Biology subjects

Husain, M.

Publications and source records attributed to Husain, M..

At least 19 recordsLinked to original sources

bioRxiv2026

A hippocampal neuroimaging signature of neurovascular insulin signalling links metabolism to mood

Depression is a leading cause of global disability and is increasingly linked to systemic metabolic dysfunctions, including insulin resistance. However, the biological pathways connecting metabolic state to affective symptoms are unresolved. A growing body of evidence indicates that insulin, beyond its role in systemic glucose homeostasis, supports brain metabolism, synaptic function and cognition, yet its contribution to mood regulation remains unclear. Here, we identify a human hippocampal metabolic signature associated with glycaemic variation in individuals with depression. Using multimodal neuroimaging, we show that hippocampal concentrations of GABA and lactate correlate with HbA1c and with mood severity, while functional connectivity between the hippocampus and the default mode network tracks affective symptoms independently of adiposity. To causally probe these relationships, we generated a mouse model of hippocampus-specific insulin receptor depletion. Unexpectedly, reducing insulin signalling at the blood brain barrier enhanced neuronal metabolism and attenuated anxiety-like behaviour. Notably, hippocampal lactate and GABA levels similarly tracked anxiety-related behaviour in mice, mirroring their association with symptom severity in humans. Together, these findings identify a conserved neurometabolic signature linking hippocampal insulin signalling to affective state, and reveal that brain insulin resistance exerts context- and cell-type-specific effects on behaviour. This work establishes a mechanistic basis for brain insulin resistance in depression and highlights the hippocampus as a critical hub for metabolic modulation of mood.

neuroscience↗

bioRxiv2026

Resident cardiac macrophages are not required for normal atrioventricular node conduction

Resident cardiac macrophages are understood to facilitate atrioventricular (AV) node conduction because they purportedly couple to AV node myocytes via connexin43 (Cx43) containing gap junctions. We tested this mechanism using biophysical modelling, high-resolution imaging of mouse and human AV conduction tissue, and pharmacological macrophage depletion. In silico, coupling macrophage membrane phenotypes to HCN4+ AV node myocytes imposed an electrotonic load that suppressed pacemaking and promoted conduction slowing, including stable 2:1 block in strand simulations. Anatomically, HCN4-defined components of the mouse AV conduction axis were essentially devoid of Cx43 and overlap of CD68+ macrophages and Cx43 was negligible in both mouse AV node and human penetrating bundle. Finally, near-complete macrophage depletion with CSF1R inhibition (PLX5622) did not alter AV electrical activity in vivo or ex vivo. Together, these data argue against a physiologically relevant role for Cx43-mediated macrophage-myocyte electrical coupling in normal AV node function. HIGHLIGHTSO_LIModelling predicts that AV node automaticity and conduction would be suppressed if macrophages coupled to AV node myocytes C_LIO_LIThe mouse AV conduction axis is essentially devoid of Cx43, currently considered responsible for macrophage-AV node myocyte coupling C_LIO_LIOverlap of macrophages and Cx43 expression is not discernible in the Cx43-expressing human distal AV node C_LIO_LIMacrophage depletion by CSF1R inhibition does not impact AV electrical activity in vivo or ex vivo C_LI

physiology↗

bioRxiv2026

Reprogramming BCMA-Targeted CAR-T Cells through γ-Secretase Modulation Blocks Antigen Shedding and Extends CAR-T Longevity

B-cell maturation antigen (BCMA) shedding by {gamma}-secretase generates soluble BCMA (sBCMA), which diminishes membrane antigen density, and limits the durability of BCMA-directed immunotherapies in multiple myeloma (MM). Here, using AI-driven diffusion modeling, we identify peptide inhibitors that selectively block {gamma}-secretase-mediated BCMA cleavage, stabilizing membrane-bound BCMA (mBCMA) without compromising cellular viability. The lead peptide, P5, suppresses sBCMA and restores mBCMA in vitro and in xenograft models. To create sustained, cell-intrinsic inhibition, we engineered Shedding Deterrent Locked-in (SHEDLOCK) CAR-T cells that locally secrete {gamma}-secretase-modulatory peptides. SHEDLOCK-1 CAR-T cells secreting P5 exhibits enhanced cytotoxicity and persistence relative to conventional CAR-T cells. Building on this, SHEDLOCK-2 CAR-T cells were generated, which secrete a modified, naturally derived peptide (nxP) that directly engages the {gamma}-secretase catalytic site, concurrently preventing BCMA shedding, and surprisingly enhancing CAR-T cell longevity by preserving telomere integrity and metabolic fitness. In MM xenograft and patient-derived models, SHEDLOCK-2 CAR-T cells demonstrate durable antitumor activity with a favorable safety profile. Together, these findings establish SHEDLOCK as a next-generation CAR-T platform and provide a strong preclinical foundation for Phase I clinical evaluation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/700575v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@3cd38aorg.highwire.dtl.DTLVardef@164167borg.highwire.dtl.DTLVardef@1ffed21org.highwire.dtl.DTLVardef@1294d06_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Dynamic updating of spatial working memory across eye movements: a computational investigation of transsaccadic integration

