Biology subjects

Iqbal, A.

Publications and source records attributed to Iqbal, A..

At least 19 recordsLinked to original sources

bioRxiv2025

Lowering the HTT1a transcript as an effective therapy for Huntingtons disease

Lowering the levels of HTT transcripts has been a major focus of therapeutic development for Huntingtons disease (HD), but which transcript should be lowered? HD is caused by a CAG repeat expansion in exon 1 of the HTT gene, and the rate of somatic expansion of this CAG repeat throughout life is now known to drive the age of onset and rate of disease progression. As the CAG repeat expands, the extent to which the HTT mRNA is alternatively processed to generate the HTT1a transcript and highly aggregation-prone and pathogenic HTT1a protein increases. Several HTT-lowering modalities have entered clinical trials that either target both HTT and HTT1a together, or full-length HTT alone. We have developed siRNAs that target the Htt1a mouse transcript (634/486) and used these, together with a potent Htt-targeting siRNA (10150) to compare the efficacy of lowering either full-length Htt or Htt1a. zQ175 and wild-type mice were treated with 10150 or 634/486 alongside control groups at 2 months of age with treatment to 6 or 10 months, or at 6 months with treatment to 10 months. The siRNA potency and durability were most effective in the hippocampus. Whilst both strategies showed benefits, despite the greater potency of 10150, targeting Htt1a was more effective at delaying HTT aggregation and transcriptional dysregulation than targeting full-length Htt. These data support HTT-lowering strategies that are designed to target the HTT1a transcript, either alone, or together with lowering full-length HTT. One Sentence SummaryLowering HTT1a transcript levels delays the onset of molecular and neuropathological phenotypes in a knock-in mouse model of Huntingtons disease.

neuroscience↗

bioRxiv2024

MortX: A Domain Generalization Benchmark for Mouse Cortex Segmentation and Registration

Mesoscale understanding of human brain development is crucial for understanding neurodevelopmental disorders. By applying AI techniques to analyze high-resolution, multi-modal brain imaging datasets across postnatal ages, researchers can study cortical development at the granular level. We introduce MortX, a benchmark dataset of the developing mouse cortex that captures multiple postnatal stages with annotations for distinct anatomical and functional subregions and layers. MortX features high-resolution imaging data including bright-field and fluorescence-labeled neuronal markers. We developed a standardized cortical atlas of genetic markers and manually registered it to brain section images for ground-truth labeling. The dataset serves as a benchmark for domain generalization in neuroimaging, enabling both classical and deep learning models to be trained on source brains and tested on unseen targets. Our results demonstrate generalized model performance and structural invariance across ages. We open-source MortX as a community resource for mouse brain segmentation and registration, emphasizing domain adaptation. This dataset addresses key challenges in mouse brain imaging and advances machine learning models that will help unravel neurodevelopmental disorders.

neuroscience↗

bioRxiv2024

Biologically Realistic Computational Primitives of Neocortex Implemented on Neuromorphic Hardware Improve Vision Transformer Performance

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers 2-3 of the primary visual cortex (V1). We investigate the role of four major cortical interneuron classes in a competitive-cooperative computational primitive and validate these circuit motifs implemented soft winner-take-all (sWTA) computation for gain modulation, signal restoration, and context-dependent multistability. Using a novel parameter mapping technique, we configured IBMs TrueNorth (TN) chip to implement sWTA computations, mirroring biological neural dynamics. Retrospectively, we observed a strong correspondence between the biophysical model and the TN hardware parameters, particularly in the roles of four key inhibitory neuron classes: Parvalbumin (feedforward inhibition), Somatostatin (feedback inhibition), VIP (disinhibition), and LAMP5 (gain normalization). Moreover, the sparse coupling of this sWTA motif was also able to simulate a two-state neural state machine on the TN chip, replicating working memory dynamics essential for cognitive tasks. Additionally, integrating the sWTA computation as a preprocessing layer in the Vision Transformer (ViT) enhanced its performance on the MNIST digit classification task, demonstrating improved generalization to previously unseen data and suggesting a mechanism akin to zero-shot learning. Our approach provides a framework for translating brain-inspired computations to neuromorphic hardware, with potential applications on platforms like Intels Loihi2 and IBMs Northpole. By integrating biophysically accurate models with neuromorphic hardware and advanced machine learning techniques, we offer a comprehensive roadmap for embedding neural computation into NeuroAI systems.

