BACKGROUND: JUNB transcription factor contributes to the formation of the ubiquitous transcriptional complex AP-1 involved in the control of many physiological and disease-associated functions. The roles of JUNB in the control of cell division and tumorigenic processes are acknowledged but still unclear. RESULTS: Here, we report the results of combined transcriptomic, genomic, and functional studies showing that JUNB promotes cell cycle progression via induction of cyclin E1 and repression of transforming growth factor (TGF)-β2 genes. We also show that high levels of JUNB switch the response of TGF-β2 stimulation from an antiproliferative to a pro-invasive one, induce endogenous TGF-β2 production by promoting TGF-β2 mRNA translation, and enhance tumor growth and metastasis in mice. Moreover, tumor genomic data indicate that JUNB amplification associates with poor prognosis in breast and ovarian cancer patients. CONCLUSIONS: Our results reveal novel functions for JUNB in cell proliferation and tumor aggressiveness through regulation of cyclin E1 and TGF-β2 expression, which might be exploited for cancer prognosis and therapy.

BACKGROUND: Despite having been extensively studied, it remains largely unclear why humans bear a particularly high risk of cancer. The antagonistic pleiotropy hypothesis predicts that primate-specific genes (PSGs) tend to promote tumorigenesis, while the molecular atavism hypothesis predicts that PSGs involved in tumors may represent recently derived duplicates of unicellular genes. However, these predictions have not been tested. RESULTS: By taking advantage of pan-cancer genomic data, we find the upregulation of PSGs across 13 cancer types, which is facilitated by copy-number gain and promoter hypomethylation. Meta-analyses indicate that upregulated PSGs (uPSGs) tend to promote tumorigenesis and to play cell cycle-related roles. The cell cycle-related uPSGs predominantly represent derived duplicates of unicellular genes. We prioritize 15 uPSGs and perform an in-depth analysis of one unicellular gene-derived duplicate involved in the cell cycle, DDX11. Genome-wide screening data and knockdown experiments demonstrate that DDX11 is broadly essential across cancer cell lines. Importantly, non-neutral amino acid substitution patterns and increased expression indicate that DDX11 has been under positive selection. Finally, we find that cell cycle-related uPSGs are also preferentially upregulated in the highly proliferative embryonic cerebrum. CONCLUSIONS: Consistent with the predictions of the atavism and antagonistic pleiotropy hypotheses, primate-specific genes, especially those PSGs derived from cell cycle-related genes that emerged in unicellular ancestors, contribute to the early proliferation of the human cerebrum at the cost of hitchhiking by similarly highly proliferative cancer cells.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

The fundamental challenge of multi-sample structural variant (SV) analysis such as merging and benchmarking is identifying when two SVs are the same. Common approaches for comparing SVs were developed alongside technologies which produce ill-defined boundaries. As SV detection becomes more exact, algorithms to preserve this refined signal are needed. Here, we present Truvari-an SV comparison, annotation, and analysis toolkit-and demonstrate the effect of SV comparison choices by building population-level VCFs from 36 haplotype-resolved long-read assemblies. We observe over-merging from other SV merging approaches which cause up to a 2.2× inflation of allele frequency, relative to Truvari.

A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.

BACKGROUND: Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation. RESULTS: We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically. CONCLUSIONS: ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices-iterative QC, providing a data-driven QC framework compatible with observed biological diversity.

Most single-cell RNA sequencing (scRNA-seq) analyses begin with cell clustering; thus, the clustering accuracy considerably impacts the validity of downstream analyses. In contrast with the abundance of clustering methods, the tools to assess the clustering accuracy are limited. We propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. As a result, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

