Redox post-translational modifications on cysteine thiols (redox PTMs) have profound effects on protein structure and function, thus enabling regulation of various biological processes. Redox proteomics approaches aim to characterize the landscape of redox PTMs at the systems level. These approaches facilitate studies of condition-specific, dynamic processes implicating redox PTMs and have furthered our understanding of redox signaling and regulation. Mass spectrometry (MS) is a powerful tool for such analyses which has been demonstrated by significant advances in redox proteomics during the last decade. A group of well-established approaches involves the initial blocking of free thiols followed by selective reduction of oxidized PTMs and subsequent enrichment for downstream detection. Alternatively, novel chemoselective probe-based approaches have been developed for various redox PTMs. Direct detection of redox PTMs without any enrichment has also been demonstrated given the sensitivity of contemporary MS instruments. This review discusses the general principles behind different analytical strategies and covers recent advances in redox proteomics. Several applications of redox proteomics are also highlighted to illustrate how large-scale redox proteomics data can lead to novel biological insights.

In discovery proteomics, as well as many other "omic" approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Cardiovascular diseases (CVDs) are among the most morbid and deadly types of diseases worldwide, while the existing therapeutic approaches all have their limitations. Mouse heart undergoes a very complex postnatal developmental process, including the 1-week window in which cardiomyocytes (CMs) maintain relatively high cell activity. The underlying mechanism provides an attractive direction for CVDs treatment. Herein, we collected ventricular tissues from mice of different ages from E18.5D to P8W and performed iTRAQ-based quantitative proteomics to characterize the atlas of cardiac development. A total of 3422 proteins were quantified at all selected time points, revealing critical proteomic changes related to cardiac developmental events such as the metabolic transition from glycolysis to beta-oxidation. A cluster of significantly dysregulated proteins containing proteins that have already been reported to be associated with cardiac regeneration (Erbb2, Agrin, and Hmgb) was identified. Meanwhile, the peroxisome proliferator-activated receptor (PPAR) signaling pathway (Cpt1α, Hmgcs2, Plin2, and Fabp4) was also found specifically enriched. We further revealed that bezafibrate, a pan-activator of PPAR signaling pathway markedly enhanced H9C2 cardiomyocyte activity via enhancing Cpt1α expression. This work provides new hint that activation of PPAR signaling pathway could potentially be a therapeutic strategy for the treatment of CVDs.

Microglia are dynamic resident immune cells of the central nervous system (CNS) that sense, survey, and respond to changes in their environment. In disease states, microglia transform from homeostatic to diverse molecular phenotypic states that play complex and causal roles in neurologic disease pathogenesis, as evidenced by the identification of microglial genes as genetic risk factors for neurodegenerative disease. While advances in transcriptomic profiling of microglia from the CNS of humans and animal models have provided transformative insights, the transcriptome is only modestly reflective of the proteome. Proteomic profiling of microglia is therefore more likely to provide functionally and therapeutically relevant targets. In this review, we discuss molecular insights gained from transcriptomic studies of microglia in the context of Alzheimer's disease as a prototypic neurodegenerative disease, and highlight existing and emerging approaches for proteomic profiling of microglia derived from in vivo model systems and human brain.

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.

trans-Translation is the most effective ribosome rescue system known in bacteria. While it is essential in some bacteria, Bacillus subtilis possesses two additional alternative ribosome rescue mechanisms that require the proteins BrfA or RqcH. To investigate the physiology of trans-translation deficiency in the model organism B. subtilis, we compared the proteomes of B. subtilis 168 and a ΔssrA mutant in the mid-log phase using gel-free label-free quantitative proteomics. In chemically defined medium, the growth rate of the ssrA deletion mutant was 20% lower than that of B. subtilis 168. An 35 S-methionine incorporation assay demonstrated that protein synthesis rates were also lower in the ΔssrA strain. Alternative rescue factors were not detected. Among the 34 proteins overrepresented in the mutant strain were eight chemotaxis proteins. Indeed, both on LB agar and minimal medium the ΔssrA strain showed an altered motility and chemotaxis phenotype. Despite the lower growth rate, in the mutant proteome ribosomal proteins were more abundant while proteins related to amino acid biosynthesis were less abundant than in the parental strain. This overrepresentation of ribosomal proteins coupled with a lower protein synthesis rate and down-regulation of precursor supply reflects the slow ribosome recycling in the trans-translation-deficient mutant.

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the "resolution revolution," their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.