neuroscience↗

bioRxiv2024

Segment AnyNeuron

Image segmentation plays an integral part in neuroimage analysis and is crucial for understanding brain disorders. Deep Learning (DL) models have shown exponential success in computer vision tasks over the years, including image segmentation. However, to achieve optimal performance, DL models require extensive annotated data for training, which is often the bottleneck to expediting brain-wide image analysis. For segmenting cellular structures such as neurons, the annotation process is cumbersome and time-consuming due to the inherent structural, intensity, and background variations present in the data caused by genetic markers, imaging techniques, etc. We propose an Active Learning-based neuron segmentation framework (Segment AnyNeuron), which incorporates state-of-the-art image segmentation modules - Detectron2 and HQ SAM, and requires minimal ground truth annotation to achieve high precision for brain-wide segmentation of neurons. Our framework can classify and segment completely unseen neuronal data by selecting the most representative samples for manual annotation, thus avoiding the cold-start problem common in Active Learning. We demonstrate the effectiveness of our framework for automated brain-wide segmentation of neurons on a variety of open-source neuron imaging datasets, acquired from different scanners and a variety of transgenic mouse lines.

neuroscience↗

bioRxiv2024

NeuroAtlas: An Artificial Intelligence-based Framework for Annotation, Segmentation and Registration of Large Scale Biomedical Imaging Data

With increasing neuroimaging modalities and data diversity, mapping brain regions to a standard atlas template has become a challenging problem. Machine learning in general and deep learning, in particular, have been providing robust solutions for several neuroimaging tasks, including brain image registration and segmentation. However, these methods require a large amount of data for groundtruth labels, annotated by human experts, which is time-consuming. In this work, we introduce NeuroAtlas, an AI-based framework for atlas generation and brain region segmentation. We showcase an end-to-end solution for brain registration and segmentation by providing i) a deep learning modeling suite with a variety of high-performing model architectures to map a brain atlas onto the input brain section and ii) a Graphical User Interface (GUI)-based plugin for large-scale data annotation with a feature of modifying the predicted labels for active learning. We demonstrate a robust performance of our framework on the human brains, captured through various imaging modalities and age groups, and demonstrate its application for mouse brains as well. NeuroAtlas tool will be open-sourced and entirely compatible with both local as well as cloud-based computing so that users can easily adapt to their neuroimaging custom datasets.

neuroscience↗

bioRxiv2024

Multimodal 3D Image Registration for Mapping Brain Disorders

We introduce an AI-driven approach for robust 3D brain image registration, addressing challenges posed by diverse hardware scanners and imaging sites. Our model trained using an SSIM-driven loss function, prioritizes structural coherence over voxel-wise intensity matching, making it uniquely robust to inter-scanner and intra-modality variations. This innovative end-to-end framework combines global alignment and non-rigid registration modules, specifically designed to handle structural, intensity, and domain variances in 3D brain imaging data. Our approach outperforms the baseline model in handling these shifts, achieving results that align closely with clinical ground-truth measurements. We demonstrate its efficacy on 3D brain data from healthy individuals and dementia patients, with particular success in quantifying brain atrophy, a key biomarker for Alzheimers disease and other brain disorders. By effectively managing variability in multisite, multi-scanner neuroimaging studies, our approach enhances the precision of atrophy measurements for clinical trials and longitudinal studies. This advancement promises to improve diagnostic and prognostic capabilities for neurodegenerative disorders.

neuroscience↗

bioRxiv2024

CellSeg3D: self-supervised 3D cell segmentation for microscopy

Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package called CellSeg3D that provides access to these models in Jupyter Notebooks and in a napari GUI plugin. Recognizing the scarcity of high-quality 3D ground truth data, we created a fully human-annotated mesoSPIM dataset to advance evaluation and benchmarking in the field. To assess model performance, we benchmarked our approach across four diverse datasets: the newly developed mesoSPIM dataset, a 3D platynereis-ISH-Nuclei confocal dataset, a separate 3D Platynereis-Nuclei light-sheet dataset, and a challenging and densely packed Mouse-Skull-Nuclei confocal dataset. We demonstrate that our self-supervised model, WNet3D - trained without any ground truth labels - achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts.

cell biology↗

bioRxiv2024

Absolute quantitative and base-resolution sequencing reveals comprehensive landscape of pseudouridine across the human transcriptome

