Biology subjects

Bhatt, R.

Publications and source records attributed to Bhatt, R..

16 recordsLinked to original sources

bioRxiv2026

Predicting VHH-Fc Developability from Large-Scale IgG Data

The VHH-Fc antibody scaffold is an emerging therapeutic modality. No public large-scale, standardized developability VHH-Fc dataset exists. Filling that gap, we introduce GDPa5, a 160-member VHH-Fc library profiled across 10 biophysical assays on the PROPHET-Ab platform. Cross format models trained on the developability properties of 559 IgGs outperformed intra-format models trained on GDPa5 alone, which is an advantage driven by the larger scale of standardized IgG data rather than by format. The most accurately predicted properties were heparin binding (HAC, Spearman {rho}=0.82), hydrophobicity (HIC, {rho}=0.63), and self-association (AC-SINS, {rho}=0.62), all of which are largely governed by antibody surface properties. Tabular neural networks (TabICLv2, TabPFN v2.5), applied here for the first time to antibody developability prediction, outperformed conventional modeling approaches. Adding experimental HIC and HAC measurements as model inputs improved prediction of the more complex polyreactivity liability (PR-CHO, {Delta}{rho} = +0.10), supporting a tiered assay strategy that extends predictive performance while limiting experimental burden. We demonstrate through this work that IgG-trained models are a practical, data-efficient starting point for VHH-Fc developability prediction.

biophysics↗

bioRxiv2026

Decoding Bispecific Antibody Developability: Design Rules and Predictive Models from a 160-Member Library

Bispecific antibodies deliver functional outcomes that monospecific antibodies cannot, yet emergent self-association, polyreactivity, and aggregation often degrade their developability relative to their parental arms. Whether bispecific developability inherits from the parents or is driven by the format has not been tested at scale. We characterized 160 bispecific antibodies and their 65 parental arms on a uniform knobs-into-holes CrossMab IgG1 scaffold across 10 assays on the PROPHET-Ab high-throughput platform. Bispecific developability separates into three classes of inheritance. Hydrophobicity and surface charge inherit cleanly from the parents (Spearman {rho} {approx} 0.85 to 0.95), so parental-level screening predicts bispecific fate. Self-association and polyreactivity inherit partially ({rho} {approx} 0.60 to 0.88), with mechanistically interpretable emergent outliers driven in part by Fv-Fv charge complementarity and a parental biophysical ceiling on the hydrophobicity (HIC) by surface-charge (HAC) plane. Thermostability is poorly predicted from parental antibodies ({rho} < 0.4), so it requires bispecific-level testing. The class framework yields actionable selection rules: triage hydrophobicity and charge at the parental level, avoid pairing two high-HIC x high-HAC arms, pair opposite-sign Fv charges to suppress self-association but re-validate at the formulation buffer, and measure thermostability on the bispecific itself. This work charts a tractable path from monospecific sequence to bispecific developability prediction. SignificanceBispecific antibodies are a fast-growing therapeutic class, yet the rational design of well-behaving bispecific antibodies from validated monospecific antibody building blocks remains challenging. A key bottleneck is the lack of comprehensive, high-quality public datasets linking parental antibody developability properties to corresponding bispecific antibody developability properties. We address this gap by releasing a dataset comprising 160 bispecific antibodies and the 65 parental monospecific antibodies profiled in 10 developability assays. The data show that bispecific antibody developability is complex. Some properties are easily predictable from the parents, whereas others emerge in the bispecific format or from the bispecific format itself. The factors that govern each property can be identified empirically and used to make practical selection decisions. The mechanistic explanations and predictive models reported here establish a compact set of actionable rules. Together, they define a framework for using computational pipelines to convert monospecific antibodies into bispecific antibodies with drug-like developability properties, enabling faster and more effective generation of high-quality bispecific antibodies for diverse therapeutic applications.

biophysics↗

bioRxiv2026

Reprogramming tumour-associated macrophages from immune suppressive to inflammatory state by Checkpoint kinase 1 inhibitor combination treatment

