Biology subjects

Bandyopadhyay, S.

Publications and source records attributed to Bandyopadhyay, S..

48 records · Page 3Linked to original sources

bioRxiv2019

Coordinated Postnatal Trajectories of T Cells and Microbiota in Preterm and Full- term Newborns

Neonatal immune-microbiota co-development is poorly understood, yet appropriate recognition of - and response to - pathogens and commensal microbiota is critical to health. In this longitudinal study of 148 pre- and 119 full-term infants from birth through one year of age we found that postmenstrual age, or weeks from conception, not post-natal age, is the dominant factor influencing T cell and mucosal microbiota development. Numerous features of the T cell and microbiota functional development remain unexplained, however, by either age metric and are instead shaped by discrete peri- and post-natal events. Most strikingly, we establish that prenatal antibiotics or infection disrupt the normal T cell population developmental trajectory, influencing subsequent respiratory microbial colonization and predicting respiratory morbidity. In this way, early exposures imprint the postnatal immune-microbiota axis and place an infant at significant risk for respiratory morbidity in early childhood. One Sentence SummaryT cells and microbiota follow predictable, coordinated trajectories in newborns, and their coordination is an important determinant of illness in the first year. Graphical AbstractNasal and rectal microbiome was assessed weekly in the NICU, monthly post-discharge, and when respiratory illness symptoms occurred. T cells were characterized at birth (cord blood), 36-42 weeks postmenstrual age, and at 12 months. Quarterly respiratory morbidity surveys determined the 12-month outcome of persistent respiratory disease. More frequent illnesses and chronic disease outcomes occurred by one year of age if development of these systems was discordant or disrupted. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/736090v3_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1f08adeorg.highwire.dtl.DTLVardef@12d3671org.highwire.dtl.DTLVardef@151396borg.highwire.dtl.DTLVardef@190dcff_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2019

CODC: A copula based model to identify differential coexpression.

Differential coexpression has recently emerged as a new way to establish a fundamental difference in expression pattern among a group of genes between two populations. Earlier methods used some scoring techniques to detect changes in correlation patterns of a gene pair in two conditions. However, modeling differential coexpression by mean of finding differences in the dependence structure of the gene pair has hitherto not been carried out. We exploit a copula-based framework to model differential coexpression between gene pair in two different conditions. The Copula is used to model the dependency between expression profiles of a gene pair. For a gene pair, the distance between two joint distributions produced by copula is served as differential coexpression. We used five pan-cancer TCGA RNA-Seq data to evaluate the model which outperforms the existing state-of-the-art. Moreover, the proposed model can detect a mild change in the coexpression pattern across two conditions. For noisy expression data, the proposed method performs well because of the popular scale-invariant property of copula. Additionally, we have identified differentially coexpressed modules by applying hierarchical clustering on the distance matrix. The identified modules are analyzed through Gene Ontology terms and KEGG pathway enrichment analysis.

bioinformatics↗

bioRxiv2019

dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data

DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves interfacing with multiple programming languages viz. R, python and C, hindering seamless installation and portability. Here we present dropClust2, a complete R package thats not only fast but also minimally resource intensive. DropClust2 features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets.\n\nAvailability and implementationdropClust2 is freely available at https://debsinha.shinyapps.io/dropClust/ as an online web service and at https://github.com/debsin/dropClust as an R package.

bioinformatics↗

bioRxiv2019

Integration of pathway, cellular and genetic context reveals principles of synthetic lethality that affect reproducibility

Synthetic lethal screens have the potential to identify new vulnerabilities incurred by specific cancer mutations but have been hindered by lack of agreement between studies. Using KRAS as a model, we identified that published synthetic lethal screens significantly overlap at the pathway rather than gene level. Analysis of pathways encoded as protein networks identified synthetic lethal candidates that were more reproducible than those previously reported. Lack of overlap likely stems from biological rather than technical limitations as most synthetic lethal phenotypes were strongly modulated by changes in cellular conditions or genetic context, the latter determined using a pairwise genetic interaction map that identified numerous interactions that suppress synthetic lethal effects. Accounting for pathway, cellular and genetic context nominates a DNA repair dependency in KRAS-mutant cells, mediated by a network containing BRCA1. We provide evidence for why most reported synthetic lethals are not reproducible which is addressable using a multi-faceted testing framework.\n\nSTATEMENT OF SIGNIFICANCESynthetic lethal screens have the power to identify new targets in cancer, although have thus far come up short of expectation. We use computational and experimental approaches to delineate principles for identifying robust synthetic lethal targets that could aid in the development of effective new therapeutic strategies for genetically defined cancers.

