Biology subjects

Bhattacharya, S.

Publications and source records attributed to Bhattacharya, S..

At least 145 records · Page 8Linked to original sources

bioRxiv2020

NBS1-CtIP-Mediated DNA End Resection Regulates cGAS Binding to Micronuclei

Cyclic GMP-AMP synthase (cGAS), an important component of immune signaling, is hyperactivated in cells defective for DNA damage response (DDR) signaling. However, a direct role for DDR factors in the regulation of cGAS functions is mostly unknown. Here, we provide novel evidence that Nijmegen breakage syndrome 1 (NBS1) protein, a well-studied DNA double-strand break (DSB) sensor, in coordination with ATM, a protein kinase, and CtBP-interacting protein (CtIP), a DNA end resection factor, functions as an upstream regulator of cGAS binding to micronuclei. Upon NBS1 binding to micronuclei via its fork-head-associated domain, it recruits ATM and CtIP via its N- and C-terminal domains, respectively. Subsequently, ATM stabilizes NBS1s interaction with micronuclei, and CtIP converts DSB ends into single-strand DNA ends, and these two key events preclude cGAS from binding to micronuclei. Notably, we show that purified cGAS cannot form a complex with DNA substrates that mimic resected DNA ends in vitro. Thus, NBS1 together with its binding partners modify the chromatin architecture of the micronuclei and that plays a critical role in cGASs binding to micronuclei.

immunology↗

bioRxiv2020

Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

Crystallography and NMR system (CNS) is currently the de facto standard for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding difficult free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to several popular CNS- and fragment-based approaches, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS.Author summary Predicting the folded and functional 3-dimensional structure of a protein molecule from its amino acid sequence is of central importance to structural biology. Recently, promising advances have been made in ab initio protein folding due to the reasonably accurate estimation of inter-residue interaction maps at increasingly higher resolutions that range from binary contacts to finer-grained distances. Despite the progress in predicting the interaction maps, approaches for turning the residue-residue interactions projected in these maps into their precise spatial positioning heavily rely on a decade-old experimental structure determination protocol that is not suitable for predictive modeling. This paper presents a new hierarchical structure modeling method, DConStruct, which can better exploit the information encoded in the interaction maps at multiple granularities, from binary contact maps to distance-based hybrid maps at tri-level thresholding, for improved ab initio folding. Multiple large-scale benchmarking experiments show that our proposed method can substantially improve the folding accuracy for both soluble and membrane proteins compared to state-of-the-art approaches. DConStruct is licensed under the GNU General Public License v3 and freely available at https://github.com/Bhattacharya-Lab/DConStruct.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

Capturing limbal epithelial stem cell population dynamics, signature and their niche

Stem cells (SCs) are traditionally viewed as rare, slow-cycling cells that follow deterministic rules dictating their self-renewal or differentiation. It was several decades ago, when limbal epithelial SCs (LSCs) that regenerate the corneal epithelium were one of the first sporadic, quiescent SCs ever discovered. However, LSC dynamics, heterogeneity and genetic signature are largely unknown. Moreover, recent accumulating evidence strongly suggested that epithelial SCs are actually abundant, frequently dividing cells that display stochastic behavior.In this work, we performed an in-depth analysis of the murine limbal epithelium by single-cell RNA sequencing and quantitative lineage tracing. The generated data provided an atlas of cell states of the corneal epithelial lineage, and particularly, revealed the co-existence of two novel LSC populations that reside in separate and well-defined sub-compartments. In the “outer” limbus, we identified a primitive widespread population of quiescent LSCs (qLSCs) that uniformly express Krt15/Gpha2/Ifitm3/Cd63 proteins, while the “inner” limbus host prevalent active LSCs (aLSCs) co-expressing Krt15-GFP/Atf3/Mt1-2/Socs3. Analysis of LSC population dynamics suggests that while qLSCs and aLSCs possess different proliferation rates, they both follow similar stochastic rules that dictate their self-renewal and differentiation. Finally, T cells were distributed in close proximity to qLSCs. Indeed, their absence or inhibition resulted in the loss of quiescence and delayed wound healing. Taken together, we propose that divergent regenerative strategies are tailored to properly support tissue-specific physiological constraints. The present study suggests that in the case of the cornea, quiescent epithelial SCs are abundant, follow stochastic rules and neutral drift dynamics.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cell biology↗

bioRxiv2020

Single cell transcriptomic profiling identifies molecular phenotypes of newborn human lung cells