BackgroundTumour-associated macrophages (TAMs) play critical roles within the tumour microenvironment regulating immune evasion and therapeutic response. Previously, we have shown that the combination of Checkpoint kinase 1 inhibitor (CHK1i) with a subclinical dose of hydroxyurea (LDHU) reprograms the tumour immune microenvironment to a pro-inflammatory status. MethodsWe investigated a tumour-restricted Fcgr4 (Cd16.2) expressing macrophage population in multiple murine tumour models and the impact of CHK1i+LDHU on this population, using conventional and imaging flow cytometry as well as single-cell sequencing. ResultsTranscriptional profiling using CITE-seq and single-cell RNA sequencing reveals that Fcgr4 TAMs closely resemble Fcgr4- TAMs but display modest enrichment of interferon-associated and inflammatory gene programs, consistent with a functionally biased state rather than a distinct lineage. Importantly, we show that a highly tumour selective CHK1i+LDHU therapy shifts TAMs toward a more inflammatory phenotype while preserving dominant immunosuppressive features. Depletion of CSF1R macrophages enhanced CD8 T cell activation without influencing tumour growth but significantly augmented therapeutic efficacy of CHK1i+LDHU. ConclusionTogether, these findings define a novel TAM population and establish how targeted therapy reshapes, but does not fully overcome, TAM-mediated immune regulation.

cancer biology↗

bioRxiv2026

A framework for quantifying the mechanics of dexterous grasp

AO_SCPLOWBSTRACTC_SCPLOWA hallmark of primate behavior is the exceptional ability to dexterously grasp and manipulate objects, yet the investigation of the neural mechanisms that support manual dexterity has been hindered by technical challenges. Optical hand tracking is complicated by frequent occlusions, and contact forces are hard to measure with sufficient precision. Furthermore, while monitoring the kinematics during reaching phase, and the contact forces during object manipulation phase is difficult, joint torques are impossible to measure directly. While challenging, the ability to estimate joint torques in the complex primate hand could provide an invaluable continuous mechanical description spanning both phases. With this in mind, we have developed an experimental apparatus and data processing pipeline for quantifying these variables describing prehension. The apparatus presented objects of various sizes and orientations throughout the workspace, evoking different grasping strategies. Object surfaces were instrumented with thousands of pressure-sensitive elements, enabling high-resolution measurement of distributed contact forces. Simultaneously, eight high-speed cameras were used to reconstruct hand and arm movements with markerless tracking, triangulating 3D landmarks, and mapping them onto a musculoskeletal model, enabling estimation of time-varying joint angles. This posture quantification allowed contact forces to be automatically assigned to specific hand segments, in close agreement with manual human annotations. We used the reconstructed movements and contact forces with the musculoskeletal model of the hand to compute inverse dynamics, yielding joint torques throughout behavior, unifying the hand kinematics and grasp forces into a single physical description. Throughout the processing, we identified individual neurons in the motor cortex of monkeys that were related to grasp force, kinematics, and torques. Together, this framework enabled a comprehensive and precise physical characterization of primate manual behavior, providing a foundation for investigating the neural mechanisms of manual dexterity.

neuroscience↗

bioRxiv2025

Automated Eight-Stage Classification of Drosophila melanogaster Using Transfer-Learning CNNs with Mobile Live-Inference Deployment

Drosophila melanogaster is a foundational model organism whose rapid development and genetic tractability underpin research in genetics, development, and disease. Manual staging of its embryonic, larval, and pupal phases is slow and error-prone. An automated eight-class classifier is introduced to distinguish egg, first-, second-, and third-instar larvae, as well as white, brown, eye, and black pupae, from stereo-microscope images. By fine-tuning ImageNet-pretrained CNNs (ResNet-50, InceptionV3, ConvNeXtSmall) on a balanced dataset ([~]300 images per class), the best-performing model (ResNet-50) achieves 85% accuracy (F1 = 0.85) on a held-out validation set, significantly outperformed alternatives. Primary misclassifications align with subtle morphological transitions between adjacent stages. To facilitate broad adoption, the ResNet-50 model has been deployed in a lightweight Streamlit app offering live-camera inference ({approx}8 FPS on mobile). All code, pretrained weights, and data are publicly available, enabling scalable, high-throughput Drosophila staging for diverse experimental workflows.

bioinformatics↗

bioRxiv2025

Using targeted therapy to promote a pro-inflammatory tumour microenvironment and anti-tumour immune response in high grade serous ovarian cancer