Hepatocyte nuclear factor 4-alpha (HNF4α) is a master regulator gene belonging to the nuclear receptor superfamily and is involved in regulating a wide range of critical biological processes in different organs. Structurally, the HNF4A locus is organized into two independent promoters and is subjected to alternative splicing to produce twelve distinct isoforms. However, little is known about the biological impact of each isoform and the mechanisms by which they regulate transcription. Proteomic analyses have led to the identification of proteins that interact with specific HNF4α isoforms. The identification and validation of these interactions and their roles in the co-regulation of targeted gene expression are essential to better understand the role of this transcription factor in different biological processes and pathologies. This review addresses the discoveries of different HNF4α isoforms and the main functions of the P1 and P2 isoform subgroups. It also provides information on the most recent focus areas in research on the nature and function of proteins associated with each of the isoforms in some biological contexts.

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.

Post-translational methylation of proteins, which occurs in arginines and lysines, modulates several biological processes at different levels of cell signaling. Recently, methylation has been demonstrated in the regulation beyond histones, for example, in the dynamics of protein-protein and protein-nucleic acid interactions. However, the presence and role of non-histone methylation in Trypanosoma cruzi, the etiologic agent of Chagas disease, has not yet been elucidated. Here, we applied mass spectrometry-based-proteomics (LC-MS/MS) to profile the methylproteome of T. cruzi epimastigotes, describing a total of 1252 methyl sites in 824 proteins. Functional enrichment and protein-protein interaction analysis show that protein methylation impacts important biological processes of the parasite, such as translation, RNA and DNA binding, amino acid, and carbohydrate metabolism. In addition, 171 of the methylated proteins were previously reported to bear phosphorylation sites in T. cruzi, including flagellar proteins and RNA binding proteins, indicating that there may be an interplay between these different modifications in non-histone proteins. Our results show that a broad spectrum of functions is affected by methylation in T. cruzi, indicating its potential to impact important processes in the biology of the parasite and other trypanosomes.

Chlorobaculum tepidum is an anaerobic green sulfur bacterium which oxidizes sulfide, elemental sulfur, and thiosulfate for photosynthetic growth. It can also oxidize sulfide to produce extracellular S0 globules, which can be further oxidized to sulfate and used as an electron donor. Here, we performed label-free quantitative proteomics on total cell lysates prepared from different metabolic states, including a sulfur production state (10 h post-incubation [PI]), the beginning of sulfur consumption (20 h PI), and the end of sulfur consumption (40 h PI), respectively. We observed an increased abundance of the sulfide:quinone oxidoreductase (Sqr) proteins in 10 h PI indicating a sulfur production state. The periplasmic thiosulfate-oxidizing Sox enzymes and the dissimilatory sulfite reductase (Dsr) subunits showed an increased abundance in 20 h PI, corresponding to the sulfur-consuming state. In addition, we found that the abundance of the heterodisulfide-reductase and the sulfhydrogenase operons was influenced by electron donor availability and may be associated with sulfur metabolism. Further, we isolated and analyzed the extracellular sulfur globules in the different metabolic states to study their morphology and the sulfur cluster composition, yielding 58 previously uncharacterized proteins in purified globules. Our results show that C. tepidum regulates the cellular levels of enzymes involved in sulfur metabolism in response to the availability of reduced sulfur compounds.

Enhancers are non-coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time-consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high-throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)-based prediction methods for enhancer identification and related databases has been provided. The existing enhancer-prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML-based predictors.

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Lactate is closely related to various cellular processes, such as angiogenesis, responses to hypoxia, and macrophage polarization, while regulating natural immune signaling pathways and promoting neurogenesis and cognitive function. Lysine lactylation (Kla) is a novel posttranslational modification, the examination of which may lead to new understanding of the nonmetabolic functions of lactate and the various physiological and pathological processes in which lactate is involved, such as infection, tumorigenesis and tumor development. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), researchers have identified lactylation in human gastric cancer cells and some other species, but no research on lactylation in human lungs has been reported. In this study, we performed global profiling of lactylation in human lungs under normal physiological conditions, and 724 Kla sites in 451 proteins were identified. After comparing the identified proteins with those reported in human lactylation datasets, 141 proteins that undergo lactylation were identified for the first time in this study. Our work expands the database on human lactylation and helps advance the study on lactylation function and regulation under physiological and pathological conditions.

