Biology subjects

Rahman, A. H.

Publications and source records attributed to Rahman, A. H..

13 recordsLinked to original sources

bioRxiv2022

Terraces in Species Tree Inference from Gene Trees

A terrace in a phylogenetic tree space is a region where all trees contain the same set of subtrees, due to certain patterns of missing data among the taxa sampled, resulting in an identical optimality score for a given data set. This was first investigated in the context of phylogenetic tree estimation from sequence alignments using maximum likelihood (ML) and maximum parsimony (MP). The concept of terraces was later extended to the species tree inference problem from a collection of gene trees, where a set of equally optimal species trees was referred to as a "pseudo" species tree terrace. Pseudo terraces do not consider the topological proximity of the trees in terms of the induced subtrees resulting from certain patterns of missing data. In this study, we mathematically characterize species tree terraces and investigate the mathematical properties and conditions that lead multiple species trees to induce/display an identical set of locus-specific subtrees owing to missing data. We report that species tree terraces are agnostic to gene tree topologies and the discordance therein. Therefore, we introduce and characterize a special type of gene tree topology-aware terrace which we call "peak terrace", and investigate conditions on the patterns of missing data that give rise to peak terraces. In addition to the theoretical and analytical results, we empirically investigated different challenges as well as various opportunities pertaining to the multiplicity of equally good species trees in terraced landscapes. Based on an extensive experimental study involving both simulated and real biological datasets, we present the prevalence of species tree terraces and the resulting ambiguity created for tree search algorithms. Remarkably, our findings indicate that the identification of terraces and the trees within them can substantially enhance the accuracy of summary methods. Furthermore, we demonstrate that reasonably accurate branch support can be computed by leveraging trees sourced from these terraces.

bioinformatics↗

bioRxiv2022

Spatial positioning and matrix programs of cancer-associated fibroblasts promote T cell exclusion in human lung tumors

It is currently accepted that activated cancer-associated fibroblasts (CAF) participate in T cell exclusion from tumor nests, but it remains unclear how they promote barrier phenotypes, and whether specific subsets are involved. Here, using single-cell RNA sequencing coupled with multiplex imaging on a large cohort of lung tumors, we identify four main CAF populations, of which only two are associated with T cell exclusion: (i) MYH11+SMA+ CAF, which are present in early-stage tumors and form a single-cell layer lining cancer aggregates, and (ii) FAP+SMA+ CAF, which appear in more advanced tumors and organize in patches within the stroma or in multiple layers around tumor nests. Both CAF populations show a contractility phenotype together with dense and aligned matrix fiber deposition compared to the T cell-permissive CAF. Yet they express distinct matrix genes, including COL4A1/COL9A1 (MYH11+SMA+ CAF) and COL11A1/COL12A1 (FAP+SMA+ CAF). Hereby, we uncovered unique molecular programs of CAF driving T cell marginalization, whose targeting should increase immunotherapy efficacy in patients bearing T cell-excluded tumors. SIGNIFICANCEThe cellular and molecular programs driving T cell marginalization in solid tumors remain unclear. Here, we describe two CAF populations associated with T cell exclusion in human lung tumors. We demonstrate the importance of pairing molecular and spatial analysis of the tumor microenvironment, a prerequisite to develop new strategies targeting T cell-excluding CAF.

cancer biology↗

bioRxiv2022

Shift of lung macrophage composition is associated with COVID-19 disease severity and recovery

Though it has been 2 years since the start of the Coronavirus Disease 19 (COVID-19) pandemic, COVID-19 continues to be a worldwide health crisis. Despite the development of preventive vaccines, very little progress has been made to identify curative therapies to treat COVID-19 and other inflammatory diseases which remain a major unmet need in medicine. Our study sought to identify drivers of disease severity and death to develop tailored immunotherapy strategies to halt disease progression. Here we assembled the Mount Sinai COVID-19 Biobank which was comprised of ~600 hospitalized patients followed longitudinally during the peak of the pandemic. Moderate disease and survival were associated with a stronger antigen (Ag) presentation and effector T cell signature, while severe disease and death were associated with an altered Ag presentation signature, increased numbers of circulating inflammatory, immature myeloid cells, and extrafollicular activated B cells associated with autoantibody formation. Strikingly, we found that in severe COVID-19 patients, lung tissue resident alveolar macrophages (AM) were not only severely depleted, but also had an altered Ag presentation signature, and were replaced by inflammatory monocytes and monocyte-derived macrophages (MoM{Phi}). Notably, the size of the AM pool correlated with recovery or death, while AM loss and functionality were restored in patients that recovered. These data therefore suggest that local and systemic myeloid cell dysregulation is a driver of COVID-19 severity and that modulation of AM numbers and functionality in the lung may be a viable therapeutic strategy for the treatment of critical lung inflammatory illnesses.

