Biology subjects

Mehta, A.

Publications and source records attributed to Mehta, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Mutational fitness landscapes of diverse human influenza H1N1 neuraminidases

While neuraminidase (NA) is emerging as a promising influenza vaccine target, it evolves rapidly and undergoes antigenic drift. However, our understanding of its evolutionary constraints remains limited. Here, we systematically quantified the replication fitness effects of >6,000 mutations for each of three antigenically diverse H1N1 NAs using deep mutational scanning. Moderate correlations among H1N1 NA fitness landscapes revealed mutations with strain-dependent replication fitness effects. Additionally, cross-subtype comparison between N1 and N2 NAs uncovered similar epistatic effects that were enriched in the C-terminal interprotomer interface. We also showed that NA mutational fitness strongly correlated between in vivo and in vitro. Characterization of 40 individually constructed NA mutants further demonstrated that NA mutational fitness depended more on its surface protein expression than on its total protein expression or endoplasmic reticulum stress induction. Overall, the large dataset generated in this work provides critical insights into the evolutionary constraints of NA.

microbiology↗

bioRxiv2026

An open imaging and AI resource enabling unbiased quantification of extrachromosomal DNA at scale

Quantitative imaging of extrachromosomal DNA (ecDNA) is increasingly important for studying cancer heterogeneity and adaptation, yet automated analysis has been limited by the absence of accessible imaging data, gold standard annotations and adaptable computational tools. Here we establish an open resource for computational ecDNA imaging, integrating 2,986 native-resolution metaphase FISH image sets with manual annotations, standardized benchmarks and open-source quantification frameworks. We use this resource to systematically compare existing and newly developed approaches spanning rule-based computer vision, deep-learning-based segmentation and probabilistic localization. This comparison reveals count-dependent underestimation that distorts ecDNA copy-number distributions and motivates ecCount, a probabilistic localization method developed here to preserve individual ecDNA signals and quantitative burden. ecCount achieves an object-level F1 score of 0.939 on held-out images with minimal count bias. Together, the images, annotations, retrainable models, evaluation tools and guided workflows provide community infrastructure for applying, adapting and improving automated ecDNA quantification across experimental systems.

genetics↗

bioRxiv2026

PQBP1 couples HIV-1 capsid recognition to cGAS recruitment through conformational remodeling

Pattern recognition receptors (PRRs) must selectively engage pathogen-derived signals to potentiate inflammation and antimicrobial responses. Polyglutamine-binding protein 1 (PQBP1) acts upstream of cyclic GMP-AMP synthase (cGAS) during HIV-1 infection through the recognition of viral capsid. However, the mechanism by which capsid binding enables cGAS recruitment remains unclear. As an intrinsically disordered protein, PQBP1 samples an ensemble of conformational states whose distribution depends on ligand engagement. Here we show that capsid engagement shifts this population toward a conformation competent for cGAS binding. Capsid binding at the N-terminus of PQBP1 redistributes conformational sampling within the WW domain and distal polar-rich domain (PRD). Alanine substitutions within these capsid-responsive regions retain capsid binding yet disrupt infection-dependent cGAS recruitment, indicating that capsid binding and cGAS recruitment depend on distinct regions of PQBP1. Together, these findings define a mechanism by which HIV-1 capsid engagement remodels PQBP1 into a cGAS-competent state, coupling capsid recognition to innate immune activation. IMPORTANCEPolyglutamine-binding protein 1 (PQBP1) initiates innate immune detection of HIV-1 by recognizing the incoming viral capsid. We show that capsid engagement reshapes PQBP1 conformational dynamics, and we identify distal regions required for infection-dependent cGAS association. These findings provide a mechanistic framework for how pathogen recognition is coupled to downstream innate immune activation.

molecular biology↗

bioRxiv2026

Discovery of Tcf7 regulators with clonally-resolved CRISPR screens identifies Trim28 as a mediator of CD8 T cell differentiation in tumors

