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IntroductionThe advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for the characterization of community connectivity, i.e., the relative similarity of cells within and between found communities—an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment.MethodsTo address this gap, we introduce the analysis of community connectivity (ACC), which facilitates understanding of the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for the integration of spatial and gene expression information to achieve ACC.ResultsWe demonstrate BANYAN's ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data. Next, we use BANYAN to implement ACC across a wide range of real data scenarios, including 10 × Visium data of melanoma brain metastases and invasive ductal carcinoma, and NanoString CosMx data of human-small-cell lung cancer, each of which reveals distinct cliques of interacting cell sub-populations. An R package banyan is available at https://github.com/dongjunchung/banyan.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant is highly transmissible and evades pre-established immunity. Messenger RNA (mRNA) vaccination against ancestral strain spike protein can induce intact T-cell immunity against the Omicron variant, but efficacy of booster vaccination in patients with late-stage lung cancer on immune-modulating agents including anti-programmed cell death protein 1(PD-1)/programmed death-ligand 1 (PD-L1) has not yet been elucidated.Methods We assessed T-cell responses using a modified activation-induced marker assay, coupled with high-dimension flow cytometry analyses. Peripheral blood mononuclear cells (PBMCs) were stimulated with various viral peptides and antigen-specific T-cell responses were evaluated using flow cytometry.Results Booster vaccines induced CD8+ T-cell response against the ancestral SARS-CoV-2 strain and Omicron variant in both non-cancer subjects and patients with lung cancer, but only a marginal induction was detected for CD4+ T cells. Importantly, antigen-specific T cells from patients with lung cancer showed distinct subpopulation dynamics with varying degrees of differentiation compared with non-cancer subjects, with evidence of dysfunction. Notably, female-biased T-cell responses were observed.Conclusion We conclude that patients with lung cancer on immunotherapy show a substantial qualitative deviation from non-cancer subjects in their T-cell response to mRNA vaccines, highlighting the need for heightened protective measures for patients with cancer to minimize the risk of breakthrough infection with the Omicron and other future variants.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Abstract Mankind’s quest for a manned mission to Mars is placing increased emphasis on the development of innovative radio-protective countermeasures for long-term space travel. Hibernation confers radio-protective effects in hibernating animals, and this has led to the investigation of synthetic torpor to mitigate the deleterious effects of chronic low-dose-rate radiation exposure. Here we describe an induced torpor model we developed using the zebrafish. We explored the effects of radiation exposure on this model with a focus on the liver. Transcriptomic and behavioural analyses were performed. Radiation exposure resulted in transcriptomic perturbations in lipid metabolism and absorption, wound healing, immune response, and fibrogenic pathways. Induced torpor reduced metabolism and increased pro-survival, anti-apoptotic, and DNA repair pathways. Coupled with radiation exposure, induced torpor led to a stress response but also revealed maintenance of DNA repair mechanisms, pro-survival and anti-apoptotic signals. To further characterise our model of induced torpor, the zebrafish model was compared with hepatic transcriptomic data from hibernating grizzly bears (Ursus arctos horribilis) and active controls revealing conserved responses in gene expression associated with anti-apoptotic processes, DNA damage repair, cell survival, proliferation, and antioxidant response. Similarly, the radiation group was compared with space-flown mice revealing shared changes in lipid metabolism.

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.

We assessed vaccine-induced antibody responses to the SARS-CoV-2 ancestral virus and Omicron variant before and after booster immunization in 57 patients with B cell malignancies. Over one-third of vaccinated patients at the pre-booster time point were seronegative, and these patients were predominantly on active cancer therapies such as anti-CD20 monoclonal antibody. While booster immunization was able to induce detectable antibodies in a small fraction of seronegative patients, the overall booster benefit was disproportionately evident in patients already seropositive and not receiving active therapy. While ancestral virus– and Omicron variant–reactive antibody levels among individual patients were largely concordant, neutralizing antibodies against Omicron tended to be reduced. Interestingly, in all patients, including those unable to generate detectable antibodies against SARS-CoV-2 spike, we observed comparable levels of EBV- and influenza-reactive antibodies, demonstrating that B cell–targeting therapies primarily impair de novo but not preexisting antibody levels. These findings support rationale for vaccination before cancer treatment.