The brain continuously integrates rapidly changing visual input across eye movements to maintain stable perception, yet the precise mechanisms underpinning dynamic working memory and how these break down in brain diseases remain unclear. We developed a novel eye-tracking paradigm and computational models to investigate how spatial and colour information are updated across saccades. Our findings reveal that saccades selectively impair spatial but not colour memory. Computational modelling identified that spatial representations are maintained in a dual eye-centred frame of reference which is actively updated by a noisy memory of saccades but is vulnerable to interference. Using this model, we found that specific mechanistic failures in initial encoding and memory decay, rather than the saccadic updating process itself, account for spatial working memory deficits in Alzheimers and Parkinsons disease. These results provide a mechanistic understanding of how dynamic spatial memory operates in health and its disruption in neurodegenerative disorders.

neuroscience↗

bioRxiv2025

Human whole epigenome modelling for clinical applications with Pleiades

Gene regulation in humans extends beyond the four-letter genetic code. DNA methylation, in particular, functions as a critical epigenetic regulator, dynamically programming cellular identity, adapting gene expression in response to environmental cues, and underpinning the onset and progression of numerous diseases. Here we present Pleiades, a series of whole-genome epigenetic foundation models spanning three sizes: 90M, 600M, and 7B parameters. Pleiades is trained upon an extensive proprietary corpus of human methylation and genomic data, totalling 1.9T tokens. We introduce alignment embeddings and stacked hierarchical attention techniques to provide precise epigenetic modelling without the need for extended context lengths. Collectively, these advances enable Pleiades to perform a diverse range of down-stream biological and clinical tasks, including genomic regulatory prediction, realistic generation of cell-free DNA fragments and fragment-level cell-type-of-origin classification, within a unified and scalable computational framework. We specifically apply Pleiades to the early detection of clinical Alzheimers disease and Parkinsons disease from plasma cell-free DNA, achieving high-accuracy detection (AUROC 0.89 for AD and 0.84 for PD) using a minimally invasive blood test. Combined with plasma pTau-217, Pleiades reaches an AUROC of 0.97 for AD, underscoring the promise of multimodal epigenomic and proteomic approaches. Using mechanistic interpretability, we ground Pleiades latent features in interpretable biological signals, relating its Alzheimers predictions to fragmentomic and epigenomic properties of cfDNA. These findings demonstrate the potential of genome-wide epigenomic modelling as a clinically translatable paradigm for diagnostics and precision medicine, though prospective validation in larger, more diverse cohorts will be required to confirm clinical utility.

genomics↗

bioRxiv2024

A computational approach to understanding effort-based decision-making in depression

BackgroundMotivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. MethodsEffort-based decision-making was assessed using the Apple Gathering Task, where participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. ResultsFour fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity. ConclusionsThis work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

neuroscience↗

bioRxiv2024

Differential effects of aging and epilepsy in discriminating and reactivating memories

How are memories stored and subsequently retrieved? Pattern separation and pattern completion are distinct neurocognitive hippocampal processes that are thought to facilitate encoding and retrieval of memories. However, the way in which these operations affect the quality of memory remains unclear and there is currently no single behavioural task in humans to measure both mechanisms simultaneously. Here, we describe a novel paradigm which provides a distinct measure of each process and apply it to people with focal epilepsy, a condition associated with hippocampal dysfunction, comparing them with healthy young and older participants. Pattern separation and pattern completion were observed in healthy younger individuals. A specific deficit in pattern completion was identified in people with focal epilepsy, while the healthy older cohort showed degraded ability to perform pattern separation. To understand the underlying mechanisms, we simulated human performance using an auto-associative neural network. Modelling indicated that disruption in different populations of neurons could explain the observed memory profiles. These findings show that pattern separation and pattern completion are distinct processes that can be measured from a single behavioural task and are differentially affected by focal epilepsy and aging.

neuroscience↗

bioRxiv2024

Working memory as a representational template for reinforcement learning

Working memory (WM) and reinforcement learning (RL) both influence decision-making, but how they interact to affect behaviour remains unclear. We assessed whether RL is influenced by the format of visual stimuli in WM, either feature-based or unified, object-based representations. In a pre-registered paradigm, participants learned stimulus-action combinations, mapping four stimuli onto two feature dimensions to one of two actions through probabilistic feedback. In parallel, participants retained the RL stimulus in WM and were asked to recall this stimulus after each trial. Crucially, the format of representation probed in WM was manipulated, with blocks encouraging either separate features or bound objects to be remembered. Incentivising a feature-based WM representation facilitated feature-based learning, shown by an improved choice strategy. This reveals a role of WM in providing sustained internal representations that are harnessed by RL, providing a framework by which these two cognitive processes cooperate.