Stem-like TCF7+ CD8 T cells sustain anti-tumor responses and support immune checkpoint blockade. We systematically identified regulators of this cell state using genome-wide CRISPR screens in primary T cells in vitro. Using random barcodes to link clonal relationships with guide identity and transcriptional states in single cells, we inferred differentiation trajectories and differentiation rates of CD8 T cells in tumors, while mitigating confounding clonal bias. We found that Trim28-deficient T cells in tumors were enriched in the TCF7+ cell state, depleted in cycling and terminal effector states, and uniquely generated a tissue-resident memory (TRM)-like state with increased chromatin accessibility at known TRM loci as well as repeat elements. Despite the increase in TCF7+ CD8 T cells, loss of Trim28 did not improve tumor control, likely because of reduced effector differentiation, highlighting the need for tuning the balance and dynamics of stem-like versus effector states for effective tumor clearance.

immunology↗

bioRxiv2026

Classification of image category based on spatially distributed, transient high-frequency events

Transient high-frequency activity (HFA) in local field potentials exhibits substantial variability across trials and behavioral states, yet its functional role in sensory representation remains poorly understood. Here, we tested whether transient HFA events carry stimulus-related information through their temporal, spectral, and phase-dependent properties. Using intracranial recordings from 21 human participants performing a visual localizer task, we extracted transient HFA events and quantified their features using information-theoretic and decoding analyses. Individual event features carried only modest information about stimulus identity, and response magnitude and morphology-related properties contributed minimally to decoding performance. Instead, decoding was dominated by temporal alignment and low-frequency phase. Critically, representing transient events as distributed low-frequency phase configurations across electrodes substantially improved cross-trial decoding performance, whereas disrupting distributed phase structure eliminated this decoding advantage. These findings indicate that stimulus-related structure in transient HFA does not primarily arise from isolated local event properties, but instead emerges through distributed, phase-dependent network dynamics. More broadly, the results provide a framework for understanding how transient high-frequency neural activity contributes to sensory representations across distributed cortical networks.

neuroscience↗

bioRxiv2026

A joint metabolic-integrative regime of consciousness: evidence from FDG-PET and TMS-EEG

Conscious experience is associated with both sufficient cerebral metabolic support and preserved large-scale neural integration, yet these two constraints have largely been studied in isolation. Whether consciousness requires their joint satisfaction -- and whether either alone is sufficient -- remains an open question. Here, we test the prediction that conscious states occupy a joint metabolic-integrative regime defined by the co-occurrence of adequate metabolic support and preserved perturbational complexity. We performed a constraint-driven cross-study synthesis of published benchmarks from [18F]- fluorodeoxyglucose positron emission tomography (FDG-PET) and transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Metabolic values (expressed as percentage of normal cortical metabolism) and perturbational complexity index (PCI) values were mapped across canonical states spanning disorders of consciousness, sleep, and anaesthesia into a shared two-dimensional state space. A metabolic lower bound of [~]42-46% of normal cortical metabolism and a complexity threshold of PCI* {approx} 0.31 consistently separated unconscious from conscious conditions across independent cohorts. Of 15 conditions with complete data on both axes, all 9 conscious states occupied the joint regime (above both thresholds) and all 6 unconscious states fell below at least one threshold (Fishers exact test, two-tailed p {approx} 2.00 x 10-4); metabolic support and perturbational complexity were strongly correlated across conditions (Spearmans {rho} = 0.95). The sole off-diagonal condition -- NREM sleep, with preserved metabolism ([~]56% of normal) but low complexity (PCI {approx} 0.23) -- was unconscious, consistent with the prediction that metabolic support is necessary but not sufficient without preserved integrative dynamics. These findings support a joint metabolic-integrative constraint on conscious experience. The framework yields a clear falsification target: reportable experience should not occur in conditions falling outside this joint regime. Prospective within-subject studies combining FDG-PET and TMS-EEG are needed to evaluate this prediction at the individual level.