BACKGROUNDHigh-grade serous ovarian cancer (HGSOC) is characterized by elevated replication stress and an immunosuppressive microenvironment. A synergistic combination of checkpoint kinase 1 inhibitor (CHK1i) with low-dose hydroxyurea (LDHU) promotes a unique ATR-independent moderate replication stress response with potent anti-tumour effects. The ability of this approach to reprogram the tumour immune microenvironment (TIME) to overcome the immunosuppression and promote an anti-tumour immune response in HGSOC is the focus of this study. METHODSWe investigated the therapeutic potential of CHK1i+LDHU in established HGSOC cell cultures, fresh tumour cell explants from HGSOC patient ascites, and syngeneic mouse models, assessing tumour cell killing, immunogenic cell death, pro-inflammatory cytokine/chemokine expression, and anti-tumour immune responses. RESULTSCHK1i+LDHU effectively killed ovarian cancer cells regardless of chemotherapy resistance, BRCA2 mutation and homologous recombination repair status in vitro. In vivo, treatment significantly reduced tumour burden and ascites accumulation. CHK1i+LDHU enhanced expression of pro-inflammatory cytokines/chemokines and triggered immunogenic cell death in tumour. In syngeneic models, treatment promoted CD8+ cytotoxic T cell-dependent anti-tumour responses and reduced immunosuppressive signalling within the TIME. CONCLUSIONSCHK1i+LDHU is a promising therapy for chemotherapy-resistant HGSOC, combining direct cytotoxic effects with reprogramming the TIME to reduce immunosuppression and activate a CD8+ T cell-dependent anti-tumour response.

cancer biology↗

bioRxiv2025

Affordable plasmonic biosensing: democratizing SERS with scalable, field-compatible substrate fabrication

Efficient and accurate plasmonic biosensing in the field remains a significant challenge. Despite its potential to revolutionize point-of-care (PoC) diagnostics through its unparalleled sensitivity and precise molecular fingerprinting capabilities, adoption of traditional surface-enhanced Raman spectroscopy (SERS) in the field has not proven feasible yet. High production and material costs, complex fabrication methods, reliance on specialized equipment, and persistent issues with sensor stability and reproducibility continue to impede development for PoC use. Addressing these challenges, this study innovates a democratized and cost-effective fabrication kit that enables the production of SERS substrates using commonly available materials and straightforward electrochemistry techniques without compromising on sensitivity and reproducibility. Importantly, this method leverages commercially available bottled water and simple battery-powered fabrication, thereby eliminating reliance on a power grid and enabling the local production of biosensors in resource-restricted and conflict-affected areas. The cost of producing the fabrication kit is $39.54 with raw materials purchased at bulk retail prices, while the cost of consumables for fabricating each test is just 1.33{cents}. To ensure real-world feasibility, we conducted a comprehensive reproducibility analysis, where consistent plasmonic enhancement was observed across multiple production batches. Furthermore, we demonstrated their efficacy in two critical applications: the rapid detection of bacteria and pesticides. We detect trace levels of pesticides such as Thiram and Thiabendazole down to 0.1 ppm using a digital SERS approach. We also demonstrated the identification of bacteria isolated from culture, namely Escherichia coli and Bacillus subtilis. We envision that this label-free, high-sensitivity substrate, when paired with portable Raman spectrometers, could open doors for a new era of field-deployable biosensing, paving the way for its adoption for a plethora of applications, from public health to food testing.

bioengineering↗

bioRxiv2025

A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training

Antibodies must bind their targets with high affinity and specificity to achieve useful therapeutic activity. They must also possess suitable developability properties (e.g., thermostability, solubility, viscosity, polyreactivity) to ensure favorable manufacturing, formulation, and in vivo performance. Both binding and developability properties are inherent to a given antibody amino acid sequence. Identification or selection of antibodies possessing suitable binding characteristics is now routine, and de novo computational design models, trained on extensive complementarity-determining region sequence and structural data, are rapidly improving. Developability properties, however, remain difficult to predict largely due to insufficient training data, with empirical testing being heavily used to avoid challenges in late-stage antibody development. To fill this gap, we built a high-throughput antibody developability assay platform designed to generate the large datasets needed to train improved machine learning (ML) models. We optimized and automated known developability assays [Jain et al., 2017], and developed a robust integrated data analytics pipeline. Here we report data on 246 antibodies--representing 106 approved, 135 clinical-stage, and 5 preregistration/withdrawn molecules--across a panel of 10 developability assays, in a "tidy data" format suitable for AI/ML modeling. We used these data to develop an XGBoost [Chen et al., 2016] ML model that better predicts similarity to approved antibodies compared to conventional use of developability warning thresholds. Additionally, we confirm that preliminary predictive models do improve with more training data. Our high-throughput PROPHET-Ab platform enables data generation at the scale needed to develop improved ML models to predict antibody developability. SignificanceSuccessful antibody drugs exhibit important "developability" properties, beyond tight and specific binding to their target, including high expressibility, high stability and solubility, low aggregation propensity, low viscosity, low polyreactivity, and long in vivo half-life. Collectively, developability properties predict favorable manufacturing, storage, administration, and safety, and deficiencies in these properties increase risk for clinical failure. Despite progress in developing machine learning models to predict structure and binding, antibody developability models lag, largely due to a lack of sufficiently large training datasets. We have built a high-throughput platform, PROPHET-Ab, that enables data generation at the scale needed to train improved AI/ML models to predict antibody developability.

