Biology subjects

Sinha, S.

Publications and source records attributed to Sinha, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Stretching the structural envelope of isomeric imatinib analogs that reduce β-amyloid production by modulating both β- and γ-secretase cleavages of APP

We previously showed that the anticancer drug imatinib mesylate (IMT, trade name: Gleevec) and a chemically distinct compound, DV2-103 (a kinase-inactive derivative of the potent Abl and Src kinase inhibitor, PD173955) lower A{beta} levels at low micromolar concentrations primarily through a lysosome-dependent mechanism that renders APP less susceptible to proteolysis by BACE1 without directly inhibiting BACE1 enzymatic activity, or broadly inhibiting the processing of other BACE1 substrates. Additionally, IMT indirectly inhibits {gamma}-secretase and stimulates autophagy, and thus may decrease A{beta} levels through multiple pathways. In two recent studies we demonstrated similar effects on APP metabolism caused by derivatives of IMT and DV2-103. In the present study we investigated how so many structurally diverse compounds affect APP metabolism in the same way, with similar potencies and production of APP metabolites. To this end, we synthesized and tested radically altered IMT regioisomers that possess medium structural similarity to IMT. Independent of structural similarity, these isomers manifest widely differing potencies in altering APP metabolism. These will enable us to choose the most potent isomers for further derivatization.

pharmacology and toxicology↗

bioRxiv2024

Unveiling Cas8 Dynamics and Regulation within a transposon-encoded Cascade-TniQ Complex

The Vibrio cholerae Cascade-TniQ complex unveiled a new paradigm in biology, demonstrating that CRISPR-associated proteins can direct DNA transposition. Despite the tremendous potential of "knocking in" genes at desired sites, the mechanisms underlying DNA binding and transposition remain elusive. In this system, a conformational change of the Cas8 protein is essential for DNA binding, yet how it occurs is unclear. Here, structural modeling and free energy simulations reconstruct the Cas8 helical bundle and reveal an open-to-close conformational transition at key steps of the complexs function. We show that when Cascade-TniQ binds RNA, the Cas8 bundle changes conformation mediated by the interaction with the Cas7.1 protein. This interaction alleviates unfavorable contacts and synchronizes Cas8s shift with neighboring subunits, lowering the barrier for the transition to the open state, a critical requirement for DNA binding. As DNA fully pairs with RNA, the open state becomes increasingly accessible, favoring interactions with DNA and aiding the formation of an R-loop. These outcomes provide the first dynamic representation of a critical conformational change in one of the largest CRISPR systems and illustrate its role at critical steps of the Cascade-TniQ biophysical function, advancing our understanding of nucleic acid binding and transposition mechanisms.

biophysics↗

bioRxiv2024

Liver-specific Mettl14 deletion induces nuclear heterotypia and dysregulates RNA export machinery

Modification of RNA with N6-methyladenosine (m6A) has gained attention in recent years as a general mechanism of gene regulation. In the liver, m6A, along with its associated machinery, has been studied as a potential biomarker of disease and cancer, with impacts on metabolism, cell cycle regulation, and pro-cancer state signaling. However these observational data have yet to be causally examined in vivo. For example, neither perturbation of the key m6A writers Mettl3 and Mettl14, nor the m6A readers Ythdf1 and Ythdf2 have been thoroughly mechanistically characterized in vivo as they have been in vitro. To understand the functions of these machineries, we developed mouse models and found that deleting Mettl14 led to progressive liver injury characterized by nuclear heterotypia, with changes in mRNA splicing, processing and export leading to increases in mRNA surveillance and recycling.

molecular biology↗

bioRxiv2024

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multiple studies have extensively charted the TMEs impact on immunotherapy, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of bulk transcriptomics to associate gene expression of individual cell types in the TME with clinical response. Employing DECODEM to analyze gene expression of breast cancer patients treated with neoadjuvant chemotherapy across three bulk cohorts, we find that the expression of specific immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, CAFs) are highly predictive of chemotherapy response, achieving the same prediction levels as the expression of malignant cells. Notably, ensemble models integrating the estimated expression of different cell types perform best and outperform models built on the original tumor bulk expression. These findings and the models generalizability are further tested and validated in two single-cell (SC) cohorts of triple negative breast cancer. To investigate the possible role of immune cell-cell interactions (CCIs) in mediating chemotherapy response, we extended DECODEM to DECODEMi to identify such key functionally important CCIs, validated in SC data. Our findings highlight the importance of active pre-treatment immune infiltration for chemotherapy success. DECODEM and DECODEMi are made publicly available to facilitate studying the role of the TME in mediating response in a wide range of cancer indications and treatments.

bioinformatics↗

bioRxiv2024

A chemically defined and xeno-free hydrogel system for regenerative medicine.

