Biology subjects

Sen, S.

Publications and source records attributed to Sen, S..

138 records · Page 8Linked to original sources

bioRxiv2019

The expanded BXD family of mice: A cohort for experimental systems genetics and precision medicine

The challenge of precision medicine is to model complex interactions among DNA variants, sets of phenotypes, and complex environmental factors and confounders. We have expanded the BXD family, creating a powerful and extensible test bed for experimental precision medicine and an ideal cohort to study gene-by-environmental interactions.\n\nThese BXD segregate for over 6 million variants, with a mean minor allele frequency close to 0.5. We have increased the family two-fold to 150 inbred strains, all derived from C57BL/6J and DBA/2J. We have also generated updated and comprehensive genotypes and an unrivaled deep phenome.\n\nApproximately 10,000 recombinations have been located, allowing precision of quantitative trait loci mapping of {+/-}2.0 Mb over much of the genome and {+/-}0.5 Mb for Mendelian loci. The BXD phenome includes more than 100 omics data sets and >7000 quantitative and clinical phenotypes, all of which is publicly available.\n\nThe BXD family is an enduring, collaborative, and replicable resource to test causal and mechanistic links between genomes and phenomes at many stages and under a wide variety of treatments and interventions.

genomics↗

bioRxiv2019

Genomic Prediction of Depression Risk and Resilience Under Stress

Advancing our ability to predict who is likely to develop depression in response to stress holds great potential in reducing the burden of the disorder. Large-scale genome-wide association studies (GWAS) of depression have, for the first time, provided a basis for meaningful depression polygenic risk score construction (MDD-PRS). The Intern Health Study utilizes the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the MDD-PRS derived from the PGC2/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.082, p=2.1x10-12) and that MDD-PRS was significantly more strongly associated with depression under stress than at baseline (MDD-PRS x stress interaction - beta=0.029, p=0.02). While known risk factors accounted for 85.6% of the association between MDD-PRS and depression at baseline, they only accounted for 55.4% of the association between MDD-PRS and depression under stress, suggesting that MDD-PRS can add unique predictive power to existing models of depression under stress. Further, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that polygenic risk score holds promise in furthering our ability to predict vulnerability and resilience under stress.

genomics↗

bioRxiv2019

Cleaning genotype data from Diversity Outbred mice

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in complex quantitative traits. Data cleaning is more difficult in multiparent populations (experimental crosses derived from more than two founder strains), as individual SNP markers have incomplete information. We describe our process for cleaning SNP microarray genotype data from Diversity Outbred (DO) mice, illustrated with genotype data from the MegaMUGA array on a set of 291 DO mice. We first consider the proportion of missing genotypes in each mouse, as an indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to check mouse sex. We use the proportion of matching SNP genotypes in pairs of mice to detect sample duplicates. We use a hidden Markov model to calculate genotype probabilities across the genome for each mouse, which we use to estimate the number of crossovers in each mouse, and to identify potential genotyping errors. The estimated genotyping error rate, by mouse sample and by SNP marker, is a useful diagnostic. We also consider the SNP genotype frequencies by mouse and by marker, with markers grouped according to the minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele intensities from the microarray, colored by both observed and predicted genotypes, can be revealing about the underlying cause. But the detection of low-quality samples is more important than the detection of low-quality markers.

genetics↗

bioRxiv2018

Neuroblast-specific chromatin landscapes allow integration of spatial and temporal cues to generate neuronal diversity in Drosophila