Abstract Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain–containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy. Methods Using a systems analytical approach, we studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial to discern the impact of CD24Fc treatment on immune homeostasis. We performed high dimensional spectral flow cytometry and measured the levels of a broad array of cytokines and chemokines to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. Results Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found that patients with severe COVID-19 had systemic hyper-activation of multiple cellular compartments, including CD8+ T cells, CD4+ T cells, and CD56+ natural killer cells. Treatment with CD24Fc blunted this systemic inflammation, inducing a return to homeostasis in NK and T cells without compromising the anti-Spike protein antibody response. CD24Fc significantly attenuated the systemic cytokine response and diminished the cytokine coexpression and network connectivity linked with COVID-19 severity and pathogenesis. Conclusions Our data demonstrate that CD24Fc rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19.

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Background Immune checkpoint blockade (ICB) has revolutionized cancer immunotherapy. However, most patients with cancer fail to respond clinically. One potential reason is the accumulation of immunosuppressive transforming growth factor β (TGFβ) in the tumor microenvironment (TME). TGFβ drives cancer immune evasion in part by inducing regulatory T cells (Tregs) and limiting CD8+ T cell function. Glycoprotein-A repetitions predominant (GARP) is a cell surface docking receptor for activating latent TGFβ1, TGFβ2 and TGFβ3, with its expression restricted predominantly to effector Tregs, cancer cells, and platelets.Methods We investigated the role of GARP in human patients with cancer by analyzing existing large databases. In addition, we generated and humanized an anti-GARP monoclonal antibody and evaluated its antitumor efficacy and underlying mechanisms of action in murine models of cancer.Results We demonstrate that GARP overexpression in human cancers correlates with a tolerogenic TME and poor clinical response to ICB, suggesting GARP blockade may improve cancer immunotherapy. We report on a unique anti-human GARP antibody (named PIIO-1) that specifically binds the ligand-interacting domain of all latent TGFβ isoforms. PIIO-1 lacks recognition of GARP-TGFβ complex on platelets. Using human LRRC32 (encoding GARP) knock-in mice, we find that PIIO-1 does not cause thrombocytopenia; is preferentially distributed in the TME; and exhibits therapeutic efficacy against GARP+ and GARP- cancers, alone or in combination with anti-PD-1 antibody. Mechanistically, PIIO-1 treatment reduces canonical TGFβ signaling in tumor-infiltrating immune cells, prevents T cell exhaustion, and enhances CD8+ T cell migration into the TME in a C-X-C motif chemokine receptor 3 (CXCR3)-dependent manner.Conclusion GARP contributes to multiple aspects of immune resistance in cancer. Anti-human GARP antibody PIIO-1 is an efficacious and safe strategy to block GARP-mediated LTGFβ activation, enhance CD8+ T cell trafficking and functionality in the tumor, and overcome primary resistance to anti-PD-1 ICB. PIIO-1 therefore warrants clinical development as a novel cancer immunotherapeutic.

Summary: The cancer research field is finally starting to unravel the mystery behind why males have a higher incidence and mortality rate than females for nearly all cancer types of the non-reproductive systems. Here, we explain how sex – specifically sex chromosomes and sex hormones – drives differential adaptive immunity across immune-related disease states including cancer, and why males are consequently more predisposed to tumor development. We highlight emerging data on the roles of cell-intrinsic androgen receptors in driving CD8+ T cell dysfunction or exhaustion in the tumor microenvironment and summarize ongoing clinical efforts to determine the impact of androgen blockade on cancer immunotherapy. Finally, we outline a framework for future research in cancer biology and immuno-oncology, underscoring the importance of a holistic research approach to understanding the mechanisms of sex dimorphisms in cancer, so sex will be considered as an imperative factor for guiding treatment decisions in the future.