A huge number of high-quality predicted protein structures are now publicly available. However, many of these structures contain non-globular regions, which diminish the performance of downstream structural bioinformatic applications. In this study, we develop AlphaCutter for the removal of non-globular regions from predicted protein structures. A large-scale cleaning of 542,380 predicted SwissProt structures highlights that AlphaCutter is able to (1) remove non-globular regions that are undetectable using pLDDT scores and (2) preserve high integrity of the cleaned domain regions. As useful applications, AlphaCutter improved the folding energy scores and sequence recovery rates in the re-design of domain regions. On average, AlphaCutter takes less than 3 s to clean a protein structure, enabling efficient cleaning of the exploding number of predicted protein structures. AlphaCutter is available at https://github.com/johnnytam100/AlphaCutter. AlphaCutter-cleaned SwissProt structures are available for download at https://doi.org/10.5281/zenodo.7944483.

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein-protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.

As a prevalent cancer type, hepatocellular carcinoma (HCC) accounts for a number of tumor-related deaths worldwide. Substantial efforts from various aspects, including RNA and proteins, have been devoted to understanding the mechanisms of HCC and proposing therapeutic schemes correspondingly. Within one of the important fields in cancer research - protein post-translational modifications (PTMs), recent discoveries revealed much broader landscapes of lysine lactylation (Kla) distributed in whole human proteome. Upon realizing the relation between Kla and cancers, Hong et al. (Proteomics 2023, 23, 2200432) comprehensively profiled lactylproteome in HCC tissues for the first time. All collected and processed samples were categorized into normal liver tissues, HCC without metastasis or HCC with lung metastasis tissues. As a result, 2045 Kla modification sites from 960 proteins were identified and 1438 sites from 772 proteins were quantifiably measured. Many differentially expressed Kla-proteins emerged and meant to contribute HCC formation and metastatsis. Among them, specific Kla sites from ubiquitin specific peptidase 14 (USP14) and ATP-binding cassette family 1 (ABCF1) were respectively verified as diagnostic indicators to characterize HCC and its metastasis. This work was of great significance, and made impacts in terms of further discovery of HCC rationale, as well as diagnosis of HCC status and targeted therapies.

3',5'-cyclic adenosine monophosphate (cAMP) is finally recognized as an essential signaling molecule in plants where cAMP-dependent processes include responses to hormones and environmental stimuli. To better understand the role of 3',5'-cAMP at the systems level, we have undertaken a phosphoproteomic analysis to elucidate the cAMP-dependent response of tobacco BY-2 cells. These cells overexpress a molecular "sponge" that buffers free intracellular cAMP level. The results show that, firstly, in vivo cAMP dampening profoundly affects the plant kinome and notably mitogen-activated protein kinases, receptor-like kinases, and calcium-dependent protein kinases, thereby modulating the cellular responses at the systems level. Secondly, buffering cAMP levels also affects mRNA processing through the modulation of the phosphorylation status of several RNA-binding proteins with roles in splicing, including many serine and arginine-rich proteins. Thirdly, cAMP-dependent phosphorylation targets appear to be conserved among plant species. Taken together, these findings are consistent with an ancient role of cAMP in mRNA processing and cellular programming and suggest that unperturbed cellular cAMP levels are essential for cellular homeostasis and signaling in plant cells.

Structural characterization of protein interactions is essential for our ability to understand and modulate physiological processes. Computational approaches to modeling of protein complexes provide structural information that far exceeds capabilities of the existing experimental techniques. Protein structure prediction in general, and prediction of protein interactions in particular, has been revolutionized by the rapid progress in Deep Learning techniques. The work of Schweke et al. (Proteomics 2023, 23, 2200323) presents a community-wide study of an important problem of distinguishing physiological protein-protein complexes/interfaces (experimentally determined or modeled) from non-physiological ones. The authors designed and generated a large benchmark set of physiological and non-physiological homodimeric complexes, and evaluated a large set of scoring functions, as well as AlphaFold predictions, on their ability to discriminate the non-physiological interfaces. The problem of separating physiological interfaces from non-physiological ones is very difficult, largely due to the lack of a clear distinction between the two categories in a crowded environment inside a living cell. Still, the ability to identify key physiologically significant interfaces in the variety of possible configurations of a protein-protein complex is important. The study presents a major data resource and methodological development in this important direction for molecular and cellular biology.