Pseudouridine ({Psi}) is one of the most abundant modifications in cellular RNA. However, its function remains elusive, mainly due to the lack of highly sensitive and accurate detection methods. To address this challenge, we introduced 2-bromoacrylamide-assisted cyclization sequencing (BACS) for quantitative profiling of {Psi} at single-base resolution. Based on novel bromoacrylamide cyclization chemistry, BACS enables a {Psi}-to-C transition. Compared to previous methods, BACS allowed the precise identification of {Psi} positions, especially in densely modified {Psi} regions and consecutive uridine sequences. BACS successfully detected all known {Psi} sites in human rRNA and spliceosomal snRNAs and generated the first quantitative {Psi} map of human snoRNA and tRNA. Furthermore, BACS simultaneously detected adenosine-to-inosine (A-to-I) editing sites and N1-methyladenosine (m1A). Depletion of three key pseudouridine synthases (PUS) enabled us to elucidate the targets and sequence motifs of TRUB1, PUS7, and PUS1 in HeLa cells. We further applied BACS to Epstein-Barr virus (EBV)-encoded small RNAs (EBERs) and identified a highly abundant {Psi}114 site in EBER2. Surprisingly, applying BACS to a panel of RNA viruses demonstrated the absence of {Psi} in their viral transcripts or genomes, shedding light on differences in pseudouridylation between virus families. We anticipate BACS to serve as a powerful tool to uncover the biological importance of {Psi} in future studies.

molecular biology↗

bioRxiv2023

Exploring the Therapeutic Potential of Cannabis Constituents in Parkinson's Disease: Insights from Molecular Docking Studies

Cannabis, often known as marihuana, marijuana, hashish, and hash, belongs to the genus Cannabis sativa L. This plant has excellent potential for the treatment of several brain disorders. Phytochemical compounds in this plant act as antioxidants, preserving synaptic plasticity and preventing neuronal degeneration. The neurodegenerative condition Parkinsons has emerged as one of the most significant health concerns of the twenty-first century. A detailed in silico molecular docking study was carried out to assess the neuroprotective effects of cannabis compounds against four potential targets of PD, including monoamine oxidase B (MAO-B), catechol-O-methyltransferase (COMT), alpha-synuclein (ASN), and Adenosine A2A receptor (A2A). Physicochemical properties, drug-likeness, toxicity, and ADMET profiles were also investigated. In this docking study, the cannabis compound cannabicyclol showed a superior docking score of -10.8 kcal/mol with the MAO-B protein. Based on these results, cannabicyclol and the target protein MAO-B were used to perform MD simulations to analyze their stability at 100 ns. Furthermore, it is crucial to carry out in vitro and in vivo investigations to enhance the potency of cannabis components and understand the processes underlying the suppression of Parkinsons disease-related enzymes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/566677v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@15f455borg.highwire.dtl.DTLVardef@1c26038org.highwire.dtl.DTLVardef@3b32c6org.highwire.dtl.DTLVardef@82926b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Domain-Invariant Brainstem Nuclei Segmentation and Signal Quantification

AO_SCPLOWBSTRACTC_SCPLOWBrainstem nuclei are hard to distinguish due to very few distinctive features which makes detecting them with high accuracy extremely difficult. We introduce StARQ that builds on SeBRe, a deep learning-based framework to segment regions of interest. StARQ provides new functionalities for automated segmentation of brainstem nuclei at high granularity, and quantification of underlying neural features such as axonal tracings, and synaptic punctae. StARQ will serve as a toolbox for generalized brainstem analysis, enabling reliable high-throughput computational analysis with open-source models.

neuroscience↗

bioRxiv2023

In Silico Analysis of Drug Off-Target Effects on Diverse Isoforms of Cervical Cancer for Enhanced Therapeutic Strategies

Cervical cancer is a severe medical issue as 500,000 new cases of cervical cancer are identified in the world every year. The selection and analysis of the suitable gene target are the most crucial in the early phases of drug design. The emphasis at one protein while ignoring its several isoforms or splice variations may have unexpected therapeutic or harmful side effects. In this work, we provide a computational analysis of interactions between cervical cancer drugs and their targets that are influenced by alternative splicing. By using open-accessible databases, we targeted 45 FDA-approved cervical cancer drugs targeting various genes having more than two distinct protein-coding isoforms. Binding pocket interactions revealed that many drugs do not have possible targets at the isoform level. In terms of size, shape, electrostatic characteristics, and structural analysis have shown that various isoforms of the same gene with distinct ligand-binding pocket configurations. Our results emphasized the risks of ignoring possibly significant interactions at the isoform level by concentrating just on the canonical isoform and promoting consideration of the impacts of cervical cancer drugs on- and off-target at the isoform level to further research.

