Biology subjects

Sengupta, S.

Publications and source records attributed to Sengupta, S..

11 recordsLinked to original sources

bioRxiv2018

Circadian control of lung inflammation in influenza infection

Influenza is a leading cause of respiratory mortality and morbidity. While inflammation is necessary for fighting infection, a fine balance of anti-viral defense and host tolerance is necessary for recovery. Circadian rhythms have been known to modulate inflammation. However, the importance of diurnal variability in the timing of influenza infection is not well understood. Here we demonstrate that endogenous rhythms influence the cellular response to infection in bronchoalveolar lavage (BAL), the pulmonary transcriptomic profile and lesional histology. This time dependent variability does not reflect alterations in viral replication. Rather, we found that better time-dependent outcomes were associated with a preponderance of NK and NKT cells and lower proportion of monocytes in the lung. Thus, host tolerance, rather than viral burden underlies the diurnal gating of influenza induced lung injury.\n\nSignificance statementOur work demonstrates the importance of circadian rhythms in influenza infection --a condition with significant public health implications. Our findings, which establish the role of the circadian rhythms in maintaining the balance between host tolerance pathways and anti-viral responses confers a new framework for evaluating the relevance of circadian influences on immunity.

immunology↗

bioRxiv2018

Iso-relevance Functions - A Systematic Approach to Ranking Genomic Features by Differential Effect Size

It is common to measure large numbers of features to identify those differing between experimental conditions; for example using RNA-Seq to search for differentially expressed genes. Ranking by p-value allows for statistical control, but has well known issues: unreliability without many replicates; and significance of biologically irrelevant effect sizes. As a result prioritization is typically performed in conjunction with effect size; the canonical one being "fold-change" [Formula]. However fold-change has several issues: division by zero, sensitivity to small values in the denominator, insensitivity to magnitude (1 over 2 equals 100 over 200). To mitigate these problems adding 1 to all values is a widely used heuristic; which we show using real and simulated data is typically highly sub-optimal, while the value 20 is nearly optimal in all cases. From another point of view, adding a fixed "pseudocount" to all values is essentially re-defining effect size from fold-change to something else. To explore this further, we axiomatize the concept of effect size and use this mathematical framework to study the problem in general. We also present the remarkable finding that pseudocounts strike a balance between sorting by fold-change and sorting by difference. Therefore, optimization is equivalent to finding the most harmonious balance between these two extremes. Lastly, the framework is illustrated on a fundamentally different type of problem, that of ranking di-codons by their differential abundance in the ORFeome of different species, where p-values are unavailable and one must solve the problem directly with effect sizes.

bioinformatics↗

bioRxiv2018

On identifying collective displacements in apo-proteins that reveal eventual binding pathways

Binding of small molecules to proteins often involves large conformational changes in the latter, which open up pathways to the binding site. Observing and pinpointing these rare events in large scale, all-atom, computations of specific protein-ligand complexes, is expensive and to a great extent serendipitous. Further, relevant collective variables which characterise specific binding or un-binding scenarios are still difficult to identify despite the large body of work on the subject. Here, we show that possible primary and secondary binding pathways can be discovered from short simulations of the apo-protein without waiting for an actual binding event to occur. We use a projection formalism, introduced earlier to study deformation in solids, to analyse local atomic displacements into two mutually orthogonal subspaces -- those which are \"affine\" i.e. expressible as a homogeneous deformation of the native structure, and those which are not. The susceptibility to non-affine displacements among the various residues in the apo-protein is then shown to correlate with typical binding pathways and sites crucial for allosteric modifications. We validate our observation with all-atom computations of three proteins, T4-Lysozyme, Src kinase and Cytochrome P450.

biophysics↗

bioRxiv2018

The Evolution of Antibiotic Production Rate in a Spatial Model of Bacterial Competition