immunology↗

bioRxiv2021

Perturb-map enables CRISPR genomics with spatial resolution and identifies regulators of tumor immune composition

The cellular architecture of a tumor, particularly immune composition, has a major impact on cancer outcome, and thus there is an interest in identifying genes that control the tumor microenvironment (TME). While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying gene functions operating extracellularly or within a tissue context. To address this, we developed an approach for spatial functional genomics called Perturb-map, which utilizes protein barcodes (Pro-Code) to enable spatial detection of barcoded cells within tissue. We show >120 Pro-Codes can be imaged within a tumor, facilitating spatial mapping of 100s of cancer clones. We applied Perturb-map to knockout dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Additionally, we paired Perturb-map and spatial transcriptomics for unbiased molecular analysis of Pro-Code/CRISPR lesions. Our studies found in Tgfbr2 knockout lesions, the TME was converted to a mucinous state and T-cells excluded, which was concomitant with increased TGF{beta} expression and pathway activation, suggesting Tgfbr2 loss on lung cancer cells enhanced suppressive effects of TGF{beta} on the TME. These studies establish Perturb-map for functional genomics within a tissue at single cell-resolution with spatial architecture preserved.

immunology↗

bioRxiv2021

Multimodal Single-Cell Characterization of the Human Granulocyte Lineage

High throughput single cell transcriptomics (scRNA-seq) has been successfully applied to characterize immune cell heterogeneity across a diverse range of settings; however, analysis of human granulocytes remains a significant challenge due to their low gene expression transcript detection. Consequently, granulocytes are typically either absent or highly under-represented and inaccurately enumerated in most human scRNA-seq datasets. Here, we apply multi-modal CITE-seq profiling to characterize granulocytes in human whole blood and bone marrow, and we show that these populations can be accurately detected and analyzed using the antibody-based modality, and that their frequencies and phenotype align well with antibody-based characterization of the same samples using CyTOF. These analyses also clearly highlight extremely low gene transcript detection across the entire granulocyte lineage including the earliest neutrophil progenitor populations when using the 10X Genomics platform. By contrast, when performing parallel analyses of the same samples using the BD Rhapsody platform, we recovered a much higher proportion of granulocyte gene transcripts, enabling true multi-modal characterization of human granulocyte heterogeneity.

immunology↗

bioRxiv2020

Donor-Specific Plasma Factors Contribute to Aberrant Staining Artifacts with a Commercial Lyophilized Mass Cytometry Antibody Panel

High-dimensional mass cytometry (CyTOF) phenotyping allows for the routine measurement of over 40 parameters and is increasingly being utilized across a wide range of studies. However, CyTOF-specific panel design and optimization represent challenges to wider adoption and standardization of immune profiling with CyTOF. To address this, Fluidigm recently commercialized its MaxPar Direct Immune Profiling Assay (MDIPA), which comprises a lyophilized 30-marker antibody panel that is able to identify all major circulating immune cell subsets and offers a streamlined solution for standardized human immune monitoring. However, in the course of applying the MDIPA to characterize large numbers of whole blood samples, we observed several instances of unusual aberrant staining patterns, most notably CD19 expression on non-B cells, which can potentially confound data analysis and lead to erroneous interpretation of results when using this assay. Here, we report that this complex phenomenon is mediated by donor-specific plasma factors that mediate non-specific interactions between specific antibodies in the MDIPA panel. Our findings additionally suggest specific strategies that can be used to mitigate the issue, including the use of PBMCs or lysed/washed whole blood to remove endogenous plasma prior to staining, or blocking specific antibodies in the MDIPA panel.

immunology↗

bioRxiv2020

Genome Wide Association Studies on 7 Yield-relatedTraits of 183 Rice Varieties in Bangladesh

