5NosoAE is a webserver that can be used for nosocomial bacterial analysis including the identification of similar strains based on antimicrobial resistance profiles (antibiogram) and the spatiotemporal distribution visualization and phylogenetic analysis of identified strains with similar antibiograms. The extensive use of antibiotics has caused many pathogenic bacteria to develop multiple drug resistance, resulting in clinical infection treatment challenges and posing a major threat to global public health. Relevant studies have investigated the key determinants of antimicrobial resistance in the whole-genome sequence of bacteria. However, a web server is currently not available for performing large-scale strain searches according to antimicrobial resistance profiles and visualizing epidemiological information including the spatiotemporal distribution, antibiogram heatmap, and phylogeny of identified strains. Here, we implemented these functions in the new server, referred to as 5NosoAE. This server accepts the genome sequence file in the FASTA format of five nosocomial bacteria, namely Acinetobacter baumannii, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecium and Staphylococcus aureus for query. All visualizations are implemented in JavaScript and PHP. This server will be useful for physicians and epidemiologists involved in research on infectious disease. The 5NosoAE platform is available at https://nosoae.imst.nsysu.edu.tw.

It is of vital importance to understand the population structure, dissect the genetic bases of performance traits, and make proper strategies for selection in breeding programs. However, there is no single webserver covering the specific needs in aquaculture. We present Aquaculture Molecular Breeding Platform (AMBP), the first web server for genetic data analysis in aquatic species of farming interest. AMBP integrates the haplotype reference panels of 18 aquaculture species, which greatly improves the accuracy of genotype imputation. It also supports multiple tools to infer genetic structures, dissect the genetic architecture of performance traits, estimate breeding values, and predict optimum contribution. All the tools are coherently linked in a web-interface for users to generate interpretable results and evaluate statistical appropriateness. The webserver supports standard VCF and PLINK (PED, MAP) files, and implements automated pipelines for format transformation and visualization to simplify the process of analysis. As a demonstration, we applied the webserver to Pacific white shrimp and Atlantic salmon datasets. In summary, AMBP constitutes comprehensive resources and analytical tools for exploring genetic data and guiding practical breeding programs. AMBP is available at http://mgb.qnlm.ac.

In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein-protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.

Protein structure exhibits greater complexity and diversity than DNA structure, and usually affects the interpretation of the function, interactions and biological annotations. Reduced amino acid alphabets (Raaa) exhibit a powerful ability to decrease protein complexity and identify functional conserved regions, which motivated us to create RaacFold. The RaacFold provides 687 reduced amino acid clusters (Raac) based on 58 reduction methods and offers three analysis tools: Protein Analysis, Align Analysis, and Multi Analysis. The Protein Analysis and Align Analysis provide reduced representations of sequence-structure according to physicochemical similarities and computational biology strategies. With the simplified representations, the protein structure can be viewed more concise and clearer to capture biological insight than the unreduced structure. Thus, the design of artificial protein will be more convenient, and redundant interference is avoided. In addition, Multi Analysis allows users to explore biophysical variation and conservation in the evolution of protein structure and function. This supplies important information for the identification and exploration of the nonhomologous functions of paralogs. Simultaneously, RaacFold provides powerful 2D and 3D rendering performance with advanced parameters for sequences, structures, and related annotations. RaacFold is freely available at http://bioinfor.imu.edu.cn/raacfold.

The GrAfSS (Graph theoretical Applications for Substructure Searching) webserver is a platform to search for three-dimensional substructures of: (i) amino acid side chains in protein structures; and (ii) base arrangements in RNA structures. The webserver interfaces the functions of five different graph theoretical algorithms - ASSAM, SPRITE, IMAAAGINE, NASSAM and COGNAC - into a single substructure searching suite. Users will be able to identify whether a three-dimensional (3D) arrangement of interest, such as a ligand binding site or 3D motif, observed in a protein or RNA structure can be found in other structures available in the Protein Data Bank (PDB). The webserver also allows users to determine whether a protein or RNA structure of interest contains substructural arrangements that are similar to known motifs or 3D arrangements. These capabilities allow for the functional annotation of new structures that were either experimentally determined or computationally generated (such as the coordinates generated by AlphaFold2) and can provide further insights into the diversity or conservation of functional mechanisms of structures in the PDB. The computed substructural superpositions are visualized using integrated NGL viewers. The GrAfSS server is available at http://mfrlab.org/grafss/.

Viruses can cross species barriers and cause unpredictable outbreaks in man with substantial economic and public health burdens. Broad-spectrum antivirals, (BSAs, compounds inhibiting several human viruses), and BSA-containing drug combinations (BCCs) are deemed as immediate therapeutic options that fill the void between virus identification and vaccine development. Here, we present DrugVirus.info 2.0 (https://drugvirus.info), an integrative interactive portal for exploration and analysis of BSAs and BCCs, that greatly expands the database and functionality of DrugVirus.info 1.0 webserver. Through the data portal that now expands the spectrum of BSAs and provides information on BCCs, we developed two modules for (i) interactive analysis of users' own antiviral drug and combination screening data and their comparison with published datasets, and (ii) exploration of the structure-activity relationship between various BSAs. The updated portal provides an essential toolbox for antiviral drug development and repurposing applications aiming to identify existing and novel treatments of emerging and re-emerging viral threats.

Extensive amounts of data from next-generation sequencing and omics studies have led to the accumulation of information that provides insight into the evolutionary landscape of related proteins. Here, we present OrthoQuantum, a web server that allows for time-efficient analysis and visualization of phylogenetic profiles of any set of eukaryotic proteins. It is a simple-to-use tool capable of searching large input sets of proteins. Using data from open source databases of orthologous sequences in a wide range of taxonomic groups, it enables users to assess coupled evolutionary patterns and helps define lineage-specific innovations. The web interface allows to perform queries with gene names and UniProt identifiers in different phylogenetic clades and supplement presence with an additional BLAST search. The conservation patterns of proteins are coded as binary vectors, i.e., strings that encode the presence or absence of orthologous proteins in other genomes. These strings are used to calculate top-scoring correlation pairs needed for finding co-inherited proteins which are simultaneously present or simultaneously absent in specific lineages. Profiles are visualized in combination with phylogenetic trees in a JavaScript-based interface. The OrthoQuantum v1.0 web server is freely available at http://orthoq.bioinf.fbb.msu.ru along with documentation and tutorial.

MicroRNAs (miRNAs) are small non-coding RNAs that are among the main post-transcriptional regulators of gene expression. A number of data collections and prediction tools have gathered putative or confirmed targets of these regulators. It is often useful, for discovery and validation, to harness such collections to perform target enrichment analysis in given transcriptional signatures or gene-sets in order to predict involved miRNAs. While several methods have been proposed to this end, a flexible and user-friendly interface for such analyses using various approaches and collections is lacking. enrichMiR (https://ethz-ins.org/enrichMiR/) addresses this gap by enabling users to perform a series of enrichment tests, based on several target collections, to rank miRNAs according to their likely involvement in the control of a given transcriptional signature or gene-set. enrichMiR results can furthermore be visualised through interactive and publication-ready plots. To guide the choice of the appropriate analysis method, we benchmarked various tests across a panel of experiments involving the perturbation of known miRNAs. Finally, we showcase enrichMiR functionalities in a pair of use cases.

Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency.

AlphaKnot is a server that measures entanglement in AlphaFold-solved protein models while considering pLDDT confidence values. AlphaKnot has two main functions: (i) providing researchers with a webserver for analyzing knotting in their own AlphaFold predictions and (ii) providing a database of knotting in AlphaFold predictions from the 21 proteomes for which models have been published prior to 2022. The knotting is defined in a probabilistic fashion. The knotting complexity of proteins is presented in the form of a matrix diagram which shows users the knot type for the entire polypeptide chain and for each of its subchains. The dominant knot types as well as the computed locations of the knot cores (i.e. minimal portions of protein backbones that form a given knot type) are shown for each protein structure. Based mainly on the pLDDT confidence values, entanglements are classified as Knots, Unsure, and Artifacts. The database portion of the server can be used, for example, to examine protein geometry and entanglement-function correlations, as a reference set for protein modeling, and for facilitating evolutional studies. The AlphaKnot server can be found at https://alphaknot.cent.uw.edu.pl/.

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

Human complex traits and common diseases show tissue- and cell-type- specificity. Recently, single-cell RNA sequencing (scRNA-seq) technology has successfully depicted cellular heterogeneity in human tissue, providing an unprecedented opportunity to understand the context-specific expression of complex trait-associated genes in human tissue-cell types (TCs). Here, we present the first web-based application to quickly assess the cell-type-specificity of genes, named Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA, available at https://bioinfo.uth.edu/webcsea/). Specifically, we curated a total of 111 scRNA-seq panels of human tissues and 1,355 TCs from 61 different general tissues across 11 human organ systems. We adapted our previous decoding tissue-specificity (deTS) algorithm to measure the enrichment for each tissue-cell type (TC). To overcome the potential bias from the number of signature genes between different TCs, we further developed a permutation-based method that accurately estimates the TC-specificity of a given inquiry gene list. WebCSEA also provides an interactive heatmap that displays the cell-type specificity across 1355 human TCs, and other interactive and static visualizations of cell-type specificity by human organ system, developmental stage, and top-ranked tissues and cell types. In short, WebCSEA is a one-click application that provides a comprehensive exploration of the TC-specificity of genes among human major TC map.