Biofabricated scaffolds facilitate bona fide cellular interactions, cell type specification, and the formation of three-dimensional tissue architecture from human pluripotent stem cells (hPSCs). However, xenogenic biomaterials are poorly defined, and synthetic biomaterials remain underdeveloped and understudied, hindering regulatory approval for clinical use and preventing the translation of lab-grown therapies. Here, we describe a protein screen-based hydrogel system biofabricated from clinical-grade human components. We show that "Alphagel", a base hydrogel comprising human embryonic matrices, supports the trilineage differentiation of hPSCs into neural, cardiac, and liver tissue. Alphagel is also shown to be biocompatible and biodegradable in vivo. Further, upon adding select proteins from maturing human foetal liver to Alphagel, we show that the resulting hydrogel (termed "Hepatogel") enhances the differentiation of hPSC-derived hepatocytes (H-iHeps) compared with Matrigel. Importantly, when injected into mice livers, Hepatogel significantly improves the retention of H-iHeps compared to standard aqueous cell injections. Altogether, our results provide proof of concept that customisable and organ-specific hydrogel systems are a valuable tool for developing clinically translatable therapies for regenerative medicine and tissue engineering.

bioengineering↗

bioRxiv2024

Proliferation-driven mechanical feedback regulates cell dynamics in growing tissues

Local stresses in a tissue, a collective property, regulate cell division and apoptosis. In turn, cell growth and division induce active stresses in the tissue. As a consequence, there is a feed-back between cell growth and local stresses. However, how the cell dynamics depend on local stress-dependent cell division and the feedback strength is not fully understood. Here, we probe the consequences of stress-mediated growth and cell division on cell dynamics using agent-based simulations of a two-dimensional growing tissue. We discover a rich dynamical behavior of individual cells, ranging from jamming (mean square displacement, {Delta}(t) [~] t with less than unity), to hyperdiffusion ( > 2) depending on cell division rate and the strength of the mechanical feedback. Strikingly, {Delta}(t) is determined by the tissue growth law, which quantifies cell proliferation (number of cells N (t) as a function of time). The growth law (N (t) [~] t{lambda} at long times) is regulated by the critical pressure that controls the strength of the mechanical feedback and the ratio between cell division-apoptosis rates. We show that{lambda} [~] , which implies that higher growth rate leads to a greater degree of cell migration. The variations in cell motility are linked to the emergence of highly persistent forces extending over several cell cycle times. Our predictions are testable using cell-tracking imaging techniques.

biophysics↗

bioRxiv2024

H2O2 mediated MAPK-mTOR crosstalk determines the XIAP and FLIP degradation pathways and development of resistance to Imatinib

Abnormal or aggregated proteins degradation is very crucial to protect the cell against proteotoxic stress. Cellular proteins are usually degraded in a ubiquitin-dependent manner by proteasomes and macro-autophagy/autophagy. Both processes use ubiquitin, binding of which target them to proteasomes via ubiquitin-like domains or to phagophores (the precursors to autophagosomes) via Atg8/LC3 binding motifs. Since both pathways use ubiquitin, the question that arises is how degradative pathway choice is achieved. Here in this paper, we have shown that mTOR pathway acts as major player in deciding which degradation pathway to choose. Here, we used Imatinib resistant (K562R) and Imatinib-sensitive (K562S) K562 cell as our previous study showed that Ubiquitin-proteasome pathway is selected for degradation of two proteins XIAP and FLIP in Imatinib-resistant K562 cells. H2O2 treatment in K562R cells degrades XIAP and FLIP via proteasomal pathway whereas H2O2 degrades them via lysosomal pathway in K562S cells. We found H2O2 mediated mTOR activation suppressed autophagy pathway in K562R cells and protein takes proteasomal degradation rout. However, proteins get degraded via autophagy when mTOR remains suppressed in K562S cells. We also found that H2O2-mediated activation of activation of ERK in turn activated mTOR in K562R cells because ROS inhibited ERK phosphorylation and thus mTOR remained inhibited in K562S cells. Moreover, we found that endogenous H2O2 levels are way higher in K562R cells than in K562S cells. Finally, and most interestingly, our data clearly suggested that activation of mTOR is responsible for developing resistance to Imatinib in K562R cells. Thus, this paper delineates a very crucial role of H2O2 and mTOR in both the selection of protein degradation pathway and development of drug resistant.