neuroscience↗

bioRxiv2023

Beta and theta oscillations track effort and previous reward in human basal ganglia and prefrontal cortex during decision making

Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we investigate this with chronic intracranial recordings from prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinsons disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12-20 Hz) oscillations tracking subjective effort on a single trial basis and PFC theta (4-7 Hz) signaling previous trial reward. Stimulation of PFC increased overall acceptance of offers in addition to increasing the impact of reward on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, as well as supporting a causal role of PFC for such choices.

neuroscience↗

bioRxiv2023

B Cells Promote T Cell Immunosenescence and Mammalian Aging Parameters

A dysregulated adaptive immune system is a key feature of aging, and is associated with age-related chronic diseases and mortality. Most notably, aging is linked to a loss in the diversity of the T cell repertoire and expansion of activated inflammatory age-related T cell subsets, though the main drivers of these processes are largely unknown. Here, we find that T cell aging is directly influenced by B cells. Using multiple models of B cell manipulation and single-cell omics, we find B cells to be a major cell type that is largely responsible for the age-related reduction of naive T cells, their associated differentiation towards pathogenic immunosenescent T cell subsets, and for the clonal restriction of their T cell receptor (TCR). Accordingly, we find that these pathogenic shifts can be therapeutically targeted via CD20 monoclonal antibody treatment. Mechanistically, we uncover a new role for insulin receptor signaling in influencing age-related B cell pathogenicity that in turn induces T cell dysfunction and a decline in healthspan parameters. These results establish B cells as a pivotal force contributing to age-associated adaptive immune dysfunction and healthspan outcomes, and suggest new modalities to manage aging and related multi-morbidity. One Sentence SummaryInsulin receptor signaling facilitates the induction of age associated B cell inflammatory changes, which drive phenotypic aging of the T cell compartment and adverse outcomes to mammalian healthspan parameters.

immunology↗

bioRxiv2023

The tumor microbiome reacts to hypoxia and can influence response to radiation treatment in colorectal cancer

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiations ability to kill cells. We investigated whether some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. We examined the RNA-seq data from the Oncology Research Information Exchange Network (ORIEN) database of colorectal cancer (CRC) patients treated with radiotherapy. For each tumor, we identified microbial RNAs and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To investigate this concept experimentally, we implanted CT26 CRC cells into both immune-competent BALB/c and immune-deficient athymic nude mice. After growth, where tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-trophic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Hypoxia appears to not only associate with different microbial populations but also elicit an adaptive transcriptional response in intratumoral microbes. SIGNIFICANCETumor hypoxia reduces radiations ability to kill cells. We explored whether some of the clinical effects of hypoxia could also be due to interaction with the tumor microbiome. Hypoxic expression scores associated with certain microbes and elicited an adaptive transcriptional response in others.

immunology↗

bioRxiv2023

The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICIs). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA-seq was conducted on the formalin-fixed paraffin-embedded (FFPE) tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival [≥]24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic}tool. The 71 patients with metastatic melanoma ranged in age from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy responsive versus non-responsive tumors. Responders showed significant enrichment of several microbes including Fusobacterium nucleatum, and non-responders showed enrichment of fungi, as well as several bacteria. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs. SignificanceWe analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy, and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

immunology↗

bioRxiv2023

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCEStudying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep-learning tool, microbial graph attention (MEGA), to refine the organisms that interact with tumors.

bioinformatics↗

bioRxiv2022

Serotonin Modulates an Inhibitory Input to the Central Amygdala from the Ventral Periaqueductal Gray

Fear is an adaptive state that drives defensive behavioral responses to specific and imminent threats. The central nucleus of the amygdala (CeA) is a critical site of adaptations that are required for the acquisition and expression of fear, in part due to alterations in the activity of inputs to the CeA. Here, we characterize a novel GABAergic input to the CeA from the ventral periaqueductal gray area (vPAG) using fiber photometry and ex vivo whole-cell slice electrophysiology combined with optogenetics and pharmacology. GABA transmission from this ascending vPAG-CeA input was enhanced by bath application of serotonin via activation of serotonin type 2C (5HT2C) receptors. Results indicate that these receptors are presynaptic. Interestingly, we found that GABA release from the vPAG-CeA input is enhanced following fear learning via activation of 5HT2C receptors and that this pathway is dynamically engaged during fear learning. Additionally, we characterized serotonin release in the CeA during fear learning and recall for the first time using fiber photometry coupled to a serotonin biosensor. Together, these findings describe a mechanism by which serotonin modulates GABA release from ascending vPAG GABA inputs to the CeA and characterize a role for this pathway in fear learning.

