Biology subjects

Subramanian, A.

Publications and source records attributed to Subramanian, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

A Legionella toxin mimics tRNA and glycosylates the translation machinery to trigger a ribotoxic stress response.

Pathogens often secrete proteins or nucleic acids that mimic the structure and/or function of molecules expressed in their hosts. Molecular mimicry empowers pathogens to subvert critical host processes and establish infection. We report that the intracellular bacterium Legionella pneumophila secretes the toxin SidI (substrate of icm/dot transporter I), which possesses a transfer RNA (tRNA)-like shape and functions as a mannosyl transferase. The 3.1 [A] cryo-EM structure of SidI reveals an N-terminal domain that exhibits a characteristic inverted L-shape and charge distribution that is present in two other known protein mimics of tRNAs, the bacterial elongation factor EF-G and the mammalian release factor eRF1. In addition, we show that SidIs C-terminal domain adopts a glycosyl transferase B fold similar to a mannosyl transferase. This molecular coupling of the proteins fold and enzymatic function allows SidI to bind and glycosylate components of the host translation apparatus, including the ribosome, resulting in a robust block of protein synthesis that is comparable in potency to ricin, one of the most powerful toxins known. Additionally, we find that translational pausing activated by SidI elicits a stress response signature reminiscent of the ribotoxic stress response that is activated by elongation inhibitors that induce ribosome collisions. SidI-mediated effects on the ribosome activate the stress kinases ZAK and p38, which in turn drive an accumulation of the protein activating transcription factor 3 (ATF3). Intriguingly, ATF3 escapes the translation block imposed by SidI, translocates to the nucleus, and orchestrates the transcription of stress-inducible genes that promote cell death. Thus, using Legionella and its effectors as tools, we have unravelled the role of a ribosome-to-nuclear signalling pathway that regulates cell fate.

cell biology↗

bioRxiv2021

Computational Fontan Analysis: Preserving accuracy while expediting workflow

BackgroundPost-operative outcomes of the Fontan operation have been linked to graft shape after implantation. Computational fluid dynamics (CFD) simulations are used to explore different surgical options. The objective of this study is to perform a systematic in vitro validation for investigating the accuracy and efficiency of CFD simulation to predict Fontan hemodynamics. MethodsCFD simulations were performed to measure indexed power loss (iPL) and hepatic flow distribution (HFD) in 10 patient-specific Fontan models, with varying mesh and numerical solvers. The results were compared with a novel in vitro flow loop setup with 3D printed Fontan models. A high-resolution differential pressure sensor was used to measure the pressure drop for validating iPL predictions. Microparticles with particle filtering system were used to measure HFD. The computational time was measured for a representative Fontan model with different mesh sizes and numerical solvers. ResultsWhen compared to in vitro setup, variations in CFD mesh sizes had significant effect on HFD (p = 0.0002) but no significant impact on iPL (p = 0.069). Numerical solvers had no significant impact in both iPL (p = 0.50) and HFD (P = 0.55). A transient solver with 0.5 mm mesh size requires computational time 100 times more than a steady solver with 2.5 mm mesh size to generate similar results. ConclusionsThe predictive value of CFD for Fontan planning can be validated against an in vitro flow loop. The prediction accuracy can be affected by the mesh size, model shape complexity and flow competition.

bioinformatics↗

bioRxiv2021

Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics

Quality control (QC) of cells, a critical step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds on QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation in the commonly used QC criteria. We demonstrate that the QC metrics vary both at the tissue and cell state level across technologies, study conditions, and species. We propose data-driven QC (ddqc), an unsupervised adaptive quality control framework that performs flexible and data-driven quality control at the level of cell states while retaining critical biological insights and improved power for downstream analysis. On applying ddqc to 6,228,212 cells and 835 mouse and human samples, we retain a median of 39.7% more cells when compared to conventional data-agnostic QC filters. With ddqc, we recover biologically meaningful trends in gene complexity and ribosomal expression among cell-types enabling exploration of cell states with minimal transcriptional diversity or maximum ribosomal protein expression. Moreover, ddqc allows us to retain cell-types often lost by conventional QC such as metabolically active parenchymal cells, and specialized cells such as neutrophils or gastric chief cells. Taken together, our work proposes a revised paradigm to quality filtering best practices - iterative QC, providing a data-driven quality control framework compatible with observed biological diversity.

bioinformatics↗

bioRxiv2021

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity, but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOA) and gene targets, we find that the two assays provide a partially shared, but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

bioinformatics↗

bioRxiv2021

High Resolution Slide-seqV2 Spatial Transcriptomics Enables Discovery of Disease-Specific Cell Neighborhoods and Pathways