RationaleWhile animal model studies have extensively defined mechanisms controlling cell diversity in the developing mammalian lung, the limited data available from late stage human lung development represents a significant knowledge gap. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. MethodsSingle cell RNA sequencing generated transcriptional profiles of 5500 cells obtained from two one-day old human lungs (born at gestational ages of 39 and 31 weeks) from the LungMAP Human Tissue Core Biorepository at the University of Rochester. Frozen single cell isolates were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Single cell sequence data from 32000 postnatal day 1, 3, 7 and 10 mouse lung (n = 2 at each time point) cells generated by the LungMAP Research Center at Cincinnati Childrens Hospital and Medical Center, using Dropseq platform, was integrated with the human data. In situ hybridization was used to confirm the spatial location of cellular phenotypes. ResultsTranscriptional interrogation of donor newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, and immune cells and signature genes for each of these populations were identified. Computational integration of newborn human and postnatal mouse lung development cellular transcriptomes facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the human and mouse cellular transcriptomes also demonstrated cell type-specific differences in developmental states of the newborn human lung cells. In particular, matrix fibroblasts could be separated into those representative of younger cells (n=393), or older cells (n=158). This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of development. Our results indicate that integrated single cell RNA profiling of human and mouse lung will help identify common and species-specific mechanisms of lung development and respiratory disease.

genomics↗

bioRxiv2020

The disordered regions of SETD2 govern H3K36me3 deposition by regulating its proteasome-mediated decay

SETD2 is the sole methyltransferase that tri-methylates histone H3 at lysine 36 in mammals. It has an extended N-terminal region which is absent in its yeast homolog Set2. The function of this poorly characterized region in regulating SETD2 stability has been reported. However, how this region regulates SETD2 half-life and the consequences of the cellular accumulation of SETD2 is unclear. Here we show that the SETD2 N-terminal region contains disordered regions and is targeted for degradation by the proteasome. The marked increase in global H3K36me3 that occurs on the removal of the N-terminal segment results in a non-canonical distribution including reduced enrichment over gene bodies and exons. An increased SETD2 abundance leads to widespread changes in transcription and alternative splicing. Thus, the regulation of SETD2 levels through intrinsically disordered region-facilitated proteolysis is important to maintain the fidelity of transcription and splicing related processes.

biochemistry↗

bioRxiv2020

The histone methyltransferase SETD2 couples transcription and splicing by engaging pre-mRNA processing factors through its SHI domain

Heterogeneous ribonucleoproteins (hnRNPs) are RNA binding molecules that are involved in key processes such as RNA splicing and transcription. One such hnRNP protein, hnRNP L, regulates alternative splicing (AS) by binding to pre-mRNA transcripts. However, it is unclear what factors contribute to hnRNP L-regulated AS events. Using proteomic approaches, we identified several key factors that co-purify with hnRNP L. We demonstrate that one such factor, the histone methyltransferase SETD2, specifically interacts with hnRNP L in vitro and in vivo. This interaction occurs through a previously uncharacterized domain in SETD2, the SETD2-hnRNP L Interaction (SHI) domain, the deletion of which, leads to a reduced H3K36me3 deposition. Functionally, SETD2 regulates a subset of hnRNP L-targeted AS events. Our findings demonstrate that SETD2 by interacting with Pol II as well as hnRNP L, can mediate the crosstalk between the transcription and the splicing machinery.

biochemistry↗

bioRxiv2020

Ultra-structural analysis and morphological changes during the differentiation of trophozoite to cyst in Entamoeba invadens

