Biology subjects

Sinha, M.

Publications and source records attributed to Sinha, M..

16 recordsLinked to original sources

bioRxiv2026

In situ radiation damage tracking during scanning small-angle X-ray scattering

Due to the high penetration power of X-rays, X-ray imaging techniques have been employed to image cellular architecture. Among these techniques, scanning small-angle X-ray scattering (SAXS) is a powerful method for investigating ordered structures within cells. With the recent advent of high-brilliance synchrotron sources, nano-focused X-rays and fast scanning modes, scanning SAXS can now provide structural information from hydrated cells in liquid environments in little time. However, the high photon flux results in an increased dose and pronounced radiation damage to the intracellular structures. Therefore, strategies are required to monitor and quantitatively analyze the extent of damage. Here, we present a multimodal approach that employs an on-axis, high-speed, brightfield microscope to monitor cellular damage during scanning. This microscope is integrated into the scanning SAXS setup at ID13 of ESRF and records images in synchrony with SAXS data acquisition. We are able to observe and quantitatively track changes in cellular morphology \textit{in situ} and thereby obtain complementary information to that provided by scanning SAXS measurements. Our approach opens up the potential for a multifaceted investigation of radiation damage to biological cells and other sample types.

biophysics↗

bioRxiv2026

Force generation of cardiomyocytes in engineered environments

Cardiomyocyte contraction is essential for the pumping action of the heart and deteriorates after myocardial damage, either as a consequence of irreversible cardiomyocyte injury or stiffening of the extracellular matrix, a process described as fibrosis. Cell geometry and substrate stiffness not only influence sarcomere architecture and contractility, they also determine how much work the cardiomyocytes can transfer to their environment. Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes provide a model system to examine these effects in a controlled manner, enabling us to mimic key features of the cardiac environment in vitro. Here we show that geometric confinement and substrate stiffness influence different aspects of cardiomyocyte force generation. By combining traction force microscopy and live-cell imaging of hiPSC-derived cardiomyocytes on soft and patterned substrates, we find that geometric confinement leads to longer resting sarcomeres and more pronounced sarcomere shortening on substrates with both physiological (10 kPa) and fibrotic (30 kPa) stiffness. Interestingly, the substrate stiffness itself has little effect on resting sarcomere length, but influences the peak contractile stress. On 10 kPa substrates, confined cells generate higher peak contractile stresses than unconfined cells, whereas this difference is not observed at 30 kPa. Confinement also results in faster mechanical relaxation at 10 kPa, with no detectable difference at 30 kPa. The combination of defined cell geometry and physiological stiffness leads to longer resting sarcomere lengths, more pronounced sarcomere shortening, higher peak contractile stress, and faster relaxation, features associated with a more mature cardiomyocyte phenotype. These findings show that the mechanical consequences of cell geometry depend on substrate stiffness and, therefore, both aspects have to be considered when employing hiPSC-derived cardiomyocytes as a model system in mechanobiology research, disease modeling and drug testing.

biophysics↗

bioRxiv2026

Recombinant GDF11 Improves Neurological Recovery in Models of Hemorrhagic Stroke and Traumatic Brain Injury

BackgroundIntracerebral hemorrhage (ICH) and traumatic brain injury (TBI) are leading causes of long-term neurological disability and mortality worldwide, with no approved therapies that promote functional recovery. Growth differentiation factor 11 (GDF11), a circulating TGF{beta}-family protein, has shown regenerative and neurorestorative potential in models of ischemic stroke. MethodsWe evaluated recombinant GDF11 (rGDF11) in mouse models of ICH and TBI. ICH was induced by intrastriatal collagenase injection, and neurological recovery was assessed using Neuroseverity Score (NSS), Rotarod (RR), and CatWalk (CW) analyses up to 28 days post-injury. Histological assessments of vascularization, neuronal density, and microglial/macrophage density were performed 28 days after ICH. For TBI, a closed-head injury model using a pneumatic impactor was employed, and NSS and RR assessments were conducted through 28 days post-injury. ResultsrGDF11 treatment significantly improved neurobehavioral performance following ICH, including NSS, RR, and CW parameters (forelimb base of support and average speed). Histological analyses revealed enhanced vascular area and neuronal density, with reduced microglial/macrophage density in rGDF11-treated mice. Following TBI, rGDF11 accelerated functional recovery, improving RR latency by day 6 and NSS by day 28 post-injury. ConclusionrGDF11 promotes structural and functional recovery after both hemorrhagic and traumatic brain injury in mice. These findings, together with prior evidence in ischemic stroke, support rGDF11 as a promising neurorestorative biologic with broad therapeutic potential for diverse forms of brain injury. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/730114v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8c624forg.highwire.dtl.DTLVardef@8a6107org.highwire.dtl.DTLVardef@e83ce6org.highwire.dtl.DTLVardef@f65f14_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Correlative X-ray imaging and fluorescence microscopy