We consider competition between antibiotic producing bacteria, non-producers (or cheaters), and sensitive cells in a two-dimensional lattice model. Previous work has shown that these three cell types can survive in spatial models due to the presence of spatial patterns, whereas coexistence is not possible in a well-mixed system. We extend this to consider the evolution of the antibiotic production rate, assuming that the cost of antibiotic production leads to a reduction in growth rate of the producers. We find that coexistence occurs for an intermediate range of antibiotic production rate. If production rate is too high or too low, only sensitive cells survive. When evolution of production rate is allowed, a mixture of cell types arises in which there is a dominant producer strain that produces sufficient to limit the growth of sensitive cells and which is able to withstand the presence of cheaters in its own species. The mixture includes a range of low-rate producers and non-producers, none of which could survive without the presence of the dominant producer strain. We also consider the case of evolution of antibiotic resistance within the sensitive species. In order for the resistant cells to survive, they must grow faster than both the non-producers and the producers. However, if the resistant cells grow too rapidly, the producing species is eliminated, after which the resistance mutation is no longer useful, and sensitive cells take over the system. We show that there is a range of growth rates of the resistant cells where the two species coexist, and where the production mechanism is maintained as a polymorphism in the producing species and the resistance mechanism is maintained as a polymorphism in the sensitive species.\n\nAuthor SummaryNatural environments such as the soil contain many species of antibiotic producing bacteria. Antibiotics prevent the growth of sensitive species that would otherwise outcompete the more-slowly-growing antibiotic producers. The producers are also vulnerable to competition from non-producing \"cheats\" arising by mutations within the producing species that avoid the metabolic cost of antibiotic production. We consider multiple strains of producers that differ in production rate in the presence of sensitive cells of a different species. We show, in 2d simulations, that the system evolves towards a state with a dominant producer strain that is able to outcompete the sensitive cells, plus a range of low-rate producers and non-producers that can survive in the presence of the dominant producer, but not on their own. This system remains stable, despite the short-term selective advantage to reducing production rate. When resistant mutants are added to the sensitive species, we show that there is a range of growth rate of the resistant cells in which producers, non-producers, sensitive and resistant cells can all coexist - as we see in nature. Our model shows the balance of factors required to maintain resistance mechanisms and production mechanisms together within the mixture of species.

evolutionary biology↗

bioRxiv2018

A First-principles Approach to Large-scale Nuclear Architecture

Model approaches to nuclear architecture have traditionally ignored the biophysical consequences of ATP-fueled active processes acting on chromatin. However, transcription-coupled activity is a source of stochastic forces that are substantially larger than the Brownian forces present at physiological temperatures. Here, we describe a first-principles approach to large-scale nuclear architecture in metazoans that incorporates cell-type-specific active processes. The model predicts the statistics of positional distributions, shapes and overlaps of each chromosome. Our simulations reproduce common organising principles underlying large-scale nuclear architecture across human cell nuclei in interphase. These include the differential positioning of euchromatin and heterochromatin, the territorial organisation of chromosomes including both gene-density-based and size-based chromosome radial positioning schemes, the non-random locations of chromosome territories and the shape statistics of individual chromosomes. We propose that the biophysical consequences of the distribution of transcriptional activity across chromosomes should be central to any chromosome positioning code.

biophysics↗

bioRxiv2018

Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

cancer biology↗

bioRxiv2017

Mycobacterium tuberculosis LprE enhances bacterial persistence by inhibiting cathelicidin and autophagy in macrophages

Mycobacterium tuberculosis(Mtb) lipoproteins are known to facilitate bacterial survival by manipulating the host immune responses. Here, we have characterized a novel Mtb lipoprotein LprE(LprEMtb), and demonstrated its role in mycobacterial survival. LprEMtb acts by down-regulating the expression of cathelicidin, Cyp27B1, VDR and p38-MAPK via TLR-2 signaling pathway. Deletion of lprEMtb resulted in induction of cathelicidin and decreased survival in the host. Interestingly, LprEMtb was also found to inhibit autophagy mechanism to dampen host immune response. Episomal expression of LprEMtb in non-pathogenic Mycobacterium smegmatis(Msm) increased bacillary persistence by down-regulating the expression of cathelicidin and autophagy, while deletion of LprEMtb orthologue in Msm, had no effect on cathelicidin and autophagy expression. Moreover, LprEMtb blocked phago-lysosome fusion by suppressing the expression of EEA1, Rab7 and LAMP-1 endosomal markers by down-regulating IL-12 and IL-22 cytokines. Our results indicate that LprEMtb plays an important role in mycobacterial pathogenesis in the context of innate immunity.

physiology↗

bioRxiv2017

Specific cholesterol binding drives drastic structural alterations in apolipoprotein A1

