The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Neural G0: a quiescent-like state found in neuroepithelial-derived cells and glioma.

Single-cell RNA sequencing has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA-seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From these data, we constructed a cell cycle classifier that identifies traditional cell cycle phases and a putative quiescent-like state in neuroepithelial-derived cell types during mammalian neurogenesis and in gliomas. The Neural G0 markers are enriched with quiescent NSC genes and other neurodevelopmental markers found in non-dividing neural progenitors. Putative glioblastoma stem-like cells were significantly enriched in the Neural G0 cell population. Neural G0 cell populations and gene expression are significantly associated with less aggressive tumors and extended patient survival for gliomas. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down-regulation of quiescence-associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent-like state found in neuroepithelial-derived cells and gliomas.

The Transcription Factor Nfatc2 Regulates ß-Cell Proliferation and Genes Associated with Type 2 Diabetes in Mouse and Human Islets.

Human genome-wide association studies (GWAS) have shown that genetic variation at >130 gene loci is associated with type 2 diabetes (T2D). We asked if the expression of the candidate T2D-associated genes within these loci is regulated by a common locus in pancreatic islets. Using an obese F2 mouse intercross segregating for T2D, we show that the expression of ~40% of the T2D-associated genes is linked to a broad region on mouse chromosome (Chr) 2. As all but 9 of these genes are not physically located on Chr 2, linkage to Chr 2 suggests a genomic factor(s) located on Chr 2 regulates their expression in trans. The transcription factor Nfatc2 is physically located on Chr 2 and its expression demonstrates cis linkage; i.e., its expression maps to itself. When conditioned on the expression of Nfatc2, linkage for the T2D-associated genes was greatly diminished, supporting Nfatc2 as a driver of their expression. Plasma insulin also showed linkage to the same broad region on Chr 2. Overexpression of a constitutively active (ca) form of Nfatc2 induced ß-cell proliferation in mouse and human islets, and transcriptionally regulated more than half of the T2D-associated genes. Overexpression of either ca-Nfatc2 or ca-Nfatc1 in mouse islets enhanced insulin secretion, whereas only ca-Nfatc2 was able to promote ß-cell proliferation, suggesting distinct molecular pathways mediating insulin secretion vs. ß-cell proliferation are regulated by NFAT. Our results suggest that many of the T2D-associated genes are downstream transcriptional targets of NFAT, and may act coordinately in a pathway through which NFAT regulates ß-cell proliferation in both mouse and human islets.

cMonkey2: Automated, systematic, integrated detection of co-regulated gene modules for any organism.

The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens, that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2.

Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers.

Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein-DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.

A high-resolution network model for global gene regulation in Mycobacterium tuberculosis.

The resilience of Mycobacterium tuberculosis (MTB) is largely due to its ability to effectively counteract and even take advantage of the hostile environments of a host. In order to accelerate the discovery and characterization of these adaptive mechanisms, we have mined a compendium of 2325 publicly available transcriptome profiles of MTB to decipher a predictive, systems-scale gene regulatory network model. The resulting modular organization of 98% of all MTB genes within this regulatory network was rigorously tested using two independently generated datasets: a genome-wide map of 7248 DNA-binding locations for 143 transcription factors (TFs) and global transcriptional consequences of overexpressing 206 TFs. This analysis has discovered specific TFs that mediate conditional co-regulation of genes within 240 modules across 14 distinct environmental contexts. In addition to recapitulating previously characterized regulons, we discovered 454 novel mechanisms for gene regulation during stress, cholesterol utilization and dormancy. Significantly, 183 of these mechanisms act uniquely under conditions experienced during the infection cycle to regulate diverse functions including 23 genes that are essential to host-pathogen interactions. These and other insights underscore the power of a rational, model-driven approach to unearth novel MTB biology that operates under some but not all phases of infection.

A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers.