Imaging the structural organization inside cells in their native state is essential for understanding how the arrangement and interactions among molecular components give rise to biological function. Fluorescence microscopy is one of the pivotal techniques that provides molecular specificity for imaging in real space, however, the technique is limited to labeled components. X-rays, on the contrary, are sensitive to electron density contrast and therefore to label-free samples, and probe structure in reciprocal space. In particular, scanning small-angle X-ray scattering (SAXS) combines information from real and reciprocal space and enables access to intact cells, owing to the high penetration power of the X-rays. Combining both imaging modalities in a synergistic manner promises powerful tools for cellular imaging, but remains challenging, because of the differing requirements the complementary methods introduce. Here we present a correlative imaging platform that integrates a modular, compact and beamline-compatible fluorescence microscope with scanning SAXS, to enable fast sequential imaging of the identical cellular regions. We developed a dedicated microfluidics flow chamber enabling measurements under hydrated, near-native conditions. We demonstrate the utility of our methodology by investigating two different relevant cellular components, i.e., thick keratin bundles in epithelial cells that contribute to cell mechanics, and force-generating actomyosin in cardiomyocytes. Employing adapted data analysis methods, we find a good agreement between the fluorescence-derived and the SAXS-derived orientation maps. This result demonstrates that the label-free approach with SAXS captures cytoskeletal organization through-out the cell, and can be directly linked to specific molecular information provided by the complementary fluorescence imaging, in a physiologically relevant cellular environment. Our work establishes a general strategy for multimodal imaging of cellular architecture and opens ways to investigate living cells under the influence of drugs and chemical manipulation experiments.

biophysics↗

bioRxiv2025

Fast scanning small angle X-ray scattering of hydrated biological cells

Due to their high penetration depth, X-rays enable us to obtain information from the interior of whole, unsliced cells. Scanning small angle X-ray scattering (SAXS), in particular, reveals real-space images in dark field representation as well as structural information in reciprocal space. However, obtaining information on anisotropy and orientation from cells in an aqueous, close-to-physiological environment remains challenging. Here, we extend the recently introduced fast scanning SAXS mode with short exposure times of few milliseconds to such hydrated samples by combining a newly developed, X-ray compatible microfluidic sample chamber and innovative data analysis that includes an effective noise-filtering method. This strategy enables the systematic analysis of radiation damage by quantifying the SAXS signal. Our results demonstrate that scanning SAXS can be used to obtain intracellular information of fixed-hydrated cells and the approach may in the future be applicable to living cells as well. SynopsisFast scanning SAXS on biological cells in aqueous environment reveals intracellular anisotropy and orientation and allows for systematic assessment of radiation damage caused by the measurements.

biophysics↗

bioRxiv2025

A Case-Based Explainable Graph Neural Network Framework for Mechanistic Drug Repositioning

Drug repositioning offers a cost-effective alternative to traditional drug development by identifying new uses for existing drugs. Recent advances leverage Graph Neural Networks (GNN) to model complex biological data, showing promise in predicting novel drug-disease associations. However, these frameworks often lack explainability, a critical factor for validating predictions and understanding drug mechanisms. Here, we introduce Drug-Based Reasoning Explainer (DBR-X), an explainable GNN model that combines a link prediction module and a path-identification module to generate interpretable and faithful explanations. When benchmarked against other GNN link prediction frameworks, DBR-X achieves superior performance in identifying known drug-disease associations, demonstrating higher accuracy across all evaluation metrics. The quality of DBR-X biological explanations was assessed through multiple approaches: comparison with manually-curated drug mechanisms, evaluation of explanation faithfulness through deletion and insertion studies, and measurement of stability under graph perturbations. Together, our model not only advances the state-of-the-art in drug repositioning predictions but also provides multi-hop explanations that can accelerate the translation of computational predictions into clinical applications.

