Biology subjects

Sengupta, D.

Publications and source records attributed to Sengupta, D..

54 records · Page 3Linked to original sources

bioRxiv2020

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

Dynamic interactions between G protein-coupled receptors (GPCRs) and their cognate protein partners at the membrane interface control several cellular signaling pathways. An important example is the association of CXC chemokine receptor 1 (CXCR1) with its cognate chemokine, interleukin-8 (IL8 or CXCL8) that regulates neutrophil-mediated immune responses. Although the N-terminal domain of the receptor is known to confer ligand selectivity, the conformational dynamics of this intrinsically disordered region of CXCR1 in particular, and chemokine receptors in general, remains unresolved. In this work, we have explored the interaction of CXCR1 with IL8 by microsecond time scale coarse-grain simulations that were validated by atomistic models and NMR chemical shift predictions. We show that the conformational plasticity of the apo-receptor N-terminal region is restricted upon ligand binding, driving it to an open C-shaped conformation. Importantly, we validated the dynamic complex sampled in our simulations against chemical shift perturbations reported by previous NMR studies. Our results indicate that caution should be exercised when chemical shift perturbation is used as a reporter of residue contacts in such dynamic associations. We believe our results represent a step forward in devising a strategy to understand intrinsically disordered regions in GPCRs and how they acquire functionally important conformational ensembles in dynamic protein-protein interfaces. Author summaryHow cells communicate with the outside environment is intricately controlled and regulated by a large family of receptors on the cell membrane (G protein-coupled receptors or GPCRs) that respond to external signals (termed ligands). Chemokine receptors belong to this GPCR family and regulate immune responses. We analyze here the first step of binding of a representative chemokine receptor (CXCR1) with its natural ligand, interleukin 8 (IL8) by an extensive set of molecular dynamics simulations. Our work complements previous mutational and NMR experiments which lack molecular-level resolution. We show that in the inactive state, one of the extracellular domains of the CXCR1 receptor, namely the N-terminal domain, is highly flexible and like a "shape-shifter" can exist in multiple conformational states. However, when IL8 binds, the N-terminal domain undergoes a conformational freezing, and acquires a C-shaped "claw-like" structure. The complex between the receptor and IL8 is still quite dynamic as this C-shaped N-terminal domain forms an extensive but slippery interface with the ligand. We further validated these results by quantitative comparison with NMR and mutagenesis studies. Our work helps clarify the inherent disorder in N-terminal domains of chemokine receptors and demonstrates how this domain can acquire functionally important conformational states in dynamic protein-protein interfaces.

biophysics↗

bioRxiv2020

Nuclear Morphology Optimized Deep Hybrid Learning (NUMODRIL): A novel architecture for accurate diagnosis/prognosis of Ovarian Cancer

Nuclear morphological features are potent determining factors for clinical diagnostic approaches adopted by pathologists to analyse the malignant potential of cancer cells. Considering the structural alteration of nucleus in cancer cells, various groups have developed machine learning techniques based on variation in nuclear morphometric information like nuclear shape, size, nucleus-cytoplasm ratio and various non-parametric methods like deep learning have also been tested for analysing immunohistochemistry images of tissue samples for diagnosing various cancers. Our aim is to study the morphometric distribution of nuclear lamin proteins as a specific parameter in ovarian cancer tissues. Besides being the principal mechanical component of the nucleus, lamins also present a platform for binding of proteins and chromatin thereby serving a wide range of nuclear functions like maintenance of genome stability, chromatin regulation. Altered expression of lamins in different subtypes of cancer is now evident from data across the world. It has already been elucidated that in ovarian cancer, extent of alteration in nuclear shape and morphology can determine degree of genetic changes and thus can be utilized to predict the outcome of low to high form of serous carcinoma. In this work, we have performed exhaustive imaging of ovarian cancer versus normal tissue and introduced a novel Deep Hybrid Learning approach on the basis of the distribution of lamin proteins. Although developed with ovarian cancer datasets in view, this architecture would be of immense importance in accurate and fast diagnosis and prognosis of all types of cancer associated with lamin induced morphological changes and would perform across small/medium to large datasets with equal efficiency. Significance StatementWe have developed a novel Deep Hybrid Learning approach based on nuclear morphology to classify normal and ovarian cancer tissues with highest possible accuracy and speed. Ovarian cancer cells can be easily distinguished from their enlarged nuclear morphology as is evident from lamin A & B distribution pattern. This is the first report to invoke specific nuclear markers like lamin A & B instead of classical haematoxylin-eosin staining in an effort to build parametric datasets. Our approach has been shown to outperform the existing deep learning techniques in training and validation of datasets over a wide range. Therefore this method could be used as a robust model to predict malignant transformations of benign nuclei and thus be implemented in the diagnosis and prognosis of ovarian cancer in future. Most importantly, this method can be perceived as a generalized approach in the diagnosis for all types of cancer.