High resolution spatial transcriptomics is a transformative technology that enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remain largely unexplored. Here we applied Slide-seqV2 to mouse and human kidneys, in healthy and in distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in human kidney by analyzing tissue from 9 distinct donors, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially-restricted kidney filter and blood flow regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially-restricted subpopulation of epithelial cells, we also found perturbations in 77 genes associated with the unfolded protein response (UPR). Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods. One-Sentence SummaryHigh resolution Slide-seqV2 spatial transcriptomics in human and mouse kidneys.

molecular biology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗

bioRxiv2021

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

cell biology↗

bioRxiv2021

Obesity-instructed TREM2high macrophages identified by comparative analysis of diabetic mouse and human kidney at single cell resolution

Mouse models are a tool for studying the mechanisms underlying complex diseases; however, differences between species pose a significant challenge for translating findings to patients. Here, we used single-cell transcriptomics and orthogonal validation approaches to provide cross-species taxonomies, identifying shared broad cell classes and unique granular cellular states, between mouse and human kidney. We generated cell atlases of the diabetic and obese kidney using two different mouse models, a high-fat diet (HFD) model and a genetic model (BTBR ob/ob), at multiple time points along disease progression. Importantly, we identified a previously unrecognized, expanding Trem2high macrophage population in kidneys of HFD mice that matched human TREM2high macrophages in obese patients. Taken together, our cross-species comparison highlights shared immune and metabolic cell-state changes.

genomics↗

bioRxiv2021

Parental-fetal interplay of immune genes leads to intrauterine growth restriction

Intrauterine growth restriction (IUGR) of fetuses affects 5-10% of pregnancies and is associated with perinatal morbidity, mortality and long-term health issues. Understanding genetic predisposition to IUGR is challenging, owing to extensive gene polymorphisms, linkage disequilibrium, and maternal and paternal influence. Here, we demonstrate that the inhibitory receptor, KIR2DL1, expressed on maternal uterine natural killer (uNK) cells, in interaction with the paternally-inherited HLA-C*05, an HLA-C group 2 allotype, expressed on fetal trophoblast cells, causes IUGR in a humanised mouse model. Micro-CT imaging of the uteroplacental vasculature revealed reduced uterine spiral artery diameter and increased segment length, increasing fetal blood flow resistance. Single cell RNA-Seq from the maternal-fetal interface highlighted expression programs activated by KIR2DL1-induced IUGR in several placental cell types, including degradation of extracellular matrix components, angiogenesis, and uNK cell communication, suggesting a complex response underlying IUGR. As current IUGR treatments are insufficient, our findings provide important insight for drug development.

immunology↗

bioRxiv2021

NiftyTorch: A Deep Learning framework for NeuroImaging

We present NiftyTorch a Deep Learning Framework for NeuroImaging. The motivation behind the development of such a library is that there are scant amount of centralized tool for deploying 3D deep learning for NeuroImaging. In addition, most of the existing tools require expert technical knowledge in Deep Learning or programming, creating a barrier for entry. The goal is to provide a one stop package using which the users can perform classification tasks, Segmentation tasks and Image Transformation tasks. The intended audience are the members of NeuroImaging who would like to explore deep learning but have no background in coding. In this article we explore the capabilities of the framework, the performance of the framework and the future work for the framework.

neuroscience↗

bioRxiv2021

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

genomics↗

bioRxiv2021

{-}Multivariable Association Discovery in Population-scale Meta-omics Studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

microbiology↗

bioRxiv2020

A High Content Screen for Mucin-1-Reducing Compounds Identifies Fostamatinib as a Candidate for Rapid Repurposing for Acute Lung Injury during the COVID-19 pandemic

Drug repurposing is the only method capable of delivering treatments on the shortened time-scale required for patients afflicted with lung disease arising from SARS-CoV-2 infection. Mucin-1 (MUC1), a membrane-bound molecule expressed on the apical surfaces of most mucosal epithelial cells, is a biochemical marker whose elevated levels predict the development of acute lung injury (ALI) and respiratory distress syndrome (ARDS), and correlate with poor clinical outcomes. In response to the pandemic spread of SARS-CoV-2, we took advantage of a high content screen of 3,713 compounds at different stages of clinical development to identify FDA-approved compounds that reduce MUC1 protein abundance. Our screen identified Fostamatinib (R788), an inhibitor of spleen tyrosine kinase (SYK) approved for the treatment of chronic immune thrombocytopenia, as a repurposing candidate for the treatment of ALI. In vivo, Fostamatinib reduced MUC1 abundance in lung epithelial cells in a mouse model of ALI. In vitro, SYK inhibition by Fostamatinib promoted MUC1 removal from the cell surface. Our work reveals Fostamatinib as a repurposing drug candidate for ALI and provides the rationale for rapidly standing up clinical trials to test Fostamatinib efficacy in patients with COVID-19 lung injury.