Many proteins perform their functions within membraneless organelles, where they form a liquid-like condensed state, also known as droplet state. The FuzDrop method predicts the probability of spontaneous liquid-liquid phase separation of proteins and provides a sequence-based score to identify the regions that promote this process. Furthermore, the FuzDrop method estimates the propensity of conversion of proteins to the amyloid state, and identifies aggregation hot-spots, which can drive the irreversible maturation of the liquid-like droplet state. These predictions can also identify mutations that can induce formation of amyloid aggregates, including those implicated in human diseases. To facilitate the interpretation of the predictions, the droplet-promoting and aggregation-promoting regions can be visualized on protein structures generated by AlphaFold. The FuzDrop server (https://fuzdrop.bio.unipd.it) thus offers insights into the complex behavior of proteins in their condensed states and facilitates the understanding of the functional relationships of proteins.

Protein-ligand blind docking is a powerful method for exploring the binding sites of receptors and the corresponding binding poses of ligands. It has seen wide applications in pharmaceutical and biological researches. Previously, we proposed a blind docking server, CB-Dock, which has been under heavy use (over 200 submissions per day) by researchers worldwide since 2019. Here, we substantially improved the docking method by combining CB-Dock with our template-based docking engine to enhance the accuracy in binding site identification and binding pose prediction. In the benchmark tests, it yielded the success rate of ∼85% for binding pose prediction (RMSD < 2.0 Å), which outperformed original CB-Dock and most popular blind docking tools. This updated docking server, named CB-Dock2, reconfigured the input and output web interfaces, together with a highly automatic docking pipeline, making it a particularly efficient and easy-to-use tool for the bioinformatics and cheminformatics communities. The web server is freely available at https://cadd.labshare.cn/cb-dock2/.

Protein structure is key to understanding biological function. Structure comparison deciphers deep phylogenies, providing insight into functional conservation and functional shifts during evolution. Until recently, structural coverage of the protein universe was limited by the cost and labour involved in experimental structure determination. Recent breakthroughs in deep learning revolutionized structural bioinformatics by providing accurate structural models of numerous protein families for which no structural information existed. The Dali server for 3D protein structure comparison is widely used by crystallographers to relate new structures to pre-existing ones. Here, we report two most recent upgrades to the web server: (i) the foldomes of key organisms in the AlphaFold Database (version 1) are searchable by Dali, (ii) structural alignments are annotated with protein families. Using these new features, we discovered a novel functionally diverse subgroup within the WRKY/GCM1 clan. This was accomplished by linking the structurally characterized SWI/SNF and NAM families as well as the structural models of the CG-1 family and uncharacterized proteins to the structure of Gti1/Pac2, a previously known member of the WRKY/GCM1 clan. The Dali server is available at http://ekhidna2.biocenter.helsinki.fi/dali. This website is free and open to all users and there is no login requirement.

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID's regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

Integrative prioritisation promotes translational use of disease genetic findings in target discovery. I report 'PiER' (http://www.genetictargets.com/PiER), web-based facilities that support ab initio and real-time genetic target prioritisation through integrative use of human disease genetics, functional genomics and protein interactions. By design, the PiER features two facilities: elementary and combinatory. The elementary facility is designed to perform specific tasks, including three online tools: eV2CG, utilising functional genomics to link disease-associated variants (particularly located at the non-coding genome) to core genes likely responsible for genetic associations in disease; eCG2PG, using knowledge of protein interactions to 'network' core genes and additional peripheral genes, producing a ranked list of core and peripheral genes; and eCrosstalk, exploiting the information of pathway-derived interactions to identify highly-ranked genes mediating crosstalk between molecular pathways. Each of elementary tasks giving results is sequentially piped to the next one. By chaining together elementary tasks, the combinatory facility automates genetics-led and network-based integrative prioritisation for genetic targets at the gene level (cTGene) and at the crosstalk level (cTCrosstalk). Together with a tutorial-like booklet describing instructions on how to use, the PiER facilities meet multi-tasking needs to accelerate computational translational medicine that leverages human disease genetics and genomics for early-stage target discovery and drug repurposing.

Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction. Here, we present significant enhancements to PrankWeb. First, a new, more accurate evolutionary conservation estimation pipeline based on the UniRef50 sequence database and the HMMER3 package is introduced. Second, PrankWeb now allows users to enter UniProt ID to carry out LBS predictions in situations where no experimental structure is available by utilizing the AlphaFold model database. Additionally, a range of minor improvements has been implemented. These include the ability to deploy PrankWeb and P2Rank as Docker containers, support for the mmCIF file format, improved public REST API access, or the ability to batch download the LBS predictions for the whole PDB archive and parts of the AlphaFold database.

The AlignMe web server is dedicated to accurately aligning sequences of membrane proteins, a particularly challenging task due to the strong evolutionary divergence and the low compositional complexity of hydrophobic membrane-spanning proteins. AlignMe can create pairwise alignments of either two primary amino acid sequences or two hydropathy profiles. The web server for AlignMe has been continuously available for >10 years, supporting 1000s of users per year. Recent improvements include anchoring, multiple submissions, and structure visualization. Anchoring is the ability to constrain a position in an alignment, which allows expert information about related residues in proteins to be incorporated into an alignment without manual modification. The original web interface to the server limited the user to one alignment per submission, hindering larger scale studies. Now, batches of alignments can be initiated with a single submission. Finally, to provide structural context for the relationship between proteins, sequence similarity can now be mapped onto one or more structures (or structural models) of the proteins being aligned, by links to MutationExplorer, a web-based visualization tool. Together with a refreshed user interface, these features further enhance an important resource in the membrane protein community. The AlignMe web server is freely available at https://www.bioinfo.mpg.de/AlignMe/.

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

Accurate inference of gene regulatory networks (GRN) is an essential component of systems biology, and there is a constant development of new inference methods. The most common approach to assess accuracy for publications is to benchmark the new method against a selection of existing algorithms. This often leads to a very limited comparison, potentially biasing the results, which may stem from tuning the benchmark's properties or incorrect application of other methods. These issues can be avoided by a web server with a broad range of data properties and inference algorithms, that makes it easy to perform comprehensive benchmarking of new methods, and provides a more objective assessment. Here we present https://GRNbenchmark.org/ - a new web server for benchmarking GRN inference methods, which provides the user with a set of benchmarks with several datasets, each spanning a range of properties including multiple noise levels. As soon as the web server has performed the benchmarking, the accuracy results are made privately available to the user via interactive summary plots and underlying curves. The user can then download these results for any purpose, and decide whether or not to make them public to share with the community.

Establishing saturated mutagenesis in a specific gene through gene editing is an efficient approach for identifying the relationships between mutations and the corresponding phenotypes. CRISPR/Cas9-based sgRNA library screening often creates indel mutations with multiple nucleotides. Single base editors and dual deaminase-mediated base editors can achieve only one and two types of base substitutions, respectively. A new glycosylase base editor (CGBE) system, in which the uracil glycosylase inhibitor (UGI) is replaced with uracil-DNA glycosylase (UNG), was recently reported to efficiently induce multiple base conversions, including C-to-G, C-to-T and C-to-A. In this study, we fused a CGBE with ABE to develop a new type of dual deaminase-mediated base editing system, the AGBE system, that can simultaneously introduce 4 types of base conversions (C-to-G, C-to-T, C-to-A and A-to-G) as well as indels with a single sgRNA in mammalian cells. AGBEs can be used to establish saturated mutant populations for verification of the functions and consequences of multiple gene mutation patterns, including single-nucleotide variants (SNVs) and indels, through high-throughput screening.

Promoters and enhancers are sites of transcription initiation (TSSs) and carry specific histone modifications, including H3K4me1, H3K4me3, and H3K27ac. Yet, the principles governing the boundaries of such regulatory elements are still poorly characterized. Alu elements are good candidates for a boundary function, being highly abundant in gene-rich regions, while essentially excluded from regulatory elements. Here, we show that the interval ranging from TSS to first upstream Alu, accommodates all H3K4me3 and most H3K27ac marks, while excluding DNA methylation. Remarkably, the average length of these intervals greatly varies in-between tissues, being longer in stem- and shorter in immune-cells. The very shortest TSS-to-first-Alu intervals were observed at promoters active in T-cells, particularly at immune genes, where first-Alus were traversed by RNA polymerase II transcription, while accumulating H3K4me1 signal. Finally, DNA methylation at first-Alus was found to evolve with age, regressing from young to middle-aged, then recovering later in life. Thus, the first-Alus upstream of TSSs appear as dynamic boundaries marking the transition from DNA methylation to active histone modifications at regulatory elements, while also participating in the recording of immune gene transcriptional events by positioning H3K4me1-modified nucleosomes.