cancer biology↗

bioRxiv2020

Wavelet coherence phases decode the universal switching mechanism of Ras GTPase superfamily

Ras superfamily GTPases are molecular switches which regulate critical cellular processes. Extensive structural and computational studies on these G proteins have established a general framework for their switching mechanism, which involves conformational changes in their two loops, Switch I and Switch II, upon GTP binding and hydrolysis. Since the extent of these conformational changes is not uniform amongst the members of the Ras superfamily, there is no generic modus operandi defining their switching mechanism. Here, we have developed a novel approach employing wavelet coherence analysis to correlate the structural changes with their functional states. Our analysis shows that the structural coupling between the Switch I and Switch II regions is manifested in terms of conserved wavelet coherence phases, which could serve as useful parameters to define functional states of the GTPases. In oncogenic GTPases mutants, this phase coupling gets disentangled, which perhaps provides an alternative explanation for their aberrant function. We have tested the statistical significance of the observed phase angle correlations on multiple switch region conformers, generated through MD simulations.

biophysics↗

bioRxiv2020

Genetic-substructure and complex demographic history of South African Bantu speakers

South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny (separation of Nguni, Sotho-Tswana and Tsonga speakers). Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over [~]400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa.

genomics↗

bioRxiv2020

Systematic hormone-metabolite network provides insights of high salinity tolerance in Pongamia pinnata (L.) pierre

Salinity stress results significant losses in plant productivity, and loss of cultivable lands. Although Pongamia pinnata is reported to be a salt tolerant semiarid tree crop, the adaptive mechanisms to saline environment are elusive. The present investigation describes alterations in hormonal and metabolic responses in correlation with physiological and molecular variations in leaves and roots of Pongamia at sea salinity level (3% NaCl) for 8 days. At physiological level, salinity induced adjustments in plant morphology, leaf gas exchange and ion accumulation patterns were observed. Our study also revealed that phytohormones including JAs and ABA play crucial role in promoting the salt adaptive strategies such as apoplasmic Na+ sequestration and cell wall lignification in leaves and roots of Pongamia. Correlation studies demonstrated that hormones including ABA, JAs and SA showed a positive interaction with selective compatible metabolites (sugars, polyols and organic acids) to aid in maintaining osmotic balance and conferring salt tolerance to Pongamia. At the molecular level, our data showed that differential expression of transporter genes as well as antioxidant genes regulate the ionic and ROS homeostasis in Pongamia. Collectively, these results shed new insights on an integrated physiological, structural, molecular and metabolic adaptations conferring salinity tolerance to Pongamia. High lightOur data, for the first time, provide new insights for an integrated molecular and metabolic adaptation conferring salinity tolerance in Pongamia. The present investigation describes alterations in hormonal and metabolic responses in correlation with physiological and molecular variations in Pongamia at sea salinity level (3% NaCl) for 8 days.