Abstract Background Disparities in colon cancer (CC) outcomes may be due to a more aggressive phenotype in African American patients in the setting of a decreased tumor immunity, though the precise mechanism for this result has not been well elucidated. To explore the molecular factors underlying CC disparities, we compared the immunogenomic signatures of CC from African American and European American patients. Methods We identified all CC patients from the publicly available Cancer Genome Atlas for whom race and survival data are available. Immunophenotype signatures were established for African American and European American patients. Comparisons were made regarding survival and a multivariable linear regression model was created to determine the association of immune cellular components with race. Differential gene expression was also assessed. Results Of the 254 patients identified, 58 (23%) were African American and 196 (77%) were European American. African American patients had a decreased progression free survival (p = 0.04). Tumors from African American patients displayed a reduced fraction of macrophages and CD8+ T cells and an increased fraction of B cells compared with tumors from European Americans. Differences persisted when controlling for sex, age, and disease stage. Immunostimulatory and immunoinhibitory gene profiles including major histocompatibility complex expression differed by race. Conclusions Differences in the tumor immune microenvironment of African American as compared to European American CC specimens may play a role in the survival differences between the groups. These differences may provide targeted therapeutic opportunities.

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

Abstract Background In systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases. Results In order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene–gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge. Conclusions We showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene–gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.

Opioid use disorder is a psychological condition that affects over 200,000 people per year in the U.S., causing the Centers for Disease Control and Prevention to label the crisis as a rapidly spreading public health epidemic. The behavioral relationship between opioid exposure and development of opioid use disorder (OUD) varies greatly between individuals, implying existence of sup-populations with varying degrees of opioid vulnerability. However, effective pre-clinical identification of these sub-populations remains challenging due to the complex multivariate measurements employed in animal models of OUD. In this study, we propose a novel non-linear network-based data analysis workflow that employs seven behavioral traits to identify opioid use sub-populations and assesses contributions of behavioral variables to opioid vulnerability and resiliency. Through this analysis workflow we determined how behavioral variables across heroin taking, refraining and seeking interact with one another to identify potentially heroin resilient and vulnerable behavioral sub-populations. Data were collected from over 400 heterogeneous stock rats in two geographically distinct locations. Rats underwent heroin self-administration training, followed by a progressive ratio and heroin-primed reinstatement test. Next, rats underwent extinction training and a cue-induced reinstatement test. To enter the analysis workflow, we integrated data from different cohorts of rats and removed possible batch effects. We then constructed a rat-rat similarity network based on their behavioral patterns and implemented community detection on this similarity network using a Bayesian degree-corrected stochastic block model to uncover sub-populations of rats with differing levels of opioid vulnerability. We identified three statistically distinct clusters corresponding to distinct behavioral sub-populations, vulnerable, resilient and intermediate for heroin use, refraining and seeking. We implement this analysis workflow as an open source R package, named mlsbm.

Background: The purpose of this study was to identify gene expression differences associated with post-traumatic stress disorder (PTSD) and trauma exposure (TE) in a three-group study design comprised of those with and without trauma exposure and PTSD.Methods: We conducted gene expression and gene network analyses in a sample (n = 45) composed of female subjects of European Ancestry (EA) with PTSD, TE without PTSD, and controls.Results: We identified 283 genes differentially expressed between PTSD-TE groups. In an independent sample of Veterans (n = 78) a small minority of these genes were also differentially expressed. We identified 7 gene network modules significantly associated with PTSD and TE (Bonferroni corrected p ≤ 0.05), which at a false discovery rate (FDR) of q ≤ 0.2, were significantly enriched for biological pathways involved in focal adhesion, neuroactive ligand receptor interaction, and immune related processes among others.Conclusions: This study uses gene network analyses to identify significant gene modules associated with PTSD, TE, and controls. On an individual gene level, we identified a large number of differentially expressed genes between PTSD-TE groups, a minority of which were also differentially expressed in the independent sample. We also demonstrate a lack of network module preservation between PTSD and TE, suggesting that the molecular signature of PTSD and trauma are likely independent of each other. Our results provide a basis for the identification of likely disease pathways and biomarkers involved in the etiology of PTSD.