Genes regulated by the same miRNA can be discovered by virtue of their coexpression at the transcriptional level and the presence of a conserved miRNA-binding site in their 3' UTRs. Using this principle we have integrated the three best performing and complementary algorithms into a framework for inference of regulation by miRNAs (FIRM) from sets of coexpressed genes. We demonstrate the utility of FIRM by inferring a cancer-miRNA regulatory network through the analysis of 2240 gene coexpression signatures from 46 cancers. By analyzing this network for functional enrichment of known hallmarks of cancer we have discovered a subset of 13 miRNAs that regulate oncogenic processes across diverse cancers. We have performed experiments to test predictions from this miRNA-regulatory network to demonstrate that miRNAs of the miR-29 family (miR-29a, miR-29b, and miR-29c) regulate specific genes associated with tissue invasion and metastasis in lung adenocarcinoma. Further, we highlight the specificity of using FIRM inferences to identify miRNA-regulated genes by experimentally validating that miR-767-5p, which partially shares the miR-29 seed sequence, regulates only a subset of miR-29 targets. By providing mechanistic linkage between miRNA dysregulation in cancer, their binding sites in the 3'UTRs of specific sets of coexpressed genes, and their associations with known hallmarks of cancer, FIRM, and the inferred cancer miRNA-regulatory network will serve as a powerful public resource for discovery of potential cancer biomarkers.

A system-level model for the microbial regulatory genome.

Microbes can tailor transcriptional responses to diverse environmental challenges despite having streamlined genomes and a limited number of regulators. Here, we present data-driven models that capture the dynamic interplay of the environment and genome-encoded regulatory programs of two types of prokaryotes: Escherichia coli (a bacterium) and Halobacterium salinarum (an archaeon). The models reveal how the genome-wide distributions of cis-acting gene regulatory elements and the conditional influences of transcription factors at each of those elements encode programs for eliciting a wide array of environment-specific responses. We demonstrate how these programs partition transcriptional regulation of genes within regulons and operons to re-organize gene-gene functional associations in each environment. The models capture fitness-relevant co-regulation by different transcriptional control mechanisms acting across the entire genome, to define a generalized, system-level organizing principle for prokaryotic gene regulatory networks that goes well beyond existing paradigms of gene regulation. An online resource (http://egrin2.systemsbiology.net) has been developed to facilitate multiscale exploration of conditional gene regulation in the two prokaryotes.

Breaking out of the cycle: Including quiescence in cell cycle classification.

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state, and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat (https://github.com/plaisier-lab/ccafv2_R) or a PyPI package integrated with scanpy (https://pypi.org/project/ccAFv2/). We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

Systems genetics analysis of gene-by-environment interactions in human cells.

Gene by environment (GxE) interactions are clearly important in many human diseases, but they have proven to be difficult to study on a molecular level. We report genetic analysis of thousands of transcript abundance traits in human primary endothelial cell (EC) lines in response to proinflammatory oxidized phospholipids implicated in cardiovascular disease. Of the 59 most regulated transcripts, approximately one-third showed evidence of GxE interactions. The interactions resulted primarily from effects of distal-, trans-acting loci, but a striking example of a local-GxE interaction was also observed for FGD6. Some of the distal interactions were validated by siRNA knockdown experiments, including a locus involved in the regulation of multiple transcripts involved in the ER stress pathway. Our findings add to the understanding of the overall architecture of complex human traits and are consistent with the possibility that GxE interactions are responsible, in part, for the failure of association studies to more fully explain common disease variation.

miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling.

Transcriptome profiling studies have produced staggering numbers of gene co-expression signatures for a variety of biological systems. A significant fraction of these signatures will be partially or fully explained by miRNA-mediated targeted transcript degradation. miRvestigator takes as input lists of co-expressed genes from Caenorhabditis elegans, Drosophila melanogaster, G. gallus, Homo sapiens, Mus musculus or Rattus norvegicus and identifies the specific miRNAs that are likely to bind to 3' un-translated region (UTR) sequences to mediate the observed co-regulation. The novelty of our approach is the miRvestigator hidden Markov model (HMM) algorithm which systematically computes a similarity P-value for each unique miRNA seed sequence from the miRNA database miRBase to an overrepresented sequence motif identified within the 3'-UTR of the query genes. We have made this miRNA discovery tool accessible to the community by integrating our HMM algorithm with a proven algorithm for de novo discovery of miRNA seed sequences and wrapping these algorithms into a user-friendly interface. Additionally, the miRvestigator web server also produces a list of putative miRNA binding sites within 3'-UTRs of the query transcripts to facilitate the design of validation experiments. The miRvestigator is freely available at http://mirvestigator.systemsbiology.net.