BACKGROUND: Lynch syndrome (LS) is a cancer predisposition syndrome affecting more than 1 in every 300 individuals worldwide. Clinical genetic testing for LS can be life-saving but is complicated by the heavy burden of variants of uncertain significance (VUS), especially missense changes. RESULT: To address this challenge, we leverage a multiplexed analysis of variant effect (MAVE) map covering >94% of the 17,746 possible missense variants in the key LS gene MSH2. To establish this map's utility in large-scale variant reclassification, we overlay it on clinical databases of >15,000 individuals with LS gene variants uncovered during clinical genetic testing. We validate these functional measurements in a cohort of individuals with paired tumor-normal test results and find that MAVE-based function scores agree with the clinical interpretation for every one of the MSH2 missense variants with an available classification. We use these scores to attempt reclassification for 682 unique missense VUS, among which 34 scored as deleterious by our function map, in line with previously published rates for other cancer predisposition genes. Combining functional data and other evidence, ten missense VUS are reclassified as pathogenic/likely pathogenic, and another 497 could be moved to benign/likely benign. Finally, we apply these functional scores to paired tumor-normal genetic tests and identify a subset of patients with biallelic somatic loss of function, reflecting a sporadic Lynch-like Syndrome with distinct implications for treatment and relatives' risk. CONCLUSION: This study demonstrates how high-throughput functional assays can empower scalable VUS resolution and prospectively generate strong evidence for variant classification.

BACKGROUND: Heterosis is widely used in agriculture. However, its molecular mechanisms are still unclear in plants. Here, we develop, sequence, and record the phenotypes of 418 hybrids from crosses between two testers and 265 rice varieties from a mini-core collection. RESULTS: Phenotypic analysis shows that heterosis is dependent on genetic backgrounds and environments. By genome-wide association study of 418 hybrids and their parents, we find that nonadditive QTLs are the main genetic contributors to heterosis. We show that nonadditive QTLs are more sensitive to the genetic background and environment than additive ones. Further simulations and experimental analysis support a novel mechanism, homo-insufficiency under insufficient background (HoIIB), underlying heterosis. We propose heterosis in most cases is not due to heterozygote advantage but homozygote disadvantage under the insufficient genetic background. CONCLUSION: The HoIIB model elucidates that genetic background insufficiency is the intrinsic mechanism of background dependence, and also the core mechanism of nonadditive effects and heterosis. This model can explain most known hypotheses and phenomena about heterosis, and thus provides a novel theory for hybrid rice breeding in future.

BACKGROUND: The tumor microenvironment (TME) has been shown to strongly influence treatment outcome for cancer patients in various indications and to influence the overall survival. However, the cells forming the TME in gastric cancer have not been extensively characterized. RESULTS: We combine bulk and single-cell RNA sequencing from tumors and matched normal tissue of 24 treatment-naïve GC patients to better understand which cell types and transcriptional programs are associated with malignant transformation of the stomach. Clustering 96,623 cells of non-epithelial origin reveals 81 well-defined TME cell types. We find that activated fibroblasts and endothelial cells are most prominently overrepresented in tumors. Intercellular network reconstruction and survival analysis of an independent cohort imply the importance of these cell types together with immunosuppressive myeloid cell subsets and regulatory T cells in establishing an immunosuppressive microenvironment that correlates with worsened prognosis and lack of response in anti-PD1-treated patients. In contrast, we find a subset of IFNγ activated T cells and HLA-II expressing macrophages that are linked to treatment response and increased overall survival. CONCLUSIONS: Our gastric cancer single-cell TME compendium together with the matched bulk transcriptome data provides a unique resource for the identification of new potential biomarkers for patient stratification. This study helps further to elucidate the mechanism of gastric cancer and provides insights for therapy.