RNA can be extensively modified post-transcriptionally with >170 covalent modifications, expanding its functional and structural repertoire. Pseudouridine (Ψ), the most abundant modified nucleoside in rRNA and tRNA, has recently been found within mRNA molecules. It remains unclear whether pseudouridylation of mRNA can be snoRNA-guided, bearing important implications for understanding the physiological target spectrum of snoRNAs and for their potential therapeutic exploitation in genetic diseases. Here, using a massively parallel reporter based strategy we simultaneously interrogate Ψ levels across hundreds of synthetic constructs with predesigned complementarity against endogenous snoRNAs. Our results demonstrate that snoRNA-mediated pseudouridylation can occur on mRNA targets. However, this is typically achieved at relatively low efficiencies, and is constrained by mRNA localization, snoRNA expression levels and the length of the snoRNA:mRNA complementarity stretches. We exploited these insights for the design of snoRNAs targeting pseudouridylation at premature termination codons, which was previously shown to suppress translational termination. However, in this and follow-up experiments in human cells we observe no evidence for significant levels of readthrough of pseudouridylated stop codons. Our study enhances our understanding of the scope, 'design rules', constraints and consequences of snoRNA-mediated pseudouridylation.

Extrahepatic delivery of small interfering RNAs (siRNAs) may have applications in the development of novel therapeutic approaches. However, reports on such approaches are limited, and the scarcity of reports concerning the systemically targeted delivery of siRNAs with effective gene silencing activity presents a challenge. We herein report for the first time the targeted delivery of CD206-targetable chemically modified mannose-siRNA (CMM-siRNA) conjugates to macrophages and dendritic cells (DCs). CMM-siRNA exhibited a strong binding ability to CD206 and selectively delivered contents to CD206-expressing macrophages and DCs. Furthermore, the conjugates demonstrated strong gene silencing ability with long-lasting effects and protein downregulation in CD206-expressing cells in vivo. These findings could broaden the use of siRNA technology, provide additional therapeutic opportunities, and establish a basis for further innovative approaches for the targeted delivery of siRNAs to not only macrophages and DCs but also other cell types.

P-TEFb, composed of CycT1 and CDK9, regulates the elongation of transcription by RNA polymerase II. In proliferating cells, it is regulated by 7SK snRNA in the 7SK snRNP complex. In resting cells, P-TEFb is absent, because CycT1 is dephosphorylated, released from CDK9 and rapidly degraded. In this study, we identified the mechanism of this degradation. We mapped the ubiquitination and degradation of free CycT1 to its N-terminal region from positions 1 to 280. This region is ubiquitinated at six lysines, where E3 ligases Siah1 and Siah2 bind and degrade these sequences. Importantly, the inhibition of Siah1/2 rescued the expression of free CycT1 in proliferating as well as resting primary cells. We conclude that Siah1/2 are the E3 ligases that bind and degrade the dissociated CycT1 in resting, terminally differentiated, anergic and/or exhausted cells.

The essential pre-mRNA splicing factor U2AF2 (also called U2AF65) identifies polypyrimidine (Py) tract signals of nascent transcripts, despite length and sequence variations. Previous studies have shown that the U2AF2 RNA recognition motifs (RRM1 and RRM2) preferentially bind uridine-rich RNAs. Nonetheless, the specificity of the RRM1/RRM2 interface for the central Py tract nucleotide has yet to be investigated. We addressed this question by determining crystal structures of U2AF2 bound to a cytidine, guanosine, or adenosine at the central position of the Py tract, and compared U2AF2-bound uridine structures. Local movements of the RNA site accommodated the different nucleotides, whereas the polypeptide backbone remained similar among the structures. Accordingly, molecular dynamics simulations revealed flexible conformations of the central, U2AF2-bound nucleotide. The RNA binding affinities and splicing efficiencies of structure-guided mutants demonstrated that U2AF2 tolerates nucleotide substitutions at the central position of the Py tract. Moreover, enhanced UV-crosslinking and immunoprecipitation of endogenous U2AF2 in human erythroleukemia cells showed uridine-sensitive binding sites, with lower sequence conservation at the central nucleotide positions of otherwise uridine-rich, U2AF2-bound splice sites. Altogether, these results highlight the importance of RNA flexibility for protein recognition and take a step towards relating splice site motifs to pre-mRNA splicing efficiencies.

Selection of the translation start codon is a key step during protein synthesis in human cells. We obtained cryo-EM structures of human 48S initiation complexes and characterized the intermediates of codon recognition by kinetic methods using eIF1A as a reporter. Both approaches capture two distinct ribosome populations formed on an mRNA with a cognate AUG codon in the presence of eIF1, eIF1A, eIF2-GTP-Met-tRNAiMet and eIF3. The 'open' 40S subunit conformation differs from the human 48S scanning complex and represents an intermediate preceding the codon recognition step. The 'closed' form is similar to reported structures of complexes from yeast and mammals formed upon codon recognition, except for the orientation of eIF1A, which is unique in our structure. Kinetic experiments show how various initiation factors mediate the population distribution of open and closed conformations until 60S subunit docking. Our results provide insights into the timing and structure of human translation initiation intermediates and suggest the differences in the mechanisms of start codon selection between mammals and yeast.

Polycomb group proteins predominantly exist in polycomb repressive complexes (PRCs) that cooperate to maintain the repressed state of thousands of cell-type-specific genes. Targeting PRCs to the correct sites in chromatin is essential for their function. However, the mechanisms by which PRCs are recruited to their target genes in mammals are multifactorial and complex. Here we review DNA binding by polycomb group proteins. There is strong evidence that the DNA-binding subunits of PRCs and their DNA-binding activities are required for chromatin binding and CpG targeting in cells. In vitro, CpG-specific binding was observed for truncated proteins externally to the context of their PRCs. Yet, the mere DNA sequence cannot fully explain the subset of CpG islands that are targeted by PRCs in any given cell type. At this time we find very little structural and biophysical evidence to support a model where sequence-specific DNA-binding activity is required or sufficient for the targeting of CpG-dinucleotide sequences by polycomb group proteins while they are within the context of their respective PRCs, either PRC1 or PRC2. We discuss the current knowledge and open questions on how the DNA-binding activities of polycomb group proteins facilitate the targeting of PRCs to chromatin.

Histone chaperones regulate all aspects of histone metabolism. NASP is a major histone chaperone for H3-H4 dimers critical for preventing histone degradation. Here, we identify two distinct histone binding modes of NASP and reveal how they cooperate to ensure histone H3-H4 supply. We determine the structures of a sNASP dimer, a complex of a sNASP dimer with two H3 α3 peptides, and the sNASP-H3-H4-ASF1b co-chaperone complex. This captures distinct functionalities of NASP and identifies two distinct binding modes involving the H3 α3 helix and the H3 αN region, respectively. Functional studies demonstrate the H3 αN-interaction represents the major binding mode of NASP in cells and shielding of the H3 αN region by NASP is essential in maintaining the H3-H4 histone soluble pool. In conclusion, our studies uncover the molecular basis of NASP as a major H3-H4 chaperone in guarding histone homeostasis.

Cas12a is an RNA-guided endonuclease that is emerging as a powerful genome-editing tool. Here, we selected a target site on bacteriophage λ-DNA and used optical tweezers combined with fluorescence to provide mechanistic insight into wild type Cas12a and three engineered variants, where the specific dsDNA and the unspecific ssDNA cleavage are dissociated (M1 and M2) and a third one which nicks the target DNA (M3). At low forces wtCas12a and the variants display two main off-target binding sites, while on stretched dsDNA at higher forces numerous binding events appear driven by the mechanical distortion of the DNA and partial matches to the crRNA. The multiple binding events onto dsDNA at high tension do not lead to cleavage, which is observed on the target site at low forces when the DNA is flexible. In addition, activity assays also show that the preferential off-target sites for this crRNA are not cleaved by wtCas12a, indicating that λ-DNA is only severed at the target site. Our single molecule data indicate that the Cas12a scaffold presents singular mechanical properties, which could be used to generate new endonucleases with biomedical and biotechnological applications.

Genetic code expansion in multicellular organisms is currently limited to the use of repurposed amber stop codons. Here, we introduce a system for the use of quadruplet codons to direct incorporation of non-canonical amino acids in vivo in an animal, the nematode worm Caenorhabditis elegans. We develop hybrid pyrrolysyl tRNA variants to incorporate non-canonical amino acids in response to the quadruplet codon UAGA. We demonstrate the efficiency of the quadruplet decoding system by incorporating photocaged amino acids into two proteins widely used as genetic tools. We use photocaged lysine to express photocaged Cre recombinase for the optical control of gene expression and photocaged cysteine to express photo-activatable caspase for light inducible cell ablation. Our approach will facilitate the routine adoption of quadruplet decoding for genetic code expansion in eukaryotic cells and multicellular organisms.

