Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

Spatial imaging of glycoRNA in single cells with ARPLA.

Little is known about the biological roles of glycosylated RNAs (glycoRNAs), a recently discovered class of glycosylated molecules, because of a lack of visualization methods. We report sialic acid aptamer and RNA in situ hybridization-mediated proximity ligation assay (ARPLA) to visualize glycoRNAs in single cells with high sensitivity and selectivity. The signal output of ARPLA occurs only when dual recognition of a glycan and an RNA triggers in situ ligation, followed by rolling circle amplification of a complementary DNA, which generates a fluorescent signal by binding fluorophore-labeled oligonucleotides. Using ARPLA, we detect spatial distributions of glycoRNAs on the cell surface and their colocalization with lipid rafts as well as the intracellular trafficking of glycoRNAs through SNARE protein-mediated secretory exocytosis. Studies in breast cell lines suggest that surface glycoRNA is inversely associated with tumor malignancy and metastasis. Investigation of the relationship between glycoRNAs and monocyte-endothelial cell interactions suggests that glycoRNAs may mediate cell-cell interactions during the immune response.

PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition.

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition.

Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA-binding proteins.

Defects in homologous recombination DNA repair (HRD) both predispose to cancer development and produce therapeutic vulnerabilities, making it critical to define the spectrum of genetic events that cause HRD. However, we found that mutations in BRCA1/2 and other canonical HR genes only identified 10%-20% of tumors that display genomic evidence of HRD. Using a networks-based approach, we discovered that over half of putative genes causing HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA-binding protein (RBP)-encoding genes. These putative drivers of HRD were experimentally validated, cross-validated in an independent cohort, and enriched in cancer-associated genome-wide association study loci. Mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD, with implications for basic biology, genetic screening, and therapy stratification.

Defining the condensate landscape of fusion oncoproteins.

Fusion oncoproteins (FOs) arise from chromosomal translocations in ~17% of cancers and are often oncogenic drivers. Although some FOs can promote oncogenesis by undergoing liquid-liquid phase separation (LLPS) to form aberrant biomolecular condensates, the generality of this phenomenon is unknown. We explored this question by testing 166 FOs in HeLa cells and found that 58% formed condensates. The condensate-forming FOs displayed physicochemical features distinct from those of condensate-negative FOs and segregated into distinct feature-based groups that aligned with their sub-cellular localization and biological function. Using Machine Learning, we developed a predictor of FO condensation behavior, and discovered that 67% of ~3000 additional FOs likely form condensates, with 35% of those predicted to function by altering gene expression. 47% of the predicted condensate-negative FOs were associated with cell signaling functions, suggesting a functional dichotomy between condensate-positive and -negative FOs. Our Datasets and reagents are rich resources to interrogate FO condensation in the future.

Alternative splicing of GSDMB modulates killer lymphocyte-triggered pyroptosis.

Granzyme A from killer lymphocytes cleaves gasdermin B (GSDMB) and triggers pyroptosis in targeted human tumor cells, eliciting antitumor immunity. However, GSDMB has a controversial role in pyroptosis and has been linked to both anti- and protumor functions. Here, we found that GSDMB splicing variants are functionally distinct. Cleaved N-terminal (NT) fragments of GSDMB isoforms 3 and 4 caused pyroptosis, but isoforms 1, 2, and 5 did not. The nonfunctional isoforms have a deleted or modified exon 6 and therefore lack a stable belt motif. The belt likely contributes to the insertion of oligomeric GSDMB-NTs into the membrane. Consistently, noncytotoxic GSDMB-NTs blocked pyroptosis caused by cytotoxic GSDMB-NTs in a dominant-negative manner. Upon natural killer (NK) cell attack, GSDMB3-expressing cells died by pyroptosis, whereas GSDMB4-expressing cells died by mixed pyroptosis and apoptosis, and GSDMB1/2-expressing cells died only by apoptosis. GSDMB4 partially resisted NK cell-triggered cleavage, suggesting that only GSDMB3 is fully functional. GSDMB1-3 were the most abundant isoforms in the tested tumor cell lines and were similarly induced by interferon-γ and the chemotherapy drug methotrexate. Expression of cytotoxic GSDMB3/4 isoforms, but not GSDMB1/2 isoforms that are frequently up-regulated in tumors, was associated with better outcomes in bladder and cervical cancers, suggesting that GSDMB3/4-mediated pyroptosis was protective in those tumors. Our study indicates that tumors may block and evade killer cell-triggered pyroptosis by generating noncytotoxic GSDMB isoforms. Therefore, therapeutics that favor the production of cytotoxic GSDMB isoforms by alternative splicing may improve antitumor immunity.

AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders.

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer's disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases.

Transcriptomic analyses of patient peripheral blood with hemoglobin depletion reveal glioblastoma biomarkers.

Peripheral blood is gaining prominence as a noninvasive alternative to tissue biopsy to develop biomarkers for glioblastoma (GBM); however, widely utilized blood-based biomarkers in clinical settings have not yet been identified due to the lack of a robust detection approach. Here, we describe the application of globin reduction in RNA sequencing of whole blood (i.e., WBGR) and perform transcriptomic analysis to identify GBM-associated transcriptomic changes. By using WBGR, we improved the detection sensitivity of informatic reads and identified differential gene expression in GBM blood. By analyzing tumor tissues, we identified transcriptomic traits of GBM blood. Further functional enrichment analyses retained the most changed genes in GBM. Subsequent validation elicited a 10-gene panel covering mRNA, long noncoding RNA, and microRNA (i.e., GBM-Dx panel) that has translational potential to aid in the early detection or clinical management of GBM. Here, we report an integrated approach, WBGR, with comprehensive analytic capacity for blood-based marker identification.

The HAPSTR2 retrogene buffers stress signaling and resilience in mammals.

We recently identified HAPSTR1 (C16orf72) as a key component in a novel pathway which regulates the cellular response to molecular stressors, such as DNA damage, nutrient scarcity, and protein misfolding. Here, we identify a functional paralog to HAPSTR1: HAPSTR2. HAPSTR2 formed early in mammalian evolution, via genomic integration of a reverse transcribed HAPSTR1 transcript, and has since been preserved under purifying selection. HAPSTR2, expressed primarily in neural and germline tissues and a subset of cancers, retains established biochemical features of HAPSTR1 to achieve two functions. In normal physiology, HAPSTR2 directly interacts with HAPSTR1, markedly augmenting HAPSTR1 protein stability in a manner independent from HAPSTR1's canonical E3 ligase, HUWE1. Alternatively, in the context of HAPSTR1 loss, HAPSTR2 expression is sufficient to buffer stress signaling and resilience. Thus, we discover a mammalian retrogene which safeguards fitness.

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer.

Somatic mutations are a major source of cancer development, and many driver mutations have been identified in protein coding regions. However, the function of mutations located in miRNA and their target binding sites throughout the human genome remains largely unknown. Here, we built detailed cancer-specific miRNA regulatory networks across 30 cancer types to systematically analyze the effect of mutations in miRNAs and their target sites in 3' untranslated region (3' UTR), coding sequence (CDS), and 5' UTR regions. A total of 3,518,261 mutations from 9,819 samples were mapped to miRNA-gene interactions (mGI). Mutations in miRNAs showed a mutually exclusive pattern with mutations in their target genes in almost all cancer types. A linear regression method identified 148 candidate driver mutations that can significantly perturb miRNA regulatory networks. Driver mutations in 3'UTRs played their roles by altering RNA binding energy and the expression of target genes. Finally, mutated driver gene targets in 3' UTRs were significantly downregulated in cancer and functioned as tumor suppressors during cancer progression, suggesting potential miRNA candidates with significant clinical implications. A user-friendly, open-access web portal (mGI-map) was developed to facilitate further use of this data resource. Together, these results will facilitate novel noncoding biomarker identification and therapeutic drug design targeting the miRNA regulatory networks. SIGNIFICANCE: A detailed miRNA-gene interaction map reveals extensive miRNA-mediated gene regulatory networks with mutation-induced perturbations across multiple cancers, serving as a resource for noncoding biomarker discovery and drug development.

Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors.