bioinformatics↗

bioRxiv2022

Structural and Functional Characterization of Novel Phosphotyrosine Phosphatase Protein from Drosophila Melanogaster (Pupal Retina)

A novel pair of protein Tyrosine Phosphatases in Drosophila Melanogaster (pupal retina) has been identified. Phosphotyrosyl protein phosphatases (PTPs) are structurally diverse enzymes increasingly recognized having fundamental role in cellular processes including effects on metabolism, cell proliferation and differentiation. This study presents comparative homology modeling of low molecular weight phosphotyrosine protein phosphatase (PTPs) from Drosophila melanogaster (Dr-PTPs) and their complexation with potent inhibitor HEPES. The 3D structure was predicted based on sequence homology with bovine heart low molecular weight PTPs (Bh-PTPs). The sequence homology is approximately 50% identical to each other and to low molecular weight protein tyrosine phosphatases (PTPs) in other species. Comparison of the 3D structures of Bh-PTPs and Dr-PTPs (primo-2) reveals a remarkable similarity having a four stranded central parallel {beta} sheet with flanking helices on both sides, showing two right-handed {beta}--{beta} motifs. The inhibitor shows similar binding features as seen in other PTPs. The study also highlights the key catalytic residues important for target recognition and PTPs activation. The structure guided studies of both proteins clearly reveal a common mechanism of action, inhibitor binding at the active site and will expected to contribute towards the basic understanding of functional association of this enzyme with other molecules.

biochemistry↗

bioRxiv2021

A feedback loop between heterochromatin and the nucleopore complex controls germ-cell to oocyte transition during Drosophila oogenesis

Germ cells differentiate into oocytes that become totipotent upon fertilization. How the highly specialized oocyte acquires this distinct cell fate is poorly understood. During Drosophila oogenesis, H3K9me3 histone methyltransferase SETDB1 translocates from the cytoplasm to the nucleus of germ cells concurrent with oocyte specification. Here, we discovered that nuclear SETDB1 is required to silence a cohort of differentiation-promoting genes by mediating their heterochromatinization. Intriguingly, SETDB1 is also required for the upregulation of 18 of the ~30 nucleoporins (Nups) that comprise the nucleopore complex (NPC). NPCs in turn anchor SETDB1-dependent heterochromatin at the nuclear periphery to maintain H3K9me3 and gene silencing in the egg chambers. Aberrant gene expression due to loss of SETDB1 or Nups results in loss of oocyte identity, cell death and sterility. Thus, a feedback loop between heterochromatin and NPCs promotes transcriptional reprogramming at the onset of oocyte specification that is critical to establish oocyte identity.

developmental biology↗

bioRxiv2021

Relationing of Labellum and Glossae Biomorphological characteristics of honeybee workers as breeding tool on honey collection potential of Apis mellifera L. honeybee colonies

This study focused on the correlation of honey collection Potential and the length and width of labellum and glossae in worker honey bees (Apis mellifera Ligustica). Sixty honeybee A. mellifera L. colonies were selected, among these 60 colonies, 3 worker bees were sampled from each colony total numbers of samples collected were 180 adult worker foraging bees. Fifteen colonies for each group were used to check the correlation of honey production with length of labellum, width of labellum, length of glossae and the width of glossae respectively. These worker bees were bought to the laboratory frozen, boiled, dissected and mounted on the slides. Measurements of the labellum length, labellum width, glossae length and glossae width were taken by the stereomicroscope with ocular micrometer at 0.8X magnification. Correlation values for the honey collectionand length and width of labellum and glossae were high and positive. These Results support the perception that worker bees with larger labellum and glossae have more ability for honey collection Potential. It is concluded that Biomorphological characters of labellum and glossae are significantly correlated with the honeycollection Potential in A. mellifera L.

zoology↗

bioRxiv2021

Itaconate and derivatives reduce interferon responses and inflammation in influenza A infection