Alternative splicing is critical for development; however, its role in the specification of the three embryonic germ layers is poorly understood. By performing RNA-Seq on human embryonic stem cells (hESCs) and derived definitive endoderm, cardiac mesoderm, and ectoderm cell lineages, we detect distinct alternative splicing programs associated with each lineage. The most prominent splicing program differences are observed between definitive endoderm and cardiac mesoderm. Integrative multi-omics analyses link each program with lineage-enriched RNA binding protein regulators, and further suggest a widespread role for Quaking (QKI) in the specification of cardiac mesoderm. Remarkably, knockout of QKI disrupts the cardiac mesoderm-associated alternative splicing program and formation of myocytes. These changes arise in part through reduced expression of BIN1 splice variants linked to cardiac development. Mechanistically, we find that QKI represses inclusion of exon 7 in BIN1 pre-mRNA via an exonic ACUAA motif, and this is concomitant with intron removal and cleavage from chromatin. Collectively, our results uncover alternative splicing programs associated with the three germ lineages and demonstrate an important role for QKI in the formation of cardiac mesoderm.

Comment in 10.1093/nar/gkac311. 10.1093/nar/gkac334.

Comment in 10.1093/nar/gkac334. 10.1093/nar/gkac343.

The heterodimeric histone chaperone FACT, consisting of SSRP1 and SPT16, contributes to dynamic nucleosome rearrangements during various DNA-dependent processes including transcription. In search of post-translational modifications that may regulate the activity of FACT, SSRP1 and SPT16 were isolated from Arabidopsis cells and analysed by mass spectrometry. Four acetylated lysine residues could be mapped within the basic C-terminal region of SSRP1, while three phosphorylated serine/threonine residues were identified in the acidic C-terminal region of SPT16. Mutational analysis of the SSRP1 acetylation sites revealed only mild effects. However, phosphorylation of SPT16 that is catalysed by protein kinase CK2, modulates histone interactions. A non-phosphorylatable version of SPT16 displayed reduced histone binding and proved inactive in complementing the growth and developmental phenotypes of spt16 mutant plants. In plants expressing the non-phosphorylatable SPT16 version we detected at a subset of genes enrichment of histone H3 directly upstream of RNA polymerase II transcriptional start sites (TSSs) in a region that usually is nucleosome-depleted. This suggests that some genes require phosphorylation of the SPT16 acidic region for establishing the correct nucleosome occupancy at the TSS of active genes.

Telomeres, the ends of linear chromosomes, are composed of repetitive DNA sequences, histones and a protein complex called shelterin. How DNA is packaged at telomeres is an outstanding question in the field with significant implications for human health and disease. Here, we studied the architecture of telomeres and their spatial association with other chromatin domains in different cell types using correlative light and electron microscopy. To this end, the shelterin protein TRF1 or TRF2 was fused in tandem to eGFP and the peroxidase APEX2, which provided a selective and electron-dense label to interrogate telomere organization by transmission electron microscopy, electron tomography and scanning electron microscopy. Together, our work reveals, for the first time, ultrastructural insight into telomere architecture. We show that telomeres are composed of a dense and highly compacted mesh of chromatin fibres. In addition, we identify marked differences in telomere size, shape and chromatin compaction between cancer and non-cancer cells and show that telomeres are in direct contact with other heterochromatin regions. Our work resolves the internal architecture of telomeres with unprecedented resolution and advances our understanding of how telomeres are organized in situ.

Pseudogenes (genes disrupted by frameshift or in-frame stop codons) are ubiquitously present in the bacterial genome and considered as nonfunctional fossil. Here, we used RNA-seq and mass-spectrometry technologies to measure the transcriptomes and proteomes of Salmonella enterica serovars Paratyphi A and Typhi. All pseudogenes' mRNA sequences remained disrupted, and were present at comparable levels to their intact homologs. At the protein level, however, 101 out of 161 pseudogenes suggested successful translation, with their low expression regardless of growth conditions, genetic background and pseudogenization causes. The majority of frameshifting detected was compensatory for -1 frameshift mutations. Readthrough of in-frame stop codons primarily involved UAG; and cytosine was the most frequent base adjacent to the codon. Using a fluorescence reporter system, fifteen pseudogenes were confirmed to express successfully in vivo in Escherichia coli. Expression of the intact copy of the fifteen pseudogenes in S. Typhi affected bacterial pathogenesis as revealed in human macrophage and epithelial cell infection models. The above findings suggest the need to revisit the nonstandard translation mechanism as well as the biological role of pseudogenes in the bacterial genome.

Large-scale cancer genome sequencing has enabled the catalogs of somatic mutations; however, the mutational impact on intrinsically disordered protein regions (IDRs) has not been systematically investigated to date. Here, we comprehensively characterized the mutational landscapes of IDRs and found that IDRs have higher mutation frequencies across diverse cancers. We thus developed a computational method, ROI-Driver, to identify putative driver genes enriching IDR and domain hotspots in cancer. Numerous well-known cancer-related oncogenes or tumor suppressors that play important roles in cancer signaling regulation, development and immune response were identified at a higher resolution. In particular, the incorporation of IDR structures helps in the identification of novel potential driver genes that play central roles in human protein-protein interaction networks. Interestingly, we found that the putative driver genes with IDR hotspots were significantly enriched with predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular pathways. We also identified an appreciable number of clinically relevant genes enriching IDR mutational hotspots that exhibited differential expression patterns and are associated with cancer patient survival. In summary, combinations of mutational effects on IDRs significantly increase the sensitivity of driver detection and are likely to open new therapeutic avenues for various cancers.

The Dlk1-Dio3 imprinted domain is controlled by an imprinting control region (ICR) called IG-DMR that is hypomethylated on the maternal allele and hypermethylated on the paternal allele. Although several genetic mutation experiments have shown that IG-DMR is essential for imprinting control of the domain, how DNA methylation itself functions has not been elucidated. Here, we performed both gain and loss of DNA methylation experiments targeting IG-DMR by transiently introducing CRISPR/Cas9 based-targeted DNA methylation editing tools along with one guide RNA into mouse ES cells. Altered DNA methylation, particularly at IG-DMR-Rep, which is a tandem repeat containing ZFP57 methylated DNA-binding protein binding motifs, affected the imprinting state of the whole domain, including DNA methylation, imprinted gene expression, and histone modifications. Moreover, the altered imprinting states were persistent through neuronal differentiation. Our results suggest that the DNA methylation state at IG-DMR-Rep, but not other sites in IG-DMR, is a master element to determine whether the allele behaves as the intrinsic maternal or paternal allele. Meanwhile, this study provides a robust strategy and methodology to study core DNA methylation in cis-regulatory elements, such as ICRs and enhancers.

Eukaryotic uL11 contains a conserved MPPKFDP motif at the N-terminus that is not found in archaeal and bacterial homologs. Here, we determined the solution structure of human uL11 by NMR spectroscopy and characterized its backbone dynamics by 15N-1H relaxation experiments. We showed that these N-terminal residues are unstructured and flexible. Structural comparison with ribosome-bound uL11 suggests that the linker region between the N-terminal domain and C-terminal domain of human uL11 is intrinsically disordered and only becomes structured when bound to the ribosomes. Mutagenesis studies show that the N-terminal conserved MPPKFDP motif is involved in interacting with the P-complex and its extended protuberant domain of uL10 in vitro. Truncation of the MPPKFDP motif also reduced the poly-phenylalanine synthesis in both hybrid ribosome and yeast mutagenesis studies. In addition, G→A/P substitutions to the conserved GPLG motif of helix-1 reduced poly-phenylalanine synthesis to 9-32% in yeast ribosomes. We propose that the flexible N-terminal residues of uL11, which could extend up to ∼25 Å from the N-terminal domain of uL11, can form transient interactions with the uL10 that help to fetch and fix it into a position ready for recruiting the incoming translation factors and facilitate protein synthesis.

Bacterial mRNAs have short life cycles, in which transcription is rapidly followed by translation and degradation within seconds to minutes. The resulting diversity of mRNA molecules across different life-cycle stages impacts their functionality but has remained unresolved. Here we quantitatively map the 3' status of cellular RNAs in Escherichia coli during steady-state growth and report a large fraction of molecules (median>60%) that are fragments of canonical full-length mRNAs. The majority of RNA fragments are decay intermediates, whereas nascent RNAs contribute to a smaller fraction. Despite the prevalence of decay intermediates in total cellular RNA, these intermediates are underrepresented in the pool of ribosome-associated transcripts and can thus distort quantifications and differential expression analyses for the abundance of full-length, functional mRNAs. The large heterogeneity within mRNA molecules in vivo highlights the importance in discerning functional transcripts and provides a lens for studying the dynamic life cycle of mRNAs.