Protein adopts multitude of flexible and rapidly interconverting conformers, many which are governed by specific protein-interaction domains. ApoA1, a key player involved in high-density lipoprotein (HDL) regulation exists in structurally diverse forms with varying degree of cholesterol association, and each state is associated with different functional properties. While disc-shaped HDL and its oligomeric ApoA1 protein components have been the focus of several investigations, structural properties of monomeric ApoA1 are poorly understood. Here, we undertook large-scale structural analysis of ApoA1 in apo and cholesterol-bound forms using tens of independent simulations with total computing time exceeding 50 s. Examination of multiple lipid-free trajectories of monomeric ApoA1 revealed a common conformation, with distinct spatial proximity between N- and C-terminal domains. With incorporation of physiologically known cholesterol concentration ({approx}100 cholesterol molecules) in ApoA1 simulations, the monomeric protein spontaneously formed an open circular topology. Remarkably, these drastic structural perturbations are driven by specific binding site at C-terminal and a novel cholesterol binding site at the N-terminal. We proposed a mechanism of stage-wise opening of ApoA1 and demonstrated that less cholesterol concentration around interaction sites or mutation within N-terminal binding sites does not lead to open bell-shaped topology. The kinetic barriers between open and closed-states also showed an ensemble of loosely packed helix bundle (H1-H7; H4-H7) that posed as a slow-intermediate step. Lastly we performed complementary experiments, including ITC and CD measurements to confirm that structural changes are induced by ligand association and not driven by random hydrophobic effect. Collectively, our study suggests a previously unknown mechanism of cholesterol sequestering by ApoA1 that could directly aid in developing modulators for cholesterol efflux with chronic cardiovascular diseases.

biophysics↗

bioRxiv2017

Imaging-Genomics Study Of Head-Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes And Genomic Mechanisms Via Integration Of TCGA And TCIA

PurposeRecent data suggest that imaging radiomics features for a tumor could predict important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. For Head and Neck Squamous Cell Carcinoma (HNSCC), we perform a comprehensive study to discover the imaging-genomics associations and explore the potential of predicting tumor genomic alternations using radiomic features.\n\nMethodsOur retrospective study integrates whole-genome multi-omics data from The Cancer Genome Atlas (TCGA) with matched computed tomography imaging data from The Cancer Imaging Archive (TCIA) for the same set of 126 HNSCC patients. Linear regression analysis and gene set enrichment analysis are used to identify statistically significant associations between radiomic imaging features and genomic features. Random forest classifier is used to predict two key HNSCC molecular biomarkers, the status of human papilloma virus (HPV) and disruptive TP53 mutation, based on radiomic features.\n\nResultsWide-spread and statistically significant associations are discovered between genomic features (including miRNA expressions, protein expressions, somatic mutations, and transcriptional activities, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of HPV and TP53 mutation status using radiomic features achieves an area under the receiver operating characteristics curve (AUC) of 0.71 and 0.641, respectively.\n\nConclusionOur analysis suggests that radiomic features are associated with genomic characteristics in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.

cancer biology↗

bioRxiv2017

Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

Epilepsy is a neurological condition which affects the nervous system. It is a general term used for a group of disorders in which nerve cells of the brain discharge anomalous electrical impulses from time to time, causing a temporary malfunction of the other nerve cells of the brain. EEG signal provides an important cue for diagnosis and interpretation related to prognosis of epilepsy. In this work we envisage to provide novel tool which can be used to detect the prognosis of epileptic disorder by comparing linear and nonlinear modalities of EEG analysis conventionally used Power spectral analysis and a robust non linear method, Detrended Fluctuation Analysis (DFA). Publicly available dataset is used for this work consisting of 100 normal patients EEG data as control group and 100 epileptic patients EEG data for comparison. Response for different frequency bands (alpha, theta, beta) of the EEG spectrum have been analyzed using Detrended Fluctuation Analysis (DFA) and Power Spectral Intensity (PSI). The comparison of the DFA scaling exponent with the spectral power data is calculated for all the 3 different frequency bands of EEG signal provide new and interesting results which have been discussed in detail.

neuroscience↗

bioRxiv2017

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution. Here, we use whole-genome sequencing of 2,778 tumour samples from 2,658 donors to reconstruct the life history, evolution of mutational processes, and driver mutation sequences of 39 cancer types. The early phases of oncogenesis are driven by point mutations in a small set of driver genes, often including biallelic inactivation of tumour suppressors. Early oncogenesis is also characterised by specific copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma. By contrast, increased genomic instability, a nearly four-fold diversification of driver genes, and an acceleration of point mutation processes are features of later stages. Copy-number alterations often occur in mitotic crises leading to simultaneous gains of multiple chromosomal segments. Timing analysis suggests that driver mutations often precede diagnosis by many years, and in some cases decades, providing a window of opportunity for early cancer detection.

cancer biology↗