neuroscience↗

bioRxiv2026

Alzheimers Disease Brain Phenotypes are Age-dependent

A widespread view of neurodegenerative disorders, including Alzheimers Disease (AD), frames their effects as accelerated aging, with the brain-age gap (BAG, the deviation of predicted brain age from chronological age) as a staple biomarker. However, BAG relies on a fundamental, untested assumption: that AD can be identified via age-invariant brain phenotypes. Using invariant representation learning on brain MRI from 44,178 individuals, we created neural representations that optimally convey age information (age-aware) or conversely remove it (age-invariant) while minimizing reconstruction distortion. We provide the first causal evidence that age information is necessary in brain biomarkers for AD detection: age-aware representations achieve competitive state-of-the-art performance and significantly outperform age-invariant ones (0.84 vs. 0.77 AUC, p < 0.001, with external validation). This necessity reveals a conceptual flaw in BAG: by subtracting chronological age, it discards the very information essential for accurate detection. Using conditional decoders to simulate aging trajectories, we found that healthy aging and AD operate along multiple independent anatomical dimensions (deep gray matter, frontoparietal, temporal). AD patients diverge from rather than accelerate healthy aging, showing pathological temporal shifts alongside, remarkably, relative frontoparietal preservation. Furthermore, representational similarity analysis suggests that even models pretrained on non-age tasks (e.g., sex or BMI) implicitly converge toward age-related features when optimized for AD. Given that the AD phenotype cannot be decoupled from age, our results establish a hard limit for age-independent biomarkers and favor multidimensional models that preserve aging structure over unidimensional summaries like BAG.

neuroscience↗

bioRxiv2026

Structural insights into antibody responses against influenza A virus in its natural reservoir

While influenza A virus undergoes rapid antigenic drift in humans, at least some subtypes, such as H3, have relatively stable antigenicity in natural waterfowl reservoirs, despite the presence of immune pressure. However, the underlying mechanisms remain poorly understood. This study identified and characterized 187 antibodies to H3 hemagglutinin from experimentally infected mallard ducks, 18 of which were further analyzed by cryo-EM. Compared with human H3 antibodies, duck H3 antibodies exhibited higher glycan-binding propensity, more balanced immunodominance hierarchy, and targeted distinct epitopes. Other unique features of duck H3 antibodies included a convergent CDR H3-independent heavy chain-only binding mode and an N-glycosylated CDR H3 as decoy receptor. By annotating duck immunoglobulin germline genes, we also demonstrated the importance of gene conversion in duck H3 antibodies. Overall, our findings provide insights into how millennia of coevolution have shaped the interplay between influenza A virus antigenic drift and antibody responses in the natural reservoir.

immunology↗

bioRxiv2026

Modeling patient variants of Cnot1 and Cdc42bpb results in distinct forms of congenital diaphragmatic hernia in mice

Congenital diaphragmatic hernia (CDH) is a severe congenital anomaly characterized by impairment of both diaphragm and lung development in utero. CDH presents as a spectrum of forms and severities, with diaphragm defects arising in the dorsal/posterior region typically correlating with more severe pulmonary disease and higher risk of mortality than those appearing in ventral/anterior regions. The genetic etiology underlying CDH is complex, with many genes implicated showing variable expressivity and incomplete penetrance in both human patients and mouse models. Here we present in vivo validation of two genes previously unassociated with CDH: the CDC42-interacting kinase CDC42BPB; and CNOT1, a scaffolding protein of the CCR4-NOT protein complex, critical for mRNA regulation through modifications such as deadenylation. Each gene was found to have a damaging, de novo missense variant in a recent large-scale CDH patient sequencing screen. Loss of Cdc42bpb leads to ventral diaphragmatic hernias, heart septal defects and minor lung epithelial differentiation defects in mouse embryos. Installation of the orthologous patient-specific missense variant through CRISPR/Cas9 editing leads to less severe ventral diaphragm defects. Mouse embryos with either one or two copies of the orthologous Cnot1 variant, c.1867C>T (p.R623W), develop dorsal diaphragmatic hernias with low (<50%) penetrance, and mutants showed alterations in mRNA isoform expression consistent with the molecular role of Cnot1 in RNA splicing. These results underscore the power of in vivo functional modeling to validate genes and patient-specific variants uncovered by patient sequencing, reveal two previously unrecognized genetic causes of CDH, and highlight the heterogeneity of different patient anatomic presentations.

genetics↗

bioRxiv2026

MassID provides near complete annotation of metabolomics data with identification probabilities

