Biology subjects

Hossain, I.

Publications and source records attributed to Hossain, I..

10 recordsLinked to original sources

bioRxiv2026

Phosphorylation of CARM1 at Ser572 preserves muscle stem cell self-renewal by restricting premature myogenic commitment

Skeletal muscle regeneration depends on satellite cells, which must balance myogenic commitment with self-renewal to maintain regenerative capacity throughout life. The methyltransferase CARM1 is a key regulator of satellite cell fate, acting through methylation of PAX7 to promote Myf5 expression and myogenic commitment. The dystrophin glycoprotein complex regulates this pathway through p38{gamma}-mediated phosphorylation of CARM1 at Ser572, however the physiological role of this phosphorylation event in vivo remains unknown. Here, we generated a phosphorylation-deficient CARM1 knock-in mouse (CARM1 SA) to determine how CARM1 Ser572 phosphorylation regulates satellite cell fate during regenerative myogenesis. Satellite cell-derived myoblasts from CARM1 SA mice exhibited reduced PAX7 expression, impaired fusion, and abnormal myotube formation despite transcriptional signatures consistent with accelerated myogenic progression. Ex vivo single-myofiber assays revealed precocious acquisition of a committed progenitor identity accompanied by a reduction in the self-renewing satellite cell population. While muscle development and regeneration following a single injury were largely preserved, repeated regenerative challenge resulted in progressive depletion of the satellite cell pool in CARM1 SA mice. Transcriptomic analysis demonstrated minimal effects under homeostatic conditions but identified substantial alterations during regeneration, including reduced expression of extracellular matrix-associated genes, impaired TGF{beta}-SMAD signaling, decreased Tgfbr3 expression, and increased MYC activity, consistent with a loss of self-renewal programs and enhanced activation. Collectively, these findings demonstrate that phosphorylation of CARM1 at Ser572 is a critical mechanism to delay satellite cell commitment, thereby maintaining a balance between self-renewal and commitment to ensure long-term maintenance of the muscle stem cell compartment.

cell biology↗

bioRxiv2023

CyVerse: Cyberinfrastructure for Open Science

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 110,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in two countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

scientific communication and education↗

bioRxiv2023

Biologically informed NeuralODEs for genome-wide regulatory dynamics

Modeling dynamics of gene regulatory networks using ordinary differential equations (ODEs) allow a deeper understanding of disease progression and response to therapy, thus aiding in intervention optimization. Although there exist methods to infer regulatory ODEs, these are generally limited to small networks, rely on dimensional reduction, or impose non-biological parametric restrictions -- all impeding scalability and explainability. PHOENIX is a neural ODE framework incorporating prior domain knowledge as soft constraints to infer sparse, biologically interpretable dynamics. Extensive experiments - on simulated and real data - demonstrate PHOENIXs unique ability to learn key regulatory dynamics while scaling to the whole genome.

genomics↗

bioRxiv2022

ZMYM2 is essential for methylation of germline genes and active transposons in embryogenesis

ZMYM2 is a transcriptional repressor whose role in development is largely unexplored. We found that Zmym2-/- mice show embryonic lethality by E10.5. Molecular characterization of Zmym2-/- embryos revealed two distinct defects. First, they fail to undergo DNA methylation and silencing of germline gene promoters, resulting in widespread upregulation of germline genes. Second, they fail to methylate and silence the evolutionarily youngest and most active LINE element subclasses in mice. Zmym2-/- embryos show ubiquitous overexpression of LINE-1 protein as well as aberrant expression of transposon-gene fusion transcripts. Interaction and colocalization data indicate that ZMYM2 homes to germline genes via binding to the non-canonical polycomb complex PRC1.6 and to transposons via the TRIM28 complex. In the absence of ZMYM2, hypermethylation of histone 3 lysine 4 occurs at target sites, creating a chromatin landscape unfavourable for establishment of DNA methylation. ZMYM2-/- human embryonic stem cells also show aberrant upregulation and demethylation of young LINE elements, indicating a conserved role in repression of active transposons. ZMYM2 is thus an important new factor in DNA methylation patterning in early embryonic development.

molecular biology↗

bioRxiv2021

The DNMT1 inhibitor GSK-3484862 mediates global demethylation in murine embryonic stem cells

