Biology subjects

Chowdhury, A.

Publications and source records attributed to Chowdhury, A..

49 records · Page 3Linked to original sources

bioRxiv2021

Spatiotemporal solidification of α-synuclein inside the liquid droplets

Liquid-liquid phase separation (LLPS) and subsequent liquid-to-solid transition is implicated in membraneless organelles formation as well as disease associated protein aggregation. However, how liquid-to-solid transition is initiated inside a liquid droplet remains unclear. Here, using studies at single droplet resolution, we show that liquid-to-solid transition of -synuclein (-Syn) liquid droplets is associated with significant changes in the local microenvironment as well as secondary structure of the protein, which is prominently observed at the center of the liquid droplets. With the ageing of liquid droplets, the "structured" core at the center gradually expands and propagates over entire droplets. Further, during droplet fusion, smaller, homogeneous droplets progressively dissolve and supply proteins to the larger, heterogeneous droplets containing solid-like core at their center. The present study will significantly help to understand the physical mechanism of LLPS and liquid-to-solid transition in biological compartmentalization as well as in protein aggregation associated with human neurodegenerative disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/465113v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@2a3406org.highwire.dtl.DTLVardef@bd8148org.highwire.dtl.DTLVardef@172b4eaorg.highwire.dtl.DTLVardef@1c3965f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Pancreatic cancer risk predicted from disease trajectories using deep learning

Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be addressed by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a dataset of 6 million patient records with 24,000 pancreatic cancer cases in the Danish National Patient Registry (DNPR) and, for comparison, a dataset of three million records with 3,900 pancreatic cancer cases in the United States Department of Veterans Affairs (US-VA) healthcare system. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories and test the ability to predict cancer occurrence in time intervals of 3 to 60 months after risk assessment. For cancer occurrence within 36 months, the performance of the best model (AUROC=0.88, DNPR), trained and tested on disease trajectories, exceeds that of a model without longitudinal information (AUROC=0.85, DNPR). Performance decreases when disease events within a 3 month window before cancer diagnosis are excluded from training (AUROC[3m]=0.83). Independent training and testing on the US-VA dataset reaches comparable performance (AUROC=0.78, AUROC[3m]=0.76). These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of prediction-surveillance programs based on risk assessment in a large population followed by affordable surveillance of a relatively small number of patients at highest risk. Use of AI on real-world clinical records has the potential to shift focus from treatment of late-stage to early-stage cancer, benefiting patients by improving lifespan and quality of life.

bioinformatics↗

bioRxiv2021

The anti-immune dengue subgenomic flaviviral RNA is found in vesicles in mosquito saliva and associated with increased infectivity

Mosquito transmission of dengue viruses to humans starts with infection of skin resident cells at the biting site. There is great interest in identifying transmission-enhancing factors in mosquito saliva in order to counteract them. Here we report the discovery of high levels of subgenomic flaviviral RNA (sfRNA) in dengue virus 2-infected mosquito saliva. We show that salivary sfRNA is protected in detergent-sensitive, protease-resistant compartments. Furthermore, we show that incubation with mosquito saliva containing higher sfRNA levels results in higher virus infectivity in human cells. Since sfRNA potently inhibits innate immunity in human cells, we posit that sfRNA in mosquito saliva is present in extracellular vesicles that deliver it to cells at the biting site to inhibit innate immunity and enhance dengue virus transmission.

physiology↗

bioRxiv2020

South Asian patient population genetics reveal strong founder effects and high rates of homozygosity - new resources for precision medicine

