Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by respiratory distress, multiorgan dysfunction, and, in some cases, ...death. The pathological mechanisms underlying COVID-19 respiratory distress and the interplay with aggravating risk factors have not been fully defined. Lung autopsy samples from 18 patients with fatal COVID-19, with symptom onset-to-death times ranging from 3 to 47 days, and antemortem plasma samples from 6 of these cases were evaluated using deep sequencing of SARS-CoV-2 RNA, multiplex plasma protein measurements, and pulmonary gene expression and imaging analyses. Prominent histopathological features in this case series included progressive diffuse alveolar damage with excessive thrombosis and late-onset pulmonary tissue and vascular remodeling. Acute damage at the alveolar-capillary barrier was characterized by the loss of surfactant protein expression with injury to alveolar epithelial cells, endothelial cells, respiratory epithelial basal cells, and defective tissue repair processes. Other key findings included impaired clot fibrinolysis with increased concentrations of plasma and lung plasminogen activator inhibitor-1 and modulation of cellular senescence markers, including p21 and sirtuin-1, in both lung epithelial and endothelial cells. Together, these findings further define the molecular pathological features underlying the pulmonary response to SARS-CoV-2 infection and provide important insights into signaling pathways that may be amenable to therapeutic intervention.
Behavior is among the most dynamic animal phenotypes, modulated by a variety of internal and external stimuli. Behavioral differences are associated with large-scale changes in gene expression, but ...little is known about how these changes are regulated. Here we show how a transcription factor (TF), ultraspiracle (usp; the insect homolog of the Retinoid X Receptor), working in complex transcriptional networks, can regulate behavioral plasticity and associated changes in gene expression. We first show that RNAi knockdown of USP in honey bee abdominal fat bodies delayed the transition from working in the hive (primarily "nursing" brood) to foraging outside. We then demonstrate through transcriptomics experiments that USP induced many maturation-related transcriptional changes in the fat bodies by mediating transcriptional responses to juvenile hormone. These maturation-related transcriptional responses to USP occurred without changes in USP's genomic binding sites, as revealed by ChIP-chip. Instead, behaviorally related gene expression is likely determined by combinatorial interactions between USP and other TFs whose cis-regulatory motifs were enriched at USP's binding sites. Many modules of JH- and maturation-related genes were co-regulated in both the fat body and brain, predicting that usp and cofactors influence shared transcriptional networks in both of these maturation-related tissues. Our findings demonstrate how "single gene effects" on behavioral plasticity can involve complex transcriptional networks, in both brain and peripheral tissues.
Songbirds represent an important model organism for elucidating molecular mechanisms that link genes with complex behaviors, in part because they have discrete vocal learning circuits that have ...parallels with those that mediate human speech. We found that ~10% of the genes in the avian genome were regulated by singing, and we found a striking regional diversity of both basal and singing-induced programs in the four key song nuclei of the zebra finch, a vocal learning songbird. The region-enriched patterns were a result of distinct combinations of region-enriched transcription factors (TFs), their binding motifs, and presinging acetylation of histone 3 at lysine 27 (H3K27ac) enhancer activity in the regulatory regions of the associated genes. RNA interference manipulations validated the role of the calcium-response transcription factor (CaRF) in regulating genes preferentially expressed in specific song nuclei in response to singing. Thus, differential combinatorial binding of a small group of activity-regulated TFs and predefined epigenetic enhancer activity influences the anatomical diversity of behaviorally regulated gene networks.
We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular ...bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.
Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in ...behavior are poorly understood. We characterized the molecular basis of behavioral plasticity in queenless honey bee (
) colonies, where individuals engage in both reproductive and non-reproductive behaviors. Using high-throughput behavioral tracking, we discovered these colonies contain a continuum of phenotypes, with some individuals specialized for either egg-laying or foraging and 'generalists' that perform both. Brain gene expression and chromatin accessibility profiles were correlated with behavioral variation, with generalists intermediate in behavior and molecular profiles. Models of brain GRNs constructed for individuals revealed that transcription factor (TF) activity was highly predictive of behavior, and behavior-associated regulatory regions had more TF motifs. These results provide new insights into the important role played by brain GRN plasticity in the regulation of behavior, with implications for social evolution.