During routine genome duplication, many potential replication origins remain inactive or 'dormant'. Such origin dormancy is achieved, in part, by an interaction with the metabolic sensor SIRT1 deacetylase. We report here that dormant origins are a group of consistent, pre-determined genomic sequences that are distinguished from baseline (i.e. ordinarily active) origins by their preferential association with two phospho-isoforms of the helicase component MCM2. During normal unperturbed cell growth, baseline origins, but not dormant origins, associate with a form of MCM2 that is phosphorylated by DBF4-dependent kinase (DDK) on serine 139 (pS139-MCM2). This association facilitates the initiation of DNA replication from baseline origins. Concomitantly, SIRT1 inhibits Ataxia Telangiectasia and Rad3-related (ATR)-kinase-mediated phosphorylation of MCM2 on serine 108 (pS108-MCM2) by deacetylating the ATR-interacting protein DNA topoisomerase II binding protein 1 (TOPBP1), thereby preventing ATR recruitment to chromatin. In cells devoid of SIRT1 activity, or challenged by replication stress, this inhibition is circumvented, enabling ATR-mediated S108-MCM2 phosphorylation. In turn, pS108-MCM2 enables DDK-mediated phosphorylation on S139-MCM2 and facilitates replication initiation at dormant origins. These observations suggest that replication origin dormancy and activation are regulated by distinct post-translational MCM modifications that reflect a balance between SIRT1 activity and ATR signaling.

Activation-induced deaminase (AID) is a DNA-cytosine deaminase that mediates maturation of antibodies through somatic hypermutation and class-switch recombination. While it causes mutations in immunoglobulin heavy and light chain genes and strand breaks in the switch regions of the immunoglobulin heavy chain gene, it largely avoids causing such damage in the rest of the genome. To help understand targeting by human AID, we expressed it in repair-deficient Escherichia coli and mapped the created uracils in the genomic DNA using uracil pull-down and sequencing, UPD-seq. We found that both AID and the human APOBEC3A preferentially target tRNA genes and transcription start sites, but do not show preference for highly transcribed genes. Unlike A3A, AID did not show a strong replicative strand bias or a preference for hairpin loops. Overlapping uracilation peaks between these enzymes contained binding sites for a protein, FIS, that helps create topological domains in the E. coli genome. To confirm whether these findings were relevant to B cells, we examined mutations from lymphoma and leukemia genomes within AID-preferred sequences. These mutations also lacked replicative strand bias or a hairpin loop preference. We propose here a model for how AID avoids causing mutations in the single-stranded DNA found within replication forks.

Structural Maintenance of Chromosomes (SMC) complexes play essential roles in genome organization across all domains of life. To determine how the activities of these large (≈50 nm) complexes are controlled by ATP binding and hydrolysis, we developed a molecular dynamics model that accounts for conformational motions of the SMC and DNA. The model combines DNA loop capture with an ATP-induced 'power stroke' to translocate the SMC complex along DNA. This process is sensitive to DNA tension: at low tension (0.1 pN), the model makes loop-capture steps of average 60 nm and up to 200 nm along DNA (larger than the complex itself), while at higher tension, a distinct inchworm-like translocation mode appears. By tethering DNA to an experimentally-observed additional binding site ('safety belt'), the model SMC complex can perform loop extrusion (LE). The dependence of LE on DNA tension is distinct for fixed DNA tension vs. fixed DNA end points: LE reversal occurs above 0.5 pN for fixed tension, while LE stalling without reversal occurs at about 2 pN for fixed end points. Our model matches recent experimental results for condensin and cohesin, and makes testable predictions for how specific structural variations affect SMC function.

As an oncogenic transcription factor, Yin Yang 1 (YY1) regulates enhancer and promoter connection. However, gaps still exist in understanding how YY1 coordinates coactivators and chromatin enhancer elements to assemble enhancers and super-enhancers. Here, we demonstrate that a histidine cluster in YY1's transactivation domain is essential for its formation of phase separation condensates, which can be extended to additional proteins. The histidine cluster is also required for YY1-promoted cell proliferation, migration, clonogenicity and tumor growth. YY1-rich nuclear puncta contain coactivators EP300, BRD4, MED1 and active RNA polymerase II, and colocalize with histone markers of gene activation, but not that of repression. Furthermore, YY1 binds to the consensus motifs in the FOXM1 promoter to activate its expression. Wild-type YY1, but not its phase separation defective mutant, connects multiple enhancer elements and the FOXM1 promoter to form an enhancer cluster. Consistently, fluorescent in situ hybridization (FISH) assays reveal the colocalization of YY1 puncta with both the FOXM1 gene locus and its nascent RNA transcript. Overall, this study demonstrates that YY1 activates target gene expression through forming liquid-liquid phase separation condensates to compartmentalize both coactivators and enhancer elements, and the histidine cluster of YY1 plays a determinant role in this regulatory mechanism.

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

Homeostasis of meiotic DNA double strand breaks (DSB) is critical for germline genome integrity and homologous recombination. Here we demonstrate an essential role for SKP1, a constitutive subunit of the SCF (SKP1-Cullin-F-box) ubiquitin E3 ligase, in early meiotic processes. SKP1 restrains accumulation of HORMAD1 and the pre-DSB complex (IHO1-REC114-MEI4) on the chromosome axis in meiotic germ cells. Loss of SKP1 prior to meiosis leads to aberrant localization of DSB repair proteins and a failure in synapsis initiation in meiosis of both males and females. Furthermore, SKP1 is crucial for sister chromatid cohesion during the pre-meiotic S-phase. Mechanistically, FBXO47, a meiosis-specific F-box protein, interacts with SKP1 and HORMAD1 and targets HORMAD1 for polyubiquitination and degradation in HEK293T cells. Our results support a model wherein the SCF ubiquitin E3 ligase prevents hyperactive DSB formation through proteasome-mediated degradation of HORMAD1 and subsequent modulation of the pre-DSB complex during meiosis.

Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.

Comment in 10.1093/nar/gkac311. 10.1093/nar/gkac343.

The expansion of d(CGG) trinucleotide repeats (TRs) lies behind several important neurodegenerative diseases. Atypical DNA secondary structures have been shown to trigger TR expansion: their characterization is important for a molecular understanding of TR disease. CD spectroscopy experiments in the last decade have unequivocally demonstrated that CGG runs adopt a left-handed Z-DNA conformation, whose features remain uncertain because it entails accommodating GG mismatches. In order to find this missing motif, we have carried out molecular dynamics (MD) simulations to explore all the possible Z-DNA helices that potentially form after the transition from B- to Z-DNA. Such helices combine either CpG or GpC Watson-Crick steps in Z-DNA form with GG-mismatch conformations set as either intrahelical or extrahelical; and participating in BZ or ZZ junctions or in alternately extruded conformations. Characterization of the stability and structural features (especially overall left-handedness, higher-temperature and steered MD simulations) identified two novel Z-DNA helices: the most stable one displays alternately extruded Gs, and is followed by a helix with symmetrically extruded ZZ junctions. The G-extrusion favors a seamless stacking of the Watson-Crick base pairs; extruded Gs favor syn conformations and display hydrogen-bonding and stacking interactions. Such conformations could have the potential to hijack the MMR complex, thus triggering further expansion.

Nearest neighbor parameters for estimating the folding stability of RNA secondary structures are in widespread use. For helices, current parameters penalize terminal AU base pairs relative to terminal GC base pairs. We curated an expanded database of helix stabilities determined by optical melting experiments. Analysis of the updated database shows that terminal penalties depend on the sequence identity of the adjacent penultimate base pair. New nearest neighbor parameters that include this additional sequence dependence accurately predict the measured values of 271 helices in an updated database with a correlation coefficient of 0.982. This refined understanding of helix ends facilitates fitting terms for base pair stacks with GU pairs. Prior parameter sets treated 5'GGUC3' paired to 3'CUGG5' separately from other 5'GU3'/3'UG5' stacks. The improved understanding of helix end stability, however, makes the separate treatment unnecessary. Introduction of the additional terms was tested with three optical melting experiments. The average absolute difference between measured and predicted free energy changes at 37°C for these three duplexes containing terminal adjacent AU and GU pairs improved from 1.38 to 0.27 kcal/mol. This confirms the need for the additional sequence dependence in the model.

Comment in Trends Plant Sci. 2022 Nov;27(11):1087-1089.

Argonaute (Ago) proteins are programmable nucleases found in eukaryotes and prokaryotes. Prokaryotic Agos (pAgos) share a high degree of structural homology with eukaryotic Agos (eAgos), and eAgos originate from pAgos. Although eAgos exclusively cleave RNA targets, most characterized pAgos cleave DNA targets. This study characterized a novel pAgo, MbpAgo, from the psychrotolerant bacterium Mucilaginibacter paludis which prefers to cleave RNA targets rather than DNA targets. Compared to previously studied Agos, MbpAgo can utilize both 5'phosphorylated(5'P) and 5'hydroxylated(5'OH) DNA guides (gDNAs) to efficiently cleave RNA targets at the canonical cleavage site if the guide is between 15 and 17 nt long. Furthermore, MbpAgo is active at a wide range of temperatures (4-65°C) and displays no obvious preference for the 5'-nucleotide of a guide. Single-nucleotide and most dinucleotide mismatches have no or little effects on cleavage efficiency, except for dinucleotide mismatches at positions 11-13 that dramatically reduce target cleavage. MbpAgo can efficiently cleave highly structured RNA targets using both 5'P and 5'OH gDNAs in the presence of Mg2+ or Mn2+. The biochemical characterization of MbpAgo paves the way for its use in RNA manipulations such as nucleic acid detection and clearance of RNA viruses.