MotivationRice genetic diversity is regulated by multiple genes and is largely dependent on various environmental factors. Uncovering the genetic variations associated with the diversity in rice populations is the key to breed stable and high yielding rice varieties. ResultsWe performed Genome Wide Association Studies (GWAS) on 7 rice yielding traits (grain length, grain width, grain weight, panicle length, leaf length, leaf width and leaf angle) based on a population of 183 rice landraces of Bangladesh. Our GWA studies reveal various chromosomal regions and candidate genes that are associated with different traits in Bangladeshi rice varieties. Noteworthy was the recurrent implication of chromosome 10 in all three grain shape related traits (grain length, grain width, and grain weight), indicating its pivotal role in shaping rice grain morphology. Our study also underscores the involvement of transposon gene families across these three traits. For leaf related traits, chromosome 10 was found to harbor regions that are significantly associated with leaf length and leaf width. The results of these association studies support previous findings as well as provide additional insights into the genetic diversity of rice. ConclusionsThis is the first known GWAS study on various yield-related traits in the varieties of Oryza sativa available in Bangladesh - the fourth largest rice-producing country. We believe this study will accelerate rice genetics research and breeding stable high-yielding rice in Bangladesh.

bioinformatics↗

bioRxiv2020

CITEseq analysis of non-small-cell lung cancer lesions reveals an axis of immune cell activation associated with tumor antigen load and TP53 mutations

Immunotherapy is becoming a mainstay in the treatment of NSCLC. While tumor mutational burden (TMB) has been shown to correlate with response to immunotherapy, little is known about the relation of the baseline immune response with the tumor genotype. Here, we profiled 35 early stage NSCLC lesions using multiscale single cell sequencing. Unsupervised clustering identified in a subset of patients a key cellular module consisting of PDCD1+ CXCL13+ activated T cells, IgG+ plasma cells, and SPP1+ macrophages, referred to as the lung cancer activation module (LCAMhi). Transcriptional data from two NSCLC cohorts confirmed a subset of patients with LCAMhi enrichment, which was independent of overall immune cell content. The LCAMhi module strongly correlated with TMB, expression of cancer testis antigens, and with TP53 mutations in smokers and non-smokers. These data establish LCAM as a key mode of immune cell activation associated with high tumor antigen load and driver mutations.

immunology↗

bioRxiv2020

Phylogenomic terraces: presence and implication in species tree estimation from gene trees

Species tree estimation from multi-locus dataset is extremely challenging, especially in the presence of gene tree heterogeneity across the genome due to incomplete lineage sorting (ILS). Summary methods have been developed which estimate gene trees and then combine the gene trees to estimate a species tree by optimizing various optimization scores. In this study, we have formalized the concept of "phylogenomic terraces" in the species tree space, where multiple species trees with distinct topologies may have exactly the same optimization score (quartet score, extra lineage score, etc.) with respect to a collection of gene trees. We investigated the presence and implication of terraces in species tree estimation from multi-locus data by taking ILS into account. We analyzed two of the most popular ILS-aware optimization criteria: maximize quartet consistency (MQC) and minimize deep coalescence (MDC). Methods based on MQC are provably statistically consistent, whereas MDC is not a consistent criterion for species tree estimation. Our experiments, on a collection of dataset simulated under ILS, indicate that MDC-based methods may achieve competitive or identical quartet consistency score as MQC but could be significantly worse than MQC in terms of tree accuracy - demonstrating the presence and affect of phylogenomic terraces. This is the first known study that formalizes the concept of phylogenomic terraces in the context of species tree estimation from multi-locus data, and reports the presence and implications of terraces in species tree estimation under ILS.

bioinformatics↗

bioRxiv2019

Immune Profiling of Atherosclerotic Plaques Identifies Innate and Adaptive Dysregulations Associated with Ischemic Cerebrovascular Events