Target identification by modification-free proteomic approaches can potentially reveal the pharmacological mechanism of small molecular compounds. By combining the recent solvent-induced protein precipitation (SIP) method with TMT-labeling quantitative proteomics, we propose solvent-induced proteome profiling (SIPP) approach to identify small molecule-protein interactions. The SIPP approach enables to depict denaturation curves of the target protein by varying concentrations of organic solvents to induce unfolding and precipitation of the cellular proteome. By using this approach, we have successfully identified the known targets of market drugs and natural products and extended the proteome information of SIP for target identification.

Nostoc flagelliforme, a terrestrial cyanobacterium spread throughout arid and semi-arid areas, has been long known for its outstanding adaptability to extremely dry conditions. This microorganism is able to recover biological activities within hours after months of anhydrobiosis state, attracting investigation through proteomic analysis. Except for canonical proteome, microproteins encoded by small ORFs (smORFs) have recently been regarded as indispensable participants in metabolic processes. However, the involvement of smORFs in N. flagelliforme remains unknown. Here we first constructed a smORF database in N. flagelliforme using bioinformatic prediction, resulting in 6072 novel smORFs. Then LS-MS/MS analysis was applied to identify expression patterns of microproteins and seek smORFs and their encoded microprotein playing a role during rehydration. In total, 18 novel microproteins were mined based on a smORF searching strategy combined with three proteomic assays, of which five were annotated as ribosomal proteins, one as RNA polymerase subunit, and one as acetohydroxy acid isomeroreductase. We also suggested the possible functions of smORFs according to their expression pattern and discovered two neighboring and homologous smORFs. All these results will expand our knowledge of smORFs-encoded microproteins and their relation to the stress response of extremophilic microorganisms.

Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected hundreds of millions of people all over the world and thus threatens human life. Clinical evidence shows that SARS-CoV-2 infection can cause several neurological consequences, but the existing antiviral drugs and vaccines have failed to stop its spread. Therefore, an understanding of the response to SARS-CoV-2 infection of hosts is vital to find a resultful therapy. Here, we employed a K18-hACE2 mouse infection model and LC-MS/MS to systematically evaluate the acetylomes of brain cortexes in the presence and absence of SARS-CoV-2 infection. Using a label-free strategy, 3829 lysine acetylation (Kac) sites in 1735 histone and nonhistone proteins were identified. Bioinformatics analyses indicated that SARS-CoV-2 infection might lead to neurological consequences via acetylation or deacetylation of important proteins. According to a previous study, we found 26 SARS-CoV-2 proteins interacted with 61 differentially expressed acetylated proteins with high confidence and identified one acetylated SARS-CoV-2 protein nucleocapsid phosphoprotein. We greatly expanded the known set of acetylated proteins and provide the first report of the brain cortex acetylome in this model and thus a theoretical basis for future research on the pathological mechanisms and therapies of neurological consequences after SARS-CoV-2 infection.

Peptide tag systems are a robust biophysical and biochemical method that is widely used for protein detection and purification. Here, we developed a novel tag system termed "HiP4" (histidine plus four amino acids) whose epitope sequence comprises only seven amino acids (HHHDYDI) that partially overlap with the conventional 6x histidine tag (6xHis-tag). We produced a monoclonal antibody against the HiP4 tag that can be used in multiple immunoassays with high specificity and affinity. Using this system, we developed a tandem affinity purification (TAP) and mass spectrometry (TAP-MS) system for comprehensive protein interactome analysis. The integrated use of nickel bead purification followed by HiP4 tag immunoprecipitation made it possible to reduce nonspecific binding and improve selectivity, leading to the recovery of previously unrecognized proteins that interact with hepatitis B virus X (HBx) protein or TAR DNA-binding protein 43 (TARDBP or TDP-43). Our results indicate that this system may be viable as a simple and powerful tool for TAP-MS that can achieve low background and high selectivity in comprehensive protein-protein interaction analyses.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Aging is the main risk factor for the appearance of age-related neurodegenerative diseases, including Alzheimer's disease (AD). AD is the most common form of dementia, characterized by the presence of senile plaques (SPs) and neurofibrillary tangles (NFTs), the main histopathological hallmarks in AD brains. The core of these deposits are predominantly amyloid fibrils in SPs and hyperphosphorylated Tau protein in NFTs, but other molecular components can be found associated with these pathological lesions. Herein, an extensive literature review was carried out to obtain the SPs and NFTs proteomes, followed by a bioinformatic analysis and further putative biomarker validation. For SPs, 857 proteins were recovered, and, for NFTs, 627 proteins of which 375 occur in both groups and represent the common proteome. Gene Ontology (GO) enrichment analysis permitted the identification of biological processes and the molecular functions most associated with these lesions. Analysis of the SPs and NFTs common proteins unraveled pathways and molecular targets linking both histopathological events. Further, validation of a putative phosphotarget arising from the in silico analysis was performed in serum-derived extracellular vesicles from AD patients. This bioinformatic approach contributed to the identification of putative molecular targets, valuable for AD diagnostic or therapeutic intervention.