Entamoeba Histolytica, a pathogenic parasite, is the causative organism of amoebiasis and uses human colon to complete its life cycle. It destroys intestinal tissue leading to invasive disease. Since it does not form cyst in culture medium, a reptilian parasite Entamoeba invadens serves as the model system to study encystation. Detailed investigation on the mechanism of cyst formation, information on ultra-structural changes and cyst wall formation during encystation are still lacking in E. invadens. Here, we used electron microscopy to study the ultrastructural changes during cyst formation and showed that the increase in heterochromatin patches and deformation of nuclear shape were early events in encystation. These changes peaked at ~20h post induction, and normal nuclear morphology was restored by 72h. Two types of cellular structures were visible by 16h. One was densely stained and consisted of the cytoplasmic mass with clearly visible nucleus. The other consisted of membranous shells with large vacuoles and scant cytoplasm. The former structure developed into the mature cyst while the latter structure was lost after 20h, This study of ultra-structural changes during encystation in E. invadens opens up the possibilities for further investigation into the mechanisms involved in this novel process.

cell biology↗

bioRxiv2020

The plasticity of the pyruvate dehydrogenase complex confers a labile structure that is associated with its catalytic activity

The pyruvate dehydrogenase complex (PDC) is a multienzyme complex that plays a key role in energy metabolism by converting pyruvate to acetyl-CoA. An increase of nuclear PDC has been shown to be correlated with an increase of histone acetylation that requires acetyl-CoA. PDC has been reported to form a ~ 10 MDa macromolecular machine that is proficient in performing sequential catalytic reactions via its three components. In this study, we show that the PDC displays size versatility in an ionic strength-dependent manner using size exclusion chromatography of yeast cell extracts. Biochemical analysis in combination with mass spectrometry indicates that yeast PDC (yPDC) is a salt-labile complex that dissociates into sub-megadalton individual components even under physiological ionic strength. Interestingly, we find that each oligomeric component of yPDC displays a larger size than previously believed. In addition, we show that the mammalian PDC also displays this uncommon characteristic of salt-lability, although it has a somewhat different profile compared to yeast. We show that the activity of yPDC is reduced in higher ionic strength. Our results indicate that the structure of PDC may not always maintain its ~ 10 MDa organization, but is rather variable. We propose that the flexible nature of PDC may allow modulation of its activity.

biochemistry↗

bioRxiv2020

DNA methylation mediated downregulation of histone H3 variant H3.3 affects cell proliferation contributing to the development of HCC

H3.3 variant is a versatile histone important for development and disease. We report a DNA methylation dependent decrease of histone H3 variant H3.3 in hepatocellular carcinoma (HCC) development and an increase in the level of the H3.2 variant. The loss of H3.3 correlates with a decrease in the histone PTMs associated with active transcription. The overexpression of H3.3 and H3.2 did not affect global PTMs and cell physiology, probably owing to the deregulation of specific histone chaperones CAF-1 (for H3.2) and HIRA (for H3.3) that we observed in HCC. Notably, upon P150 (CAF-1 subunit) knockdown in HCC cell lines, a cell cycle arrest in S-phase was observed, possibly due to the decrease in the histone levels necessary for DNA packaging. Furthermore, H3.3 knockdown in a preneoplastic liver cell line led to an increase in cell proliferation and a decreased transcription of tumor suppressor genes, recapitulating the tumor cell phenotype. Importantly, our data suggest that the use of DNA Methyl Transferase (DNMT) and Histone Deacetylase (HDAC) inhibitors to restore the expression of H3.3 and the altered chromatin state for the better clinical management of the disease.

cancer biology↗

bioRxiv2020

Regulation of SETD2 stability by its intrinsically disordered regions maintains the fidelity of H3K36me3 deposition

The histone H3K36me3 mark regulates transcription elongation, pre-mRNA splicing, DNA methylation, and DNA damage repair. However, knowledge of the regulation of the enzyme SETD2, which deposits this functionally important mark, is very limited. Here we show that the poorly characterized N-terminal region of SETD2 plays a determining role in regulating the stability of SETD2. This stretch of 1-1403 amino acids contributes to the robust degradation of SETD2 by the proteasome. Besides, the SETD2 protein is aggregate-prone and forms insoluble bodies in nuclei especially upon proteasome inhibition. Removal of the N-terminal segment results in the stabilization of SETD2 and leads to a marked increase in global H3K36me3 which, uncharacteristically, happens in a Pol II-independent manner. Thus, the regulation of SETD2 levels through proteasomal mediated decay is important to maintain the fidelity of H3K36me3 deposition.