plant biology↗

bioRxiv2020

Deep learning discerns cancer mutation exclusivity

The exclusivity of a vast majority of cancer mutations remains poorly understood, despite the availability of large amounts of whole genome and exome sequencing data. In clinical settings, this markedly hinders the identification of the previously uncharacterized deleterious mutations due to the unavailability of matched normal samples. We employed state of the art deep learning algorithms for cross-exome learning of mutational embeddings and demonstrated their utility in sequence based detection of cancer-specific Single Nucleotide Variants (SNVs).

bioinformatics↗

bioRxiv2019

A Central Role for Canonical PRC1 in Shaping the 3D Nuclear Landscape

Polycomb group (PcG) proteins silence gene expression by chemically and physically modifying chromatin. A subset of PcG target loci are compacted and cluster in the nucleus to form observable bodies; a conformation which is thought to contribute to gene silencing. However, how these interactions influence gross nuclear organisation and their relationship with transcription remains poorly understood. Here we examine the role of Polycomb Repressive Complex 1 (PRC1) in shaping 3D genome organization in mouse embryonic stem cells (mESCs). Using a combination of imaging and Hi-C analyses we show that PRC1-mediated long-range interactions are independent of CTCF and can bridge sites at a megabase scale. Impairment of PRC1 enzymatic activity does not directly disrupt these interactions. We demonstrate that PcG targets coalesce in vivo, and that developmentally induced expression of one of the target loci disrupts this spatial arrangement. Finally, we show that transcriptional activation and the loss of PRC1-mediated interactions are seperable events. These findings provide important insights into the function of PRC1, whilst highlighting the complexity of this regulatory system. HighlightsO_LILoss of RING1B substantially disrupts nuclear architecture. C_LIO_LIPRC1 mediated looping can occur at a Mb scale and is independent of CTCF. C_LIO_LIPolycomb mediated looping is driven by canonical PRC1 complexes. C_LIO_LIMultimeric PRC1-mediated interactions occur in vitro and in vivo. C_LIO_LIDisruption of PRC1-mediated looping is independent of gene activation. C_LI

molecular biology↗

bioRxiv2019

UniPath: A uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles.

Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. Besides being robust to variability in dropout, UniPath provides consistency and scalability in estimating gene-set enrichment scores for every cell. UniPaths approach of predicting temporal-order of single-cells using their gene-set activity score enables suppression of known covariates. UniPath based analysis of mouse cell atlas yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs and helped in annotating many unlabeled cells. By enabling unconventional analysis, UniPath also proves to be useful in inferring context-specific regulation in cancer cells.

genomics↗

bioRxiv2019

Integrative analysis and machine learning based characterization of single circulating tumor cells

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic systems for label-free enrichment of CTCs.

genomics↗

bioRxiv2019

Deep Active Lesion Segmentation

Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs). Our DALS framework benefits from an improved level-set ACM formulation with a per-pixel-parameterized energy functional and a novel multiscale encoder-decoder CNN that learns an initialization probability map along with parameter maps for the ACM. We evaluate our lesion segmentation model on a new Multiorgan Lesion Segmentation (MLS) dataset that contains images of various organs, including brain, liver, and lung, across different imaging modalities--MR and CT. Our results demonstrate favorable performance compared to competing methods, especially for small training datasets.

bioengineering↗

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↗

bioRxiv2018

deepMc: deep Matrix Completion for imputation of single cell RNA-seq data

Single cell RNA-seq has fueled discovery and innovation in medicine over the past few years and is useful for studying cellular responses at individual cell resolution. But, due to paucity of starting RNA, the data acquired is highly sparse. To address this, We propose a deep matrix factorization based method, deepMc, to impute missing values in gene-expression data. For the deep architecture of our approach, We draw our motivation from great success of deep learning in solving various Machine learning problems. In this work, We support our method with positive results on several evaluation metrics like clustering of cell populations, differential expression analysis and cell type separability.

