Biology subjects

Muhammad, T.

Publications and source records attributed to Muhammad, T..

17 recordsLinked to original sources

bioRxiv2025

MeCP2 NID interaction with RNA: Implications for Rett Syndrome-Relevant Protein Regulation

Mutations in the MECP2 gene cause the progressive neurodevelopmental disorder Rett syndrome. Pathogenic missense mutation hotspots exist in the proteins Methyl DNA binding Domain (MBD), and the NCoR Interaction Domain (NID), indicating these regions as critical for MeCP2 function. The NID binds to a co-repressor complex allowing transcriptional repression at target genes. A putative RNA Binding Domain (RBD) was identified that overlaps with the NID, yet the role that RNA interaction plays in MeCP2 function remains underexplored. Using cell-based and in vitro molecular assays, we validated RNA interaction at the NID/RBD of MeCP2 both to a dsRNA probe in vitro and to the lncRNA NEAT1_2 in cells. As expected, this region did not appear to affect MeCP2-chromatin interactions; however, we found that RNA-RBD interaction precludes MeCP2-NCoR binding in cells. Taken together, we find that RNA interaction at this non-canonical RNA binding domain regulates important MeCP2-protein interactions and therefore may be a key part of the pathophysiology of Rett syndrome.

molecular biology↗

bioRxiv2025

An Innovative Framework for Heart Sound Classification Integrating Adaptive Fuzzy Rank-Based Ensemble of Transfer Learning Models with Multi-Dimensional Features

Heart sound identification for diagnosing cardiovascular diseases is a complex challenge due to the intricate, spectro-temporal characteristics of conditions like Aortic Stenosis, Mitral Regurgitation, and multi-valvular disorders. Conventional methods frequently inadequately encompass the complete range of diagnostic characteristics, depending on fragmented or simplistic approaches that lack generalizability across varied patient demographics, clinical settings, and recording circumstances. To address these limitations, we offer an innovative, multi-dimensional feature fusion framework that integrates Wavelet Scattering Transform (WST) and Mel-Frequency Cepstral Coefficients (MFCC) to capture both temporal stability and perceptually optimal frequency patterns from cardiac sounds. This method is further refined by an adaptive fuzzy rank-based ensemble technique utilizing the Gompertz function, which dynamically modifies model weights according to confidence metrics, hence assuring more dependable and precise predictions amidst fluctuating clinical uncertainty. We meticulously assess our model utilizing eight advanced fine-tuned transfer learning architectures across four feature extraction techniques (WST, MFCC, STFT, Multi-Dimensional) on the clinically validated BUET Multi-disease Heart Sound (BMD-HS) dataset. This dataset comprises 864 phonocardiogram recordings from 108 participants with echocardiographically confirmed diagnoses. The multi-dimensional feature fusion method attains 97% accuracy with EfficientNetB2, whereas the adaptive fuzzy ensemble strategy achieves 98% accuracy, surpassing both individual models and conventional ensemble methods. Moreover, Explainable AI with Audio-LIME offers transparent, clinically interpretable insights, achieving fidelity scores surpassing 0.85 and clinical relevance ratings exceeding 90%, facilitating the identification of critical time-frequency regions that hold diagnostic significance. This system establishes a novel benchmark for heart sound classification, enhances the differentiation of intricate valvular disorders, and provides dependable, evidence-based decision support for cardiovascular diagnosis in clinical settings. Our research illustrates the capability for resilient, scalable, and interpretable heart sound classification systems that can improve clinical decision-making and promote the integration of automated diagnostic tools in healthcare.

bioengineering↗

bioRxiv2024

Identification of Antiviral Drug Candidates Against Monkeypox DNA Polymerase and Profilin-like Protein A42R Utilizing an In-Silico Approach