cell biology↗

bioRxiv2020

RAAS blockade, kidney disease, and expression of ACE2, the entry receptor for SARS-CoV-2, in kidney epithelial and endothelial cells

SARS-CoV-2, the coronavirus that causes COVID-19, binds to angiotensin-converting enzyme 2 (ACE2) on human cells. Beyond the lung, COVID-19 impacts diverse tissues including the kidney. ACE2 is a key member of the Renin-Angiotensin-Aldosterone System (RAAS) which regulates blood pressure, largely through its effects on the kidney. RAAS blockers such as ACE inhibitors (ACEi) and Angiotensin Receptor Blockers (ARBs) are widely used therapies for hypertension, cardiovascular and chronic kidney diseases, and therefore, there is intense interest in their effect on ACE2 expression and its implications for SARS-CoV-2 pathogenicity. Here, we analyzed single-cell and single-nucleus RNA-seq of human kidney to interrogate the association of ACEi/ARB use with ACE2 expression in specific cell types. First, we performed an integrated analysis aggregating 176,421 cells across 49 donors, 8 studies and 8 centers, and adjusting for sex, age, donor and center effects, to assess the relationship of ACE2 with age and sex at baseline. We observed a statistically significant increase in ACE2 expression in tubular epithelial cells of the thin loop of Henle (tLoH) in males relative to females at younger ages, the trend reversing, and losing significance with older ages. ACE2 expression in tLoH increases with age in females, with an opposite, weak effect in males. In an independent cohort, we detected a statistically significant increase in ACE2 expression with ACEi/ARB use in epithelial cells of the proximal tubule and thick ascending limb, and endothelial cells, but the association was confounded in this small cohort by the underlying disease. Our study illuminates the dynamics of ACE2 expression in specific kidney cells, with implications for SARS-CoV-2 entry and pathogenicity.

genomics↗

bioRxiv2020

Alternative polyadenylation is a determinant of oncogenic Ras function

Alternative polyadenylation of pre-mRNA has been recently shown to play important roles in development and cancer. Activating mutations in the Ras oncogene are common drivers of many human cancers but the mechanisms by which they cooperate with alternative polyadenylation are not known. By exploiting the genetics of C. elegans, we identified cfim-1/CFIm25, a subunit of the alternative polyadenylation machine, as a key determinant of hyperactive Ras function. Ablation of cfim-1 increased penetrance of multivulva phenotype in let-60/Ras gain-of-function (gf) mutant through shortening of transcripts at the 3 untranslated region, including p21 activated kinase pak-1/PAK1 and multidrug transporter mrp-5/ABCC1. Depletion of CFIm25 in human KRAS-driven cancer cells resulted in a similar shortening of 3 untranslated regions in the PAK1 and ABCC1 transcripts, which caused an epithelial-to-mesenchymal transition and increased cell migration. Exploiting the mechanisms by which alternative polyadenylation affects activated oncogene output could offer novel approaches for the treatment of Ras-driven tumors.

cancer biology↗

bioRxiv2020

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

bioinformatics↗

bioRxiv2020

Improving Deconvolution Methods in Biology through Open Innovation Competitions: An Application to the Connectivity Map

Do machine learning methods improve standard deconvolution techniques for gene expression data? This paper uses a unique new dataset combined with an open innovation competition to evaluate a wide range of gene-expression deconvolution approaches developed by 294 competitors from 20 countries. The objective of the competition was to separate the expression of individual genes from composite measures of gene pairs. Outcomes were evaluated using direct measurements of single genes from the same samples. Results indicate that the winning algorithm based on random forest regression outperformed the other methods in terms of accuracy and reproducibility. More traditional gaussian-mixture methods performed well and tended to be faster. The best deep learning approach yielded outcomes slightly inferior to the above methods. We anticipate researchers in the field will find the dataset and algorithms developed in this study to be a powerful research tool for benchmarking their deconvolution methods and a useful resource for multiple applications.

genomics↗

bioRxiv2019

Non-oncology drugs are a source of previously unappreciated anti-cancer activity

Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at https://depmap.org.

cancer biology↗