With the advent of single-cell RNA sequencing (scRNA-seq), one major challenging is the so-called 'dropout' events that distort gene expression and remarkably influence downstream analysis in single-cell transcriptome. To address this issue, much effort has been done and several scRNA-seq imputation methods were developed with two categories: model-based and deep learning-based. However, comprehensively and systematically comparing existing methods are still lacking. In this work, we use six simulated and two real scRNA-seq datasets to comprehensively evaluate and compare a total of 12 available imputation methods from the following four aspects: (i) gene expression recovering, (ii) cell clustering, (iii) gene differential expression, and (iv) cellular trajectory reconstruction. We demonstrate that deep learning-based approaches generally exhibit better overall performance than model-based approaches under major benchmarking comparison, indicating the power of deep learning for imputation. Importantly, we built scIMC (single-cell Imputation Methods Comparison platform), the first online platform that integrates all available state-of-the-art imputation methods for benchmarking comparison and visualization analysis, which is expected to be a convenient and useful tool for researchers of interest. It is now freely accessible via https://server.wei-group.net/scIMC/.

The -1 programmed ribosomal frameshifting (-1 PRF) has been explored as a gene regulatory circuit for synthetic biology applications. The -1 PRF usually uses an RNA pseudoknot structure as the frameshifting stimulator. Finding a ligand-responsive pseudoknot with efficient -1 PRF activity is time consuming and is becoming a bottleneck for its development. Inserting a guanine to guanine (GG)-mismatch pair in the 5'-stem of a small frameshifting pseudoknot could attenuate -1 PRF activity by reducing stem stability. Thus, a ligand-responsive frameshifting pseudoknot can be built using GG-mismatch-targeting small molecules to restore stem stability. Here, a pseudoknot requiring stem-loop tertiary interactions for potent frameshifting activity was used as the engineering template. This considerably amplified the effect of mismatch destabilization, and led to creation of a mammalian -1 PRF riboswitch module capable of mediating premature translation termination as a synthetic regulatory mode. Application of the synthetic circuit allowed ligand-dependent ATF6N mimic formation for the activation of protein folding-related genes involved in the unfolded protein response without an ER-stress inducing agent. With the availability of mismatch-targeting molecules, the tailored module thus paves the way for various mismatch plug-ins to streamline highly efficient orthogonal ligand-dependent -1 PRF stimulator development in the synthetic biology toolbox.

Mammalian orthoreovirus (MRV) is a double-stranded RNA virus from the Reoviridae family presenting a promising activity as an oncolytic virus. Recent studies have underlined MRV's ability to alter cellular alternative splicing (AS) during infection, with a limited understanding of the mechanisms at play. In this study, we investigated how MRV modulates AS. Using a combination of cell biology and reverse genetics experiments, we demonstrated that the M1 gene segment, encoding the μ2 protein, is the primary determinant of MRV's ability to alter AS, and that the amino acid at position 208 in μ2 is critical to induce these changes. Moreover, we showed that the expression of μ2 by itself is sufficient to trigger AS changes, and its ability to enter the nucleus is not required for all these changes. Moreover, we identified core components of the U5 snRNP (i.e. EFTUD2, PRPF8, and SNRNP200) as interactors of μ2 that are required for MRV modulation of AS. Finally, these U5 snRNP components are reduced at the protein level by both MRV infection and μ2 expression. Our findings identify the reduction of U5 snRNP components levels as a new mechanism by which viruses alter cellular AS.

Viral mRNAs that lack a 5' m7GTP cap and a 3' poly-A tail rely on structural elements in their untranslated regions (UTRs) to form unique RNA-protein complexes that regulate viral translation. Recent studies of the barley yellow dwarf virus (BYDV) have revealed eukaryotic initiation factor 3 (eIF3) plays a significant role in facilitating communication between its 5' and 3' UTRs by binding both UTRs simultaneously. This report uses in vitro translation assays, fluorescence anisotropy binding assays, and selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) footprinting to identify secondary structures that are selectively interacting with eIF3. SHAPE data also show that eIF3 alters its interaction with BYDV structures when another factor crucial for BYDV translation, eIF4F, is introduced by the 3' BYDV translational enhancer (BTE). The observed BTE and eIF4F-induced shift of eIF3 position on the 5' UTR and the translational effects of altering eIF3-binding structures (SLC and SLII) support a new model for BYDV translation initiation that requires the reorientation of eIF3 on BYDV UTRs. This eIF3 function in BYDV translation initiation is both reminiscent of and distinct from eIF3-RNA interactions found in other non-canonically translating mRNAs (e.g. HCV). This characterization of a new role in translation initiation expands the known functionality of eIF3 and may be broadly applicable to other non-canonically translating mRNAs.

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein-protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Proteins isolated from natural sources can be composed of a mixture of isoforms with similar physicochemical properties that coexist in the final steps of purification. Yet, even where unverified, the assumed sequence is enforced throughout the structural studies. Herein, we propose a novel perspective to address the usually neglected sequence heterogeneity of natural products by integrating biophysical, genetic and structural data in our program SEQUENCE SLIDER. The aim is to assess the evidence supporting chemical composition in structure determination. Locally, we interrogate the experimental map to establish which side chains are supported by the structural data, and the genetic information relating sequence conservation is integrated into this statistic. Hence, we build a constrained peptide database, containing most probable sequences to interpret mass spectrometry data (MS). In parallel, we perform MS de novo sequencing with genomic-based algorithms to detect point mutations. We calibrated SLIDER with Gallus gallus lysozyme, whose sequence is unequivocally established and numerous natural isoforms are reported. We used SLIDER to characterize a metalloproteinase and a phospholipase A2-like protein from the venom of Bothrops moojeni and a crotoxin from Crotalus durissus collilineatus. This integrated approach offers a more realistic structural descriptor to characterize macromolecules isolated from natural sources.

Epigenome-wide association studies often detect many differentially methylated sites, and many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated that CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here, we present MethReg, an R/Bioconductor package that analyzes matched DNA methylation and gene expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize and annotate CpG sites with high regulatory potential. At these CpG sites, TF-target gene associations are often only present in a subset of samples with high (or low) methylation levels, so they can be missed by analyses that use all samples. Using colorectal cancer and Alzheimer's disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

Th17 cells are essential for protection against extracellular pathogens, but their aberrant activity can cause autoimmunity. Molecular mechanisms that dictate Th17 cell-differentiation have been extensively studied using mouse models. However, species-specific differences underscore the need to validate these findings in human. Here, we characterized the human-specific roles of three AP-1 transcription factors, FOSL1, FOSL2 and BATF, during early stages of Th17 differentiation. Our results demonstrate that FOSL1 and FOSL2 co-repress Th17 fate-specification, whereas BATF promotes the Th17 lineage. Strikingly, FOSL1 was found to play different roles in human and mouse. Genome-wide binding analysis indicated that FOSL1, FOSL2 and BATF share occupancy over regulatory regions of genes involved in Th17 lineage commitment. These AP-1 factors also share their protein interacting partners, which suggests mechanisms for their functional interplay. Our study further reveals that the genomic binding sites of FOSL1, FOSL2 and BATF harbour hundreds of autoimmune disease-linked SNPs. We show that many of these SNPs alter the ability of these transcription factors to bind DNA. Our findings thus provide critical insights into AP-1-mediated regulation of human Th17-fate and associated pathologies.

Human endogenous retroviruses, also called LTR elements, can be bound by transcription factors and marked by different histone modifications in different biological contexts. Recently, individual LTR or certain subclasses of LTRs such as LTR7/HERVH and LTR5_Hs/HERVK families have been identified as cis-regulatory elements. However, there are still many LTR elements with unknown functions. Here, we dissected the landscape of histone modifications and regulatory map of LTRs by integrating 98 ChIP-seq data in human embryonic stem cells (ESCs), and annotated the active LTRs enriching enhancer/promoter-related histone marks. Notably, we found that MER57E3 functionally acted as proximal regulatory element to activate respective ZNF gene. Additionally, HERVK transcript could mainly function in nucleus to activate the adjacent genes. Since LTR5_Hs/LTR5 was bound by many early embryo-specific transcription factors, we further investigated the expression dynamics in different pluripotent states. LTR5_Hs/LTR5/HERVK exhibited higher expression level in naïve ESCs and extended pluripotent stem cells (EPSCs). Functionally, the LTR5_Hs/LTR5 with high activity could serve as a distal enhancer to regulate the host genes. Ultimately, our study not only provides a comprehensive regulatory map of LTRs in human ESCs, but also explores the regulatory models of MER57E3 and LTR5_Hs/LTR5 in host genome.