bioinformatics↗

bioRxiv2024

Weighted Ensemble Approach for Knowledge Graph completion improves performance

This study introduces a weighted ensemble method called "WeightedKgBlend" for link prediction in knowledge graphs which combines the predictive capabilities of two types of Knowledge Graph completion methods: knowledge graph embedding and path based reasoning. By dynamically assigning weights based on individual model performance, WeightedKgBlend surpasses standalone methods, demonstrating superior predictive accuracy when tested to discover drug-disease candidates over a large-scale biomedical knowledge graph called Mechanistic Repositioning Network. This research highlights the efficacy of an integrated approach combining multiple methods in drug discovery, showcasing improved performance and the potential for transformative insights in the realm of biomedical knowledge discovery.

bioinformatics↗

bioRxiv2023

Universal Digital High Resolution Melt for the detection of pulmonary mold infections

BackgroundInvasive mold infections (IMIs) such as aspergillosis, mucormycosis, fusariosis, and lomentosporiosis are associated with high morbidity and mortality, particularly in immunocompromised patients, with mortality rates as high as 40% to 80%. Outcomes could be substantially improved with early initiation of appropriate antifungal therapy, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high resolution melting analysis (U-dHRM) may enable rapid and robust diagnosis of IMI. This technology aims to accomplish timely pathogen detection at the single genome level by conducting broad-based amplification of microbial barcoding genes in a digital polymerase chain reaction (dPCR) format, followed by high-resolution melting of the DNA amplicons in each digital reaction to generate organism-specific melt curve signatures that are identified by machine learning. MethodsA universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these 19 fungal melt curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage (BAL) samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. ResultsU-dHRM achieved an average of 97% fungal organism identification accuracy and a turn-around-time of 4hrs. Pathogenic molds (Aspergillus, Mucorales, Lomentospora and Fusarium) were detected by U-dHRM in 73% of BALF samples suspected of IMI. Mixtures of pathogenic molds were detected in 19%. U-dHRM demonstrated good sensitivity for IMI, as defined by current diagnostic criteria, when clinical findings were also considered. ConclusionsU-dHRM showed promising performance as a separate or combination diagnostic approach to standard mycological tests. The speed of U-dHRM and its ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples as well as detect emerging opportunistic pathogens may provide information that could aid in treatment decisions and improve patient outcomes.

microbiology↗

bioRxiv2023

Repeated FRAP of the actin-binding protein CapG in the cell nucleus - a functional assay for EGF signaling in the single live breast cancer cell

Compartmentalization and differential distribution of proteins within a cell maintain cellular function and viability. CapG is the only gelsolin-related actin-binding protein that distributes in steady state diffusively throughout cytoplasm and cell nucleus. To detect changes in CapGs nuclear shuttling in response to external stimuli on the single cell level, we established repeated FRAP experiments of one and the same breast cancer cell. With this experimental set up, we found that ATP-depletion reversibly decreased CapGs shuttling into the cell nucleus. The addition of epidermal growth factor (EGF) increased CapGs nuclear shuttling within minutes. Serum-starvation doubled the number of breast cancer cells from 40% to 80% displaying increased CapG shuttling in response to EGF. Testing five different potential CapG phosphorylation sites, we found that serine 70 mediates the increase in CapGs nuclear shuttling triggered by EGF. Thus, repeated FRAP of CapG in the cell nucleus can be used as functional readout of signaling cascades in the same single live breast cancer cell.