Liquid chromatography coupled to mass spectrometry (LC/MS) is a powerful tool in metabolomics research, generating tens-of-thousands of signals from a single biological sample. However, current software solutions for unbiased assessment of metabolomics data analysis are limited by complex sources of noise and non-quantitative metabolite identifications that make results difficult to interpret. Here, we present MassID, a cloud-based untargeted metabolomics pipeline that aims to overcome the innate challenges of unbiased metabolite analysis and perform end-to-end data processing, transforming raw spectra to normalized and identified metabolite profiles. MassID incorporates a suite of software functionalities, including deep learning-based peak detection and comprehensive noise filtering. In addition, with MassID we introduce a novel software module: DecoID2 that enables probabilistic metabolite identification for false discovery rate (FDR)-controlled metabolomics. When applied to a human plasma dataset, MassID results in near-complete signal annotation, identification of >4,000 metabolites (including >1,200 compounds at an FDR <5%) across four complementary LC/MS runs, and enables integrated downstream analyses to understand biochemical dysregulation at both the molecular and pathway level. When compared to the Metabolomics Standards Initiative (MSI) confidence levels, identification probability generally correlated with MSI levels. However, only 356/418 of MSI Level 1 compounds were identified with <5% FDR and the remaining 884 FDR < 5% compounds were identified from MSI L2-L3 compounds, highlighting the enhanced specificity and discovery potential achieved by MassID.

bioinformatics↗

bioRxiv2026

Small molecule inhibition of the mitochondrial lipid transfer protein STARD7 attenuates influenza viral replication

The increasing appearance of drug-resistant and zoonotic influenza strains highlights an urgent need for host-directed antivirals that offer broad-spectrum activity and a higher barrier to resistance. Here, we describe the characterization of M4, a small-molecule identified from a high-thoughput screen that potently inhibits influenza A and B viruses. Mechanistic studies reveal that M4 suppresses replication by causing nuclear retention of the viral ribonucleoprotein (vRNP) complex. Chemoproteomic profiling identified the lipid transfer protein STARD7 as the primary cellular target, and genetic depletion of STARD7 phenocopies the antiviral effects of M4. Additional studies localizes the M4 binding site to cysteine 302 within the lipid-binding domain of STARD7, supporting a model in which STARD7-dependent lipid transfer activity promotes efficient vRNP trafficking and nuclear export. Combining M4 with baloxavir enhances antiviral efficacy in a murine infection model, providing in vivo support for a host-directed strategy. Together, these results identify STARD7 as a metabolic checkpoint licensing vRNP export and establish a proof of concept for therapeutic intervention with small molecule inhibitors. Author SummaryInfluenza viruses continue to cause widespread illness and pose an ongoing pandemic threat, in part because existing antiviral drugs can lose effectiveness as the virus evolves resistance. To address this challenge, we focused on identifying therapies that target host cell processes required for viral replication, rather than viral proteins themselves. In this study, we describe a small molecule, M4, that inhibits replication of both influenza A and B viruses by blocking a critical step in the viral life cycle. We found that M4 acts on a host protein called STARD7, which helps move certain lipids to the right places inside cells. When STARD7 is inhibited, the influenza viral ribonucleoprotein (vRNP) complex becomes trapped in the cell nucleus and cannot reach the cytoplasm, preventing the virus from completing its replication cycle. Disrupting STARD7 genetically produces the same effect, confirming that this host protein is important for influenza replication. These findings point to a previously unrecognized host gating mechanism that controls nuclear export of the vRNP complex during infection. Although M4 alone showed limited activity in animals, combining it with an existing antiviral drug strongly improved antiviral efficacy. Together, our results reveal a new host pathway that influenza viruses rely on and support host-directed combination approaches to strengthen antiviral treatment and help counter drug resistance.