For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the 'Secondary Metabolite Transcriptomic Pipeline' (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest.

The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques that monitor different complementary signals during unfolding. The CalFitter webserver has already proved integral to deriving invaluable energy parameters by global data analysis. Here, we introduce CalFitter 2.0, which newly incorporates singular value decomposition (SVD) of multi-wavelength spectral datasets into the global fitting pipeline. Processed time- or temperature-evolved SVD components can now be fitted together with other experimental data types. Moreover, deconvoluted basis spectra provide spectral fingerprints of relevant macrostates populated during unfolding, which greatly enriches the information gains of the CalFitter output. The SVD analysis is fully automated in a highly interactive module, providing access to the results to users without any prior knowledge of the underlying mathematics. Additionally, a novel data uploading wizard has been implemented to facilitate rapid and easy uploading of multiple datasets. Together, the newly introduced changes significantly improve the user experience, making this software a unique, robust, and interactive platform for the analysis of protein thermal denaturation data. The webserver is freely accessible at https://loschmidt.chemi.muni.cz/calfitter.

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

Understanding the functions and origins of proteins requires splitting these macromolecules into fragments that could be independent in terms of folding, activity, or evolution. For that purpose, structural domains are the typical level of analysis, but shorter segments, such as subdomains and supersecondary structures, are insightful as well. Here, we propose SWORD2, a web server for exploring how an input protein structure may be decomposed into 'Protein Units' that can be hierarchically assembled to delimit structural domains. For each partitioning solution, the relevance of the identified substructures is estimated through different measures. This multilevel analysis is achieved by integrating our previous work on domain delineation, 'protein peeling' and model quality assessment. We hope that SWORD2 will be useful to biologists searching for key regions in their proteins of interest and to bioinformaticians building datasets of protein structures. The web server is freely available online: https://www.dsimb.inserm.fr/SWORD2.

Biomedical researchers take advantage of high-throughput, high-coverage technologies to routinely generate sets of genes of interest across a wide range of biological conditions. Although these technologies have directly shed light on the molecular underpinnings of various biological processes and diseases, the list of genes from any individual experiment is often noisy and incomplete. Additionally, interpreting these lists of genes can be challenging in terms of how they are related to each other and to other genes in the genome. In this work, we present GenePlexus (https://www.geneplexus.net/), a web-server that allows a researcher to utilize a powerful, network-based machine learning method to gain insights into their gene set of interest and additional functionally similar genes. Once a user uploads their own set of human genes and chooses between a number of different human network representations, GenePlexus provides predictions of how associated every gene in the network is to the input set. The web-server also provides interpretability through network visualization and comparison to other machine learning models trained on thousands of known process/pathway and disease gene sets. GenePlexus is free and open to all users without the need for registration.

Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods in recent years; however, many tools developed in the field stay exclusive to only a few bioinformatic groups. To perform a complete RNA 3D structure modeling analysis as proposed by the RNA-Puzzles community, researchers must familiarize themselves with a quite complex set of tools. In order to facilitate the processing of RNA sequences and structures, we previously developed the rna-tools package. However, using rna-tools requires the installation of a mixture of libraries and tools, basic knowledge of the command line and the Python programming language. To provide an opportunity for the broader community of biologists to take advantage of the new developments in RNA structural biology, we developed rna-tools.online. The web server provides a user-friendly platform to perform many standard analyses required for the typical modeling workflow: 3D structure manipulation and editing, structure minimization, structure analysis, quality assessment, and comparison. rna-tools.online supports biologists to start benefiting from the maturing field of RNA 3D structural bioinformatics and can be used for educational purposes. The web server is available at https://rna-tools.online.

Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer driver genes can not be targeted directly, suggesting an indirect approach that considers alternative functionally related targets in the gene interaction network. Once potential drug targets have been identified, it is essential to consider all available drugs. Since tools that offer support for systematic discovery of drug repurposing candidates in oncology are lacking, we developed CADDIE, a web application integrating six human gene-gene and four drug-gene interaction databases, information regarding cancer driver genes, cancer-type specific mutation frequencies, gene expression information, genetically related diseases, and anticancer drugs. CADDIE offers access to various network algorithms for identifying drug targets and drug repurposing candidates. It guides users from the selection of seed genes to the identification of therapeutic targets or drug candidates, making network medicine algorithms accessible for clinical research. CADDIE is available at https://exbio.wzw.tum.de/caddie/ and programmatically via a python package at https://pypi.org/project/caddiepy/.

The visualization of biological sequences with various functional elements is fundamental for the publication of scientific achievements in the field of molecular and cellular biology. However, due to the limitations of the currently used applications, there are still considerable challenges in the preparation of biological schematic diagrams. Here, we present a professional tool called IBS 2.0 for illustrating the organization of both protein and nucleotide sequences. With the abundant graphical elements provided in IBS 2.0, biological sequences can be easily represented in a concise and clear way. Moreover, we implemented a database visualization module in IBS 2.0, enabling batch visualization of biological sequences from the UniProt and the NCBI RefSeq databases. Furthermore, to increase the design efficiency, a resource platform that allows uploading, retrieval, and browsing of existing biological sequence diagrams has been integrated into IBS 2.0. In addition, a lightweight JS library was developed in IBS 2.0 to assist the visualization of biological sequences in customized web services. To obtain the latest version of IBS 2.0, please visit https://ibs.renlab.org.

The NCBI Sequence Read Archive currently hosts microRNA sequencing data for over 800 different species, evidencing the existence of a broad taxonomic distribution in the field of small RNA research. Simultaneously, the number of samples per miRNA-seq study continues to increase resulting in a vast amount of data that requires accurate, fast and user-friendly analysis methods. Since the previous release of sRNAtoolbox in 2019, 55 000 sRNAbench jobs have been submitted which has motivated many improvements in its usability and the scope of the underlying annotation database. With this update, users can upload an unlimited number of samples or import them from Google Drive, Dropbox or URLs. Micro- and small RNA profiling can now be carried out using high-confidence Metazoan and plant specific databases, MirGeneDB and PmiREN respectively, together with genome assemblies and libraries from 441 Ensembl species. The new results page includes straightforward sample annotation to allow downstream differential expression analysis with sRNAde. Unassigned reads can also be explored by means of a new tool that performs mapping to microbial references, which can reveal contamination events or biologically meaningful findings as we describe in the example. sRNAtoolbox is available at: https://arn.ugr.es/srnatoolbox/.

Deep learning has been applied for solving many biological problems, and it has shown outstanding performance. Applying deep learning in research requires knowledge of deep learning theories and programming skills, but researchers have developed diverse deep learning platforms to allow users to build deep learning models without programming. Despite these efforts, it is still difficult for biologists to use deep learning because of limitations of the existing platforms. Therefore, a new platform is necessary that can solve these challenges for biologists. To alleviate this situation, we developed a user-friendly and easy-to-use web application called DLEB (Deep Learning Editor for Biologists) that allows for building deep learning models specialized for biologists. DLEB helps researchers (i) design deep learning models easily and (ii) generate corresponding Python code to run directly in their machines. DLEB provides other useful features for biologists, such as recommending deep learning models for specific learning tasks and data, pre-processing of input biological data, and availability of various template models and example biological datasets for model training. DLEB can serve as a highly valuable platform for easily applying deep learning to solve many important biological problems. DLEB is freely available at http://dleb.konkuk.ac.kr/.

Immune checkpoint blockade (ICB) therapy has been successfully applied to clinically therapeutics in multiple cancers, but its efficacy varies greatly among different patients and cancer types. Therefore, the construction of gene signatures to identify patients who could benefit from ICB therapy is particularly important for precision cancer treatment. However, due to the lack of a user-friendly platform, the construction of such gene signatures is a great challenge for clinical investigators who have limited programming skills. In light of this challenge, we developed a web server called Tumor Immunotherapy Response Signature Finder(TIRSF) for the construction of gene signatures to predict ICB therapy response in cancer patients. TIRSF consists of three functional modules. The first module is the Signature Discovery module which provides signature construction and performance evaluation functionalities. The second is a module for response prediction based on the TIRSF signatures, which enables response prediction and prognostic analysis of immunotherapy samples. The last is a module for response prediction based on existing signatures. This module currently integrates 24 published signatures for ICB therapy response prediction. Together, all of above features can be freely accessed at http://tirsf.renlab.org/.

Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.

Author information: (1)Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. (2)Swiss Institute for Bioinformatics, University of Lausanne, Lausanne, Switzerland. (3)HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. (4)Protein Function development, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. (5)Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain. (6)Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Tokyo, Japan. (7)Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain. (8)Department of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France. (9)BiGEst-ICube Platform, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France. (10)Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA. (11)Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden. (12)European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. (13)Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan. (14)Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain. (15)Centro de Investigaciones Biomédicas en Red de Enfermedades Infecciosas, Barcelona, Spain. (16)Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA. (17)Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA. (18)Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, UK. (19)Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge, UK. (20)Department of Computer Science, University College London, London, UK. (21)Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, UK. (22)Computer Science Department, ETH Zurich, Zurich, Switzerland.