biophysics↗

bioRxiv2024

Explicitly Acquired Interrelations Among Mental Schema Reduces Cognitive Load and Facilitates Emergence of Novel Responses in Mice and Artificial Neural Networks.

Mammalian brain has evolved to infer from past experiences and elicit context relevant novel behavioural responses hitherto unexpressed by the animal. However, little is known about how prior knowledge influences the emergence of such responses. Remarkably, the brain not only arrives at these responses through logical inferences based on previous leanings, but also acquire new related information, without causing catastrophic interference. Mental schemas have often been proposed as the framework for this phenomenon. In this study, using mice as a model animal, we show that schematic networks not only enhance the cognitive load handling capacity (CLHC) and prevent catastrophic interference, but also facilitate the generation of novel, contextually relevant responses. Interestingly, when the animals were trained in a paradigm that did not invoke the pre-formed mental schema, we observed neither an enhancement to CLHC nor a generation of novel context relevant responses. Based on the principles of mental schemas discovered in our animal experiments, we developed a biologically plausible artificial neural network (ANN) that avoids catastrophic interference and captures the learning properties observed in our experiments. The custom architecture of this ANN enables it to generate responses similar to those of animals in novel scenarios. Significance StatementLittle is known about the role of mental schemas in preventing memory interference--a process in which overlapping or similar memories hinder the acquisition and retention of related information. In this study, we demonstrate that mental schemas enhance cognitive load handling capacity and improve the ability to solve novel but related problems. Using mice as a model, we show that the mere existence of a mental schema is not enough for improved cognitive load handling; instead, the relationship between existing and new information must be explicitly established during the learning process. Based on these findings, we developed a minimalistic artificial neural network (ANN) that effectively mimics this behaviour. These insights pave the way for developing more efficient learning and teaching strategies.

neuroscience↗

bioRxiv2024

The Genetic Architecture of the Human Corpus Callosum and its Subregions

The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive functions, and representing information in multiple dimensions. Understanding which genetic variants underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility to neuropsychiatric disorders, can provide molecular insights into the CCs role in mediating cortical development and its contribution to neuropsychiatric disease. To characterize the morphometry of the midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract, parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685). We then examined evidence for generalization to the non-European participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin, suggesting transcriptional activity regulation in early development. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified. These results provide insight into variability of corpus callosum development, its genetic influence on the cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.

neuroscience↗

bioRxiv2024

Same Sentences, Different Grammars, Different Brain Responses: An MEG study on Case and Agreement Encoding in Hindi and Nepali Split-Ergative Structures

At first glance, the brains language network appears to be universal, but languages clearly differ. How does the brain adapt to the specific details of individual grammatical systems? Here, we present an MEG study on case and agreement in Hindi and Nepali. Both languages use split-ergative case systems. However, these systems interact with verb agreement differently - in Hindi, case features conspire to determine which noun phrase (NP) the verb agrees with (subject, object, or neither), but in Nepali the verb always agrees with the subject NP. We found that left inferior frontal and left anterior temporal regions are sensitive to case features in both languages. Across case configurations, these same brain areas in Hindi participants show different patterns of activity for sentences that require masculine vs. feminine marking on the verb, before the comprehenders encounter it. Additionally, the left temporoparietal junction in Hindi shows different activity for subject and object agreement configurations. Both findings are not observed in Nepali participants. We suggest that this brain response demonstrates a unique-to-Hindi selection of an agreement controller and pre-encoding of the verbs morphological features. This shows that brain activity reflects psycholinguistic processes that are intimately tied to grammatical features. HighlightsO_LIThe left inferior frontal lobe and the left anterior temporal lobe distinguish accusative objects versus bare object NPs in Hindi and Nepali, and pre-emptively encode gender agreement features in Hindi. C_LIO_LIThe left inferior parietal lobe shows a differential sensitivity to object-agreement and subject-agreement constructions in Hindi that is absent in Nepali C_LIO_LIMEG can reveal differences in neural activity that reflect specific requirements of different grammatical systems C_LI

neuroscience↗

bioRxiv2023

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks

We present a novel and interpretable approach for predicting small-molecule binding affinities using context explanation networks (CENs). Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of pre-calculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs. inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each pre-calculated term to the final affinity prediction, with implications for subsequent lead optimization.

pharmacology and toxicology↗

bioRxiv2023

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

In brain imaging research, it is becoming standard practice to remove the face from the individuals 3D structural MRI scan to ensure data privacy standards are met. Face removal - or defacing - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on brain age prediction - a common benchmarking task of predicting a subjects chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

bioinformatics↗

bioRxiv2022

Identification of Banana heat responsive long non-coding RNAs and their gene expression analysis.