cancer biology↗

bioRxiv2023

Drug Repositioning using Consilience of Knowledge Graph Completion Methods

While link prediction methods in knowledge graphs have been increasingly utilized to locate potential associations between compounds and diseases, they suffer from lack of sufficient evidence to explain why a drug and a disease may be indicated. This is especially true for knowledge graph embedding (KGE) based methods where a drug-disease indication is linked only by information gleaned from a vector representation. Complementary pathwalking algorithms can increase the confidence of drug repurposing candidates by traversing a knowledge graph. However, these methods heavily weigh the relatedness of drugs, through their targets, pharmacology or shared diseases. Furthermore, these methods can rely on arbitrarily extracted paths as evidence of a compound to disease indication and lack the ability to make predictions on rare diseases. In this paper, we evaluate seven link prediction methods on a vast biomedical knowledge graph for drug repurposing. We follow the principle of consilience, and combine the reasoning paths and predictions provided by path-based reasoning approaches with those of KGE methods to identify putative drug repurposing indications. Finally, we highlight the utility of our approach through a potential repurposing indication.

bioinformatics↗

bioRxiv2022

ARAX: a graph-based modular reasoning tool for translational biomedicine

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSWith the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine--supporting that language--that federates semantically integrated knowledge-bases. ResultsWe introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface. ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the users query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning, and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAXs application and utility in specific disease contexts, we present several use-case examples. Availability and ImplementationThe source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web application programming interface (API) endpoint at arax.rtx.ai/api/arax/v1.3/ui/. Contactdmk333@psu.edu

bioinformatics↗

bioRxiv2022

Silencing KCC2 in mouse dorsal hippocampus compromises spatial and contextual memory

Delayed upregulation of the neuronal chloride extruder KCC2 underlies the progressive shift in GABA signaling polarity during development. Conversely, KCC2 downregulation is observed in a variety of neurological and psychiatric disorders often associated with cognitive impairment. Reduced KCC2 expression and function in mature networks may disrupt GABA signaling and promote anomalous network activities underlying these disorders. However, the causal link between KCC2 downregulation, altered brain rhythmogenesis and cognitive function remains elusive. Here, by combining behavioral exploration with in vivo electrophysiology we assessed the impact of chronic KCC2 silencing in mouse dorsal hippocampus and showed it compromises both spatial and contextual memory. This was associated with altered hippocampal rhythmogenesis and neuronal hyperexcitability, with increased CA1 pyramidal cell burst firing during non-REM sleep. Reducing neuronal excitability with terbinafine, a specific Task-3 leak potassium channel activator, occluded the impairment of contextual memory upon KCC2 silencing. Our results establish a causal relationship between KCC2 expression and cognitive performance and suggest that impaired rhythmopathies and neuronal hyperexcitability are central to the deficits caused by KCC2 silencing in the adult mouse brain.

neuroscience↗

bioRxiv2021

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

BackgroundBiomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). ResultsTo create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building--and hosting a web API for querying--a biomedical knowledge graph that uses an Extract-Transform-Load (ETL) approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. ConclusionRTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

bioinformatics↗

bioRxiv2020

Deep immune profiling reveals targetable mechanisms of immune evasion in checkpoint blockade-refractory glioblastoma

BackgroundGlioblastoma (GBM) is refractory to checkpoint blockade immunotherapy (CPI). We sought to determine to what extent this immune evasion is due to intrinsic properties of the tumor cells versus the specialized immune context of the brain, and if it can be reversed. MethodsWe used CyTOF mass cytometry to compare the tumor immune microenvironments (TIME) of human tumors that are generally CPI-refractory (GBM and sarcoma) or CPI-responsive (renal cell carcinoma), as well as mouse models of GBM that are CPI-responsive (GL261) or CPI-refractory (SB28). We further compared SB28 tumors grown intracerebrally versus subcutaneously to determine how tumor site affects TIME and responsiveness to dual CTLA-4/PD-1 blockade. Informed by these data, we explored rational immunotherapeutic combinations. ResultsCPI-sensitivity in human and mouse tumors was associated with increased T cells and dendritic cells, and fewer myeloid cells, in particular PD-L1+ tumor associated macrophages. The SB28 mouse model of GBM responded to CPI when grown subcutaneously but not intracerebrally, providing a system to explore mechanisms underlying CPI resistance in GBM. The response to CPI in the subcutaneous SB28 model required CD4 T cells and NK cells, but not CD8 T cells. Recombinant FLT3L expanded dendritic cells, improved antigen-specific T cell priming, and prolonged survival of mice with intracerebral SB28 tumors, but at the cost of increased Tregs. Targeting PD-L1 also prolonged survival, especially when combined with stereotactic radiation. ConclusionsOur data suggest that a major obstacle for effective immunotherapy of GBM is the low antigenicity of the tumor cells coupled with poor antigen presentation in the brain, rather than intrinsic immunosuppressive properties of GBM tumor cells. Deep immune profiling identified dendritic cells and PD-L1+ tumor-associated macrophages as promising targetable cell populations, which was confirmed using therapeutic interventions in vivo. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/404939v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@bdd0e4org.highwire.dtl.DTLVardef@1dd335forg.highwire.dtl.DTLVardef@157bfa6org.highwire.dtl.DTLVardef@1e76151_HPS_FORMAT_FIGEXP M_FIG C_FIG In BriefIn mice and humans, tumors that were sensitive to checkpoint blockade had consistent immunological features. A mouse model of glioma that is refractory to checkpoint blockade was sensitized by increasing antigen presentation through a variety of approaches.