Genetic screens are widely exploited to develop novel therapeutic approaches for cancer treatment. With recent advances in single-cell technology, single-cell CRISPR screen (scCRISPR) platforms provide opportunities for target validation and mechanistic studies in a high-throughput manner. Here, we aim to establish scCRISPR platforms which are suitable for immune-related screens involving multiple cell types. We integrated two scCRISPR platforms, namely Perturb-seq and CROP-seq, with both in vitro and in vivo immune screens. By leveraging previously generated resources, we optimized experimental conditions and data analysis pipelines to achieve better consistency between results from high-throughput and individual validations. Furthermore, we evaluated the performance of scCRISPR immune screens in determining underlying mechanisms of tumor intrinsic immune regulation. Our results showed that scCRISPR platforms can simultaneously characterize gene expression profiles and perturbation effects present in individual cells in different immune screen conditions. Results from scCRISPR immune screens also predict transcriptional phenotype associated with clinical responses to cancer immunotherapy. More importantly, scCRISPR screen platforms reveal the interactive relationship between targeting tumor intrinsic factors and T cell-mediated antitumor immune response which cannot be easily assessed by bulk RNA-seq. Collectively, scCRISPR immune screens provide scalable and reliable platforms to elucidate molecular determinants of tumor immune resistance.

Single-cell transcriptomic mapping of intestinal epithelium that undergoes 3D morphogenesis and mechanodynamic stimulation in a gut-on-a-chip.

The human gut-on-a-chip has demonstrated in vivo-relevant cellular fidelity and physiological functions of Caco-2 intestinal epithelium compared to its static cultures. However, transcriptomic dynamics that controls the morphogenic and mechanodynamic perturbation of Caco-2 epithelium in a microphysiological culture remain elusive. Single-cell transcriptomic analysis revealed that a gut-on-a-chip culture drives three clusters that illustrate distinct gene expressions and their spatial representation in three-dimensional (3D) epithelial layers. A pseudotemporal trajectory analysis elucidated the evolutionary transition from a homogeneous ancestral genotype in Transwell cultures into heterogeneous transcriptomes in gut-on-a-chip cultures, verified in cell cycle perturbation, cytodifferentiation, and intestinal functions in digestion, transport, and drug metabolism. Furthermore, the inversed transcriptomic signature of oncogenes and tumor-suppressor genes of Caco-2 cells confirmed that gut-on-a-chip cultures induce post-mitotic reprogramming of cancer-associated genes. Our study suggests that a physiological gut-on-a-chip culture induces the transcriptomic perturbation of Caco-2 epithelium to elicit in vivo-relevant morphogenesis and restoration of normal physiological functions.

Circulating metabolites associated with tumor hypoxia and early response to treatment in bevacizumab-refractory glioblastoma after combined bevacizumab and evofosfamide.

Glioblastomas (GBM) are the most common and aggressive form of primary malignant brain tumor in the adult population, and, despite modern therapies, patients often develop recurrent disease, and the disease remains incurable with median survival below 2 years. Resistance to bevacizumab is driven by hypoxia in the tumor and evofosfamide is a hypoxia-activated prodrug, which we tested in a phase 2, dual center (University of Texas Health Science Center in San Antonio and Dana Farber Cancer Institute) clinical trial after bevacizumab failure. Tumor hypoxic volume was quantified by 18F-misonidazole PET. To identify circulating metabolic biomarkers of tumor hypoxia in patients, we used a high-resolution liquid chromatography-mass spectrometry-based approach to profile blood metabolites and their specific enantiomeric forms using untargeted approaches. Moreover, to evaluate early response to treatment, we characterized changes in circulating metabolite levels during treatment with combined bevacizumab and evofosfamide in recurrent GBM after bevacizumab failure. Gamma aminobutyric acid, and glutamic acid as well as its enantiomeric form D-glutamic acid all inversely correlated with tumor hypoxia. Intermediates of the serine synthesis pathway, which is known to be modulated by hypoxia, also correlated with tumor hypoxia (phosphoserine and serine). Moreover, following treatment, lactic acid was modulated by treatment, likely in response to a hypoxia mediated modulation of oxidative vs glycolytic metabolism. In summary, although our results require further validation in larger patients' cohorts, we have identified candidate metabolic biomarkers that could evaluate the extent of tumor hypoxia and predict the benefit of combined bevacizumab and evofosfamide treatment in GBM following bevacizumab failure.

Biomolecular Condensation: A New Phase in Cancer Research.