Population-scale genetic studies can identify drug targets and allow disease risk to be predicted with resulting benefit for management of individual health risks and system-wide allocation of health care delivery resources. Although population-scale projects are underway in many parts of the world, genetic variation between population groups means that additional projects are warranted. South Asia has a population whose genetics is the least characterized of any of the worlds major populations. Here we describe GenomeAsia studies that characterize population structure in South Asia and that create tools for economical and accurate genotyping at population-scale. Prior work on population structure characterized isolated population groups, the relevance of which to large-scale studies of disease genetics is unclear. For our studies we used whole genome sequence information from 4,807 individuals recruited in the health care delivery systems of Pakistan, India and Bangladesh to ensure relevance to population-scale studies of disease genetics. We combined this with WGS data from 927 individuals from isolated South Asian population groups, and developed a custom SNP array (called SARGAM) that is optimized for future human genetic studies in South Asia. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of homozygosity that approach 100 times that seen in outbred populations. We describe founder effects that increase the power to associate functional variants with disease processes and that make South Asia a uniquely powerful place for population-scale genetic studies.

genomics↗

bioRxiv2020

JNK pathway restricts DENV, ZIKV and CHIKV infection by activating complement and apoptosis in mosquito salivary glands

Arbovirus infection of Aedes aegypti salivary glands (SGs) determines transmission. However, there is a dearth of knowledge on SG immunity. Here, we characterized SG immune response to dengue, Zika and chikungunya viruses using high-throughput transcriptomics. The three viruses regulate components of Toll, IMD and JNK pathways. However, silencing of Toll and IMD components showed variable effects on SG infection by each virus. In contrast, regulation of JNK pathway produced consistent responses. Virus infection increased with depletion of component Kayak and decreased with depletion of negative regulator Puckered. Virus-induced JNK pathway regulates complement and apoptosis in SGs via TEP20 and Dronc, respectively. Individual and co-silencing of these genes demonstrate their antiviral effects and that both may function together. Co-silencing either TEP20 or Dronc with Puckered annihilates JNK pathway antiviral effect. We identified and characterized the broad antiviral function of JNK pathway in SGs, expanding the immune arsenal that blocks arbovirus transmission.

microbiology↗

bioRxiv2020

CluStrat: a structure informed clustering strategy for population stratification

Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. Recent independent studies failed to replicate the strong evidence of selection for height across Europe implying the shortcomings of standard population stratification correction approaches. Here, we present CluStrat, a stratification correction algorithm for complex population structure that leverages the linkage disequilibrium (LD)-induced distances between individuals. CluStrat performs agglomerative hierarchical clustering using the Mahalanobis distance and then applies sketching-based randomized ridge regression on the genotype data to obtain the association statistics. With the growing size of data, computing and storing the genome wide covariance matrix is a non-trivial task. We get around this overhead by computing the GRM directly using a connection between statistical leverage scores and the Mahalanobis distance. We test CluStrat on a large simulation study of discrete and admixed, arbitrarily-structured sub-populations identifying two to three-fold more true causal variants when compared to Principal Component (PC) based stratification correction methods while trading off for a slightly higher spurious associations. Applying CluStrat on WTCCC2 Parkinsons disease (PD) data, we identified loci mapped to a host of genes associated with PD such as BACH2, MAP2, NR4A2, SLC11A1, UNC5C to name a few. Availability and ImplementationCluStrat source code and user manual is available at: https://github.com/aritra90/CluStrat

genetics↗

bioRxiv2019

Daratumumab induces mechanisms of immune activation through CD38+ NK cell targeting

Daratumumab (Dara), a multiple myeloma (MM) therapy, is an antibody against the surface receptor CD38, which is expressed not only on plasma cells but also on NK cells and monocytes. Correlative data have highlighted the immune-modulatory role of Dara, despite the paradoxical observation that Dara regimens decrease the frequency of total NK cells. Here we show that, despite this reduction, NK cells play a pivotal role in Dara anti-MM activity. CD38 on NK cells is essential for Dara-induced immune modulation, and its expression is restricted to NK cells with effector function. We also show that Dara induces rapid CD38 protein degradation associated with NK cell activation, leaving an activated CD38-negative NK cell population. CD38+ NK cell targeting by Dara also promotes monocyte activation, inducing an increase in T cell costimulatory molecules (CD86/80) and enhancing anti-MM phagocytosis activity ex-vivo and in vivo. In support of Daras immunomodulating role, we show that MM patients that discontinued Dara therapy because of progression maintain targetable unmutated surface CD38 expression on their MM cells, but retain effector cells with impaired cellular immune function. In summary, we report that CD38+ NK cells may be an unexplored therapeutic target for priming the immune system of MM patients.