During early neurogenesis in flies and mice, spatial and temporal cues interact to specify neuronal diversity, yet in no organism is it known how spatial and temporal cues are integrated. We used Targeted DamID (TaDa) to identify the genomic binding sites of the temporal transcription factor Hunchback in two adjacent Drosophila neuroblasts (NB5-6 and NB7-4). Hunchback targets were different in each neuroblast. Profiling chromatin accessibility showed that each neuroblast had a distinct chromatin landscape: Hunchback-bound loci in NB5-6 were in open chromatin, but the same loci in NB7-4 were in closed chromatin. Moreover, binding of the spatial factor Gsb/Pax3, essential for NB5-6 specification, was correlated with open chromatin and Hunchback-enriched loci in NB5-6, but not NB7-4. We propose early-acting spatial factors establish a unique chromatin landscape in each neuroblast, thereby restricting temporal factor binding to different loci in each neuroblast, resulting in different neurons in each neuroblast lineage.\n\nImpact statementIntegration of spatial and temporal identity during Drosophila neurogenesis is due to spatial factors generating neuroblast-specific chromatin thereby biasing subsequent temporal transcription factor binding and producing neuroblast-specific neurons.

developmental biology↗

bioRxiv2018

Activation of Multiple Signalling Pathways by P152Lp53 Mutant Reveals New Gain-of-function Implicating Tumorigenesis

TP53 is the most frequently mutated tumor suppressor gene in context of all varieties of cancer, yet biochemical characterization of several reported mutations with probable biological significance have not been accomplished. We have identified a relatively rare proline to leucine mutation (P152L) in p53 in an Indian oral cancer patient sample at the fade end of its DNA binding domain (DBD). Although P152Lp53 DBD potentially binds to DNA, the full length protein is completely devoid of DNA binding ability at its cognate site. Interestingly, P152Lp53 can efficiently tetramerize. Significantly, this mutant when expressed in p53 null cell line, was found to induce cell mobility, proliferation, and invasion as compared to vector control. Also, enhanced tumorigenic potential was observed when cells expressing P152Lp53 were xenografted into nude mice, the mechanistic details of which were also investigated upon where several of the pathways were found to be upregulated such as Cell-Cell/Cell-ECM signalling, EGFR signalling, Rho-GTPase signalling. Taken together, this study establishes P152Lp53 as a new gain of function mutant.

cancer biology↗

bioRxiv2018

Matrix linear models for high-throughput chemical genetic screens

We develop a flexible and computationally efficient approach for analysing high throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. The goal is to detect interactions between genes and stresses. Typically, this is achieved by grouping the mutants and stresses into categories, and performing modified t-tests for each combination. This approach does not have a natural extension if mutants or stresses have quantitative or non-overlapping annotations (eg. if conditions have doses, or a mutant falls into more than one category simultaneously). We develop a matrix linear model framework that allows us to model relationships between mutants and conditions in a simple, yet flexible multivariate framework. It encodes both categorical and continuous relationships to enhance detection of associations. To handle large datasets, we develop a fast estimation approach that takes advantage of the structure of matrix linear models. We evaluate our methods performance in simulations and in an E. coli chemical genetic screen, comparing it with an existing univariate approach based on modified t-tests. We show that matrix linear models perform slightly better than the univariate approach when mutants and conditions are classified in non-overlapping categories, and substantially better when conditions can be ordered in dosage categories. Our approach is much faster computationally and is scalable to larger datasets. It is an attractive alternative to current methods, and provides a natural framework extensible to larger, and more complex chemical genetic screens. A Julia implementation of matrix linear models and the code used for the analysis in this paper can be found at https://bitbucket.org/jwliang/mlm_packages and https://bitbucket.org/jwliang/mlm_gs_supplement, respectively.

bioinformatics↗

bioRxiv2018

R/qtl2: software for mapping quantitative trait loci with high dimensional data and multi-parent populations

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multi-parent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2 comes with a test framework and is free and open source software written in the R and C++ programming languages.

genetics↗

bioRxiv2018

Single Cell Transcriptomics of Pancreatic Cancer Precursors Demonstrates Epithelial and Microenvironmental Heterogeneity as an Early Event in Neoplastic Progression