BACKGROUND: Despite its conserved role on gene expression and transposable element (TE) silencing, genome-wide CG methylation differs substantially between wild Arabidopsis thaliana accessions. RESULTS: To test our hypothesis that global reduction of CG methylation would reduce epigenomic, transcriptomic, and phenotypic diversity in A. thaliana accessions, we knock out MET1, which is required for CG methylation, in 18 early-flowering accessions. Homozygous met1 mutants in all accessions suffer from common developmental defects such as dwarfism and delayed flowering, in addition to accession-specific abnormalities in rosette leaf architecture, silique morphology, and fertility. Integrated analysis of genome-wide methylation, chromatin accessibility, and transcriptomes confirms that MET1 inactivation greatly reduces CG methylation and alters chromatin accessibility at thousands of loci. While the effects on TE activation are similarly drastic in all accessions, the quantitative effects on non-TE genes vary greatly. The global expression profiles of accessions become considerably more divergent from each other after genome-wide removal of CG methylation, although a few genes with diverse expression profiles across wild-type accessions tend to become more similar in mutants. Most differentially expressed genes do not exhibit altered chromatin accessibility or CG methylation in cis, suggesting that absence of MET1 can have profound indirect effects on gene expression and that these effects vary substantially between accessions. CONCLUSIONS: Systematic analysis of MET1 requirement in different A. thaliana accessions reveals a dual role for CG methylation: for many genes, CG methylation appears to canalize expression levels, with methylation masking regulatory divergence. However, for a smaller subset of genes, CG methylation increases expression diversity beyond genetically encoded differences.

BACKGROUND: DNA N6-methyldeoxyadenosine (6mA) is rarely present in mammalian cells and its nuclear role remains elusive. RESULTS: Here we show that hypoxia induces nuclear 6mA modification through a DNA methyltransferase, METTL4, in hypoxia-induced epithelial-mesenchymal transition (EMT) and tumor metastasis. Co-expression of METTL4 and 6mA represents a prognosis marker for upper tract urothelial cancer patients. By RNA sequencing and 6mA chromatin immunoprecipitation-exonuclease digestion followed by sequencing, we identify lncRNA RP11-390F4.3 and one novel HIF-1α co-activator, ZMIZ1, that are co-regulated by hypoxia and METTL4. Other genes involved in hypoxia-mediated phenotypes are also regulated by 6mA modification. Quantitative chromatin isolation by RNA purification assay shows the occupancy of lncRNA RP11-390F4.3 on the promoters of multiple EMT regulators, indicating lncRNA-chromatin interaction. Knockdown of lncRNA RP11-390F4.3 abolishes METTL4-mediated tumor metastasis. We demonstrate that ZMIZ1 is an essential co-activator of HIF-1α. CONCLUSIONS: We show that hypoxia results in enriched 6mA levels in mammalian tumor cells through METTL4. This METTL4-mediated nuclear 6mA deposition induces tumor metastasis through activating multiple metastasis-inducing genes. METTL4 is characterized as a potential therapeutic target in hypoxic tumors.

BACKGROUND: Chinese cabbage (Brassica rapa ssp. pekinensis) experienced a whole-genome triplication event and thus has three subgenomes: least fractioned, medium fractioned, and most fractioned subgenome. Environmental changes affect leaf development, which in turn influence the yield. To improve the yield and resistance to different climate scenarios, a comprehensive understanding of leaf development is required including insights into the full diversity of cell types and transcriptional networks underlying their specificity. RESULTS: Here, we generate the transcriptional landscape of Chinese cabbage leaf at single-cell resolution by performing single-cell RNA sequencing of 30,000 individual cells. We characterize seven major cell types with 19 transcriptionally distinct cell clusters based on the expression of the reported marker genes. We find that genes in the least fractioned subgenome are predominantly expressed compared with those in the medium and most fractioned subgenomes in different cell types. Moreover, we generate a single-cell transcriptional map of leaves in response to high temperature. We find that heat stress not only affects gene expression in a cell type-specific manner but also impacts subgenome dominance. CONCLUSIONS: Our study highlights the transcriptional networks in different cell types and provides a better understanding of transcriptional regulation during leaf development and transcriptional response to heat stress in Chinese cabbage.