cancer biology↗

bioRxiv2019

conLSH: Context based Locality Sensitive Hashing for Mapping of noisy SMRT Reads

Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of long and noisy reads demanding cutting edge research to get most out of it. To deal with the high error probability of SMRT data, a novel contextual Locality Sensitive Hashing (conLSH) based algorithm is proposed in this article, which can effectively align the noisy SMRT reads to the reference genome. Here, sequences are hashed together based not only on their closeness, but also on similarity of context. The algorithm has [Formula] space requirement, where n is the number of sequences in the corpus and{rho} is a constant. The indexing time and querying time are bounded by [Formula] and [Formula] respectively, where P2 > 0, is a probability value. This algorithm is particularly useful for retrieving similar sequences, a widely used task in biology. The proposed conLSH based aligner is compared with rHAT, popularly used for aligning SMRT reads, and is found to comprehensively beat it in speed as well as in memory requirements. In particular, it takes approximately 24.2% less processing time, while saving about 70.3% in peak memory requirement for H.sapiens PacBio dataset.

bioinformatics↗

bioRxiv2018

Anemia Diagnosis on a Simple Paper-based Assay

In developing countries, the maternal and neonatal mortality rate is often affected by prenatal period anemia, a preventable and ubiquitous impairment attributed due to low hemoglobin (Hgb) concentration. We report the development of a simple, frugal (~ 0.02 $ per test), rapid and high fidelity paper-based colorimetric microfluidic device for point-of-care (POC) detection of anemia. We validate our findings with 32 blood samples collected from different patients covering a wide spectrum of anemia and subsequently, compare with standard pathological results measured using a hematology analyzer. POC based Hgb estimates are correlated with the pathological gold standard estimates of Hgb levels (r = 0.909), and the POC test method yielded similar sensitivity and specificity for detecting mild anemia (n = 8) (<11 g/dl) (sensitivity: 87.5%, specificity: 100 %) and for severe anemia (n = 3) (<7 g/dl) (sensitivity: 100 %, specificity: 100 %). The estimated Hgb levels are, within 1.5 g/dl from the pathological estimate, for 91 % of the blood samples. Results demonstrate the elevated efficacy and viability of this POC colorimetric diagnostic test, in comparison to the state-of-the-art complex and expensive diagnostic tests for anemia detection.

bioengineering↗

bioRxiv2018

Novel immune cell subtypes linked to survival among African American women with triple-negative breast cancer

Triple negative breast cancer (TNBC) is an aggressive disease that is twice as likely to be diagnosed in African American (AA) women compared to white women, with poor clinical outcomes. Tumor infiltrating lymphocytes (TILs) are associated with improved survival for TNBC, but the relevance of TILs and immune cell subtypes to survival in AA women with TNBC is unknown. We evaluated histopathologic TIL counts and molecular characteristics among 60 AA women diagnosed with TNBC with linkage to clinical outcomes using data from the Metropolitan Detroit Cancer Surveillance System. We utilized whole genome expression profiling of TN tumors and cell type deconvolution analysis to evaluate the underlying mechanisms and immune cell subtypes associated with survival patterns in the context of TILs. TILs were significantly associated with improved survival [1-10% Hazard Ratio (HR)=0.32, 95% Confidence Interval (CI) 0.12-0.90, p=0.031; >10% HR=0.18, 95% CI 0.05-0.67, 9.9x10-3]. 524 transcripts (326 coding, 198 non-coding) were associated with TIL levels, 34 of which were associated with both TILs and survival (p<0.05). While only naive B cells were associated with survival when considering individual cell types [Median HR=2.43, 95% CI 1.07-5.55, p=0.035], increased naive B cells, plasma cells, and activated NK cells, and decreased resting mast cells, M1 macrophages, and monocytes were associated with transcripts that predicted worse survival. These data provide evidence for novel roles for these immune cells types in TNBC, and further studies are needed to validate these findings and identify determinants of patterns of immune response in TNBC relevant to the AA population.\n\nSummaryWe found that increased naive B cells, plasma cells, and activated natural killer cells, and decreased resting mast cells, M1 macrophages, and monocytes were associated with expression biomarkers of worse survival among African American women with triple negative breast cancer.