BackgroundEarly detection of pancreatic ductal adenocarcinoma (PDAC) remains elusive. Precursor lesions of PDAC, specifically, intraductal papillary mucinous neoplasms (IPMNs) represent a bona fide pathway to invasive neoplasia, although the molecular correlates of progression remain to be fully elucidated. Single cell transcriptomics provides a unique avenue for dissecting both the epithelial and microenvironmental heterogeneity that accompany multistep progression from non-invasive IPMNs to PDAC.\n\nMethodsSingle cell RNA-sequencing was performed through droplet-based sequencing on 5,403 cells from two low-grade IPMNs (LGD-IPMN), two high-grade IPMNs (HGD-IPMN), and two PDACs (all surgically resected).\n\nResultsAnalysis of single cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of non-invasive dysplasia to invasive cancer. While HGD-IPMNs expressed many core-signaling pathways described in PDAC, LGD-IPMNs harbored subsets of single cells with a transcriptomic profile that overlapped with invasive cancer. Notably, a pro-inflammatory immune component was readily seen in low-grade IPMNs, comprised of cytotoxic T-cells, activated T-helper cells, and dendritic cells, which was progressively depleted during neoplastic progression, accompanied by infiltration of myeloid-derived suppressor cells. Finally, stromal myofibroblast populations were heterogeneous, and acquired a previously described tumor-promoting and immune-evading phenotype during invasive carcinogenesis.\n\nConclusionsThis study demonstrates the ability to perform high resolution profiling of the transcriptomic changes that occur during multistep progression of cystic PDAC precursors to cancer. Notably, single cell analysis provides an unparalleled insight into both the epithelial and microenvironmental heterogeneity that accompany early cancer pathogenesis, and might be a useful substrate to identify targets for cancer interception.

pathology↗

bioRxiv2017

Cross talk between photo-pigments and graphene electron cloud - Designing a biodiode

We report emergence of a new electrical material by growing photosynthetic biofilm on a Dirac material, graphene. The material showed new conducting as well as semiconducting properties. Frequency dependent capacitive spectra further indicated presence of electrical isosbestic points(at 0.8 and 9MHz), implying two state dieletric transitions at critical frequencies. A notable reult was a Schottky diode like behavior in the IV curve. Voltage dependent conductance with conductance peaks near the Schottky diode threshold was observed. We obtained facilitated growth of photosynthetic biofilm in presence of graphene. Lastly higher bacterial metabolism was seen in graphene incorporated biofilm. For this zero band gap Dirac material this can only be interpreted as coupling of the electron transport chain of the bacterial biofilm and the graphene electron cloud.

biophysics↗

bioRxiv2017

Emerging Electrical Properties of Graphene incorporated Photosynthetic Biofilms

We report emergence of a new electrical material by growing photosynthetic biofilm on a Dirac material, graphene. The material showed new conducting as well as semiconducting properties. Frequency dependent capacitive spectra further indicated presence of electrical isosbestic points(at 0.8 and 9MHz), implying two state dieletric transitions at critical frequencies. A notable reult was a Schottky diode like behavior in the IV curve. Voltage dependent conductance with conductance peaks near the Schottky diode threshold was observed. We obtained facilitated growth of photosynthetic biofilm in presence of graphene. Lastly higher bacterial metabolism i was seen in the biofilm in contact with graphene as compared to its normal growth condition. For this zero band gap Dirac material this can only be interpreted as coupling of the electron transport chain of the bacterial biofilm and the graphene electron cloud.

biophysics↗

bioRxiv2017

Whole Genome Sequencing of Pharmacogenetic Drug Response in Racially and Ethnically Diverse Children with Asthma