BACKGROUND: Many biological processes, such as cell division cycle and drug resistance, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass spectrometry with sufficiently high throughput and accuracy. RESULTS: Here, we describe nPOP, a method that enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8-20 nl. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 pl volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated glass slide. Protein covariation analysis identifies cell cycle dynamics that are similar and dynamics that differ between cell types, even within subpopulations of melanoma cells delineated by markers for drug resistance priming. Melanoma cells expressing these markers accumulate in the G1 phase of the cell cycle, display distinct protein covariation across the cell cycle, accumulate glycogen, and have lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibit gradients of protein abundance, suggesting transition states. Within this subpopulation, proteins functioning in oxidative phosphorylation covary with each other and inversely with proteins functioning in glycolysis. This protein covariation suggests divergent reliance on energy sources and its association with other biological functions. These results are validated by different mass spectrometry methods. CONCLUSIONS: nPOP enables flexible, automated, and highly parallelized sample preparation for single-cell proteomics. This allows for quantifying protein covariation across thousands of single cells and revealing functionally concerted biological differences between closely related cell states. Support for nPOP is available at https://scp.slavovlab.net/nPOP .

Advancing crop genomics requires efficient genetic systems enabled by high-quality personalized genome assemblies. Here, we introduce RagTag, a toolset for automating assembly scaffolding and patching, and we establish chromosome-scale reference genomes for the widely used tomato genotype M82 along with Sweet-100, a new rapid-cycling genotype that we developed to accelerate functional genomics and genome editing in tomato. This work outlines strategies to rapidly expand genetic systems and genomic resources in other plant species.

Read alignment is often the computational bottleneck in analyses. Recently, several advances have been made on seeding methods for fast sequence comparison. We combine two such methods, syncmers and strobemers, in a novel seeding approach for constructing dynamic-sized fuzzy seeds and implement the method in a short-read aligner, strobealign. The seeding is fast to construct and effectively reduces repetitiveness in the seeding step, as shown using a novel metric E-hits. strobealign is several times faster than traditional aligners at similar and sometimes higher accuracy while being both faster and more accurate than more recently proposed aligners for short reads of lengths 150nt and longer. Availability: https://github.com/ksahlin/strobealign.

BACKGROUND: G-quadruplexes (G4s) are unique noncanonical nucleic acid secondary structures, which have been proposed to physically interact with transcription factors and chromatin remodelers to regulate cell type-specific transcriptome and shape chromatin landscapes. RESULTS: Based on the direct interaction between G4 and natural porphyrins, we establish genome-wide approaches to profile where the iron-liganded porphyrin hemin can bind in the chromatin. Hemin promotes genome-wide G4 formation, impairs transcription initiation, and alters chromatin landscapes, including decreased H3K27ac and H3K4me3 modifications at promoters. Interestingly, G4 status is not involved in the canonical hemin-BACH1-NRF2-mediated enhancer activation process, highlighting an unprecedented G4-dependent mechanism for metabolic regulation of transcription. Furthermore, hemin treatment induces specific gene expression profiles in hepatocytes, underscoring the in vivo potential for metabolic control of gene transcription by porphyrins. CONCLUSIONS: These studies demonstrate that G4 functions as a sensor for natural porphyrin metabolites in cells, revealing a G4-dependent mechanism for metabolic regulation of gene transcription and chromatin landscapes, which will deepen our knowledge of G4 biology and the contribution of cellular metabolites to gene regulation.

Expansions of short tandem repeats (STRs) cause many rare diseases. Expansion detection is challenging with short-read DNA sequencing data since supporting reads are often mapped incorrectly. Detection is particularly difficult for "novel" STRs, which include new motifs at known loci or STRs absent from the reference genome. We developed STRling to efficiently count k-mers to recover informative reads and call expansions at known and novel STR loci. STRling is sensitive to known STR disease loci, has a low false discovery rate, and resolves novel STR expansions to base-pair position accuracy. It is fast, scalable, open-source, and available at: github.com/quinlan-lab/STRling .

Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.