epidemiology↗

bioRxiv2018

Neonate gut and respiratory microbiota: coordinated development through time and space

Background: Postnatal development of the microbiota in early life influences immunity, metabolism, neurodevelopment and long-term infant health. Microbiome development occurs at multiple body sites, each with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development and risk of respiratory diseases.\n\nResults: Gut and respiratory microbiota collected as longitudinal rectal, throat and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their composition. Multiple methods were used to relate the occurrence of CSTs to several measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post menstrual age (PMA: equal to GA plus WOL). Manifestation of CSTs followed one of three patterns with respect to infant maturity. First, chronological: independent of infant maturity (GA) at birth, and strongly associated with post-natal age (WOL). Second, idiosyncratic: primarily dependent on maturity (GA) at birth, with persistent differences in CST occurrence between pre- and full-term infants through the first year of life. Third, convergent: CSTs appear earlier in infants with greater maturity (GA) at birth, but after a sufficient post-natal interval their occurrence in pre-term infants reaches parity with full-term infants. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Significant canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between more proximal locations.\n\nConclusion: Cross-body site associations of developing infant microbiota suggest the importance of research and clinical practices that focus on dynamic interactions between multiple microbial communities to elucidate and promote systemic microbiota development.

developmental biology↗

bioRxiv2017

A quantitative chemotherapy genetic interaction map reveals new factors associated with PARP inhibitor resistance

Nearly every cancer patient is treated with chemotherapy yet our understanding of factors that dictate response and resistance to such agents remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells that charts the impact of knockdown of 625 cancer and DNA repair related genes on sensitivity to 29 drugs, covering all classes of cancer chemotherapeutics. This quantitative map is predictive of interactions maintained in cancer cell lines and can be used to identify new cancer-associated DNA repair factors, predict cancer cell line responses to therapy and prioritize drug combinations. We identify that GPBP1 loss in breast and ovarian cancer confers resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map may help navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.

cancer biology↗

bioRxiv2017

dropClust: Efficient clustering of ultra-large scRNA-seq data

Droplet based single cell transcriptomics has recently enabled parallel screening of tens of thousands of single cells. Clustering methods that scale for such high dimensional data without compromising accuracy are scarce. We exploit Locality Sensitive Hashing, an approximate nearest neighbor search technique to develop a de novo clustering algorithm for large-scale single cell data. On a number of real datasets, dropClust outperformed the existing best practice methods in terms of execution time, clustering accuracy and detectability of minor cell sub-types.

genomics↗

bioRxiv2017

Kinome rewiring reveals AURKA is a molecular barrier to the efficacy of PI3K/mTOR-pathway inhibitors in breast cancer

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest. We hypothesized that dynamic changes in signaling, including adaptation and feedback, limit drug efficacy. Using a quantitative chemoproteomics approach we mapped dynamic changes in the kinome in response to various agents and identified signaling changes that correlate with drug sensitivity. Measurement of dynamics across a panel of breast cancer cell lines identified that maintenance of CDK4 and AURKA activity was associated with drug resistance. We tested whether incomplete inhibition of CDK4 or AURKA was a source of therapy failure and found that inhibition of either was sufficient to sensitize most breast cancer cells to PI3K, AKT, and mTOR inhibitors. In particular, drug combinations including the AURKA inhibitor MLN8237 were highly synergistic and induced apoptosis through enhanced suppression of mTOR signaling to S6 and 4E-BP1 leading to tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of target therapy and indicates that Aurora kinase co-inhibition could unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer.

cancer biology↗

bioRxiv2016

Connecting tumor genomics with therapeutics through multi-dimensional network modules

Recent efforts have catalogued genomic, transcriptomic, epigenetic and proteomic changes in tumors, but connecting these data with effective therapeutics remains a challenge. In contrast, cancer cell lines can model therapeutic responses but only partially reflect tumor biology. Bridging this gap requires new methods of data integration to identify a common set of pathways and molecular events. Using MAGNETIC, a new method to integrate molecular profiling data using functional networks, we identify 219 gene modules in TCGA breast cancers that capture recurrent alterations, reveal new roles for H3K27 tri-methylation and accurately quantitate various cell types within the tumor microenvironment. We show that a significant portion of gene expression and methylation in tumors is poorly reproduced in cell lines due to differences in biology and microenvironment and MAGNETIC identifies therapeutic biomarkers that are robust to these differences. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.

bioinformatics↗