Biology subjects

Shenoy, A.

Publications and source records attributed to Shenoy, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Innate sensorimotor policies constrain cerebellar adaptive correction

Innate sensorimotor policies enable rapid and reliable behavior but can become ineffective when environments change. How these policies interact with feedback-driven processes to support behavioral flexibility remains unclear. Using closed-loop virtual reality in larval zebrafish, we reversed the visual consequences of self-generated turns. Larvae initially followed their innate mapping and then rapidly switched to corrective turns in the opposite direction. Purkinje cell ablation abolished this correction while preserving innate optomotor responses. Imaging revealed outcome-invariant sensorimotor representations in granule cells and an outcome-dependent modulation of inferior olive neurons and Purkinje cells. Correction weakened as the optic-flow angle increased, despite persistent error-related representations. The innate behavioral repertoire also shifted as the optic-flow angle increased, making corrective turns less likely. An actor-critic model initialized with the measured behavioral repertoire reproduced the limits of correction. Together, these results show that adaptive behavior emerges from the interaction between an innate feed-forward policy and an olivo-cerebellar instructive signal, revealing innate behavioral structure as a fundamental constraint on cerebellar adaptation.

neuroscience↗

bioRxiv2026

Organism-scale annotation with Pan-human Azimuth

Single-cell atlases now span many human tissues, but inconsistent annotations across studies limit their utility as a unified reference. We introduce Pan-human Azimuth, a supervised neural network that maps human cells from diverse tissues and datasets onto a single hierarchical organism-scale typology. Developed through NIH HuBMAP, the model is trained on a uniquely curated corpus designed to maximize diversity across tissues and technologies while enforcing uniform, interpretable annotations and stringent quality control. The use of a single organism-wide reference enables us to map tens of millions of cells in the Tabula Sapiens and scBaseCamp repositories, perform cross-tissue comparisons across thousands of samples, and identify striking tissue specialization among fibroblast states. Pan-human Azimuth naturally extends to annotating spatial transcriptomic data, recovering canonical kidney cortical structures and distinguishing glomerular states consistent with expert pathology. We release Pan-human Azimuth alongside cloud, R, and Python interfaces to facilitate standardized organism-wide single-cell analysis.

genomics↗

bioRxiv2026

Leveraging AI and structural proteomics for rational design of a KAT6A degrader

While targeted protein degraders such as PROTACs are a clinically proven therapeutic strategy, the discovery of novel degraders remains hampered by trial-and-error process. To address this challenge, we developed the AIMS platform, which combines structural proteomics with AI models for rational PROTAC design. AIMS is an end-to-end toolkit for PROTAC optimization, encompassing structure solving using proteomics and AI, prediction of ADME and degradation properties, and prospective ranking of compound design ideas. Altogether, this integrated platform successfully enabled the multi-parameter optimization of a potent and bioavailable in vivo validated KAT6A degrader, establishing a versatile framework for PROTAC development across various targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/727609v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@13596d0org.highwire.dtl.DTLVardef@140500eorg.highwire.dtl.DTLVardef@147e585org.highwire.dtl.DTLVardef@12dbdfe_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Oncogene-Mechanics Axis: KRAS G12C Confers Agility Enabling Malignant Mechano-responses to Peristalsis in Colorectal Cancer

Oncogene activity and mechanical forced individually and collective drive colorectal cancer, yet the integration of these signals is unknown. We used a patented peristalsis bioreactor to determine how oncogenic KRAS G12C mutations alter the cellular response to colonic peristalsis. Although both ehalthy intestinal cells and KRAS G12C cells sensed peristalsis via ERK phosphorylation, their mechano-responses diverged significantly. Peristalsis triggered a 9-fold enrichment of LGR5+ cancer stem cells in KRAS G12C cancer cells, an effect absent in healthy controls. Using Brillouin microscopy, we discovered that KRAS G12C induced a more agile and deformable mechano-phenotype by lowering intracellular viscosity, a state further amplified by peristalsis. This agility allowed KRAS G12C cancer cells to leverage, rather than resist peristalsis, resulting in LGR5 enrichment and malignant progression. Pharmacologic inhibition of KRAS G12C reverse the mechano-phenotype, while introducing KRAS G12C into healthy cells recapitulated it. Our findings identify a novel KRAS oncogene-mechanics axis, suggesting that targeting the cell's mechanical state could be a powerful complement to emerging KRAS-directed therapies.

bioengineering↗

bioRxiv2025

Reconstructing developmental and disease progression with sample-level embeddings