Data independent acquisition (DIA/SWATH) MS is a primary strategy in quantitative proteomics. diaPASEF is a recent adaptation using trapped ion mobility spectrometry (TIMS) to improve selectivity/sensitivity. Complex DIA spectra are typically analyzed with reference to spectral libraries. The best-established method for generating libraries uses offline fractionation to increase depth of coverage. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow DIA windows that cover different mass ranges of the complete precursor space, have been introduced that performed comparably to deep offline fractionation-based libraries. We investigated whether an analogous GPF-based approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data. We developed a rapid library generation approach using an IM-GPF acquisition scheme in the m/z versus 1/K0 space requiring seven injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation. We found that library generation by IM-GPF outperformed direct library generation from diaPASEF and had performance approaching that of the deep library. This establishes the IM-GPF scheme as a pragmatic approach to rapid library generation for analysis of diaPASEF data.

Cattle-yak, the interspecific hybrid between yak and taurine cattle, exhibits male-specific sterility. Massive loss of spermatogenic cells, especially spermatocytes, results in azoospermia in these animals. Currently, the mechanisms underlying meiosis block and defects in spermatocyte development remain elusive. The present study was designed to investigate the differences in the protein composition of spermatocytes isolated from 12-month-old yak and cattle-yak testes. Histological analysis confirmed that spermatocytes were the most advanced germ cells in the testes of yak and cattle-yak at this developmental stage. Comparative proteomic analysis identified a total of 452 differentially abundant proteins (DAPs) in the fluorescence-activated cell sorting (FACS) isolated spermatocytes from cattle-yak and yak. A total of 291 proteins were only present in yak spermatocytes. Gene Ontology analysis revealed that the downregulated DAPs were mostly enriched in the cellular response to DNA damage stimulus and double-strand breaks (DSBs) repair via break-induced replication, while the proteins specific for yak were related to cell division and cycle, spermatogenesis, and negative regulation of the extrinsic apoptotic signaling pathway. Ultimately, these DAPs were related to the critical process for spermatocyte meiotic events, including DSBs, homologous recombination, synapsis, crossover formation, and germ cell apoptosis. The database composed of proteins associated with spermatogenesis, including KPNA2, HTATSF1, TRIP12, STIP1, LZTFL1, LARP7, MTCH2, STK31, ROMO1, CDK5AP2, DNMT1, RBM44, and CHRAC1, is the focus of further research on male hybrid sterility. In total, these results provide insight into the molecular mechanisms underlying failed meiotic processes and male infertility in cattle-yak.

Rice is a major component of the human diet and feeds more than 50 million people across the globe. We previously developed two pigmented rice cultivars, Super-hongmi (red seeds) and Super-jami (black seeds), that are highly rich in antioxidants and exhibit high levels of radical scavenging activities. However, the molecular mechanism underlying the accumulation of pigments and different antioxidants in these rice cultivars remains largely elusive. Here, we report the proteome profiles of mature Super-hongmi and Super-jami seeds, and compared them with the Hopum (white seeds) using a label-free quantitative proteomics approach. This approach led to the identification of 5127 rice seed proteins of which 1628 showed significant changes in the pigmented rice cultivar(s). The list of significantly modulated proteins included a phytoene desaturase (PDS3) which suggested accumulation of ζ-carotene in red seeds while the black seeds seem to accumulate more of anthocyanins because of the higher abundance of dihydroflavonol 4-reductase. Moreover, proteins associated with lignin and tocopherol biosynthesis were highly increased in both red and black cultivars. Taken together, these data report the seed proteome of three different colored rice seeds and identify novel components associated with pigment accumulation in rice.