neuroscience↗

bioRxiv2021

Hypersensitivity to uncertainty is key feature of subjective cognitive impairment

With an increasingly ageing global population, more people are presenting with concerns about their cognitive function, but not all have an underlying neurodegenerative diagnosis. Subjective cognitive impairment (SCI) is a common condition describing self-reported deficits in cognition without objective evidence of cognitive impairment. Many individuals with SCI suffer from depression and anxiety, which have been hypothesised to account for their cognitive complaints. Despite this association between SCI and affective features, the cognitive and brain mechanisms underlying SCI are poorly understood. Here, we show that people with SCI are hypersensitive to uncertainty and that this might be a key mechanism accounting for their affective burden. Twenty-seven individuals with SCI performed an information sampling task, where they could actively gather information prior to decisions. Across different conditions, SCI participants sampled faster and obtained more information than matched controls to resolve uncertainty. Remarkably, despite their urgent sampling behaviour, SCI participants were able to maintain their efficiency. Hypersensitivity to uncertainty indexed by this sampling behaviour correlated with the severity of affective burden including depression and anxiety. Analysis of MRI resting functional connectivity revealed that both uncertainty hypersensitivity and affective burden were associated with stronger insular-hippocampal connectivity. These results suggest that altered uncertainty processing is a key mechanism underlying the psycho-cognitive manifestations in SCI and implicate a specific brain network target for future treatment.

neuroscience↗

bioRxiv2021

Distinct neural representations for prosocial and self-benefitting effort

Prosocial behaviours - actions that benefit others - are central to individual and societal well-being. Most prosocial acts are effortful. Yet, how the brain encodes effort costs when actions benefit others is unknown. Here, using a combination of multivariate representational similarity analysis and model-based univariate analysis during fMRI, we reveal how the costs of prosocial efforts are processed. Strikingly, we identified a unique neural signature of effort in the anterior cingulate gyrus for prosocial acts both when choosing to help others and when exerting force for their benefit. This pattern was absent for similar self-benefitting behaviour and correlated with individual levels of empathy. In contrast, the ventral tegmental area and the ventral insula signalled subjective value preferentially when choosing whether to exert effort to benefit oneself. These findings demonstrate partially distinct brain areas guide the evaluation and exertion of effort costs when acts are prosocial or self-benefitting.

neuroscience↗

bioRxiv2021

Nucleus accumbens D1-receptors regulate and focus transitions to reward-seeking action

While it is well established that dopamine transmission is integral in mediating the influence of reward expectations on reward-seeking actions, the precise causal role of dopamine transmission in moment-to-moment cue-driven behavioural control remains contentious. This is a particular issue in situations where it is necessary to refrain from responding to achieve a beneficial outcome. To examine this, we manipulated dopamine transmission pharmacologically as rats performed a Go/No-Go task that required them to either make or withhold action to gain either a small or large reward. Stimulation of D1Rs, both globally and locally in the nucleus accumbens core (NAcC) region consistently disrupted No-Go performance, potentiating inappropriate responses that clustered strongly just after cue presentation. D1R blockade did not, however, improve rats ability to withhold responses, but instead primarily disrupted performance on Go trials. While global D1R blockade caused a general reduction of invigoration of reward seeking actions, intra-NAcC administration of the D1R antagonist by contrast increased the likelihood that Go trial performance was in an "unfocused" state. Such a state was characterised, both on and off drug, by a reduction in the precision and speed of responding even though the appropriate action sequence was often executed. These findings suggests that the balance of activity at NAcC D1Rs plays a key role in enabling the rapid activation of a focused, reward-seeking state to enable animals to efficiently and accurately achieve their goal.

neuroscience↗

bioRxiv2021

Reduced cortico-muscular beta coupling in Parkinsons disease predicts motor impairment

Long-range communication through the motor system is thought to be facilitated by phase coupling between neural activity in the 15-30 Hz beta range. During periods of sustained muscle contraction (grip), such coupling is manifest between motor cortex and the contralateral forearm muscles - measured as the cortico-muscular coherence (CMC). We examined alterations in CMC in individuals with Parkinsons disease (PD), while equating grip strength between individuals with PD (off their medication) and healthy control participants. We show a marked reduction in beta CMC in the PD group, even though the grip strength was comparable between the two groups. Moreover, the reduced CMC was related to motor symptoms, so that individuals with lower CMC also displayed worse motor symptoms. These findings highlight the CMC as a simple, effective, and clinically relevant neural marker of PD pathology, with the potential to aid monitoring of disease progression and the efficacy of novel treatments for PD.

neuroscience↗