biochemistry↗

bioRxiv2020

Application of singe nuclei RNA sequencing to assess the hepatic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin

Cell-specific transcriptional responses are lost in the averages of bulk RNA sequencing. We performed single nuclei RNA sequencing (snSeq) on frozen liver samples from male C57BL/6 mice in response to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Approximately 19,907 hepatic genes were detected across 16,015 sequenced nuclei from control and treated samples. Eleven cell-(sub)types were identified including distinct hepatocyte sub-populations, consistent with the cell diversity of the liver. TCDD increased macrophages from 0.5% to 24.7%, while neutrophils were only present in treated samples. The number of differentially expressed genes correlated with the basal expression level of Ahr. In addition to expected functional enrichments within each cell-(sub)type, RAS signaling was enriched in nonparenchymal cells. snSeq also identified a Kupffer cell subtype highly expressing Gpnmb, consistent with a dietary NASH model. Overall, snSeq distinguished cell-specific transcriptional changes and population shifts consistent with the hepatotoxicity of TCDD.

molecular biology↗

bioRxiv2020

Phenotypic switching and adaptive strategies of cancer cells in response to stress: insights from live cell imaging and mathematical modeling

A major challenge in cancer therapy is drug resistance, which is typically attributed to acquired mutations and tumor heterogeneity. However, emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance, although, the details of such mechanisms are poorly understood. Here, by combining real time cellular growth data with mathematical modeling, we showed that the cisplatin-sensitive and tolerant lung cancer cells when co-cultured in cisplatin-free and cisplatin-treated environments, exhibit drastically different group strategies in response to environmental changes. While tolerant cells exhibited a persister-like behaviour and were attenuated by sensitive cells, sensitive cells learned to evade chemotherapy from tolerant cells when co-cultured. Further, tolerant cells could switch phenotypes to become sensitive, although high cisplatin concentrations suppressed this switching. Finally, switching cisplatin administration from continuous to intermittent suppressed the emergence of tolerant cells, suggesting that intermittent rather than continuous chemotherapy may result in better outcomes in lung cancer.

cancer biology↗

bioRxiv2020

Integrated role of microRNA-30e-5p through targeting negative regulators of innate immune pathways during HBV infection and SLE

Precise regulation of innate immunity is crucial for the development of appropriate host immunity against microbial infections and the maintenance of immune homeostasis. The microRNAs are small non-coding RNA, post-transcriptional regulator of multiple genes and act as a rheostat for protein expression. Here, we identified microRNA(miR)-30e-5p (miR-30e) induced by the hepatitis B virus (HBV) and other viruses that act as a master regulator for innate immune responses. Moreover, pegylated type I interferons treatment to HBV patients for viral reduction also reduces the miRNA. Additionally, we have also shown the immuno-pathological effects of miR-30e in systemic lupus erythematous (SLE) patients and SLE mouse model. Mechanistically, the miR-30e targets multiple negative regulators namely TRIM38, TANK, ATG5, ATG12, BECN1, SOCS1, SOCS3 of innate immune signaling pathways and enhances innate immune responses. Furthermore, sequestering of endogenous miR-30e in PBMCs of SLE patients and SLE mouse model respectively by the introduction of antagomir and locked nucleic acid based inhibitor significantly reduces type I interferon and pro-inflammatory cytokines. Collectively, our study demonstrates the novel role of miR-30e in innate immunity and its prognostic and therapeutic potential in infectious and autoimmune diseases.

immunology↗

bioRxiv2020

A robust and interpretable, end-to-end deep learning model for cytometry data

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Traditional approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large CyTOF studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model and identified a CD27-CD94+ CD8+ T cell population significantly associated with latent CMV infection. Finally, we provide a tutorial for creating, training and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (github.com/hzc363/DeepLearningCyTOF).