The development of the Artemis programme with the goal of returning to the moon is spurring technology advances that will eventually take humans to Mars and herald a new era of interplanetary space travel. However, long-term space travel poses unique challenges including exposure to ionising radiation from galactic cosmic rays and potential solar particle events, exposure to microgravity and specific nutritional challenges arising from earth independent exploration. Ionising radiation is one of the major obstacles facing future space travel as it can generate oxidative stress and directly damage cellular structures such as DNA, in turn causing genomic instability, telomere shortening, extracellular-matrix remodelling and persistent inflammation. In the gastrointestinal tract (GIT) this can lead to leaky gut syndrome, perforations and motility issues, which impact GIT functionality and affect nutritional status. While current countermeasures such as shielding from the spacecraft can attenuate harmful biological effects, they produce harmful secondary particles that contribute to radiation exposure. We hypothesised that induction of a torpor-like state would confer a radioprotective effect given the evidence that hibernation extends survival times in irradiated squirrels compared to active controls. To test this hypothesis, a torpor-like state was induced in zebrafish using melatonin treatment and reduced temperature, and radiation exposure was administered twice over the course of 10 days. The protective effects of induced-torpor were assessed via RNA sequencing and qPCR of mRNA extracted from the GIT. Pathway and network analysis were performed on the transcriptomic data to characterise the genomic signatures in radiation, torpor and torpor + radiation groups. Phenotypic analyses revealed that melatonin and reduced temperature successfully induced a torpor-like state in zebrafish as shown by decreased metabolism and activity levels. Genomic analyses indicated that low dose radiation caused DNA damage and oxidative stress triggering a stress response, including steroidal signalling and changes to metabolism, and cell cycle arrest. Torpor attenuated the stress response through an increase in pro-survival signals, reduced oxidative stress via the oxygen effect and detection and removal of misfolded proteins. This proof-of-concept model provides compelling initial evidence for utilizing an induced torpor-like state as a potential countermeasure for radiation exposure.

In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining and provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. We evaluate the utility and performance of GAIL with applications to gene signatures associated with systemic lupus erythematosus and breast cancer. Results show that GAIL allows effective interrogation and visualization of gene-gene networks and their subnetworks, which facilitates biological understanding of gene-gene associations. GAIL is available at http://chunglab.io/GAIL/.

In spite of accumulating evidence suggesting that different complex traits share a common risk basis, namely pleiotropy, effective investigation of pleiotropic architecture still remains challenging. In order to address this challenge, we developed ShinyGPA, an interactive and dynamic visualization toolkit to investigate pleiotropic structure. ShinyGPA requires only the summary statistics from genome-wide association studies (GWAS), which reduces the burden on researchers using this tool. ShinyGPA allows users to effectively investigate genetic relationships among phenotypes using a flexible low-dimensional visualization and an intuitive user interface. In addition, ShinyGPA provides joint association mapping functionality that can facilitate biological understanding of the pleiotropic architecture. We analyzed GWAS summary statistics for 12 phenotypes using ShinyGPA and obtained visualization results and joint association mapping results that are well supported by the literature. The visualization produced by ShinyGPA can also be used as a hypothesis generating tool for relationships between phenotypes, which might also be used to improve the design of future genetic studies. ShinyGPA is currently available at https://dongjunchung.github.io/GPA/.

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.

Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.

ABSTRACT Iron, a major protein cofactor, is essential for most organisms. Despite the well-known effects of O2 on the oxidation state and solubility of iron, the impact of O2 on cellular iron homeostasis is not well understood. Here we report that in Escherichia coli K-12, the lack of O2 dramatically changes expression of genes controlled by the global regulators of iron homeostasis, the transcription factor Fur and the small RNA RyhB. Using chromatin immunoprecipitation sequencing (ChIP-seq), we found anaerobic conditions promote Fur binding to more locations across the genome. However, by expression profiling, we discovered that the major effect of anaerobiosis was to increase the magnitude of Fur regulation, leading to increased expression of iron storage proteins and decreased expression of most iron uptake pathways and several Mn-binding proteins. This change in the pattern of gene expression also correlated with an unanticipated decrease in Mn in anaerobic cells. Changes in the genes posttranscriptionally regulated by RyhB under aerobic and anaerobic conditions could be attributed to O2-dependent changes in transcription of the target genes: aerobic RyhB targets were enriched in iron-containing proteins associated with aerobic energy metabolism, whereas anaerobic RyhB targets were enriched in iron-containing anaerobic respiratory functions. Overall, these studies showed that anaerobiosis has a larger impact on iron homeostasis than previously anticipated, both by expanding the number of direct Fur target genes and the magnitude of their regulation and by altering the expression of genes predicted to be posttranscriptionally regulated by the small RNA RyhB under iron-limiting conditions. IMPORTANCE Microbes and host cells engage in an “arms race” for iron, an essential nutrient that is often scarce in the environment. Studies of iron homeostasis have been key to understanding the control of iron acquisition and the downstream pathways that enable microbes to compete for this valuable resource. Here we report that O2 availability affects the gene expression programs of two Escherichia coli master regulators that function in iron homeostasis: the transcription factor Fur and the small RNA regulator RyhB. Fur appeared to be more active under anaerobic conditions, suggesting a change in the set point for iron homeostasis. RyhB preferentially targeted iron-containing proteins of respiration-linked pathways, which are differentially expressed under aerobic and anaerobic conditions. Such findings may be relevant to the success of bacteria within their hosts since zones of reduced O2 may actually reduce bacterial iron demands, making it easier to win the arms race for iron.