Aggressive behaviour associated with territorial defence is widespread and has fitness consequences. However, excess aggression can interfere with other important biological functions such as ...immunity and energy homeostasis. How the expression of complex behaviours such as aggression is regulated in the brain has long intrigued ethologists, but has only recently become amenable for molecular dissection in non-model organisms. We investigated the transcriptomic response to territorial intrusion in four brain regions in breeding male threespined sticklebacks using expression microarrays and quantitative polymerase chain reaction (qPCR). Each region of the brain had a distinct genomic response to a territorial challenge. We identified a set of genes that were upregulated in the diencephalon and downregulated in the cerebellum and the brain stem. Cis-regulatory network analysis suggested transcription factors that regulated or co-regulated genes that were consistently regulated in all brain regions and others that regulated gene expression in opposing directions across brain regions. Our results support the hypothesis that territorial animals respond to social challenges via transcriptional regulation of genes in different brain regions. Finally, we found a remarkably close association between gene expression and aggressive behaviour at the individual level. This study sheds light on the molecular mechanisms in the brain that underlie the response to social challenges.
A fundamental problem in meta-analysis is how to systematically combine information from multiple statistical tests to rigorously evaluate a single overarching hypothesis. This problem occurs in ...systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other complex phenotypes are influenced by intrinsic and environmental determinants that act on the transcriptome, but little is known about how these determinants interact at the molecular level. We developed an informatic technique that identifies statistically significant meta-associations between gene expression patterns and transcription factor combinations. Deploying this technique for brain transcriptome profiles from ca. 400 individual bees, we show that diverse determinants of behavior rely on shared combinations of transcription factors. These relationships were revealed only when we considered complex and variable regulatory rules, suggesting that these shared transcription factors are used in distinct ways by different determinants. This regulatory code would have been missed by traditional gene coexpression or cis -regulatory analytic methods. We expect that our meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.
Alkali bees (
) are solitary relatives of the halictine bees, which have become an important model for the evolution of social behavior, but for which few solitary comparisons exist. These ...ground-nesting bees defend their developing offspring against pathogens and predators, and thus exhibit some of the key traits that preceded insect sociality. Alkali bees are also efficient native pollinators of alfalfa seed, which is a crop of major economic value in the United States. We sequenced, assembled, and annotated a high-quality draft genome of 299.6 Mbp for this species. Repetitive content makes up more than one-third of this genome, and previously uncharacterized transposable elements are the most abundant type of repetitive DNA. We predicted 10,847 protein coding genes, and identify 479 of these undergoing positive directional selection with the use of population genetic analysis based on low-coverage whole genome sequencing of 19 individuals. We found evidence of recent population bottlenecks, but no significant evidence of population structure. We also identify 45 genes enriched for protein translation and folding, transcriptional regulation, and triglyceride metabolism evolving slower in alkali bees compared to other halictid bees. These resources will be useful for future studies of bee comparative genomics and pollinator health research.
Better tools are needed to enable researchers to quickly identify and explore effective and interpretable feature-based explanations for discriminating multi-class genomic datasets, e.g., healthy ...versus diseased samples. We develop an interactive exploration tool, GENVISAGE, which rapidly discovers the most discriminative feature pairs that separate two classes of genomic objects and then displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially for large numbers of objects and features, we propose a suite of optimizations to make GENVISAGE responsive at scale and demonstrate that our optimizations lead to a 400× speedup over competitive baselines for multiple biological datasets. We apply our rapid and interpretable tool to identify literature-supported pairs of genes whose transcriptomic responses significantly discriminate several chemotherapy drug treatments. With its generalizable optimizations and framework, GENVISAGE opens up real-time feature-based explanation generation to data from massive sequencing efforts, as well as many other scientific domains.
•Finding feature pairs that separate object classes in genomic datasets is important•Our interactive GENVISAGE tool rapidly identifies and visualizes these feature pairs•Several optimizations make GENVISAGE up to 400× faster than baseline approaches•GENVISAGE finds supported gene pairs that discriminate between drug treatments
A fundamental task in the analysis of genomics datasets is identifying features that can explain the difference between two groups of biological samples. As studies and data repositories that enable simultaneous analysis of thousands of samples become widespread, it is imperative that feature identification tools return interpretable and significant results rapidly, allowing researchers to interactively generate and explore hypotheses on these massive datasets. Our tool, GENVISAGE, is built around a framework that identifies pairs of features that strongly separate samples of different classes. An extensive suite of optimization techniques enables us to extract literature-supported feature pairs with accompanying interpretable visualizations from exceptionally large genomic datasets in real time. The GENVISAGE optimizations and webserver instance provide a blueprint for future online tools providing interactive feature exploration in massive datasets from genomics and other domains.
Identifying features that most strongly separate samples from two biological classes is fundamental in the analysis of genomic datasets. This task is typically addressed by finding (1) single features using univariate statistical methods or (2) multi-feature combinations from time-intensive machine learning. Here we present GENVISAGE, a tool that enables researchers to interactively identify visually interpretable and significant feature pairs that separate the classes. With this highly optimized tool, researchers can instantaneously generate and explore hypotheses on very massive genomic datasets.
ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This ...has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called "STAP," to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed ("primary") TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ≤150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA.
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