Single-cell genomics is transformative for characterizing cellular heterogeneity, but many translational questions require comparing samples rather than individual cells. Typical analyses compare "case" and "control" groups, ignoring sample-level variation within them. Here we present scSLIDE, a framework that transforms each samples single-cell data into a compact profile describing where its cells fall in high-dimensional space. By comparing these density profiles, scSLIDE calculates embeddings to characterization variation across samples. Applied to COVID-19 infection, Alzheimers disease, and zebrafish embryogenesis, we show that scSLIDE can be used to cluster patients, reconstruct sample-level disease trajectories, and identify coordinated cellular programs across samples. We discover independent axes of infection and severity, a molecular disease progression that aligns with pathology-based estimates of neurodegeneration, and embryonic "pseudostages" varying across and within timepoints. In each case, we demonstrate how case-control analyses collapse rich biological variation into binary labels, and how sample-level embedding represents a powerful analytical alternative.

genomics↗

bioRxiv2025

A zebrafish circuit for behavioral credit assignment

Improving behavioral performance relies on the ability to associate decisions with their positive and negative outcomes. Although neurons that associate actions with their consequences have been identified across multiple brain regions, the circuit-level mechanisms underlying this integration remain poorly understood. Here, using an operant thermoregulatory assay, we show that larval zebrafish maintain a short-term memory of action-outcome associations and that the dorsal habenula-interpeduncular nucleus (dHb-IPN) pathway is necessary for this process. Consistently, a population of intermediate IPN neurons encodes actions only when they lead to a thermal reward, suggesting a major role in establishing short-term associations and influencing subsequent decisions. We then combine circuit mapping and axon imaging to show that actions and reward signals are conveyed by GABAergic prepontine and glutamatergic dHb neurons, respectively. Finally, the integration between motor and sensory streams relies on presynaptic GABAB receptor-mediated modulation of dHb axon terminals by prepontine neurons. These results link a crucial computation for adaptive behavior to a specific circuit mechanism.

neuroscience↗

bioRxiv2024

Loss of Zmiz1 in mice leads to impaired cortical development and autistic-like behaviors

De novo mutations in transcriptional regulators are emerging as key risk factors contributing to the etiology of neurodevelopmental disorders. Human genetic studies have recently identified ZMIZ1 and its de novo mutations as causal of a neurodevelopmental syndrome strongly associated with intellectual disability, autism, ADHD, microcephaly, and other developmental anomalies. However, the role of ZMIZ in brain development or how ZMIZ1 mutations cause neurological phenotypes is unknown. Here, we generated a forebrain-specific Zmiz1 mutant mouse model that develops brain abnormalities, including cortical microcephaly, corpus callosum dysgenesis, and abnormal differentiation of upper-layer cortical neurons. Behaviorally, Zmiz1 mutant mice show alterations in motor activity, anxiety, communication, and social interactions with strong sex differences, resembling phenotypes associated with autism. Molecularly, Zmiz1 deficiency leads to transcriptomic changes disrupting neurogenesis, neuron differentiation programs, and synaptic signaling. We identified Zmiz1-mediated downstream regulation of key neurodevelopmental factors, including Lhx2, Auts2, and EfnB2. Importantly, reactivation of the EfnB2 pathway by exogenous EFNB2 recombinant protein rescues the dendritic outgrowth deficits in Zmiz1 mutant cortical neurons. Overall, our in vivo findings provide insight into Zmiz1 function in cortical development and reveal mechanistic underpinnings of ZMIZ1 syndrome, thereby providing valuable information relevant to future studies on this neurodevelopmental disorder.

neuroscience↗

bioRxiv2023

Zmiz1 is a novel regulator of lymphatic endothelial cell gene expression and function

Zinc Finger MIZ-Type Containing 1 (Zmiz1), also known as ZIMP10 or RAI17, is a transcription cofactor and member of the Protein Inhibitor of Activated STAT (PIAS) family of proteins. Zmiz1 is critical for a variety of biological processes including vascular development. However, its role in the lymphatic vasculature is unknown. In this study, we utilized human dermal lymphatic endothelial cells (HDLECs) and an inducible, lymphatic endothelial cell (LEC)-specific Zmiz1 knockout mouse model to investigate the role of Zmiz1 in LECs. Transcriptional profiling of Zmiz1-deficient HDLECs revealed downregulation of genes crucial for lymphatic vessel development. Additionally, our findings demonstrated that loss of Zmiz1 results in reduced expression of proliferation and migration genes in HDLECs and reduced proliferation and migration in vitro. We also presented evidence that Zmiz1 regulates Prox1 expression in vitro and in vivo by modulating chromatin accessibility at Prox1 regulatory regions. Furthermore, we observed that loss of Zmiz1 in mesenteric lymphatic vessels significantly reduced valve density. Collectively, our results highlight a novel role of Zmiz1 in LECs and as a transcriptional regulator of Prox1, shedding light on a previously unknown regulatory factor in lymphatic vascular biology.