cancer biology↗

bioRxiv2020

Modulation of fungal virulence through CRZ1 regulated F-BAR-dependent actin remodeling and endocytosis in chickpea infecting phytopathogen Ascochyta rabiei

Polarized hyphal growth of filamentous pathogenic fungi is an essential event for host penetration and colonization. The long-range early endosomal trafficking during the hyphal growth is crucial for nutrient uptake, sensing of host-specific cues, and regulation of effector production. Bin1/Amphiphysin/Rvs167 (BAR) domain-containing proteins mediate fundamental cellular processes, including membrane remodeling and endocytosis. Here, we identified an F-BAR domain protein (ArF-BAR) in the necrotrophic fungus Ascochyta rabiei and demonstrate its involvement in endosome-dependent fungal virulence on the host plant, Cicer arietinum. We show that ArF-BAR regulates endocytosis at the hyphal tip, localizes to the early endosomes, and is involved in actin dynamics. Functional studies involving gene knockout and complementation experiments reveal that ArF-BAR is essential for virulence. The loss-of-function of ArF-BAR results in delayed formation of first septa from the hyphal tip, crucial for host penetration and proliferation. ArF-BAR was induced in response to oxidative stress and infection and localized to endocytic vesicles within the fungal hyphae. We also show that ArF-BAR is able to tubulate synthetic liposomes, suggesting the functional role of F-BAR domain in membrane tubule formation in vivo. Further, our studies identified a stress-induced transcription factor, ArCRZ1 (Calcineurin-responsive zinc finger 1) as key regulator for transcriptional reprogramming of ArF-BAR. We propose a model in which ArCRZ1 functions upstream of ArF-BAR to regulate fungal pathogenesis through a mechanism that involves membrane remodeling and actin cytoskeleton regulation. Author summaryBAR-domain superfamily is known to mold amorphous lipid bilayer into defined tubular shapes and critical for endosome formation and trafficking. Although these processes are studied earlier in the context of their structural and biochemical properties, there is limited evidence on the direct role of F-BAR domain proteins in the pathophysiological development of other economically important fungi. Our study assumes functional significance for plant infection as we identified an F-BAR domain-containing protein that is regulated by a distinct transcriptional network. We characterized F-BAR in a necrotrophic fungal pathogen, Ascochyta rabiei that causes the Ascochyta blight (AB) disease in chickpea plants. Additionally, we have also identified a calcium-regulated CRZ1 transcription factor that regulates the transcription of ArF-BAR. Our study will help to understand the complex interplay underlying the endosome formation required for fungal virulence.

plant biology↗

bioRxiv2020

Voting-based integration algorithm improves causal network learning from interventional and observational data: an application to cell signaling network inference

In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a new method, "Learn and Vote," for inferring causal interactions from multi-experiment datasets. In our method, experiment-specific networks are learned from the data and then combined by weighted averaging to construct a consensus network. Through empirical studies on synthetic and real-world datasets, we found that for most of the larger-sized network datasets that we analyzed, our method is more accurate than state-of-the-art network inference approaches.

bioinformatics↗