cancer biology↗

bioRxiv2019

Feedback regulation of BMP signaling by C. elegans cuticle collagens

Cellular responsiveness to environmental cues, including changes in extracellular matrix (ECM), is critical for normal processes such as development and wound healing, but can go awry, as in oncogenesis and fibrosis. One type of molecular pathway allowing this responsiveness is the bone morphogenetic protein (BMP) signaling pathway. Due to their broad and potent functions, BMPs and their signaling pathways are highly regulated at multiple levels. In Caenorhabditis elegans, the BMP ligand DBL-1 is a major regulator of body size. We have previously shown that DBL-1/BMP signaling determines body size through transcriptional regulation of cuticle collagen genes. We have now obtained evidence of feedback regulation of DBL-1/BMP by collagen genes. We analyzed four DBL-1-regulated collagen genes that affect body size. Here we show that inactivation of any one of these cuticle collagen genes reduces DBL-1/BMP signaling, as measured by a Smad activity reporter. Furthermore, we find that depletion of these collagens reduces GFP::DBL-1 fluorescence, and acts unexpectedly at the level of dbl-1 transcription. We therefore conclude that cuticle, a type of ECM, impinges on DBL-1/BMP expression and signaling. In contrast to other characterized examples, however, the feedback regulation of DBL-1/BMP signaling by collagens is likely to be contact-independent, due to the physical separation of the cuticle from DBL-1-expressing cells in the ventral nerve cord. Our results provide an entry point into a novel mechanism of regulation of BMP signaling, with broader implications for mechanical regulation of gene expression in general.

developmental biology↗

bioRxiv2019

Liquid-liquid phase separation and liquid-to-solid transition mediate α-synuclein amyloid fibril containing hydrogel formation

-Synuclein (-Syn) aggregation and amyloid formation is directly linked with Parkinsons disease (PD) pathogenesis. However, the early events involved in this process remain unclear. Here, using in vitro reconstitution and cellular model, we show that liquid-liquid phase separation (LLPS) of -Syn precedes its aggregation. In particular, in vitro generated -Syn liquid-like droplets eventually undergo a liquid-to-solid transition and form amyloid-hydrogel containing oligomers and fibrillar species. Factors known to aggravate -Syn aggregation such as low pH, phosphomimic substitution, and familial PD mutation also promote -Syn LLPS and its subsequent maturation. We further demonstrate -Syn liquid droplet formation in cells, under oxidative stress. These cellular -Syn droplets eventually transform into perinuclear aggresomes, the process regulated by microtubules. The present work provides detailed insights into the phase separation behavior of natively unstructured -Syn and its conversion to a disease-associated aggregated state, which is highly relevant in PD pathogenesis.

neuroscience↗

bioRxiv2019

Association between body mass index (BMI) and hypertension in South Asian population: Evidence from Demographic and Health Survey