Molecular subtypes of ALS are associated with differences in patient prognosis.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood clinical heterogeneity, underscored by significant differences in patient age at onset, symptom progression, therapeutic response, disease duration, and comorbidity presentation. We perform a patient stratification analysis to better understand the variability in ALS pathology, utilizing postmortem frontal and motor cortex transcriptomes derived from 208 patients. Building on the emerging role of transposable element (TE) expression in ALS, we consider locus-specific TEs as distinct molecular features during stratification. Here, we identify three unique molecular subtypes in this ALS cohort, with significant differences in patient survival. These results suggest independent disease mechanisms drive some of the clinical heterogeneity in ALS.

Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.

Somatic mutations occur as random genetic changes in genes through protein-affecting mutations (PAMs), gene fusions, or copy number alterations (CNAs). Mutations of different types can have a similar phenotypic effect (i.e., allelic heterogeneity) and should be integrated into a unified gene mutation profile. We developed OncoMerge to fill this niche of integrating somatic mutations to capture allelic heterogeneity, assign a function to mutations, and overcome known obstacles in cancer genetics. Application of OncoMerge to TCGA Pan-Cancer Atlas increased detection of somatically mutated genes and improved the prediction of the somatic mutation role as either activating or loss of function. Using integrated somatic mutation matrices increased the power to infer gene regulatory networks and uncovered the enrichment of switch-like feedback motifs and delay-inducing feedforward loops. These studies demonstrate that OncoMerge efficiently integrates PAMs, fusions, and CNAs and strengthens downstream analyses linking somatic mutations to cancer phenotypes.

Single cell analysis reveals satellite cell heterogeneity for proinflammatory chemokine expression.

Background: The expression of proinflammatory signals at the site of muscle injury are essential for efficient tissue repair and their dysregulation can lead to inflammatory myopathies. Macrophages, neutrophils, and fibroadipogenic progenitor cells residing in the muscle are significant sources of proinflammatory cytokines and chemokines. However, the inducibility of the myogenic satellite cell population and their contribution to proinflammatory signaling is less understood. Methods: Mouse satellite cells were isolated and exposed to lipopolysaccharide (LPS) to mimic sterile skeletal muscle injury and changes in the expression of proinflammatory genes was examined by RT-qPCR and single cell RNA sequencing. Expression patterns were validated in skeletal muscle injured with cardiotoxin by RT-qPCR and immunofluorescence. Results: Satellite cells in culture were able to express Tnfa, Ccl2, and Il6, within 2 h of treatment with LPS. Single cell RNA-Seq revealed seven cell clusters representing the continuum from activation to differentiation. LPS treatment led to a heterogeneous pattern of induction of C-C and C-X-C chemokines (e.g., Ccl2, Ccl5, and Cxcl0) and cytokines (e.g., Tgfb1, Bmp2, Il18, and Il33) associated with innate immune cell recruitment and satellite cell proliferation. One cell cluster was enriched for expression of the antiviral interferon pathway genes under control conditions and LPS treatment. Activation of this pathway in satellite cells was also detectable at the site of cardiotoxin induced muscle injury. Conclusion: These data demonstrate that satellite cells respond to inflammatory signals and secrete chemokines and cytokines. Further, we identified a previously unrecognized subset of satellite cells that may act as sensors for muscle infection or injury using the antiviral interferon pathway.

Revealing the biology behind MRI signatures in high grade glioma.

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Induction of Aryl Hydrocarbon Receptor-Mediated Cancer Cell-Selective Apoptosis in Triple-Negative Breast Cancer Cells by a High-Affinity Benzimidazoisoquinoline.

Triple-negative breast cancer (TNBC) remains a disease with a paucity of targeted treatment opportunities. The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that is involved in a wide range of physiological processes, including the sensing of xenobiotics, immune function, development, and differentiation. Different small-molecule AhR ligands drive strikingly varied cellular and organismal responses. In certain cancers, AhR activation by select small molecules induces cell cycle arrest or apoptosis via activation of tumor-suppressive transcriptional programs. AhR is expressed in triple-negative breast cancers, presenting a tractable therapeutic opportunity. Here, we identify a novel ligand of the aryl hydrocarbon receptor that potently and selectively induces cell death in triple-negative breast cancer cells and TNBC stem cells via the AhR. Importantly, we found that this compound, Analog 523, exhibits minimal cytotoxicity against multiple normal human primary cells. Analog 523 represents a high-affinity AhR ligand with potential for future clinical translation as an anticancer agent.