Monkeypox virus (MPXV) is emerging as a major concern in the field of infectious diseases. Current treatments are limited, highlighting the need for new therapeutic options. The use of computational methods, such as molecular docking and molecular dynamic (MD) simulations, is a valuable approach in identifying potential compounds that can target specific proteins of the virus, like the DNA polymerase and profilin-like protein A42R in this case, with the aim of controlling the disease. Our study focused on screening various libraries of compounds for predicted binding to MPXV DPol and A42R proteins, with the top-performing molecules identified based on their docking scores. Among these, Dorsilurin K and Mangostin in complex with DPol, whereas [2-oxo-2-[3-(3,4,5,6-tetrahydro-2H-azepin-7-ylsulfamoyl)anilino]ethyl] 3,5-dimethylbenzoate and N-[4-[2-[4-(4-methylphenyl)sulfonylpiperazin-1-yl]-2-oxoethoxy]phenyl]furan-2-carboxamide in complex with A42R stand out with notably high docking scores, suggesting they may have a good affinity for binding to the DPol and A42R proteins of MPXV respectively. MD simulations confirmed the stability of these ligand-protein complexes followed by evaluation of the ADMET and oral bioavailability analysis. However, it is important that computational methods can suggest promising candidates, in vitro and eventually in vivo studies are essential to validate these therapeutic candidates. Further studies on these compounds will provide insights into their efficacy, safety, and potential side effects. In conclusion, this study offers promising avenues for developing potential treatments for Monkeypox. If the identified compounds prove effective in further studies, it could be a significant breakthrough in managing this zoonotic disease.

pharmacology and toxicology↗

bioRxiv2024

PTCHD1 interacts with the SNARE-associated protein SNAPIN in vitro via its first exoplasmic loop

BackgroundPatched domain-containing 1 (PTCHD1) is a susceptibility gene for autism spectrum disorder and intellectual disability. Its function in brain development and neurotransmission remains elusive. Studies have sought to characterize PTCHD1 function by elucidating its neural network of interacting proteins. However, given the current paucity of functional information, many PTCHD1 missense variants in clinical databases are classified as variants of uncertain significance (VUSs), severely limiting the healthcare resources available to patients and families. MethodsA yeast two-hybrid assay was used to identify synaptic PTCHD1-interacting proteins. Candidate binding partners were validated by cloning; transient over-expression in HEK293T cells, followed by co-immunoprecipitation and immunoblotting; and immunocytochemistry in differentiated P19 cells. To evaluate the pathogenicity of clinical missense variants, site-directed mutagenesis was employed, followed by transient over-expression and immunocytochemistry in non-neuronal (HEK293T) and neuronal (Neuro-2A cells) systems. ResultsA novel interaction was identified between the first lumenal loop of PTCHD1 and the SNARE-associated protein SNAPIN, which is implicated in synaptic vesicle exocytosis. Clinically associated missense variants within this region did not disrupt SNAPIN binding, indicating that the pathoetiology of these variants is unrelated to this interaction. However, six of the 12 missense variants tested exhibited pronounced retention within the endoplasmic reticulum, and impaired neuronal and non-neuronal trafficking to the plasma membrane. ConclusionsThese data yield insights regarding the role of PTCHD1 in neurodevelopment and neurotransmission, and suggest a neuropathological mechanism for missense variants. These findings provide a platform for diagnostic assay and VUS interpretation, allowing for clinical re-classification of these variants.

molecular biology↗

bioRxiv2024

Non-cell-autonomous regulation of germline proteostasis by insulin/IGF-1 signaling via the intestinal peptide transporter PEPT-1

Gametogenesis involves active protein synthesis and heavily relies on proteostasis. How animals regulate germline proteostasis at the organismal level is poorly understood. Taking C. elegans as a model, we show that germline proteostasis requires coupled activities of HSF-1-dependent protein folding and insulin/IGF-1 signaling controlled protein synthesis. Depletion of HSF-1 from germ cells impairs chaperone gene expression, causing protein degradation and aggregation and, consequently, declines in fecundity and gamete quality. Reduced insulin/IGF-1 signaling confers germ cells resilience to limited protein folding capacity and proteotoxic stress by lowering ribosome biogenesis and the rate of translation. Interestingly, insulin/IGF-1 signaling promotes the expression of the evolutionarily conserved intestinal peptide transporter PEPT-1 via its downstream transcription factor FOXO/DAF-16, therefore allowing dietary proteins to be incorporated into an amino acid pool that fuels protein synthesis in the germline. We propose that this non-cell-autonomous pathway plays a critical role in regulating proteostasis in gametogenesis. TeaserInsulin/IGF-1 signaling regulates proteostasis in gametogenesis via the control of dietary protein absorption.