cancer biology↗

bioRxiv2024

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Developmental dynamics involve the specification of diverse cell types and their spatial organization into multicellular niches. Here, we combine single-cell and spatial multiomics to define 19 distinct tissue niches in the developing heart, leading to the development of a context-aware, resolution-agnostic niche classification tool (TissueTypist). Applying high-resolution spatial profiling to the developing sinoatrial node, we resolve three pacemaker cell subtypes arrayed along a linear axis. First trimester subpopulations, such as the pacemaker cells in the sinus horn and sinoatrial node head region, display neuro-attractant programmes and interact with parasympathetic neurons via interactions including Semaphorin-Plexin signalling. Temporal trajectories map maturation of atrial and ventricular cardiomyocytes, uncovering a lipid-metabolic switch and potential key regulators of cell type identity. In the ventricle, we identify cellular and transcriptional gradients along both pseudotime and transmural axes, offering new molecular insights into myocardial compaction and maturation. Comparative profiling of euploid and trisomy 21 hearts shows a depletion of compact cardiomyocytes and heightened apoptosis, validated in isogenic-matched trisomy 21 and euploid iPSC-derived cardiomyocytes. This implicates disrupted myocardial growth may be a mechanism for Downs syndrome-associated congenital heart disease. Overall, we deliver a spatially resolved framework of human cardiac development, enabling systematic exploration of developmental niches in health and disease.

developmental biology↗

bioRxiv2024

APOE3-R136S mutation confers resilience against tau pathology via cGAS-STING-IFN inhibition

The Christchurch mutation (R136S) on the APOE3 (E3S/S) gene is associated with attenuation of tau load and cognitive decline despite the presence of a causal PSEN1 mutation and high levels of amyloid beta pathology in the carrier1. However, the specific molecular mechanisms enabling the E3S/S mutation to mitigate tau-induced neurodegeneration remain unclear. Here, we replaced mouse ApoE with wild-type human E3 or E3S/S on a tauopathy background. The R136S mutation markedly decreased tau load and protected against tau-induced synaptic loss, myelin loss, and reduction in theta and gamma powers. Additionally, the R136S mutation reduced interferon response to tau pathology in both mouse and human microglia, suppressing cGAS-STING activation. Treating tauopathy mice carrying wild-type E3 with a cGAS inhibitor protected against tau-induced synaptic loss and induced similar transcriptomic alterations to those induced by the R136S mutation across brain cell types. Thus, suppression of microglial cGAS-STING-IFN pathway plays a central role in mediating the protective effects of R136S against tauopathy. One-sentence summaryThe R136S mutation on APOE3 enhances resistance to tau-related pathology and toxicity by downregulating the cGAS-STING-IFN signaling pathway.

neuroscience↗

bioRxiv2024

High-resolution atlas of the developing human heart and the great vessels

The human heart and adjoining great vessels consist of multiple cell types essential for life, yet many remain uncharacterised molecularly during development. Here, we performed a high-resolution profiling of the developing heart and great vessels between 4 and 20 post-conception weeks using single-cell and spatial transcriptomics defining 63 cell types with distinct identity and location-specific signatures. We reveal previously unreported molecular identities in cell types, including the pericardium and the ductus arteriosus. In the cardiomyocytes, we identify signatures of the trabeculated-compact, and right-left axes of ventricular cardiomyocytes. In vessels, we distinguish the constituents belonging to either coronary or great vessels. We confirm our transcriptional findings spatially, revealing nuanced signatures with specific zonation patterns and validating this atlas as a curated transcriptional reference for future studies. We leverage the temporal scope of the presented atlas to build CMageR, a predictive pipeline for scRNA-seq combining cardiac cell annotation with a transcriptional cardiac clock of single-cell developmental age for each cell type. Our cardiomyocyte clock captures dynamic biology, revealing core functional changes and novel markers of maturity during the first and second trimester. Finally, we benchmark in vitro models, suggesting a transcriptional right-chamber bias in stem cell derived cardiomyocytes with the oldest model age-matched to 12 post-conception weeks. Collectively, our work provides a high-resolution atlas of human cardiac development to enhance our understanding of function in development, health, and disease, and a foundation for building a rich reference to benchmark and improve in vitro models.

developmental biology↗

bioRxiv2024

Cellular flows initiate left-right patterning prior to laterality gene expression in amniotes