BACKGROUND: The pathogen landscape in the Early European Middle Ages remains largely unexplored. Here, we perform a systematic pathogen screening of the rural community Lauchheim "Mittelhofen," in present-day Germany, dated to the Merovingian period, between fifth and eighth century CE. Skeletal remains of individuals were subjected to an ancient DNA metagenomic analysis. Genomes of the detected pathogens were reconstructed and analyzed phylogenetically. RESULTS: Over 30% of the individuals exhibit molecular signs of infection with hepatitis B virus (HBV), parvovirus B19, variola virus (VARV), and Mycobacterium leprae. Seven double and one triple infection were detected. We reconstructed four HBV genomes and one genome each of B19, VARV, and M. leprae. All HBV genomes are of genotype D4 which is rare in Europe today. The VARV strain exhibits a unique pattern of gene loss indicating that viruses with different gene compositions were circulating in the Early Middle Ages. The M. leprae strain clustered in branch 3 together with the oldest to-date genome from the UK. CONCLUSIONS: The high burden of infectious disease, together with osteological markers of physiological stress, reflect a poor health status of the community. This could have been an indirect result of the climate decline in Europe at the time, caused by the Late Antique Little Ice Age (LALIA). Our findings suggest that LALIA may have created an ecological context in which persistent outbreaks set the stage for major epidemics of severe diseases such as leprosy and smallpox hundreds of years later.

BACKGROUND: The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. RESULTS: We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. CONCLUSIONS: A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

BACKGROUND: Short tandem repeats (STRs) compose approximately 3% of the genome, and mutations at STR loci have been linked to dozens of human diseases including amyotrophic lateral sclerosis, Friedreich ataxia, Huntington disease, and fragile X syndrome. Improving our understanding of these mutations would increase our knowledge of the mutational dynamics of the genome and may uncover additional loci that contribute to disease. To estimate the genome-wide pattern of mutations at STR loci, we analyze blood-derived whole-genome sequencing data for 544 individuals from 29 three-generation CEPH pedigrees. These pedigrees contain both sets of grandparents, the parents, and an average of 9 grandchildren per family. RESULTS: We use HipSTR to identify de novo STR mutations in the 2nd generation of these pedigrees and require transmission to the third generation for validation. Analyzing approximately 1.6 million STR loci, we estimate the empirical de novo STR mutation rate to be 5.24 × 10-5 mutations per locus per generation. Perfect repeats mutate about 2 × more often than imperfect repeats. De novo STRs are significantly enriched in Alu elements. CONCLUSIONS: Approximately 30% of new STR mutations occur within Alu elements, which compose only 11% of the genome, but only 10% are found in LINE-1 insertions, which compose 17% of the genome. Phasing these mutations to the parent of origin shows that parental transmission biases vary among families. We estimate the average number of de novo genome-wide STR mutations per individual to be approximately 85, which is similar to the average number of observed de novo single nucleotide variants.

BACKGROUND: Eukaryotic ribosomes are widely presumed to scan mRNA for the AUG codon to initiate translation in a strictly 5'-3' movement (i.e., strictly unidirectional scanning model), so that ribosomes initiate translation exclusively at the 5' proximal AUG codon (i.e., the first-AUG rule). RESULTS: We generate 13,437 yeast variants, each with an ATG triplet placed downstream (dATGs) of the annotated ATG (aATG) codon of a green fluorescent protein. We find that out-of-frame dATGs can inhibit translation at the aATG, but with diminishing strength over increasing distance between aATG and dATG, undetectable beyond ~17 nt. This phenomenon is best explained by a Brownian ratchet mechanism of ribosome scanning, in which the ribosome uses small-amplitude 5'-3' and 3'-5' oscillations with a net 5'-3' movement to scan the AUG codon, thereby leading to competition for translation initiation between aAUG and a proximal dAUG. This scanning model further predicts that the inhibitory effect induced by an out-of-frame upstream AUG triplet (uAUG) will diminish as uAUG approaches aAUG, which is indeed observed among the 15,586 uATG variants generated in this study. Computational simulations suggest that each triplet is scanned back and forth approximately ten times until the ribosome eventually migrates to downstream regions. Moreover, this scanning process could constrain the evolution of sequences downstream of the aATG to minimize proximal out-of-frame dATG triplets in yeast and humans. CONCLUSIONS: Collectively, our findings uncover the basic process by which eukaryotic ribosomes scan for initiation codons, and how this process could shape eukaryotic genome evolution.