Although there has been a well-established association between adiposity and hypertension, whether such associations are heterogeneous for South Asian populations or for different socioeconomic groups is not well-known. We analysed the recent Demographic and Health Survey (DHS) data from Bangladesh, India, and Nepal to estimate the age-specific prevalence of hypertension and the association of body mass index (BMI) with hypertension. We used multiple logistic regressions to estimate the odds ratios (ORs) with 95% confidence intervals (CIs) of hypertension for overweight and obesity as well as for each 5-unit increase in BMI. The overall prevalence for hypertension among participants aged 35-44 years were 17.4%, 20%, and 22.5% for Bangladesh, India, and Nepal, respectively. For all age groups, men were more likely to be hypertensive than women in India and Nepal, but not in Bangladesh. Overweight and obesity were associated with higher odds of hypertension in all countries. For each 5 kg/m2 increase in BMI, the ORs for hypertension were 1.79 (95% CI: 1.65-1.93), 1.59 (95% CI: 1.58-1.61), and 2.03 (95% CI: 1.90-2.16) in Bangladesh, India, and Nepal, respectively. The associations between BMI and hypertension were consistent across various subgroups defined by sex, age, urbanicity, educational attainment and households wealth index. Our study shows that the association of BMI with hypertension is stronger for South Asian populations, and public health measures to reduce population-level reduction in BMI would also help in lowering the burden of hypertension.

epidemiology↗

bioRxiv2018

Non-Canonical Activation of CREB Mediates Neuroprotection in a C. elegans Model of Excitotoxic Necrosis

Excitotoxicity, caused by exaggerated neuronal stimulation by Glutamate (Glu), is a major cause of neurodegeneration in brain ischemia. While we know that neurodegeneration is triggered by overstimulation of Glu-Receptors (GluRs), the subsequent mechanisms that lead to cellular demise remain controversial. Surprisingly, signaling downstream of GluRs can also activate neuroprotective pathways. The strongest evidence involves activation of the transcription factor cAMP Response Element Binding-protein (CREB), widely recognized for its importance in synaptic plasticity. Canonical views describe CREB as a phosphorylation-triggered transcription factor, where transcriptional activation involves CREB phosphorylation and association with CREB Binding Protein (CBP). However, given CREBs ubiquitous cross-tissue expression, the multitude of cascades leading to CREB phosphorylation, and its ability to regulate thousands of genes, it remains unclear how CREB exerts closely-tailored, differential neuroprotective responses in excitotoxicity. A non-canonical, alternative cascade for activation of CREB-mediated transcription involves the CREB co-factor cAMP-regulated transcriptional co-activator (CRTC), and may be independent of CREB phosphorylation. To identify cascades that activate CREB in excitotoxicity we use a C. elegans model of neurodegeneration by excitotoxic necrosis. We demonstrate that CREBs neuroprotective effect is conserved, and seems most effective in neurons with moderate Glu exposure. We find that factors mediating canonical CREB activation are not involved. Instead, phosphorylation-independent CREB activation in nematode excitotoxic necrosis hinges on CRTC. CREB-mediated transcription that depends on CRTC, but not on CREB phosphorylation, might lead to expression of a specific subset of neuroprotective genes. Elucidating conserved mechanisms of excitotoxicity-specific CREB activation can help us focus on core neuroprotective programs in excitotoxicity.

neuroscience↗

bioRxiv2017

Functional Compensation Between Hematopoietic Stem Cells In Vivo

In most organ systems, regeneration is a coordinated effort that involves many stem cells, but little is known about whether and how individual stem cells compensate for the functional deficiencies of other stem cells. Functional compensation between stem cells is critically important during disease progression and treatment. Here, we show how individual hematopoietic stem cells (HSCs) in a mouse heterogeneously compensate for the deficiencies of other HSCs during lymphopoiesis by increasing their clonal expansion at specific differentiation stages. This compensation rescues the overall blood supply and influences blood cell types outside of the deficient lineages in distinct patterns. We have identified the molecular regulators and signaling pathways in HSCs that are involved in this process. Our data demonstrate how stem cells interact with each other to constitute a coordinated network that is robust enough to withstand minor functional disruptions. Exploiting the innate compensation capacity of stem cell networks may improve the prognosis and treatment of many diseases.

systems biology↗

bioRxiv2017

Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular architecture and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is now more than ever accessible to researchers. This allows for collection of large amount of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs to resort to the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation.\n\nWe approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.

bioinformatics↗