Metastatic triple-negative breast cancer (TNBC) has a low 5-year survival rate of below 30% with systemic chemotherapy being the most widely used treatment. Bovine milk-derived extracellular vesicles (MEVs) have been previously demonstrated to have anti-cancer attributes. In this study, we isolated bovine MEVs from commercial milk and characterised them according to MISEV guidelines. Bovine MEVs sensitised TNBC cells to doxorubicin, resulting in reduced metabolic potential and cell-viability. Label-free quantitative proteomics of cells treated with MEVs and/or doxorubicin suggested that combinatorial treatment depleted various pro-tumorigenic interferon-inducible gene products and proteins with metabolic function, previously identified as therapeutic targets in TNBC. Combinatorial treatment also led to reduced abundance of various STAT proteins and their downstream oncogenic targets with roles in cell-cycle and apoptosis. Taken together, this study highlights the ability of bovine MEVs to sensitise TNBC cells to standard-of-care therapeutic drug doxorubicin, paving the way for novel treatment regimens.

The retinal pigment epithelial (RPE)/choroid complex regulates myopia development, but the precise pathogenesis of myopia remains unclear. We aimed to investigate the changes in RPE/choroid complex metabolism in a form deprivation myopia model after dopamine D2 receptor (D2R) modulation. Guinea pigs were randomly divided into normal (NC), form deprivation myopia (FDM), and FDM treated with dopamine D2R antagonist groups. Differential metabolites were screened using SIMCA-P software and MetaboAnalyst metabolomics analysis tool. Functions of differential metabolites were analyzed using KEGG enrichment pathways. Relative to the NC group, 38 differential metabolites were identified, comprising 29 increased metabolites (including nicotinic acid, cytosine, and glutamate) and 9 decreased metabolites, of which proline exhibited the largest decrease. Pathway analysis revealed regulation of arginine/proline and aspartate/glutamate metabolism. Intravitreal D2R antagonist injection increased proline concentrations and activated arginine/proline and purine metabolism pathways. In sum, D2R antagonists alleviated the myopia trend of refractive biological parameters in form deprivation myopic guinea pigs, suggesting the involvement of dopamine D2R signaling in myopia pathogenesis. The RPE/choroid may provide glutamate to the retina by activating proline metabolism via metabolic coupling with the retina. Dopamine D2R antagonism may modulate proline/arginine metabolic pathways in the RPE/choroid and regulate metabolism, information presentation, and myopia.

The use of poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) as carriers for chemotherapeutic drugs is regarded as an actively targeted nano-therapy for the specific delivery of anti-cancer drugs to target cells. However, the exact mechanism by which PLGA NPs boost anticancer cytotoxicity at the molecular level remains largely unclear. This study employed different molecular approaches to define the response of carcinoma FaDu cells to different types of treatment, specifically: paclitaxel (PTX) alone, drug free PLGA NPs, and PTX-loaded PTX-PLGA NPs. Functional cell assays revealed that PTX-PLGA NPs treated cells had a higher level of apoptosis than PTX alone, whereas the complementary, UHPLC-MS/MS (TIMS-TOF) based multi-omics analyses revealed that PTX-PLGA NPs treatment resulted in increased abundance of proteins associated with tubulin, as well as metabolites such as 5-thymidylic acid, PC(18:1(9Z)/18:1(9Z0), vitamin D, and sphinganine among others. The multi-omics analyses revealed new insights about the molecular mechanisms underlying the action of novel anticancer NP therapies. In particular, PTX-loaded NPs appeared to exacerbate specific changes induced by both PLGA-NPs and PTX as a free drug. Hence, the PTX-PLGA NPs' molecular mode of action, seen in greater detail, depends on this synergy that ultimately accelerates the apoptotic process, resulting in cancer cell death.

Matrix-assisted laser desorption/ionization (MALDI) imaging of proteolytic peptides from formalin-fixed paraffin embedded (FFPE) tissue sections could be integrated in the portfolio of molecular pathologists for protein localization and tissue classification. However, protein identification can be very tedious using MALDI-time-of-flight (TOF) and post-source decay (PSD)-based fragmentation. Hereby, we implemented an R package and Shiny app to exploit liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic biomarker discovery data for more specific identification of peaks observed in bottom-up MALDI imaging data. The package is made available under the GPL 3 license. The Shiny app can directly be used at the following address: https://biosciences.shinyapps.io/Maldimid.