A bilateral body plan is predominant throughout the animal kingdom. Bilaterality of amniote embryos becomes recognizable as midline morphogenesis begins at gastrulation, bisecting an embryonic field into the left and right sides, and left-right asymmetry patterning follows. While a series of laterality genes expressed after the left-right compartmentalization has been extensively studied, the laterality patterning prior to and at the initiation of midline morphogenesis has remained unclear. Here, through a biophysical quantification in a high spatial and temporal resolution, applied to a chick model system, we show that a large-scale bilateral counter-rotating cellular flow, termed as polonaise movements, display left-right asymmetries in early gastrulation. This cell movement starts prior to the formation of the primitive streak (the earliest midline structure) and the subsequent appearance of Hensens node (the left-right organizer). The cellular flow speed and vorticity unravel the location and timing of the left-right asymmetries. The bilateral flows displayed a Right dominance after six hours since the start of cell movements. Mitotic arrest that diminishes primitive streak formation resulted in changes in the bilateral flow pattern, but the Right dominance persisted. Our data indicate that the left-right asymmetry in amniote gastrula becomes detectable earlier than suggested by current models, which assume that the asymmetric regulation of the laterality signals at the node leads to the left-right patterning. More broadly, our results suggest that physical processes can play an unexpected but significant role in influencing left-right laterality during embryonic development. Significance StatementBilaterians are defined by a bilaterally symmetrical body plan. Vertebrates exhibit external bilateral symmetry but display left-right (LR) asymmetry in their internal organs. Amniote embryos switch the patterning of internal organs from bilateral symmetry to LR-asymmetry. Using chick embryos as a model system, here we examined the initiation of LR symmetry breaking. Our biophysical approaches to quantify cellular flows inferred that LR symmetry breaking occurs before the formation of Hensens node, a LR organizer, which serves as a signaling center for LR patterning-gene programs. Our work demonstrates that quantitative biophysical parameters can help unravel the initiation of LR symmetry breaking, suggesting an involvement of physical mechanisms in this critical biological patterning process.

biophysics↗

bioRxiv2024

Genotyping of paired clinical isolates using PvCSP, PvMSP3α PvMSP3βand exploring STRs to differentiate between relapse and reinfection in P. vivax

The challenge of eliminating Vivax malaria is due to the relapses caused by hypnozoites. Despite several attempts to identify molecular markers to differentiate between relapse and new infection, a reliable marker has not yet been established. To address this issue, a genomic study was conducted on paired samples of patients who had experienced Plasmodium vivax infection twice. Genotyping was performed on paired samples from 10 vivax malaria patients using five molecular markers to distinguish between relapse and new infection. Our findings indicate that one sample represented a second episode that was a relapse of the first, while another sample had three episodes, two of which were relapse episodes. We combined our clinical records with molecular inferences to identify each pair as a relapse. This particular study provides a momentary view of forthcoming research endeavors that could be undertaken to distinguish between a relapse and a novel infection. Given the notable genetic variability of Plasmodium vivax, it is crucial to harness various markers to discern between relapse and new infection. The findings of this study are poised to be of immense utility for upcoming marker research in the aforesaid aspect.

genomics↗

bioRxiv2024

PROTGOAT : Improved automated protein function predictions using Protein Language Models

Accurate prediction of protein function is crucial for understanding biological processes and various disease mechanisms. Current methods for protein function prediction relies primarily on sequence similarities and often misses out on important aspects of protein function. New developments in protein function prediction methods have recently shown exciting progress via the use of large transformer-based Protein Language Models (PLMs) that allow for the capture of nuanced relationships between amino acids in protein sequences which are crucial for understanding their function. This has enabled an unprecedented level of accuracy in predicting the functions of previously little understood proteins. We here developed an ensemble method called PROTGOAT based on embeddings extracted from multiple and diverse pre-trained PLMs and existing text information about the protein in published literature. PROTGOAT outperforms most current state-of-the-art methods, ranking fourth in the Critical Assessment of Functional Annotation (CAFA 5), a global competition benchmarking such developments among 1600 methods tested. The high performance of our method demonstrates how protein function prediction can be improved through the use of an ensemble of diverse PLMs. PROTGOAT is publicly available for academic use and can be accessed here: https://github.com/zongmingchua/cafa5

bioinformatics↗

bioRxiv2024

Structure, motion, and multiscale search of traveling networks

Network models are widely applied to describe connectivity and flow in diverse systems. In contrast, the fact that many connected systems move through space as the result of dynamic restructuring has received little attention. Therefore, we introduce the concept of traveling networks, and we analyze a tree-based model where the leaves are stochastically manipulated to grow, branch, and retract. We derive how these restructuring rates determine key attributes of network structure and motion, enabling a compact understanding of higher-level network behaviors such as multiscale search. These networks self-organize to the critical point between exponential growth and decay, allowing them to detect and respond to environmental signals with high sensitivity. Finally, we demonstrate how the traveling network concept applies to real-world systems, such as slime molds, the actin cytoskeleton, and human organizations, exemplifying how restructuring rules and rates in general can select for versatile search strategies in real or abstract spaces.