molecular biology↗

bioRxiv2023

M-Ionic: Prediction of metal ion binding sites from sequence using residue embeddings

MotivationUnderstanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (PLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the PLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal-binding. ResultsOn independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall=84.6%) in distinguishing metal-binding from non-binding proteins compared to AUROC of 0.74 (recall =61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e., Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the PLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. Availability and ImplementationM-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data. Contactarne@bioinfo.se Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

The structural basis of hyperpromiscuity in a core combinatorial network of Type II toxin-antitoxin and related phage defence systems

Toxin-antitoxin (TA) systems are a large group of small genetic modules found in prokaryotes and their mobile genetic elements. Type II TAs are encoded as bicistronic (two-gene) operons that encode two proteins: a toxin and a neutralising antitoxin. Using our tool NetFlax (standing for Network-FlaGs for toxins and antitoxins) we have performed a large-scale bioinformatic analysis of proteinaceous TAs, revealing interconnected clusters constituting a core network of TA-like gene pairs. To understand the structural basis of toxin neutralisation by antitoxins, we have predicted the structures of 3,419 complexes with AlphaFold2. Together with mutagenesis and functional assays, our structural predictions provide insights into the neutralising mechanism of the hyperpromiscuous Panacea antitoxin domain. In antitoxins composed of standalone Panacea, the domain mediates direct toxin neutralisation, while in multidomain antitoxins the neutralisation is mediated by other domains, such as PAD1, Phd-C and ZFD. We hypothesise that Panacea acts as a sensor that regulates TA activation. We have experimentally validated 16 new NetFlax TA systems. We used functional domain annotations and with metabolic labelling assays to predict their potential mechanisms of toxicity (such as disruption of membrane integrity, inhibition of cell division and abrogation of protein synthesis) as well as biological functions (such as antiphage defence). The interactive version of the NetFlax TA network that includes structural predictions can be accessed at http://netflax.webflags.se/. Significance statementToxin-antitoxin systems are enigmatic components of microbial genomes, with their biological functions being a conundrum of debate for decades. Increasingly, TAs are being found to have a role in defence against bacteriophages. By mapping and experimentally validating a core combinatorial network of TA systems and high-throughput prediction of structural interfaces, we uncover the evolutionary scale of TA partner swapping and discover new toxic effectors. We validate the predicted toxin:antitoxin complex interfaces of four TA systems, uncovering the evolutionary malleable mechanism of toxin neutralisation by Panacea-containing PanA antitoxins. We find TAs are evolutionarily related to several other phage defence systems, cementing their role as important molecular components of the arsenal of microbial warfare.

microbiology↗

bioRxiv2023

Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation

Emerging evidence reinforce the role of non-genetic adaptive resistance to chemotherapy, that involves rewiring of transcriptional programs in surviving tumors. We combined longitudinal transcriptomics with temporal pattern analysis to dissect patient-specific emergence of resistance in breast cancer. Matched triplets of tumor biopsies (pre-treatment, post-treatment and adjacent normal) were collected from breast cancer patients who received neo-adjuvant chemotherapy. Full transcriptome was analyzed by longitudinal pattern classification to follow patient-specific expression modulations. We found that dynamics of gene expression dictates resistance-related modulations. The results unraveled important principles in emergence of adaptive resistance: 1. Genes with resistance patterns are already dysregulated in the primary tumor, supporting a primed drug-tolerant state. 2. In each patient, multiple resistance-related genes are rewired but converge into few dysregulated modules. 3. Rewiring of diverse genes and pathway dysregulation vary among individuals who receive the same treatments. Patient-specific chemoresistome maps disclosed tumors acquired resistance and exposed their vulnerabilities. Mapping the complexity of dysregulated pathways in individual patients revealed important insights on adaptive resistance mechanisms. To survive the toxic drug effect, tumor cells either sustain a drug-tolerant state or intensify it, specifically bypassing the drugs interference. Depicting an individual road map to resistance can offer personalized therapeutic strategies.

cancer biology↗

bioRxiv2022

Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

MotivationDespite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established. ResultsWe analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g., TM-score) but poorly on another (e.g., DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures. AvailabilityAll scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark. Contactarne@bioinfo.se Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