Over the past decade, the majority of the mammalian genome considered to be noncoding has been revealed to be able to produce proteins. Many RNA molecules, mis-annotated as noncoding, actually are predicted to code for proteins. Some of those proteins have been identified and verified to play critical roles in multiple biological processes. The lipid droplet (LD) is a unique cellular organelle bound with a phospholipid monolayer membrane, and is closely associated with cellular lipid metabolism and metabolic disorders. However, it is still unclear how a protein targets to LDs. Here we identified a new protein on LDs, LDANP2, which is encoded by noncoding RNA, through a proteomics-based strategy. The key sequence for its localization on LDs, Truncation 3, is predicted to form an amphipathic helix. Surprisingly, the deletion of the first amino acid in Truncation 3 resulted in mitochondrial localization. How the types of amino acids would determine the LD or mitochondrial localizations of the protein was studied. The findings introduce a useful strategy to mine for new proteins and would provide clues to the understanding of how a protein would find its right organelle, with phospholipid monolayer or bilayer membrane.

Pancreatic cancer is one of the most lethal cancer types and is becoming a leading cause of cancer-related deaths. The limited benefit offered by chemotherapy agents has propelled the search for alternative approaches that target specific molecular drivers of cancer growth and progression. Mutant KRas and effector pathways Raf/MEK/ERK and PI3K/Akt are key players in pancreatic cancer; however, preclinical studies have shown adaptive tumour response to combined MEK and PI3K kinase inhibition leading to treatment resistance. There is a critical unmet need to decipher the molecular basis underlying adaptation to this targeted approach. Here, we aimed to identify common protein expression alterations associated with adaptive resistance in KRas-mutant pancreatic cancer cells, and test if it can be overcome by selected already available small molecule drugs. We found a group of 14 proteins with common expression change in resistant cells, including KRas, caveolin-1, filamin-a, eplin, IGF2R and cytokeratins CK-8, -18 and -19. Notably, several proteins have previously been observed in pancreatic cancer cells with intrinsic resistance to the combined kinase inhibition treatment, suggesting a proteomic signature. We also found that resistant cells are sensitive to small molecule drugs ERK inhibitor GDC-0994, S6K1 inhibitor DG2 and statins.

Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co-purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme-linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace-level HCPs. Here, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC-MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub-ng/mg of mAb level.

Caspase-9 is the major apical caspase responsible for triggering the intrinsic apoptotic pathway. Our previous study indicated that specific inhibition of caspase-9 caused microscopically evident alterations in appearance of the primary chondrogenic cultures which cannot be explained by decrease in apoptosis. To describe a complex molecular background of this effect, proteomics analysis of control and caspase-9 inhibitor-treated chondrogenic cultures were performed. Proteins were extracted, identified and quantified using LC-MS in both data dependent and data independent acquisition (DIA) mode. While directDIA analysis of diaPASEF data obtained using timsTOF Pro LC-MS system revealed 7849 protein groups (Q-value <0.01), a parallel analysis of iTRAQ-2DLC-MS3 and conventional DIA-MS data identified only 5146 and 4098 protein groups, respectively, showing diaPASEF a superior method for the study. The detailed analysis of diaPASEF data disclosed 236/551 significantly down-/up-regulated protein groups after caspase-9 inhibition, respectively (|log2FC|>0.58, Q value <0.05). Classification of downregulated proteins revealed changes in extracellular matrix organization, collagen metabolism, and muscle system processes. Moreover, deregulations suggest a switch from glycolytic to lipid based metabolism in the inhibited cells. No essential changes were found in the proteins involved in apoptosis. The data indicate new non-apoptotic participation of caspases in chondrocyte homeostasis with potential applications in cartilage pathophysiology.

Grain size is one of the most important agronomic traits for grain yield determination in rice. To better understand the proteins that are regulated by the grain size regulatory gene OsMKK3, this gene was knocked out using the CRISPR/Cas9 system, and tandem mass tag (TMT) labeling combined with liquid chromatograph-tandem mass spectrometry analysis was performed to study the regulation of proteins in the panicle. Quantitative proteomic screening revealed a total of 106 differentially expressed proteins (DEPs) via comparison of the OsMKK3 mutant line to the wild-type YexiangB, including 15 and 91 up-regulated and down-regulated DEPs, respectively. Pathway analysis revealed that DEPs were enriched in metabolic pathways, biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, and photosynthesis. Strong interactions were detected among seven down-regulated proteins related to photosystem components in the protein-protein interaction network, and photosynthetic rate was decreased in mutant plants. The results of the liquid chromatography-parallel reaction monitoring/mass spectromery analysis and western blot analysis were consistent with the results of the proteomic analysis, and the results of the quantitative reverse transcription polymerase chain reaction analysis revealed that the expression levels of most candidate genes were consistent with protein levels. Overall, OsMKK3 controls grain size by regulating the protein content in cells. Our findings provide new candidate genes that will aid the study of grain size regulatory mechanisms associated with the mitogen-activated protein kinase (MAPK) signaling pathway.