BackgroundDNA methylation plays an important role in regulating gene expression in mammals. The covalent DNMT1 inhibitors 5-azacytidine and decitabine are widely used in research to reduce DNA methylation levels, but they impart severe cytotoxicity which limits their demethylation capability and confounds interpretation of experiments. Recently, a non-covalent inhibitor of DNMT1 called GSK-3484862 was developed by GlaxoSmithKline. We sought to determine whether GSK-3484862 can induce demethylation more effectively than 5-azanucleosides. Murine embryonic stem cells (mESC) are an ideal cell type in which to conduct such experiments, as they have a high degree of DNA methylation but tolerate dramatic methylation loss. ResultsWe determined the cytotoxicity and optimal concentration of GSK-3484862 by treating wild-type (WT) or Dnmt1/3a/3b triple knockout (TKO) mESC with different concentrations of the compound, which was obtained from two commercial sources. Concentrations of 10 {micro}M or below were readily tolerated for 14 days of culture. Known DNA methylation targets such as germline genes and GLN-family transposons were upregulated within two days of the start of GSK-3484862 treatment. By contrast, 5-azacytidine and decitabine induced weaker upregulation of methylated genes and extensive cell death. Whole genome bisulfite sequencing (WGBS) showed that treatment with GSK-3484862 induced dramatic DNA methylation loss, with global CpG methylation levels falling from near 70% in WT mESC to less than 18% after 6 days of treatment with GSK-3484862, similar to the methylation level observed in Dnmt1 deficient mESCs. ConclusionsGSK-3484862 mediates striking demethylation in mESCs with minimal non-specific toxicity.

molecular biology↗

bioRxiv2021

Mutant huntingtin crosses the neuromuscular junction and causes transmission-selective pathological alterations in huntingtin expressing myotubes

A potential explanation for the spatiotemporal accumulation of pathological lesions in the brain of patients with neurodegenerative protein misfolding diseases (PMDs) is cell-to-cell transmission of aggregation-prone, misfolded proteins. Little is known about central to peripheral transmission and its contribution to pathology. We show that transmission of Huntingtons disease- (HD-) associated mutant HTT exon 1 (mHTTEx1) occurs across the neuromuscular junctions in human iPSC cultures and in vivo in wild-type mice. We found that transmission is an active and dynamic process, that happens prior to aggregate formation and is regulated by synaptic activity. Furthermore, we find that transmitted mHTTEx1 causes HD-relevant pathology at a molecular and functional level in human muscle cells, even in the presence of ubiquitous expression mHTTEx1. With this work we uncover a casual-link between mHTTEx1 synaptic transmission and pathology, highlighting the therapeutic potential in blocking toxic protein transmission in PMDs.

neuroscience↗

bioRxiv2019

A deep learning-based model of normal histology

Deep learning models have been applied on various tissues in order to recognize malignancies. However, these models focus on relatively narrow tissue context or well-defined pathologies. Here, instead of focusing on pathologies, we introduce models characterizing the diversity of normal tissues. We obtained 1,690 slides with rat tissue samples from the control groups of six preclinical toxicology studies, on which tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From these annotated regions, we sampled small patches of 224 x 224 pixels at six different levels of magnification. Using four studies as training set and two studies as test set, we trained VGG-16, ResNet-50, and Inception-v3 networks separately at each of these magnification levels. Among these models, Inception-v3 consistently outperformed the other networks and attained accuracies up to 83.4% (top-3 accuracy: 96.3%). Further analysis showed that most tissue confusions occurred within clusters of histologically similar tissues. Investigation of the embedding layer using the UMAP method revealed not only pronounced clusters corresponding to the individual tissues, but also subclusters corresponding to histologically meaningful structures that had neither been annotated nor trained for. This suggests that the histological representation learned by the normal histology network could also be used to flag abnormal tissue as outliers in the embedding space without a need to explicitly train for specific types of abnormalities. Finally, we found that models trained on rat tissues can be used on non-human primate and minipig tissues with minimal retraining. Author contributionT.S. and H.H. contributed equally to this work. Significance statementLike many other scientific disciplines, histopathology has been profoundly impacted by recent advances in machine learning with deep neural networks. In this field, most deep learning models reported in the literature are trained on pathologies in specific tissues/contexts. Here, we aim to establish a model of normal tissues as a foundation for future models of histopathology. We build models that are specific to histopathology images and we show that their embeddings are better feature vectors for describing the underlying images than those of off-the shelf CNN models. Therefore, our models could be useful for transfer learning to improve the accuracy of other histopathology models.