microbiology↗

bioRxiv2026

Neural Decoding of Musical Engagement Reveals Tension-Release Dynamics

Music listening is one of the most compelling and rewarding activities humans engage in spontaneously. But what exactly catches people"s attention when listening to music remains unclear. Musicologists have argued that tension-release dynamics in music constitute crucial features of the listening experience. They arise from the intertwining of different low-level and high-level features and sit at the core of music enjoyment by engaging listeners dynamically. This study aims to characterize the relationship between tension dynamics and engagement during naturalistic music listening. Using canonical correlation analysis, we decoded the music envelope from EEG and ECoG responses and found that musical tension patterns, as reported by listeners, were predictive of fluctuations in the coupling between the music and the neural response. Importantly, tension dynamics were significantly correlated with neural measures of envelope tracking even after controlling for loudness and musical expectations, confirming the specific and crucial role of musical tension in engaging listeners. These results shed new light on how musical structure gives rise to internal response modulations that, in turn, dynamically reflect musical engagement. This interplay may underlie the pervasive and emotionally rewarding nature of music. Significance StatementThis work addresses a fundamental question in cognition: how engagement dynamically modulates complex auditory input processing. We investigate this issue within the well-controlled yet ecologically valid context of music perception. At the core of the musical experience lies the perception of tension - the shifting sense of expectancy, instability, and resolution that guides listening across time. We show that fluctuations in musical tension, shaped by musical structure, reliably predict modulations of auditory engagement, thereby illuminating an understudied phenomenon: what makes music engaging.

neuroscience↗

bioRxiv2025

Structural determination of the HIV-1 Variable Region 3 epitope of antibody 19b

The HIV-1 Envelope (Env) in its pre-receptor "closed" conformation is targeted by broadly neutralizing antibodies (bnAbs), while its receptor-bound "open" conformation, exposes immunodominant epitopes targeted by non-neutralizing antibodies. A human immunoglobin G (IgG) monoclonal antibody (mAb), 19b, binds an Env third variable (V3) loop epitope that is only exposed in the open Env conformation. Despite widespread use of 19b to detect the open Env conformation in immunoassays, its epitope has not yet been structurally defined. Here we determine crystal structures of ligand-free and V3 peptide-bound 19b Fab to visualize details of this interaction. 19b utilizes both its heavy and light chains to interact with the V3 loop. The 5-residue heavy chain complementarity-determining region (CDR H3) facilitates a hydrophobic pocket for V3 residues to associate with. 19b adopts a cradle binding mode with its CDRH1, CDRL2 and CDRL3 mediating interactions with V3 regions that flank the conserved GPGR/Q motif, without making substantial contacts with the GPGR arch region. Our high-resolution structures by elucidating the epitope, binding mode and the structural basis for the broad reactivity of 19b, fill a gap in our knowledge of a reagent that is widely used in immunoassays.

biochemistry↗

bioRxiv2025

Speaker Identity is Robustly Encoded in Spatial Patterns of Intracranial EEG for Attention Decoding

The human auditory cortex robustly tracks attended speech, yet it remains unclear if speaker identity is encoded in spatial patterns of neural activity independent of temporal dynamics. Here, we demonstrate that the identity of an attended speaker is reliably reflected in distinct, time-invariant spatial activation maps in human intracranial EEG (iEEG). Leveraging these "neural fingerprints", we developed a novel framework for Auditory Attention Decoding (AAD) that shifts from traditional temporal envelope tracking to spatial speaker identification. By decoupling the decoding of "who" is speaking from "when" they are speaking, our modular system achieves state-of-the-art speech extraction, particularly in short time windows (<2 seconds) where temporal models typically fail. Furthermore, we observed a reciprocal shift in neural activity during attentional switches, confirming that these spatial codes dynamically track listener intent. These findings establish that speaker identity is a robust, spatially distributed feature in the auditory cortex, offering a high-speed, complementary mechanism for neuro-steered hearing technologies.