Identification and characterization of long non-coding RNAs (lncRNAs) in the last decade has attained great attention because of their importance in gene regulatory functions in response to various plant stresses. Rising temperature is a potential threat to the agriculture world. Banana being an important economic crop, identification and characterization of genes and RNAs that regulate high temperature stress is imperative. As the prediction of lncRNAs in response to heat stress in banana is largely unknown, the present study was focused to identify the heat stress responsive lncRNA in banana (DH Pahang). Perl script was written to identify the novel transcripts, using the work flow. StringTie and CPC softwares were used and a total of 363 novel HS-lncRNA were identified in banana. Further, lncRNA were classified as 288 lincRNAs, 71 antisense LncRNA, 5 sense lncRNAs. The functional classification was done and transcripts were broadly classified into molecular function, cellular components and biological processes. Differential expression of lncRNA showed the varied patterns at different stages of heat stress. Finally, qPCR results confirmed DGE expression pattern of lncRNAs. Further, the Cytoscape analysis was performed which showed protein coding genes involved in membrane integrity and other signal transduction pathways. Taken together, these findings expand our understanding of lncRNAs as ubiquitous regulators under heat stress conditions in banana.

molecular biology↗

bioRxiv2021

High-Plex Multiomic Analysis in FFPE Tissue at Single-Cellular and Subcellular Resolution by Spatial Molecular Imaging

The Spatial Molecular Imaging platform (CosMxTM SMI, NanoString Technologies, Seattle, WA) utilizes high-plex in-situ imaging chemistry for both RNA and protein detection. This automated instrument provides 1000s of plex, at high sensitivity (1 to 2 copies/cell), very low error rate (0.0092 false calls/cell) and background ([~]0.04 counts/cell). The imaging system generates three-dimensional super-resolution localization of analytes at [~]2 million cells per sample, four samples per run. Cell segmentation is morphology-based using antibodies, compatible with FFPE samples. Multiomic data (980 RNAs, 108 proteins) were measured at subcellular resolution using FFPE tissues (non-small cell lung (NSCLC) and breast cancer) and allowed identification of over 18 distinct cell types, 10 unique tumor microenvironments, and 100 pairwise ligand-receptor interactions. Over 800,000 single cells and [~]260 million transcripts data are released into the public domain allowing extended data analysis by the entire spatial biology research community.

genomics↗

bioRxiv2021

Implicitly Learned Higher Order Associations Differentiates Recent and Remote Retrieval of Temporal Order Memory

Memory of an ordered sequence of distinct events requires encoding the temporal order as well as the intervals that separates these events. In this study, using order place association task where the animal learns to associate the location of the food pellet to the order of entry into the event arena, we probe the nature of temporal order memory in mice. In our task, individual trials, become distinct events, as the animal is trained to form unique association between entry order and a correct location. The inter-trial intervals (> 30 mins) are chosen deliberately to minimise the working memory contributions. We develop this paradigm initially using 4 order place associates and later extend it to 5 paired associates. Our results show that animals not only acquire these explicit (entry order to place) associations but also higher order associations that can only be inferred implicitly from the temporal order of these events. As an indicator of such higher order learning during the probe trail the mice exhibit predominantly prospective errors that declines proportionally with temporal distance. On the other hand, prior to acquiring the sequence the retrospective errors are dominant. Additionally, we also tested the nature of such acquisitions when temporal order CS is presented along with flavour as a compound stimulus comprising of order and flavour both simultaneously being paired with location. Results from these experiments indicate that the animal learns both order-place and flavour-place associations. Comparing with pure order place training, we find that the additional flavour in compound training did not interfere with the ability of the animals to acquire the order place associations. When tested remotely, pure order place associations could be retrieved only after a reminder training. Further higher order associations representing the temporal relationship between the events is markedly absent in the remote time.

animal behavior and cognition↗