Biology subjects

Mollah, S.

Publications and source records attributed to Mollah, S..

7 recordsLinked to original sources

bioRxiv2024

Deciphering epigenetic influence of curcumin on histone signatures in breast cancer using 3D network

Aberrant epigenetic alterations are implicated in the transformation of normal cells into cancerous ones. Unlike genetic mutations, these dysregulations can be reversible and are potential targets for anticancer drugs. Curcumin, a natural plant-derived compound, has been shown to have anticancer effects and neuroprotective properties via its influence on epigenetic regulation. However, the precise nature of these epigenetic changes and the mechanisms driving them remain largely unexplored. Moreover, there is strong evidence linking the neuroactive properties of chemotherapy drugs in the treatment of breast cancer to chemotherapy-induced peripheral neuropathy (CIPN). Therefore, studying epigenetic regulation of neuroactive compounds in breast cancer is significant in also understanding their mechanisms mediating neuropathy in breast cancer. This study aims to apply a 3D network model to profile the histone codes modified by curcumin as a neuroactive compound and to hypothesize the enzymatic pathways driving these modifications in breast cancer. Using multi-omic data from the NIH LINCS program, we identified two novel histone codes associated with curcumin, mediated through 23 phosphoproteins involved in cell signaling. These signatures were linked to genes expressed in the dorsal root ganglion (DRG), which are characterized in this study. Together, the histone and phosphoprotein profiles represent potential biomarkers for the development of chromatin-targeted therapies in breast cancer, as well as new strategies for managing neuropathy in breast cancer patients. GitHub URL Source code is available at https://github.com/smollahlab/ECHC

bioinformatics↗

bioRxiv2024

gen3DNet: An R Package for Generating 3D Network Models

MotivationNetworks are ubiquitous to organize and represent data in the real world such as the biomedical networks, social networks, etc. Due to the attractive property of networks which can explicitly capture the rich relationships among data samples, there is an increasing demand to transform linkage-free data to graph-structure data for efficient downstream analyses. Existing works typically focus on representing a single data object like gene expression profile in a homogeneous 2D network, which fail to deal with situations where two different data objects are involved to create a 3D heterogeneous network. ResultsIn this paper, we introduce an R package, gen3DNet (a generic version of the iPhDNet), for generating 3D network models from two correlated objects with shared common factors. Specifically, gen3DNet builds the relationships between samples and shared factors using the non-negative matrix factorization, where three clustering techniques are evaluated to determine the number of functional modules. In addition, it builds the relationships between samples from the two distinct data objects based on a partial least squares regression model. Usage of the package is illustrated through a real-world application. GitHub URL Source code is available at https://github.com/mollahlab/gen3DNet

bioinformatics↗

bioRxiv2024

Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without affecting cancer treatment through Inter Variability Cross-Correlation Analysis (IVCCA)

The successful treatment of side effects of chemotherapy faces two major limitations: the need to avoid interfering with pathways essential for the cancer-destroying effects of the chemotherapy drug, and the need to avoid helping tumor progression through cancer promoting cellular pathways. To address these questions and identify new pathways and targets that satisfy these limitations, we have developed the bioinformatics tool Inter Variability Cross-Correlation Analysis (IVCCA). This tool calculates the cross-correlation of differentially expressed genes, analyzes their clusters, and compares them across a vast number of known pathways to identify the most relevant target(s). To demonstrate the utility of IVCCA, we applied this platform to RNA-seq data obtained from the hearts of the animal models with oxaliplatin-induced CTX. RNA-seq of the heart tissue from oxaliplatin treated mice identified 1744 differentially expressed genes with False Discovery Rate (FDR) less than 0.05 and fold change above 1.5 across nine samples. We compared the results against traditional gene enrichment analysis methods, revealing that IVCCA identified additional pathways potentially involved in CTX beyond those detected by conventional approaches. The newly identified pathways such as energy metabolism and several others represent promising target for therapeutic intervention against CTX, while preserving the efficacy of the chemotherapy treatment and avoiding tumor proliferation. Targeting these pathways is expected to mitigate the damaging effects of chemotherapy on cardiac tissues and improve patient outcomes by reducing the incidence of heart failure and other cardiovascular complications, ultimately enabling patients to complete their full course of chemotherapy with improved quality of life and survival rates.

bioinformatics↗

bioRxiv2023

The chromatin landscape of healthy and injured cell types in the human kidney

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. However, comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measured dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We established a comprehensive and spatially-anchored epigenomic atlas to define the kidneys active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we noted distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulated the transition between health and injury, while in thick ascending limb cells this transition was regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguished two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

molecular biology↗

bioRxiv2021

NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting

MotivationHigh-throughput studies of biological systems are rapidly generating a wealth of omics-scale data. Many of these studies are time-series collecting proteomics and genomics data capturing dynamic observations. While time-series omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values resulting in data shortage. Data missing and shortage are especially problematic for downstream applications such as omics data integration and computational analyses that need complete and sufficient data representations. Data imputation and forecasting methods have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. As a result, these techniques lack the ability to capture the time-ordered nature of data and cannot handle omics data containing missing values at multiple time points. ResultsWe propose a network-based method for time-series omics data imputation and forecasting (NeTOIF) that handle omics data containing missing values at multiple time points. NeTOIF takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform time-series imputation and forecasting using a long short-term memory network. Evaluating NeTOIF with a proteomic and a genomic dataset demonstrated a distinct advantage of NeTOIF over existing data imputation and forecasting methods. The average mean square error of NeTOIF improved 11.3% for imputation and 6.4% for forcasting compared to the baseline methods. Contactsmollah@wustl.edu

bioinformatics↗

bioRxiv2020

A Non-Negative Tensor Factorization Approach to Deconvolute Microenvironment in Breast Cancer

An in-depth understanding of epithelial breast cell response to growth-promoting ligands is required to elucidate how signals from the microenvironment affect cell-intrinsic regulatory networks and their resultant cellular phenotypes, such as cell growth, progression, and differentiation. Understanding the cellular response to these signals is particularly important in understanding the mechanisms of breast cancer initiation and progression. There is increasing evidence that aberrant epigenetic marks are present in cells of the breast tumor microenvironment and are known to affect these cellular processes. However, the mechanisms by which epigenetic microenvironment signals influence these cellular phenotypes are complex and currently not well established. To deconvolute the complexity of the epigenetic microenvironment signals in breast cancer, we developed a novel tensor-based correlation method: HOCMO (Higher-Order Correlation Model), applying to proteomics time series data to reveal the four-way regulatory dynamics among signaling proteins, histones, and growth-promoting ligands across multiple time points in the breast epithelial cells. HOCMO reveals two functional modules and the onset of specific protein-histone signatures in response to growth ligands contributing to distinct cellular phenotypes indicative of breast cancer initiation and progression. We evaluate robustness of our tensor model against baseline method TensorLy and achieved slight improvement in terms of reconstruction error and execution time. HOCMO is a data independent self-supervised learning method with superior interpretability that can capture the strength of complex interactions such as inter- and intra-pathway cellular signaling networks in any diseases or biological systems.

cancer biology↗

bioRxiv2018

Creating a scalable deep learning based Named Entity Recognition Model for biomedical textual data by repurposing BioSample free-text annotations

High quality metadata annotations for data hosted in large public repositories are essential for research reproducibility, and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Informations (NCBIs) Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via Named Entity Recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic (AUROC) curve of 85.2% and 0.977 respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. AvailabilityAll the analyses, environments, and Jupyter notebooks pertaining to this manuscript are available on Github: https://github.com/cartercompbio/PredictMEE.

bioinformatics↗