neuroscience↗

bioRxiv2025

CRISPR-based neuromorphic computing for solving regression and classification

The CRISPR-dCas9 system has emerged as a versatile platform for programmable gene regulation, offering unique advantages in modularity and orthogonality for constructing synthetic genetic circuits. Here, we present a novel architecture for biomolecular neural networks based on dCas9, guide RNAs, and antisense RNA sequestration. Through mathematical modeling and steady-state analysis, we demonstrate that this system functions as a molecular perceptron with a threshold activation function analogous to a saturated rectified linear unit (ReLU). However, a critical challenge in scaling these circuits is competition for the finite dCas9 pool, whose expression must remain low to avoid cytotoxicity. We address this constraint by developing a resource-aware design framework and characterizing how shared dCas9 availability affects network performance. Our results show that for classification tasks, decision boundaries remain invariant under resource competition, while for regression tasks, node thresholds are preserved despite sensitivity in output magnitude under heterogeneous binding conditions. We demonstrate the computational capabilities of this platform through both linear and nonlinear classification problems, as well the approximation of a band-pass function as a proof-of-concept regression task. This work expands the repertoire of molecular mechanisms capable of computation and establishes design principles for implementing CRISPR-based neuromorphic circuits that can execute complex computational tasks within the biochemical constraints of living cells.

systems biology↗

bioRxiv2025

A dopamine circuit regulates locomotor initiation and persistence in Drosophila

Decisions to initiate or terminate locomotion reflect the commitment of an animal to expend energy and thus must be appropriately regulated. Dopaminergic system has been implicated in locomotor regulation but how it controls these decisions remains unclear. Here we show that a dopamine circuit in Drosophila mushroom body regulates cue-induced locomotor initiation and termination by integrating locomotor history and current motivation. This circuit consists of the locomotor-initiator mushroom body output neurons, MBON09, and the locomotor-terminator MBON21. Previous locomotor initiation by default suppresses the propensity of future initiation through depression of MBON09 activity by locomotion-sensitive dopamine afferents, preventing redundant action. Locomotor persistence is promoted through combined inhibition of MBON21 by MBON09 and by distinct dopamine afferents that receive fluctuating motivational signals. Persistent locomotion under high motivational state, in turn, causes dopamine-dependent MBON09 facilitation, reinvigorating locomotor initiation program. Our results revealed a dopaminergic mechanism to transform recent behavior and current motivation into a moment-by-moment internal state that in turn regulates locomotor decisions.

neuroscience↗

bioRxiv2025

Structure basis for distinct protective mechanisms of IGHV3-23 antibodies targeting influenza hemagglutinin stem

Characterization of antibodies targeting the conserved stem domain of influenza hemagglutinin (HA) is critical for developing broadly protective countermeasures against influenza virus. From a phage display human antibody library, this study discovers three group 1 HA-specific stem antibodies, namely HB31, HB34, and HB315, all of which are encoded by IGHV3-23. While HB31 and HB34 have minimal neutralization activity in vitro, their Fc-mediated effector functions lead to better in vivo protection than the potently neutralizing HB315. Consistently, cryo-EM analysis suggests that HB31 and HB34 have a higher Fc accessibility than HB315, based on their epitopes and approaching angles. HB31 and HB34 engage a pocket in the upper HA stem that is rarely targeted by known HA stem antibodies, whereas the epitope of HB315 involves the lower stem. Overall, our findings provide insights not only into the structure-function relationship of HA stem antibodies, but also into the design of next-generation influenza therapeutics.

immunology↗

bioRxiv2025

MutAIverse: An AI-Powered, Mechanism-backed Platform for Discovering Novel DNA Adducts and their precursor Genotoxins

Genotoxin exposure leads to DNA adduct formation, potentially causing mutations if unrepaired. Current DNA adductomics platforms or analytical workflows are limited by incomplete spectral libraries, reliance on experimentally validated adducts, limited cellular contexts, and inefficient computational methodologies. We introduce MutAIverse, an advanced AI-driven DNA adductomics analysis platform that overcomes these limitations by leveraging intracellular mechanistic modeling of genotoxin bioactivation to construct a comprehensive DNA adduct library and offers advanced spectral mapping. MutAIverse integrates experimentally validated and chemically valid putative DNA adducts, enabling interpretable retrograde tracking to parental genotoxins. Validation of MutAIverse against experimental MS/MS datasets demonstrated the detection of both known and novel adducts. Furthermore, application to in-house generated DNA adductomics data from tissue biopsies of smokeless tobacco-induced head and neck cancer patients revealed selective enrichment of both novel and known DNA adducts; a subset of them was validated using MS/MS analysis. Collectively, MutAIverse provides a robust, end-to-end, and interpretable platform for advanced DNA adductomics analysis.

bioinformatics↗