developmental biology↗

bioRxiv2023

Towards a Foundation Model of the Mouse Visual Cortex

The complexity of neural circuits makes it challenging to decipher the brains algorithms of intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brains computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

neuroscience↗

bioRxiv2023

Bipartite invariance in mouse primary visual cortex

A primary goal of sensory systems is to extract robust and meaningful features that are invariant to variations in the sensory input. Characterizing these invariances at the neuronal level is crucial for understanding how the visual system supports generalization, but the high-dimensional nature of ecological stimuli poses major challenges. Consequently, our understanding of how the brain represents invariances has historically depended on a few examples, such as phase invariance to grating stimuli in V1 complex cells. Here, we leverage the inception loop paradigm --iterating between large-scale recordings, deep learning neuronal predictive models, and in silico experiments with in vivo verification--to characterize neuronal invariances in mouse V1. Using a neuronal predictive model, we synthesized Diverse Exciting Inputs (DEIs) that strongly drive target neurons while differing substantially in image space. These DEIs revealed a novel bipartite invariance: one portion of the receptive field encodes shift-invariant, high-frequency textures, while the other encodes a fixed, low-frequency spatial pattern. This subfield division aligned with object boundaries defined by spatial frequency differences in highly activating stimuli, suggesting bi-partite invariance contributes to segmentation. Our analysis of computational models and anatomical data from the MICrONS dataset revealed a hierarchical organization of excitatory neurons in mouse V1 Layers 2/3: We found that postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. These findings suggest a synaptic-level hierarchy that progressively increases neural invariance within the primary visual cortex. Intriguingly, similar high-low frequency bipartite patterns strongly activate certain units in artificial neural networks, suggesting that universal visual representations govern both biological and artificial systems, potentially aiding in the extraction of visual features from complex backgrounds.

neuroscience↗

bioRxiv2023

Pattern completion and disruption characterize contextual modulation in mouse visual cortex

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli--where the center and surround share the same orientation--are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in v ivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.

neuroscience↗

bioRxiv2022

It takes neurons to understand neurons: Digital twins of visual cortex synthesize neural metamers

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

neuroscience↗

bioRxiv2022

Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex

More than a dozen excitatory cell types have been identified in the mouse primary visual cortex (V1) based on transcriptomic, morphological and in vitro electrophysiological features. However, the functional landscape of excitatory neurons with respect to their responses to visual stimuli is currently unknown. Here, we combined large-scale two-photon imaging and deep learning neural predictive models to study the functional organization of mouse V1 using digital twins. Digital twins enable exhaustive in silico functional characterization providing a bar code summarizing the input-output function of each neuron. Clustering the bar codes revealed a continuum of function with around 30 modes. Each mode represented a group of neurons that exhibited a specific combination of stimulus selectivity and nonlinear response properties such as cross-orientation inhibition, size-contrast tuning and surround suppression. These non-linear properties were expressed independently spanning all possible combinations across the population. This combinatorial code provides the first large-scale, data-driven characterization of the functional organization of V1. This powerful approach based on digital twins is applicable to other brain areas and to complex non-linear systems beyond the brain.

neuroscience↗

bioRxiv2021

A flow-based latent state generative model of neural population responses to natural images

We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture the stimulus-conditioned variability including noise correlations. This allows us to train the model end-to-end without the need for sophisticated probabilistic approximations associated with many latent state models for stimulus-conditioned fluctuations. We train the model on the responses of thousands of neurons from multiple areas of the mouse visual cortex to natural images. We show that our model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability. Furthermore, it successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation, and other factors that vary systematically with brain area or retinotopic location. Overall, our model accurately accounts for two critical sources of neural variability while avoiding several complexities associated with many existing latent state models. It thus provides a useful tool for uncovering the interplay between different factors that contribute to variability in neural activity.

neuroscience↗

bioRxiv2021

Behavioral state tunes mouse vision to ethological features through pupil dilation

Sensory processing changes with behavioral context to increase computational flexibility. In the visual system, active behavioral states enhance sensory responses but typically leave the preferred stimuli of neurons unchanged. Here we find that behavioral state does modulate stimulus selectivity in mouse visual cortex in the context of colored natural scenes. Using population imaging, behavior, pharmacology, and deep neural networks, we identified a shift of color selectivity towards ultraviolet stimuli exclusively caused by pupil dilation, resulting in a dynamic switch from rod to cone photoreceptors, extending their role beyond night and day vision. This facilitated the detection of ethological stimuli, such as aerial predators against the twilight sky. In contrast to previous studies that have used pupil dilation as an indirect measure of brain state, our results suggest that the brain uses pupil dilation itself to differentially recruit rods and cones on short timescales to tune visual representations to behavioral demands.

neuroscience↗

bioRxiv2021

Solution NMR and racemic crystallography provide insights into a novel structural class of cyclic plant peptides