pathology↗

bioRxiv2019

Metadata-Guided Visual Representation Learning for Biomedical Images

MotivationThe clustering of biomedical images according to their phenotype is an important step in early drug discovery. Modern high-content-screening devices easily produce thousands of cell images, but the resulting data is usually unlabelled and it requires extra effort to construct a visual representation that supports the grouping according to the presented morphological characteristics.\n\nResultsWe introduce a novel approach to visual representation learning that is guided by metadata. In high-context-screening, meta-data can typically be derived from the experimental layout, which links each cell image of a particular assay to the tested chemical compound and corresponding compound concentration. In general, there exists a one-to-many relationship between phenotype and compound, since various molecules and different dosage can lead to one and the same alterations in biological cells.\n\nOur empirical results show that metadata-guided visual representation learning is an effective approach for clustering biomedical images. We have evaluated our proposed approach on both benchmark and real-world biological data. Furthermore, we have juxtaposed implicit and explicit learning techniques, where both loss function and batch construction differ. Our experiments demonstrate that metadata-guided visual representation learning is able to identify commonalities and distinguish differences in visual appearance that lead to meaningful clusters, even without image-level annotations.\n\nNotePlease refer to the supplementary material for implementation details on metadata-guided visual representation learning strategies.

bioinformatics↗

bioRxiv2019

Morphological deconvolution of beta-lactam polyspecificity in E. coli

Beta-lactam antibiotics comprise one of the earliest known classes of antibiotic therapies. These molsecules covalently inhibit enzymes from the family of penicillin-binding proteins, which are essential to the construction of the bacterial cell wall. As a result, beta-lactams have long been known to cause striking changes to cellular morphology. The exact nature of the changes tend to vary by the precise PBPs engaged in the cell since beta-lactams exhibit a range of PBP enzyme specificity. The traditional method for exploring beta-lactam polyspecificity is a gel-based binding assay which is low-throughput and typically run ex situ in cell extracts. Here, we describe a medium-throughput, image-based assay combined with machine learning methods to automatically profile the activity of beta-lactams in E. coli cells. By testing for morphological change across a panel of strains with perturbations to individual PBP enzymes, our approach automatically and quantifiably relates different beta-lactam antibiotics according to their preferences for individual PBPs in cells. We show the potential of our approach for guiding the design of novel inhibitors towards different PBP-binding profiles by recapitulating the activity of two recently-reported PBP inhibitors.

systems biology↗

bioRxiv2018

Unsupervised phenotypic analysis of cellular images with multi-scale convolutional neural networks

Large-scale cellular imaging and phenotyping is a widely adopted strategy for understanding biological systems and chemical perturbations. Quantitative analysis of cellular images for identifying phenotypic changes is a key challenge within this strategy, and has recently seen promising progress with approaches based on deep neural networks. However, studies so far require either pre-segmented images as input or manual phenotype annotations for training, or both. To address these limitations, we have developed an unsupervised approach that exploits the inherent groupings within cellular imaging datasets to define surrogate classes that are used to train a multi-scale convolutional neural network. The trained network takes as input full-resolution microscopy images, and, without the need for segmentation, yields as output feature vectors that support phenotypic profiling. Benchmarked on two diverse benchmark datasets, the proposed approach yields accurate phenotypic predictions as well as compound potency estimates comparable to the state-of-the-art. More importantly, we show that the approach identifies novel cellular phenotypes not included in the manual annotation nor detected by previous studies.\n\nAuthor summaryCellular microscopy images provide detailed information about how cells respond to genetic or chemical treatments, and have been widely and successfully used in basic research and drug discovery. The recent breakthrough of deep learning methods for natural imaging recognition tasks has triggered the development and application of deep learning methods to cellular images to understand how cells change upon perturbation. Although successful, deep learning studies so far either can only take images of individual cells as input or require human experts to label a large amount of images. In this paper, we present an unsupervised deep learning approach that, without any human annotation, analyzes directly full-resolution microscopy images displaying typically hundreds of cells. We apply the approach to two benchmark datasets, and show that the approach identifies novel visual phenotypes not detected by previous studies.

bioinformatics↗