FBXO11 deficiency in mice impairs lung development and aggravates cigarette smoke-induced airway fibrosis

Small airway fibrosis is a common pathology of chronic obstructive pulmonary disease (COPD) and contributes to airflow obstruction. However, the underlying fibrogenic mechanism is poorly understood. Epithelial-mesenchymal transition (EMT) has been proposed as a driver of fibrosis. EMT occurs in the airways of COPD patients and smokers, but it remains elusive whether EMT may contribute to airway fibrosis. We previously reported that FBXO11 is a critical suppressor of EMT and Fbxo11 deficiency in mice causes neonatal lethality and EMT in epidermis. Here, we found that Fbxo11-deficient mouse embryonic lungs showed impaired epithelial differentiation, excess fibroblast cells surrounding the airways, and thickened interstitial mesenchyme. We further generated conditional mutant mice to ablate Fbxo11 selectively in the club airway epithelial cells in adult mice, which induced partial EMT in the airways. To determine the effect of EMT on airway fibrosis, Fbxo11 conditional mutant mice were exposed to cigarette smoke. Airway-specific loss of Fbxo11 markedly enhanced smoking-induced airway fibrotic remodeling and collagen deposition. Taken together, our study suggests that EMT in the airway epithelium exacerbates cigarette smoke-induced airway fibrosis.

cell biology↗

bioRxiv2022

Predicting the structure of large protein complexes using AlphaFold and sequential assembly

AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10-30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score [≥]0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb.

bioinformatics↗

bioRxiv2021

Towards a structurally resolved human protein interaction network

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure-based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

bioinformatics↗

bioRxiv2021

A structural biology community assessment of AlphaFold 2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

biophysics↗

bioRxiv2021

Proteomic landscape of multi-layered breast cancer internal tumor heterogeneity

Despite extensive research, internal tumor heterogeneity presents enormous challenges to achieve complete therapeutic responses. Changes in protein expression are central determinants of cancer phenotypes that reflect potential therapeutic targets. However, previous proteomic studies did not address internal heterogeneity, therefore, masked the necessary spatial resolution to achieve a comprehensive understanding of cancer complexity. Here we present the first large-scale multi-focal breast cancer proteomic study of 330 tumor regions which associated cancer cell function, pathological parameters, and spatial localization of each tumor region. We found marked internal proteomic heterogeneity even within tumors presenting homogeneous receptor expression. Additionally, analysis of the internal heterogeneity, based on coexisting receptor expression or histological patterns in single tumors, showed significant functional differences between homogeneous and heterogeneous tumors related to cancer metabolism, immunogenicity, and proliferation. We anticipate that this study will serve as a starting point towards the development of improved cancer therapy and diagnostics.

cancer biology↗

bioRxiv2019

Differential spatiotemporal targeting of Toxoplasma and Salmonella by GBP1 assembles caspase signalling platforms

Guanylate binding proteins (GBPs), a family of interferon (IFN)-inducible GTPases, can promote cell-intrinsic defense by removal of intracellular microbial replicative niches through host cell death. GBPs target pathogen-containing vacuoles or the pathogen itself, and assist in membrane-disruption and release of microbial molecules that trigger cell death by activating the inflammasomes. We previously showed that GBP1 mediates atypical apoptosis or pyroptosis of human macrophages infected with Toxoplasma gondii (Tg) or Salmonella enterica Typhimurium (STm), respectively. In mice, the p47 Immunity-related GTPases (IRGs) control the recruitment of GBPs to microbe-containing vacuoles and subsequent cell death. However, humans are devoid of functional IRGs, and the pathogen-proximal immune detection mechanisms by GBP1 are poorly understood. Here, we describe two novel single-cell assays which show that GBP1 promotes the lysis of Tg-containing vacuoles and Tg plasma membrane, resulting in the cytosolic detection of Tg-DNA. In contrast, we show GBP1 only targets cytosolic STm and does not contribute to bacterial escape into the cytosol of human macrophages. GBP1 interacts with caspase-4 and recruits it directly to the bacterial surface, where caspase-4 can be activated by LPS. During STm infection, caspase-1 cleaves and inactivates GBP1 at Asp192, a site conserved in related mammalian GBP1 proteins but not in murine Gbps. STm-infected human macrophages expressing a cleavage-deficient GBP1 mutant exhibit higher pyroptosis due to the absence of caspase-1-mediated feedback inhibition of the GBP1-caspase-4 pathway. Our comparative studies elucidate microbe-specific spatiotemporal roles of GBP1 in detecting infection and the assembly and regulation of divergent caspase signaling platforms.

microbiology↗