It is estimated that 30% of all genes in the mammalian cells are regulated by microRNA (miRNAs). The most relevant miRNAs in a cellular context are not necessarily those with the greatest change in expression levels between healthy and diseased tissue. Differentially expressed (DE) miRNAs that modulate a large number of messenger RNA (mRNA) transcripts ultimately have a greater influence in determining phenotypic outcomes and are more important in a global biological context than miRNAs that modulate just a few mRNA transcripts. Here, we describe the development of a tool, “miRmapper”, which identifies the most dominant miRNAs in a miRNA–mRNA network and recognizes similarities between miRNAs based on commonly regulated mRNAs. Using a list of miRNA–target gene interactions and a list of DE transcripts, miRmapper provides several outputs: (1) an adjacency matrix that is used to calculate miRNA similarity utilizing the Jaccard distance; (2) a dendrogram and (3) an identity heatmap displaying miRNA clusters based on their effect on mRNA expression; (4) a miRNA impact table and (5) a barplot that provides a visual illustration of this impact. We tested this tool using nonmetastatic and metastatic bladder cancer cell lines and demonstrated that the most relevant miRNAs in a cellular context are not necessarily those with the greatest fold change. Additionally, by exploiting the Jaccard distance, we unraveled novel cooperative interactions between miRNAs from independent families in regulating common target mRNAs; i.e., five of the top 10 miRNAs act in synergy.

Ubiquitous exposure to bisphenol A (BPA), an endocrine disruptor (ED), has raised concerns for both human and ecosystem health. Epigenetic factors, including microRNAs (miRNAs), are key regulators of gene expression during cancer. The effect of BPA exposure on the zebrafish epigenome remains poorly characterized. Zebrafish represents an excellent model to study cancer as the organism develops a disease that resembles human cancer. Using zebrafish as a systems toxicology model, we hypothesized that chronic BPA-exposure impacts the miRNome in adult zebrafish and establishes an epigenome more susceptible to cancer development. After a 3 week exposure to 100 nM BPA, RNA from the liver was extracted to perform high throughput mRNA and miRNA sequencing. Differential expression (DE) analyses comparing BPA-exposed to control specimens were performed using established bioinformatics pipelines. In the BPA-exposed liver, 6188 mRNAs and 15 miRNAs were differently expressed (q ≤ 0.1). By analyzing human orthologs of the DE zebrafish genes, signatures associated with non-alcoholic fatty liver disease (NAFLD), oxidative phosphorylation, mitochondrial dysfunction and cell cycle were uncovered. Chronic exposure to BPA has a significant impact on the liver miRNome and transcriptome in adult zebrafish with the potential to cause adverse health outcomes including cancer.

Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.