In the last decade, significant advances have been made towards the rational design of proteins, DNA, and other organic nanostructures. The emerging possibility to precisely engineer molecular structures resulted in a wide range of new applications in fields such as biotechnology or medicine. The complexity and size of the artificial molecular systems as well as the number of interactions are greatly increasing and are manifesting the need for computational design support. In addition, a new generation of AI-based structure prediction tools provides researchers with completely new possibilities to generate recombinant proteins and functionalized DNA nanostructures. In this study, we present Catana, a web-based modelling environment suited for proteins and DNA nanostructures. User-friendly features were developed to create and modify recombinant fusion proteins, predict protein structures based on the amino acid sequence, and manipulate DNA origami structures. Moreover, Catana was jointly developed with the novel Unified Nanotechnology Format (UNF). Therefore, it employs a state-of-the-art coarse-grained data model, that is compatible with other established and upcoming applications. A particular focus was put on an effortless data export to allow even inexperienced users to perform in silico evaluations of their designs by means of molecular dynamics simulations. Catana is freely available at http://catana.ait.ac.at/.

RSAT (Regulatory Sequence Analysis Tools) enables the detection and the analysis of cis-regulatory elements in genomic sequences. This software suite performs (i) de novo motif discovery (including from genome-wide datasets like ChIP-seq/ATAC-seq) (ii) genomic sequences scanning with known motifs, (iii) motif analysis (quality assessment, comparisons and clustering), (iv) analysis of regulatory variations and (v) comparative genomics. RSAT comprises 50 tools. Six public Web servers (including a teaching server) are offered to meet the needs of different biological communities. RSAT philosophy and originality are: (i) a multi-modal access depending on the user needs, through web forms, command-line for local installation and programmatic web services, (ii) a support for virtually any genome (animals, bacteria, plants, totalizing over 10 000 genomes directly accessible). Since the 2018 NAR Web Software Issue, we have developed a large REST API, extended the support for additional genomes and external motif collections, enhanced some tools and Web forms, and developed a novel tool that builds or refine gene regulatory networks using motif scanning (network-interactions). The RSAT website provides extensive documentation, tutorials and published protocols. RSAT code is under open-source license and now hosted in GitHub. RSAT is available at http://www.rsat.eu/.

Phylogenomics data have grown exponentially over the last decades. It is currently common for genome-wide projects to generate hundreds or even thousands of phylogenetic trees and multiple sequence alignments, which may also be very large in size. However, the analysis and interpretation of such data still depends on custom bioinformatic and visualisation workflows that are largely unattainable for non-expert users. Here, we present PhyloCloud, an online platform aimed at hosting, indexing and exploring large phylogenetic tree collections, providing also seamless access to common analyses and operations, such as node annotation, searching, topology editing, automatic tree rooting, orthology detection and more. In addition, PhyloCloud provides quick access to tools that allow users to build their own phylogenies using fast predefined workflows, graphically compare tree topologies, or query taxonomic databases such as NBCI or GTDB. Finally, PhyloCloud offers a novel tree visualisation system based on ETE Toolkit v4.0, which can be used to explore very large trees and enhance them with custom annotations and multiple sequence alignments. The platform allows for sharing tree collections and specific tree views via private links, or make them fully public, serving also as a repository of phylogenomic data. PhyloCloud is available at https://phylocloud.cgmlab.org.

Circular dichroism (CD) spectroscopy is widely used to characterize the secondary structure composition of proteins. To derive accurate and detailed structural information from the CD spectra, we have developed the Beta Structure Selection (BeStSel) method (PNAS, 112, E3095), which can handle the spectral diversity of β-structured proteins. The BeStSel webserver provides this method with useful accessories to the community with the main goal to analyze single or multiple protein CD spectra. Uniquely, BeStSel provides information on eight secondary structure components including parallel β-structure and antiparallel β-sheets with three different groups of twist. It overperforms any available method in accuracy and information content, moreover, it is capable of predicting the protein fold down to the topology/homology level of the CATH classification. A new module of the webserver helps to distinguish intrinsically disordered proteins by their CD spectrum. Secondary structure calculation for uploaded PDB files will help the experimental verification of protein MD and in silico modelling using CD spectroscopy. The server also calculates extinction coefficients from the primary sequence for CD users to determine the accurate protein concentrations which is a prerequisite for reliable secondary structure determination. The BeStSel server can be freely accessed at https://bestsel.elte.hu.

Proteins with similar phylogenetic patterns of conservation or loss across evolutionary taxa are strong candidates to work in the same cellular pathways or engage in physical or functional interactions. Our previously published tools implemented our method of normalized phylogenetic sequence profiling to detect functional associations between non-homologous proteins. However, many proteins consist of multiple protein domains subjected to different selective pressures, so using protein domain as the unit of analysis improves the detection of similar phylogenetic patterns. Here we analyze sequence conservation patterns across the whole tree of life for every protein domain from a set of widely studied organisms. The resulting new interactive webserver, DEPCOD (DEtection of Phylogenetically COrrelated Domains), performs searches with either a selected pre-defined protein domain or a user-supplied sequence as a query to detect other domains from the same organism that have similar conservation patterns. Top similarities on two evolutionary scales (the whole tree of life or eukaryotic genomes) are displayed along with known protein interactions and shared complexes, pathway enrichment among the hits, and detailed visualization of sources of detected similarities. DEPCOD reveals functional relationships between often non-homologous domains that could not be detected using whole-protein sequences. The web server is accessible at http://genetics.mgh.harvard.edu/DEPCOD.

Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work presents a significantly improved method, DEMO2, which integrates analogous template structural alignments with deep-learning techniques for high-accuracy domain structure assembly. Starting from individual domain models, inter-domain spatial restraints are first predicted with deep residual convolutional networks, where full-length structure models are assembled using L-BFGS simulations under the guidance of a hybrid energy function combining deep-learning restraints and analogous multi-domain template alignments searched from the PDB. The output of DEMO2 contains deep-learning inter-domain restraints, top-ranked multi-domain structure templates, and up to five full-length structure models. DEMO2 was tested on a large-scale benchmark and the blind CASP14 experiment, where DEMO2 was shown to significantly outperform its predecessor and the state-of-the-art protein structure prediction methods. By integrating with new deep-learning techniques, DEMO2 should help fill the rapidly increasing gap between the improved ability of tertiary structure determination and the high demand for the high-quality multi-domain protein structures. The DEMO2 server is available at https://zhanggroup.org/DEMO/.

BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, far less prone to combinatorial 'explosions' and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

Here we present ICARUS, a web server to enable users without experience in R to undertake single cell RNA-seq analysis. The focal point of ICARUS is its intuitive tutorial-style user interface, designed to guide logical navigation through the multitude of pre-processing, analysis and visualization steps. ICARUS is easily accessible through a dedicated web server (https://launch.icarus-scrnaseq.cloud.edu.au/) and avoids installation of software on the user's computer. Notable features include the facility to apply quality control thresholds and adjust dimensionality reduction and cell clustering parameters. Data is visualized through 2D/3D UMAP and t-SNE plots and may be curated to remove potential confounders such as cell cycle heterogeneity. ICARUS offers flexible differential expression analysis with user-defined cell groups and gene set enrichment analysis to identify likely affected biological pathways. Eleven organisms including human, dog, mouse, rat, zebrafish, fruit fly, nematode, yeast, cattle, chicken and pig are currently supported. Visualization of multimodal data including those generated by CITE-seq and the 10X Genomics Multiome kit is included. ICARUS incorporates a function to save the current state of analysis avoiding computationally intensive steps during repeat analysis. The complete analysis of a typical single cell RNA-seq dataset by inexperienced users may be achieved in 1-2 h.

Cells contain intracellular compartments, including membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins are localised to one or more of these cellular compartments; the correct localisation of proteins is crucial for their correct processing and function. Moreover, proteins and the cellular processes they partake in are regulated by relocalisation in response to various cellular stimuli. High-throughput 'omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through the knowledge of their subcellular localisation, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatics tool, named SubcellulaRVis, that compellingly visualises the results of GOCC enrichment for quick interpretation of the localisation of a group of proteins (rather than single proteins). We demonstrate that SubcellulaRVis precisely describes the subcellular localisation of gene lists whose locations have been previously ascertained. SubcellulaRVis can be accessed via the web (http://phenome.manchester.ac.uk/subcellular/) or as a stand-alone app (https://github.com/JoWatson2011/subcellularvis). SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise who want to analyse data related to protein (re)localisation and location-specific modules within the intracellular protein network.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

BioUML (https://www.biouml.org)-is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development-u-science-a universal scientific platform that can be configured for specific research requirements.