Itaconate has recently emerged as a metabolite with immunomodulatory properties. We evaluated effects of endogenous itaconate and exogenous itaconate, dimethyl-, and 4-octyl-itaconate on host responses to influenza A virus infection. Infection induced ACOD1 (the enzyme catalyzing itaconate synthesis) mRNA in monocytes and macrophages, which correlated with viral replication and was abrogated by itaconate treatment. Pulmonary inflammation and weight loss were greater in Acod1-/- than wild-type mice, and ectopic synthesis of itaconate in human epithelial cells reduced infection-induced inflammation. The compounds induced different recruitment programs in infected human macrophages, and transcriptome profiling revealed that they reversed infection-triggered interferon responses and modulated inflammation in cell lines, PBMC, and lung tissue. Single-cell RNA sequencing of PBMC revealed that infection induced ACOD1 exclusively in monocytes, whereas treatment silenced IFN-responses in monocytes, lymphocytes, and NK cells. Viral replication did not increase under treatment despite the dramatically repressed IFN responses, but 4-octyl itaconate inhibited viral transcription in PBMC. The results reveal dramatic reprogramming of host responses by itaconate and derivatives and their potential as adjunct treatments for hyperinflammation in viral infection.

immunology↗

bioRxiv2020

Chemical signatures of human odour generate a unique neural code in the brain of Aedes aegypti mosquitoes

A globally invasive form of the mosquito Aedes aegypti specializes in biting humans, making it an efficient vector of dengue, yellow fever, Zika, and chikungunya viruses. Host-seeking females strongly prefer human odour over the odour of non-human animals, but exactly how they distinguish the two is not known. Vertebrate odours are complex blends of volatile chemicals with many shared components, making discrimination an interesting sensory coding challenge. Here we show that human and animal odour blends evoke activity in unique combinations of olfactory glomeruli within the Aedes aegypti antennal lobe. Human blends consistently activate a universal glomerulus, which is equally responsive to diverse animal and nectar-related blends, and a more selective human-sensitive glomerulus. This dual signal robustly distinguishes humans from animals across concentrations, individual humans, and diverse animal species. Remarkably, the human-sensitive glomerulus is narrowly tuned to the long-chain aldehydes decanal and undecanal, which we show are consistently enriched in (though not specific to) human odour and which likely originate from unique human skin lipids. We propose a model of host-odour coding wherein normalization of activity in the human-sensitive glomerulus by that in the broadly-tuned universal glomerulus generates a robust discriminatory signal of the relative concentration of long-chain aldehydes in a host odour blend. Our work demonstrates how animal brains may distil complex odour stimuli of innate biological relevance into simple neural codes and reveals novel targets for the design of next-generation mosquito-control strategies.

neuroscience↗

bioRxiv2020

Developmental Divergence of Sensory Stimulus Representation in Cortical Interneurons

Two inhibitory cell types involved in modulating barrel cortex activity and perception during active whisking in adult mice, are the VIP+ and SST+ interneurons. Here we identify a developmental transition point of structural and functional rearrangements onto these interneuron types around the start of active sensation at P14. Using in vivo two-photon Ca2+ imaging, we find that before P14, both interneuron types respond stronger to a multi-whisker stimulus, whereas after P14 their responses diverge, with VIP+ cells losing their multi-whisker preference and SST+ neurons enhancing theirs. Rabies virus tracings followed by tissue clearing, as well as photostimulation-coupled electrophysiology reveal that SST+ cells receive higher cross-barrel inputs compared to VIP+ at both time points. In addition, we also uncover that whereas prior to P14 both cell types receive direct input from the sensory thalamus, after P14 VIP+ cells show reduced inputs and SST+ cells largely shift to motor-related thalamic nuclei.

neuroscience↗

bioRxiv2019

Alpha synuclein aggresomes inhibit ciliogenesis and multiple functions of the centrosome

Protein aggregates are the pathogenic hallmarks of many different neurodegenerative diseases and include the Lewy bodies found in Parkinsons disease. Aggresomes are closely-related cellular accumulations of misfolded proteins. They develop in a juxtanuclear position, adjacent to the centrosome, the microtubule organizing centre of the cell, and share some protein components. Despite the long-standing observation that aggresomes/Lewy bodies and the centrosome sit side-by-side in the cell, no studies have been done to see whether these protein accumulations impede the organelle function. We investigated whether the formation of aggresomes affected key centrosome functions: its ability to organize the microtubule network and to promote cilia formation. We find that when aggresomes are present, neuronal cells are unable to organise their microtubule network. New microtubules are not nucleated and extended, and the cells fail to respond to polarity cues. Since dopaminergic neurons are polarised, ensuring correct localisation of organelles and the effective intracellular transport of neurotransmitter vesicles, loss of centrosome activity could contribute to loss of dopaminergic function and neuronal cell death in Parkinsons disease. In addition, we provide evidence that many cell types, including dopaminergic neurons, cannot form cilia when aggresomes are present, which would affect their ability to receive extracellular signals.

neuroscience↗