Post-traumatic stress disorder (PTSD), Major Depressive Disorder (MDD), and Substance Use Disorder (SUD) have large public health impacts. Therefore, researchers have attempted to identify those at greatest risk for these phenotypes. PTSD, MDD, and SUD are in part genetically influenced. Additionally, genes in the glutamate and gamma-aminobutyric acid (GABA) system are implicated in the encoding of emotional and fear memories, and thus may impact these phenotypes. The current study examined the associations of single nucleotide polymorphisms in GAT1 individually, and at the gene level, using a principal components (PC) approach, with PTSD, PTSD comorbid with MDD, and PTSD comorbid with SUD in 486 combat-exposed veterans. Findings indicate that several GAT1 SNPs, as well as one of the GAT1 PCs, was associated with PTSD, with and without MDD and SUD comorbidity. The present study findings provide initial insights into one pathway by which shared genetic risk influences PTSD-MDD and PTSD-SUD comorbidities, and thus identify a high-risk group (based on genotype) on whom prevention and intervention efforts should be focused.

FNR is a well-studied global regulator of anaerobiosis, which is widely conserved across bacteria. Despite the importance of FNR and anaerobiosis in microbial lifestyles, the factors that influence its function on a genome-wide scale are poorly understood. Here, we report a functional genomic analysis of FNR action. We find that FNR occupancy at many target sites is strongly influenced by nucleoid-associated proteins (NAPs) that restrict access to many FNR binding sites. At a genome-wide level, only a subset of predicted FNR binding sites were bound under anaerobic fermentative conditions and many appeared to be masked by the NAPs H-NS, IHF and Fis. Similar assays in cells lacking H-NS and its paralog StpA showed increased FNR occupancy at sites bound by H-NS in WT strains, indicating that large regions of the genome are not readily accessible for FNR binding. Genome accessibility may also explain our finding that genome-wide FNR occupancy did not correlate with the match to consensus at binding sites, suggesting that significant variation in ChIP signal was attributable to cross-linking or immunoprecipitation efficiency rather than differences in binding affinities for FNR sites. Correlation of FNR ChIP-seq peaks with transcriptomic data showed that less than half of the FNR-regulated operons could be attributed to direct FNR binding. Conversely, FNR bound some promoters without regulating expression presumably requiring changes in activity of condition-specific transcription factors. Such combinatorial regulation may allow Escherichia coli to respond rapidly to environmental changes and confer an ecological advantage in the anaerobic but nutrient-fluctuating environment of the mammalian gut.

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) has been successfully used for genome-wide profiling of transcription factor binding sites, histone modifications, and nucleosome occupancy in many model organisms and humans. Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs, applications of ChIP-Seq in this domain have not reached their full potential. Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events. We generated single-end tag (SET) and paired-end tag (PET) ChIP-Seq data for σ⁷⁰ factor in Escherichia coli (E. coli). Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events, standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data. To address this problem, we developed dPeak as a high resolution binding site identification (deconvolution) algorithm. dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays. For SET data, dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS, GPS, and GEM. When coupled with PET data, dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods. Experimental validations of a subset of dPeak predictions from σ⁷⁰ PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as 2 to 21 bp resolution. Applications of dPeak to σ⁷⁰ ChIP-Seq data in E. coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data.

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is rapidly replacing chromatin immunoprecipitation combined with genome-wide tiling array analysis (ChIP-chip) as the preferred approach for mapping transcription-factor binding sites and chromatin modifications. The state of the art for analyzing ChIP-seq data relies on using only reads that map uniquely to a relevant reference genome (uni-reads). This can lead to the omission of up to 30% of alignable reads. We describe a general approach for utilizing reads that map to multiple locations on the reference genome (multi-reads). Our approach is based on allocating multi-reads as fractional counts using a weighted alignment scheme. Using human STAT1 and mouse GATA1 ChIP-seq datasets, we illustrate that incorporation of multi-reads significantly increases sequencing depths, leads to detection of novel peaks that are not otherwise identifiable with uni-reads, and improves detection of peaks in mappable regions. We investigate various genome-wide characteristics of peaks detected only by utilization of multi-reads via computational experiments. Overall, peaks from multi-read analysis have similar characteristics to peaks that are identified by uni-reads except that the majority of them reside in segmental duplications. We further validate a number of GATA1 multi-read only peaks by independent quantitative real-time ChIP analysis and identify novel target genes of GATA1. These computational and experimental results establish that multi-reads can be of critical importance for studying transcription factor binding in highly repetitive regions of genomes with ChIP-seq experiments.