Atherosclerosis is driven by multifaceted contributions of the immune system within the circulation and at vascular focal sites. Yet the specific immune dysregulations within the atherosclerotic lesions that lead to clinical cerebro- and cardiovascular complications (i.e. ischemic stroke and myocardial infarction) are poorly understood. Here, using single-cell mass cytometry with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) we found that atherosclerotic plaques were enriched in activated, differentiated, and exhausted subsets of T cells vs. blood. Next, using single-cell proteomic, transcriptomic, and cell-to-cell interaction analyses we found unique functional dysregulations of both T cells and macrophages in plaques of patients with clinically symptomatic (SYM; recent stroke of TIA) or asymptomatic (ASYM, no recent stroke) carotid artery disease. SYM plaques were enriched with a distinct CD4+ T cell subset, and T cells were activated, differentiated and presented subset specific exhaustion. SYM macrophages presented alternatively activated phenotypes including subsets associated with plaque vulnerability. In ASYM plaques, T cells and macrophages were activated and displayed a strong IL-1{beta} signaling across cell types, that was absent in SYM plaques. The identification of plaque-specific innate and adaptive immune dysregulations associated with cerebrovascular events provides the basis for the design of precisely tailored cardiovascular immunotherapies.

immunology↗

bioRxiv2019

First report of reference guided genome assembly of Black Bengal goat (Capra hircus)

ObjectivesBlack Bengal goat (Capra hircus), a member of the Bovidae family with the unique traits of high prolificacy, skin quality and low demand for food is the most socioeconomically significant goat breed in Bangladesh. Furthermore, the aptitude of adaptation and disease resistance capacity of it is highly notable which makes its whole genome information an area of research interest.\n\nData descriptionThe genomic DNA of local (Chittagong, Bangladesh) healthy Black Bengal goat (Capra hircus) was extracted and then sequenced. The de novo assembly and structural annotations are being presented here. Sequencing was done using Illumina sequencing platform and the draft genome assembled is about 3.04 Gb. 26458 Genes were annotated using Maker gene annotations tool which predicted BUSCO Gene models. Universal Single Copy Orthologs refer 82.5% completeness of the assembled genome.

genomics↗

bioRxiv2019

Development of a Comprehensive Antibody Staining Database using a Standardized Analytics Pipeline

Large-scale immune monitoring experiments (such as clinical trials) are a promising direction for biomarker discovery and responder stratification in immunotherapy. Mass cytometry is one of the tools in the immune monitoring arsenal. We propose a standardized workflow for the acquisition and analysis of large-scale mass cytometry experiments. The workflow includes two-tiered barcoding, a broad lyophilized panel, and the incorporation of a fully automated, cloud-based analysis platform. We applied the workflow to a large antibody staining screen using the LEGENDScreen kit, resulting in single-cell data for 350 antibodies over 71 profiling subsets. The screen recapitulates many known trends in the immune system and reveals potential markers for delineating MAIT cells. Additionally, we examine the effect of fixation on staining intensity and identify several markers where fixation leads to either gain or loss of signal. The standardized workflow can be seamlessly integrated into existing trials. Finally, the antibody staining data set is available as an online resource for researchers who are designing mass cytometry experiments in suspension and tissue.

immunology↗

bioRxiv2018

Multi-objective formulation of MSA for phylogeny estimation (Do phylogeny-aware measures guide towards better phylogenetic tree?)

Multiple sequence alignment (MSA) is a basic step in many analyses in computational biology, including predicting the structure and function of proteins, orthology prediction and estimating phylogenies. The objective of MSA is to infer the homology among the sequences of chosen species. Commonly, the MSAs are inferred by optimizing a single function or objective. The alignments estimated under one criterion may be different to the alignments generated by other criteria, inferring discordant homologies and thus leading to different evolutionary histories relating the sequences. In recent past, researchers have advocated for the multi-objective formulation of MSA, to address this issue, where multiple conflicting objective functions are being optimized simultaneously to generate a set of alignments. However, no theoretical or empirical justification with respect to a real-life application has been shown for a particular multi-objective formulation. In this study, we investigate the impact of multi-objective formulation in the context of phylogenetic tree estimation. Employing multi-objective metaheuristics, we demonstrate that trees estimated on the alignments generated by multi-objective formulation are substantially better than the trees estimated by the state-of-the-art MSA tools, including PASTA, MUSCLE, CLUSTAL, MAFFT etc. We also demonstrate that highly accurate alignments with respect to popular measures like sum-of-pair (SP) score and total-column (TC) score do not necessarily lead to highly accurate phylogenetic trees. Thus in essence we ask the question whether a phylogeny-aware metric can guide us in choosing appropriate multi-objective formulations that can result in better phylogeny estimation. And we answer the question affirmatively through carefully designed extensive empirical study. As a by-product we also suggest a methodology for primary selection of a set of objective functions for a multi-objective formulation based on the association with the resulting phylogenetic tree.

bioinformatics↗