Head-to-tail cyclic and disulfide-rich peptides are natural products with applications in drug design. Among these are the PawS-Derived Peptides (PDPs) produced in seeds of the daisy plant family. PDP-23 is a unique member of this class in that it is twice the typical size and adopts two {beta}-hairpins separated by a hinge region. The {beta}-hairpins - both stabilised by a single disulfide bond - fold together into a V-shaped tertiary structure creating a hydrophobic core. In water two PDP-23 molecules merge their hydrophobic cores to form a square prism quaternary structure. Here, we synthesised PDP-23 and its enantiomer comprising all D-amino acids, which allowed us to confirm these solution NMR structural data by racemic crystallography. Furthermore, we discovered the related PDP-24. NMR analysis showed that PDP-24 does not form a dimeric structure and it has poor water solubility, but in less polar solvents adopts near identical secondary and tertiary structure to PDP-23. The natural role of these peptides in plants remains enigmatic, as we did not observe any antimicrobial or insecticidal activity. However, the plasticity of these larger PDPs and their ability to change structure under different conditions make them appealing peptide drug scaffolds.

biochemistry↗

bioRxiv2020

Generalization in data-driven models of primary visual cortex

AO_SCPLOWBSTRACTC_SCPLOWDeep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation. The goal of this paper is to test whether such a representation is indeed generally characteristic for visual cortex, i.e. gener-alizes between animals of a species, and what factors contribute to obtaining such a generalizing core. To push all non-linear computations into the core where the generalizing cortical features should be learned, we devise a novel readout that reduces the number of parameters per neuron in the readout by up to two orders of magnitude compared to the previous state-of-the-art. It does so by taking advantage of retinotopy and learns a Gaussian distribution over the neurons receptive field po-sition. With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network. We then investigate whether the convolutional core indeed captures general cortical features by using the core in transfer learning to a different animal. When transferring a core trained on thousands of neurons from various animals and scans we exceed the performance of training directly on that animal by 12%, and outperform a commonly used VGG16 core pre-trained on imagenet by 33%. In addition, transfer learning with our data-driven core is more data-efficient than direct training, achieving the same performance with only 40% of the data. Our model with its novel readout thus sets a new state-of-the-art for neural response prediction in mouse visual cortex from natural images, generalizes between animals, and captures better characteristic cortical features than current task-driven pre-training approaches such as VGG16.

neuroscience↗

bioRxiv2019

A global map of orientation tuning in mouse visual cortex

In primates and most carnivores, neurons in primary visual cortex are spatially organized by their functional properties. For example, neurons with similar orientation preferences are grouped together in iso-orientation domains that smoothly vary over the cortical sheet. In rodents, on the other hand, neurons with different orientation preferences are thought to be spatially intermingled, a feature which has been termed \"salt-and-pepper\" organization. The apparent absence of any systematic structure in orientation tuning has been considered a defining feature of the rodent visual system for more than a decade, with broad implications for brain development, visual processing, and comparative neurophysiology. Here, we revisited this question using new techniques for wide-field two-photon calcium imaging that enabled us to collect nearly complete population tuning preferences in layers 2-4 across a large fraction of the mouse visual hierarchy. Examining the orientation tuning of these hundreds of thousands of neurons, we found a global map spanning multiple visual cortical areas in which orientation bias was organized around a single pinwheel centered in V1. This pattern was consistent across animals and cortical depth. The existence of this global organization in rodents has implications for our understanding of visual processing and the principles governing the ontogeny and phylogeny of the visual cortex of mammals.

neuroscience↗

bioRxiv2018

Inception in visual cortex: in vivo-silico loops reveal most exciting images

Much of our knowledge about sensory processing in the brain is based on quasi-linear models and the stimuli that optimally drive them. However, sensory information processing is nonlinear, even in primary sensory areas, and optimizing sensory input is difficult due to the high-dimensional input space. We developed inception loops, a closed-loop experimental paradigm that combines in vivo recordings with in silico nonlinear response modeling to identify the Most Exciting Images (MEIs) for neurons in mouse V1. When presented back to the brain, MEIs indeed drove their target cells significantly better than the best stimuli identified by linear models. The MEIs exhibited complex spatial features that deviated from the textbook ideal of V1 as a bank of Gabor filters. Inception loops represent a widely applicable new approach to dissect the neural mechanisms of sensation.

neuroscience↗

bioRxiv2018

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail.\n\nHere we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

neuroscience↗