bioinformatics↗

bioRxiv2018

ROSeq: A rank based approach to modelling gene expression in single cells

1Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single cell expression data. The zero-inflated version of Poisson/Negative Binomial and Log-Normal distributions have emerged as the most popular alternatives due to their ability to accommodate high dropout rates, as commonly observed in single cell data. While the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression-ranks, as robust surrogates for transcript abundance. Here we examined the performance of the Discrete Generalized Beta Distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method, to understand its advantages as compared to some of the existing best practice approaches. Besides striking a reasonable balance between Type 1 and Type 2 errors, we concluded that ROSeq, the proposed differential expression test is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq, and made it available on the Bioconductor platform.

genomics↗

bioRxiv2018

McImpute: Matrix completion based imputation for single cell RNA-seq data

MotivationSingle cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression analysis at single cell resolution, provides a window into dynamics of cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified.\n\nResultsWe introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, performance of dimensionality reduction techniques for cell visualization and gene distribution.\n\nAvailability and Implementationhttps://github.com/aanchalMongia/McImpute_scRNAseq

bioinformatics↗

bioRxiv2018

Disproportionate feedback interactions govern cell-type specific proliferation in mammalian cells

In mammalian cells, the critical decision to maintain quiescence over proliferation commitment in and around the G1-S transition depends on more than one intertwined feedback interaction, and is highly cell-type dependent. However, the precise role played by these individual feedback regulations, in order to generate such diverse nature of proliferation commitment, is still poorly understood. Herein, we propose a generic mathematical model of G1-S transition in mammalian cells that not only reconciles distinct single cell experimental observations in a cell-type specific manner, but also makes experimentally testable non-intuitive predictions. Intriguingly, The model analysis reveals that the feedback motifs responsible for the G1-S transition act in a disparate fashion to organize the cell-type specific proliferation response in different mammalian cells. The proposed model, in principle, can be effectively tuned to explore proliferation dynamics in a cell-type specific way to gain crucial insights about novel therapeutic intervention to prevent unwanted cellular proliferation.

systems biology↗

bioRxiv2017

Subtle alteration in microRNA dynamics accounts for differential nature of cellular proliferation

In the G1 phase of the mammalian cell cycle, a bi-stable steady state dynamics of the transcription factor E2F ensures that only a certain threshold level of the growth factor can induce a high expression level (on state) of E2F to initiate either normal or abnormal cellular proliferation or even apoptosis. A group of microRNAs known as the mir-17-92 cluster, which specifically inhibits E2F, can simultaneously influence the threshold level of growth factor required for E2F activation, and the corresponding expression level of E2F in the on state. However, mir-17-92 cluster can function as either oncogene or tumor suppressor in a cell-type specific manner for reasons that still remain illusive. Here we put forward a deterministic mathematical model for Myc/E2F/mir-17-92 network that demonstrates how the experimentally observed mir-17-92 mediated differential nature of the cellular proliferation can be reconciled by having conflicting steady state dynamics of E2F for different cell types. While a 2-D bifurcation study of the model rationalizes the reason behind the contrasting E2F dynamics, an intuitive sensitivity analysis of the model parameters predicts that by exclusively altering the mir-17-92 related part of the network, it is possible to experimentally manipulate the cellular proliferation in a cell-type specific fashion for therapeutic intervention.

systems 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

FORKS: Finding Orderings Robustly using K-means and Steiner trees

Recent advances in single cell RNA-seq technologies have provided researchers with unprecedented details of transcriptomic variation across individual cells. However, it has not been straightforward to infer differentiation trajectories from such data, due to the parameter-sensitivity of existing methods. Here, we present Finding Orderings Robustly using k-means and Steiner trees (FORKS), an algorithm that pseudo-temporally orders cells and thereby infers bifurcating state trajectories. FORKS, which is a generic method, can be applied to both single-cell and bulk differentiation data. It is a semi-supervised approach, in that it requires the user to specify the starting point of the time course. We systematically benchmarked FORKS and eight other pseudo-time estimation algorithms on six benchmark datasets, and found it to be more accurate, more reproducible, and more memory-efficient than existing methods for pseudo-temporal ordering. Another major advantage of our approach is its robustness - FORKS can be used with default parameter settings on a wide range of datasets.

bioinformatics↗