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.

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.

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.

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.

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.

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.

Nonredundant, isoform-specific roles of HDAC1 in glioma stem cells.

Glioblastoma (GBM) is characterized by an aberrant yet druggable epigenetic landscape. One major family of epigenetic regulators, the histone deacetylases (HDACs), are considered promising therapeutic targets for GBM due to their repressive influences on transcription. Although HDACs share redundant functions and common substrates, the unique isoform-specific roles of different HDACs in GBM remain unclear. In neural stem cells, HDAC2 is the indispensable deacetylase to ensure normal brain development and survival in the absence of HDAC1. Surprisingly, we find that HDAC1 is the essential class I deacetylase in glioma stem cells, and its loss is not compensated for by HDAC2. Using cell-based and biochemical assays, transcriptomic analyses, and patient-derived xenograft models, we find that knockdown of HDAC1 alone has profound effects on the glioma stem cell phenotype in a p53-dependent manner. We demonstrate marked suppression in tumor growth upon targeting of HDAC1 and identify compensatory pathways that provide insights into combination therapies for GBM. Our study highlights the importance of HDAC1 in GBM and the need to develop isoform-specific drugs.

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.

N6-methyladenosine mRNA marking promotes selective translation of regulons required for human erythropoiesis.

Many of the regulatory features governing erythrocyte specification, maturation, and associated disorders remain enigmatic. To identify new regulators of erythropoiesis, we utilize a functional genomic screen for genes affecting expression of the erythroid marker CD235a/GYPA. Among validating hits are genes coding for the N6-methyladenosine (m6A) mRNA methyltransferase (MTase) complex, including, METTL14, METTL3, and WTAP. We demonstrate that m6A MTase activity promotes erythroid gene expression programs through selective translation of ~300 m6A marked mRNAs, including those coding for SETD histone methyltransferases, ribosomal components, and polyA RNA binding proteins. Remarkably, loss of m6A marks results in dramatic loss of H3K4me3 marks across key erythroid-specific KLF1 transcriptional targets (e.g., Heme biosynthesis genes). Further, each m6A MTase subunit and a subset of their mRNAs targets are required for human erythroid specification in primary bone-marrow derived progenitors. Thus, m6A mRNA marks promote the translation of a network of genes required for human erythropoiesis.

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.

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.

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.

We developed the transcription factor (TF)-target gene database and the Systems Genetics Network Analysis (SYGNAL) pipeline to decipher transcriptional regulatory networks from multi-omic and clinical patient data, and we applied these tools to 422 patients with glioblastoma multiforme (GBM). The resulting gbmSYGNAL network predicted 112 somatically mutated genes or pathways that act through 74 TFs and 37 microRNAs (miRNAs) (67 not previously associated with GBM) to dysregulate 237 distinct co-regulated gene modules associated with patient survival or oncogenic processes. The regulatory predictions were associated to cancer phenotypes using CRISPR-Cas9 and small RNA perturbation studies and also demonstrated GBM specificity. Two pairwise combinations (ETV6-NFKB1 and romidepsin-miR-486-3p) predicted by the gbmSYGNAL network had synergistic anti-proliferative effects. Finally, the network revealed that mutations in NF1 and PIK3CA modulate IRF1-mediated regulation of MHC class I antigen processing and presentation genes to increase tumor lymphocyte infiltration and worsen prognosis. Importantly, SYGNAL is widely applicable for integrating genomic and transcriptomic measurements from other human cohorts.

Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells.

To identify therapeutic targets for glioblastoma (GBM), we performed genome-wide CRISPR-Cas9 knockout (KO) screens in patient-derived GBM stem-like cells (GSCs) and human neural stem/progenitors (NSCs), non-neoplastic stem cell controls, for genes required for their in vitro growth. Surprisingly, the vast majority GSC-lethal hits were found outside of molecular networks commonly altered in GBM and GSCs (e.g., oncogenic drivers). In vitro and in vivo validation of GSC-specific targets revealed several strong hits, including the wee1-like kinase, PKMYT1/Myt1. Mechanistic studies demonstrated that PKMYT1 acts redundantly with WEE1 to inhibit cyclin B-CDK1 activity via CDK1-Y15 phosphorylation and to promote timely completion of mitosis in NSCs. However, in GSCs, this redundancy is lost, most likely as a result of oncogenic signaling, causing GBM-specific lethality.

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.

Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions.

BACKGROUND: Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster. RESULTS: Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct. CONCLUSIONS: We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release.

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.

Evolution of context dependent regulation by expansion of feast/famine regulatory proteins.

BACKGROUND: Expansion of transcription factors is believed to have played a crucial role in evolution of all organisms by enabling them to deal with dynamic environments and colonize new environments. We investigated how the expansion of the Feast/Famine Regulatory Protein (FFRP) or Lrp-like proteins into an eight-member family in Halobacterium salinarum NRC-1 has aided in niche-adaptation of this archaeon to a complex and dynamically changing hypersaline environment. RESULTS: We mapped genome-wide binding locations for all eight FFRPs, investigated their preference for binding different effector molecules, and identified the contexts in which they act by analyzing transcriptional responses across 35 growth conditions that mimic different environmental and nutritional conditions this organism is likely to encounter in the wild. Integrative analysis of these data constructed an FFRP regulatory network with conditionally active states that reveal how interrelated variations in DNA-binding domains, effector-molecule preferences, and binding sites in target gene promoters have tuned the functions of each FFRP to the environments in which they act. We demonstrate how conditional regulation of similar genes by two FFRPs, AsnC (an activator) and VNG1237C (a repressor), have striking environment-specific fitness consequences for oxidative stress management and growth, respectively. CONCLUSIONS: This study provides a systems perspective into the evolutionary process by which gene duplication within a transcription factor family contributes to environment-specific adaptation of an organism.

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.