Asthma is the most common chronic disease of children, with significant racial/ethnic differences in prevalence, morbidity, mortality and therapeutic response. Albuterol, a bronchodilator medication, is the first-line therapy for asthma treatment worldwide. We performed the largest whole genome sequencing (WGS) pharmacogenetics study to date using data from 1,441 minority children with asthma who had extremely high or low bronchodilator drug response (BDR). We identified population-specific and shared pharmacogenetic variants associated with BDR, including genome-wide significant (p < 3.53 x 10-7) and suggestive (p < 7.06 x 10-6) loci near genes previously associated with lung capacity (DNAH5), immunity (NFKB1 and PLCB1), and {beta}-adrenergic signaling pathways (ADAMTS3 and COX18). Functional analyses centered on NFKB1 revealed potential regulatory function of our BDR-associated SNPs in bronchial smooth muscle cells. Specifically, these variants are in linkage disequilibrium with SNPs in a functionally active enhancer, and are also expression quantitative trait loci (eQTL) for a neighboring gene, SLC39A8. Given the lack of other asthma study populations with WGS data on minority children, replication of our rare variant associations is infeasible. We attempted to replicate our common variant findings in five independent studies with GWAS data. The age-specific associations previously found in asthma and asthma-related traits suggest that the over-representation of adults in our replication populations may have contributed to our lack of statistical replication, despite the functional relevance of the NFKB1 variants demonstrated by our functional assays. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations.\n\nAUTHOR SUMMARYAsthma is the most common chronic disease among children. Albuterol, a bronchodilator medication, is the first-line therapy for asthma treatment worldwide. In the U.S., asthma prevalence is the highest among Puerto Ricans, intermediate among African Americans and lowest in Whites and Mexicans. Asthma disparities extend to mortality, which is four- to five-fold higher in Puerto Ricans and African Americans compared to Mexicans [1]. Puerto Ricans and African Americans, the populations with the highest asthma prevalence and death rate, also have the lowest albuterol bronchodilator drug response (BDR). We conducted the largest pharmacogenetic study using whole genome sequencing data from 1,441 minority children with asthma who had extremely high or low albuterol bronchodilator drug response. We identified population-specific and shared pharmacogenetic variants associated with BDR. Our findings help inform the direction of future development of asthma medications and our study advances the foundation of precision medicine for at-risk, yet understudied, racially/ethnically diverse populations.

genetics↗

bioRxiv2017

Soft drug-resistant ovarian cancer cells invade via two distinct mechanisms utilizing myosin IIB

The failure of chemotherapeutic drugs in treatment of various cancers is attributed to the acquisition of drug resistance. However, the invasion mechanisms of drug-resistant cancer cells remains incompletely understood. Here we address this question from a biophysical perspective by mapping the phenotypic alterations in ovarian cancer cells (OCCs) resistant to cisplatin and paclitaxel. We show that cisplatin-resistant (CisR), paclitaxel-resistant (PacR) and dual drug-resistant (i.e., resistant to both drugs) OCCs are softer and more contractile than drug-sensitive cells. Protease inhibition suppresses invasion of CisR cells but not of PacR and dual cells, suggesting protease-dependent mode of invasion in CisR cells and protease-independent mode in PacR and dual cells. Despite these differences, actomyosin contractility, mediated by the RhoA-ROCK2-Myosin IIB signaling pathway regulates both modes of invasion. Myosin IIB modulates matrix metalloproteinase-9 (MMP-9) secretion in CisR cells and nuclear squeezing in PacR and dual cells, thereby highlighting its importance as a potential therapeutic target for treatment of drug-resistant ovarian cancer cells.\n\nFinancial SupportAuthors acknowledge financial support from IIT Bombay Healthcare Initiative, CSIR andDepartment of Biotechnology (Govt. of India) (Grant # BT/PR14658/MED/31/107/2010).AK and BTwere supported by fellowships from UGC and CSIR respectively (Govt. of India).\n\nAuthors declare no competing financial interests\n\nAuthor contributions: AK, PR and SS designed the experiments. AK performed most of the experiments and analyzed the data.BT and SG developed drug resistant cell lines and did RT-PCR. MM performed western blot. AD performed AFM experiments. SD performed gelatin zymography. ABB, PM, AM and AD helped with live cell imaging experiments. AK and SS wrote the manuscript. All authors read and approved the final manuscript.\n\nSummary statement: This study identifies drug-specific differences in the modes of invasion utilized by ovarian cancer cells, and demonstrates the role of myosin IIB in regulating both modes of invasion.

biophysics↗