Suppression of Ah Receptor (AhR) increases the aggressiveness of TNBC cells and 11-Cl-BBQ-activated AhR inhibits their growth.

Triple-negative breast cancer (TNBC) represents around 15% of the 2.26 million breast cancers diagnosed worldwide annually and has the worst outcome. Despite recent therapeutic advances, there remains a lack of targeted therapies for this breast cancer subtype. The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor with biological roles in regulating development, xenobiotic metabolism, cell cycle progression and cell death. AhR activation by select ligands can promote tumor suppression in multiple cancer types. AhR can negatively regulate the activity of different oncogenic signaling pathways and can directly upregulate tumor suppressor genes such as p27Kip1. To determine the role of AhR in TNBC, we generated AhR-deficient cancer cells and investigated the impact of AhR loss on TNBC cell growth phenotypes. We found that AhR-deficient MDA-MB-468 TNBC cells have increased proliferation and formed significantly more colonies compared to AhR expressing cells. These cells without AhR expression grew aggressively in vivo. To determine the molecular targets driving this phenotype, we performed transcriptomic profiling in AhR expressing and AhR knockout MDA-MB-468 cells and identified tyrosine receptor kinases, as well as other genes involved in proliferation, survival and clonogenicity that are repressed by AhR. In order to determine therapeutic targeting of AhR in TNBC, we investigated the anti-cancer effects of the novel AhR ligand 11-chloro-7H-benzimidazo[2,1-a]benzo[de]iso-quinolin-7-one (11-Cl-BBQ), which belongs to a class of high affinity, rapidly metabolized AhR ligands called benzimidazoisoquinolines (BBQs). 11-Cl-BBQ induced AhR-dependent cancer cell-selective growth inhibition and strongly inhibited colony formation in TNBC cells.

Niche adaptation by expansion and reprogramming of general transcription factors.

Numerous lineage-specific expansions of the transcription factor B (TFB) family in archaea suggests an important role for expanded TFBs in encoding environment-specific gene regulatory programs. Given the characteristics of hypersaline lakes, the unusually large numbers of TFBs in halophilic archaea further suggests that they might be especially important in rapid adaptation to the challenges of a dynamically changing environment. Motivated by these observations, we have investigated the implications of TFB expansions by correlating sequence variations, regulation, and physical interactions of all seven TFBs in Halobacterium salinarum NRC-1 to their fitness landscapes, functional hierarchies, and genetic interactions across 2488 experiments covering combinatorial variations in salt, pH, temperature, and Cu stress. This systems analysis has revealed an elegant scheme in which completely novel fitness landscapes are generated by gene conversion events that introduce subtle changes to the regulation or physical interactions of duplicated TFBs. Based on these insights, we have introduced a synthetically redesigned TFB and altered the regulation of existing TFBs to illustrate how archaea can rapidly generate novel phenotypes by simply reprogramming their TFB regulatory network.

A systems genetics approach implicates USF1, FADS3, and other causal candidate genes for familial combined hyperlipidemia.

We hypothesized that a common SNP in the 3' untranslated region of the upstream transcription factor 1 (USF1), rs3737787, may affect lipid traits by influencing gene expression levels, and we investigated this possibility utilizing the Mexican population, which has a high predisposition to dyslipidemia. We first associated rs3737787 genotypes in Mexican Familial Combined Hyperlipidemia (FCHL) case/control fat biopsies, with global expression patterns. To identify sets of co-expressed genes co-regulated by similar factors such as transcription factors, genetic variants, or environmental effects, we utilized weighted gene co-expression network analysis (WGCNA). Through WGCNA in the Mexican FCHL fat biopsies we identified two significant Triglyceride (TG)-associated co-expression modules. One of these modules was also associated with FCHL, the other FCHL component traits, and rs3737787 genotypes. This USF1-regulated FCHL-associated (URFA) module was enriched for genes involved in lipid metabolic processes. Using systems genetics procedures we identified 18 causal candidate genes in the URFA module. The FCHL causal candidate gene fatty acid desaturase 3 (FADS3) was associated with TGs in a recent Caucasian genome-wide significant association study and we replicated this association in Mexican FCHL families. Based on a USF1-regulated FCHL-associated co-expression module and SNP rs3737787, we identify a set of causal candidate genes for FCHL-related traits. We then provide evidence from two independent datasets supporting FADS3 as a causal gene for FCHL and elevated TGs in Mexicans.