Interactions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi-omics approach to unravel gut microbiome signatures from 32 biopsy-proven patients (10 simple steatosis -SS- and 22 steatohepatitis -SH-) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH.

Hypertension is one of the most important and complex risk factors for cardiovascular diseases (CVDs). By using urinary peptidomics analyses, we aimed to identify peptides associated with hypertension, building a framework for future research towards improved prediction and prevention of premature development of CVD. We included 78 hypertensive and 79 normotensive participants from the African-PREDICT study (aged 20-30 years), matched for sex (51% male) and ethnicity (49% black and 51% white). Urinary peptidomics data were acquired using capillary-electrophoresis-time-of-flight-mass-spectrometry. Hypertension-associated peptides were identified and combined into a support vector machine-based multidimensional classifier. When comparing the peptide data between the normotensive and hypertensive groups, 129 peptides were nominally differentially abundant (Wilcoxon p < 0.05). Nonetheless, only three peptides, all derived from collagen alpha-1(III), remained significantly different after rigorous adjustments for multiple comparisons. The 37 most significant peptides (all p ≤ 0.001) served as basis for the development of a classifier, with 20 peptides being combined into a unifying score, resulting in an AUC of 0.85 in the ROC analysis (p < 0.001), with 83% sensitivity at 80% specificity. Our study suggests potential value of urinary peptides in the classification of hypertension, which could enable earlier diagnosis and better understanding of the pathophysiology of hypertension and premature cardiovascular disease development.

Synovial fluid (SF) may contain cleavage products of proteolytic activities. Our aim was to characterize the degradome through analysis of proteolytic activity and differential abundance of these components in a peptidomic analysis of SF in knee osteoarthritis (OA) patients versus controls (n = 23). SF samples from end-stage knee osteoarthritis patients undergoing total knee replacement surgery and controls, that is, deceased donors without known knee disease were previously run using liquid chromatography mass spectrometry (LC-MS). This data was used to perform new database searches generating results for non-tryptic and semi-tryptic peptides for studies of degradomics in OA. We used linear mixed models to estimate differences in peptide-level expression between the two groups. Known proteolytic events (from the MEROPS peptidase database) were mapped to the dataset, allowing the identification of potential proteases and which substrates they cleave. We also developed a peptide-centric R tool, proteasy, which facilitates analyses that involve retrieval and mapping of proteolytic events. We identified 429 differentially abundant peptides. We found that the increased abundance of cleaved APOA1 peptides is likely a consequence of enzymatic degradation by metalloproteinases and chymase. We identified metalloproteinase, chymase, and cathepsins as the main proteolytic actors. The analysis indicated increased activity of these proteases irrespective of their abundance.

Cancer cachexia is a wasting syndrome characterised by the loss of fat and/or muscle mass in advanced cancer patients. It has been well-established that cancer cells themselves can induce cachexia via the release of several pro-cachectic and pro-inflammatory factors. However, it is unclear how this process is regulated and the key cachexins that are involved. In this study, we validated C26 and EL4 as cachexic and non-cachexic cell models, respectively. Treatment of adipocytes and myotubes with C26 conditioned medium induced lipolysis and atrophy, respectively. We profiled soluble secreted proteins (secretome) as well as small extracellular vesicles (sEVs) released from cachexia-inducing (C26) and non-inducing (EL4) cancer cells by label-free quantitative proteomics. A total of 1268 and 1022 proteins were identified in the secretome of C26 and EL4, respectively. Furthermore, proteomic analysis of sEVs derived from C26 and EL4 cancer cells revealed a distinct difference in the protein cargo. Functional enrichment analysis using FunRich highlighted the enrichment of proteins that are implicated in biological processes such as muscle atrophy, lipolysis, and inflammation in both the secretome and sEVs derived from C26 cancer cells. Overall, our characterisation of the proteomic profiles of the secretory factors and sEVs from cachexia-inducing and non-inducing cancer cells provides insights into tumour factors that promote weight loss by mediating protein and lipid loss in various organs and tissues. Further investigation of these proteins may assist in highlighting potential therapeutic targets and biomarkers of cancer cachexia.