biophysics↗

bioRxiv2024

IL6 mediated cFLIP downregulation increases the migratory and invasive potential of triple negative breast cancer cell line

c-FLIP (cellular FLICE-Like Inhibitor of Apoptotic protein) which belongs to the negative regulators of the death receptor pathway, control apoptosis in a number of cancers. literatures suggest that its overexpression facilitate cancer progression and also helps in the drug resistance. However, there is a very few data available to show how it control metastasis. Moreover, how cFLIP is regulated during the progression of cancer into advanced stage and its mechanism of action on tumor cell migration and invasion is yet to be known. Our TCGA data analysis has shown that cFLIP is downregulated in many cancers, including breast cancer, especially at the later stages. when, we analysed and compared early-stage breast carcinoma cell line MCF7 (luminal) and advanced stage triple negative cell line MDAMB-231, which is highly metastatic, we found a negative correlation between cFLIP expression and metastasis. Our results indicated that Il6, one of the most prominent cytokines inside tumor microenvironment, activated p38 and the later helped in cFLIP downregulation in MDA-MB-231 cell line. Moreover, we have found that cFLIP negatively regulated autophagy and this autophagy downregulation resulted in decrease in metastasis. Thus, we have shown, for the first time, a complete interconnecting pathway in which IL6 mediated p38 activation directly influences metastasis by regulating autophagy via cFLIP downregulation.

cancer biology↗

bioRxiv2023

On the role of mechanical feedback in synchronous to asynchronous transition during embryogenesis

The initial stage of Zebrafish morphogenesis is characterized by a synchronous to asynchronous transition (SAT) in cell divisions. The cells divide in unison in the synchronous phase, unlike in the asynchronous phase. Despite the widespread observation of SAT in multiple organisms, there is no theoretical framework to predict the critical number of cell cycles n* that marks the beginning of asynchronous division. Here, by probabilistically modeling cell cycle progression under the assumption that the distribution of cell division times is broadened, we predict n* and the time at which the SAT occurs. The theory, supplemented by agent-based simulations, supports the hypothesis that the SAT emerges as a consequence of biomechanical feedback on cell division. Our results are in excellent agreement with the experiments while also explaining the cell cycle lengthening that arises as a result of biomechanical feedback. The emergence of the asynchronous phase is due to increasing fluctuations in the cell cycle times with each round of cell division. We also make several testable predictions that further sheds light on the role of biomechanical feedback in growing multicellular systems, such as during tissue and tumor growth.

biophysics↗

bioRxiv2023

SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes

A common way to investigate gene regulatory mechanisms is to identify differentially expressed genes using transcriptomics, find their candidate enhancers using epigenomics, and search for over-represented transcription factor (TF) motifs in these enhancers using bioinformatics tools. A related follow-up task is to model gene expression as a function of enhancer sequences and rank TF motifs by their contribution to such models, thus prioritizing among regulators. We present a new computational tool called SEAMoD that performs the above tasks of motif finding and sequence-to-expression modeling simultaneously. It trains a convolutional neural network model to relate enhancer sequences to differential expression in one or more biological conditions. The model uses TF motifs to interpret the sequences, learning these motifs and their relative importance to each biological condition from data. It also utilizes epigenomic information in the form of activity scores of putative enhancers and automatically searches for the most promising enhancer for each gene. Compared to existing neural network models of non-coding sequences, SEAMoD uses far fewer parameters, requires far less training data, and emphasizes biological interpretability. We used SEAMoD to understand regulatory mechanisms underlying the differentiation of neural stem cell (NSC) derived from mouse forebrain. We profiled gene expression and histone modifications in NSC and three differentiated cell types and used SEAMoD to model differential expression of nearly 12,000 genes with an accuracy of 81%, in the process identifying the Olig2, E2f family TFs, Foxo3, and Tcf4 as key transcriptional regulators of the differentiation process.

bioinformatics↗

bioRxiv2023

SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction

Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a genes expression using transcription factors (TFs) expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd" is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.

bioinformatics↗