Multicellularity was a watershed development in evolution. However, it also meant that individual cells could escape regulatory mechanisms that restrict proliferation at a severe cost to the organism: cancer. From the standpoint of cellular organization, evolutionary complexity scales to organize different molecules within the intracellular milieu. The recent realization that many biomolecules can "phase-separate" into membraneless organelles, reorganizing cellular biochemistry in space and time, has led to an explosion of research activity in this area. In this review, we explore mechanistic connections between phase separation and cancer-associated processes and emerging examples of how these become deranged in malignancy. SIGNIFICANCE: One of the fundamental functions of phase separation is to rapidly and dynamically respond to environmental perturbations. Importantly, these changes often lead to alterations in cancer-relevant pathways and processes. This review covers recent advances in the field, including emerging principles and mechanisms of phase separation in cancer.

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry.

Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Timing of Resumption of Anticoagulation After Polypectomy and Frequency of Post-procedural Complications: A Post-hoc Analysis.

BACKGROUND: Optimal timing for anticoagulation resumption after polypectomy is unclear. We explored the association between timing of anticoagulation resumption and occurrence of delayed post-polypectomy bleeding (PPB) and thromboembolic (TE) events. METHODS: We performed a post-hoc analysis of patients in an earlier study whose anticoagulants were interrupted for polypectomy. We compared rates of clinically important delayed PPB and TE events in relationship to timing of anticoagulant resumption. Late resumption was defined as > 2 days after polypectomy. RESULTS: Among 437 patients, 351 had early and 86 late resumption. Compared to early resumers, late resumers had greater polypectomy complexity. PPB rate was higher (but not significantly) in the late versus early resumers (2.3% vs. 0.9%, 1.47% greater, 95% CI [- 2.58 to 5.52], p = 0.26). TE events were more frequent in late versus early resumers [0% vs. 1.2% at 30 days, 0% vs. 2.3%, 95% CI 0.3-8, (p = 0.04) at 90 days]. On multivariate analysis, timing of restarting anticoagulation was not a significant predictor of PPB (OR 0.97, 95% CI 0.61-1.44, p = 0.897). Significant predictors were number of polyps ≥ 1 cm (OR 4.14, 95% CI 1.27-13.66, p = 0.014) and use of fulguration (OR 11.43, 95% CI 1.35-80.80, p = 0.014). CONCLUSIONS: Physicians delayed anticoagulation resumption more commonly after complex polypectomies. The timing of restarting anticoagulation was not a significant risk factor for PPB and late resumers had significantly higher rates of TE events within 90 days. Considering the potentially catastrophic consequences of TE events and the generally benign outcome of PPBs, clinicians should be cautious about delaying resumption of anticoagulation after polypectomy.

mi-IsoNet: systems-scale microRNA landscape reveals rampant isoform-mediated gain of target interaction diversity and signaling specificity.

MicroRNA (miRNA) is not a single sequence, but a series of multiple variants (also termed isomiRs) with sequence and expression heterogeneity. Whether and how these isoforms contribute to functional variation and complexity at the systems and network levels remain largely unknown. To explore this question systematically, we comprehensively analyzed the expression of small RNAs and their target sites to interrogate functional variations between novel isomiRs and their canonical miRNA sequences. Our analyses of the pan-cancer landscape of miRNA expression indicate that multiple isomiRs generated from the same miRNA locus often exhibit remarkable variation in their sequence, expression and function. We interrogated abundant and differentially expressed 5' isomiRs with novel seed sequences via seed shifting and identified many potential novel targets of these 5' isomiRs that would expand interaction capabilities between small RNAs and mRNAs, rewiring regulatory networks and increasing signaling circuit complexity. Further analyses revealed that some miRNA loci might generate diverse dominant isomiRs that often involved isomiRs with varied seeds and arm-switching, suggesting a selective advantage of multiple isomiRs in regulating gene expression. Finally, experimental validation indicated that isomiRs with shifted seed sequences could regulate novel target mRNAs and therefore contribute to regulatory network rewiring. Our analysis uncovers a widespread expansion of isomiR and mRNA interaction networks compared with those seen in canonical small RNA analysis; this expansion suggests global gene regulation network perturbations by alternative small RNA variants or isoforms. Taken together, the variations in isomiRs that occur during miRNA processing and maturation are likely to play a far more complex and plastic role in gene regulation than previously anticipated.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate 'edgetic' mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Integrated Genomic Characterization of the Human Immunome in Cancer.

Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output across cancer types is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in patients with cancer. To do so, we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Identified mutations were enriched in essential protein domains and genes identified by NIPPER were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted therapeutics. SIGNIFICANCE: This study demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.