bioinformatics↗

bioRxiv2020

DisCovER: distance-based covariational threading for weakly homologous proteins

MotivationThreading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence-based predicted contact or distance information is used. Contact- or distance-assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query-template alignment. ResultsWe present a new distance- and orientation-based covariational threading method called DisCovER by effectively integrating information from inter-residue distance and orientation along with the topological network neighborhood of a query-template alignment. Our method first selects a subset of templates using standard profile-based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance- and orientation-based query-template alignment using an iterative double dynamic programming framework. Multiple large-scale benchmarking results on query proteins classified as hard targets from the Continuous Automated Model Evaluation (CAMEO) experiment and from the current literature show that our method outperforms several existing state-of-the-art threading approaches; and that the integration of the neighborhood effect with the inter-residue distance and orientation information synergistically contributes to the improved performance of DisCovER. Availabilityhttps://github.com/Bhattacharya-Lab/DisCovER Contactbhattacharyad@auburn.edu

bioinformatics↗

bioRxiv2020

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

MotivationProtein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction. ResultsWe present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently out-performs existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep. Availabilityhttps://github.com/Bhattacharya-Lab/QDeep Contactbhattacharyad@auburn.edu

bioinformatics↗

bioRxiv2019

Morphologically and physiologically diverse fruits of two Lepidium species differ in allocation of glucosinolates into immature and mature seed and pericarp

The morphology and physiology of diaspores play crucial roles in determining the fate of seeds in unpredictable habitats. In some genera of the Brassicaceae different types of diaspores can be found. Lepidium appelianum produces non-dormant seeds within indehiscent fruits while in L. campestre dormant seeds are released from dehiscent fruits. These different diaspore types offer an excellent model system to analyse the allocation of relevant defence compounds into different tissues, which may maximise diaspore fitness. Total glucosinolate concentration and composition were analysed in immature and mature seeds and pericarps of L. appelianum and L. campestre using high-performance liquid chromatography. Moreover, transgenic RNAi L. campestre lines were used for comparison that produce indehiscent fruits due to silencing of LcINDEHISCENCE, the INDEHISCENCE ortholog of L. campestre. Total glucosinolate concentrations were lower in green compared to mature seeds in all studied Lepidium species and transgenic lines. In contrast, indehiscent fruits of L. appelianum maintained their total glucosinolate concentration in mature pericarps compared to green ones, while in dehiscent L. campestre and in indehiscent RNAi-LcIND L. campestre a significant decrease in total glucosinolate concentrations from green to mature pericarps could be detected. Regarding the distribution of glucosinolate classes, high concentrations of 4-methoxyindol-3-ylmethyl glucosinolate were found in mature seeds of L. appelianum, while no indole glucosinolates were detected in mature diaspores of L. campestre. The diaspores of the latter species may rather depend on aliphatic and aromatic glucosinolates for long-term protection. The allocation patterns of glucosinolates correlate with the morpho-physiologically distinct fruits of L. appelianum and L. campestre and may be explained by the distinct dispersal strategies and the dormancy status of both species.

plant biology↗

bioRxiv2019

Social status influences normal and pathological behaviors in mice, a role for dopamine and stress signaling

The rules leading to the emergence of a social organization and the role of social hierarchy on normal and pathological behaviours remain elusive. Here we show that groups of four isogenic male mice rapidly form enduring social ranks in a dominance hierarchy. Highest ranked individuals display enhanced anxiety and working memory, are more social and more susceptible to stress-related maladaptive behaviours. Are these differences causes or consequences to social life? We show that anxiety emerges from life in colony whereas sociability is a pre-existing trait. Strikingly, highest ranked individuals exhibit lower bursting activity of VTA dopamine neurons. Both pharmacogenetic inhibition of this neuronal population and the genetic inactivation of glucocorticoid receptor signalling in dopamine-sensing brain areas promote the accession to higher social ranks. Altogether, these results indicate that the shaping of social fate relies upon the interplay of dopamine system and stress response, impacting individual behaviour and potentially mental health.

animal behavior and cognition↗