Biology subjects

Ahmed, K. T.

Publications and source records attributed to Ahmed, K. T..

7 recordsLinked to original sources

bioRxiv2025

Dimethylindanoylindole isomers revealed different pharmacological profiles at CB1 and CB2 cannabinoid receptors

Synthetic cannabinoid receptor agonists (SCRAs) represent one of the most rapidly growing classes of novel psychoactive substances (NPS) and exhibit higher efficacy and potency at CB1 and CB2 cannabinoid receptors (CB1 and CB2) compared to {Delta}9-tetrahydrocannabinol, contributing to distinct adverse effects absent in cannabis use. The indole-based SCRAs GBD-002 and GBD-003, incorporating 2,2-dimethylindane, differ by a single substituent position yet display markedly different receptor binding profiles. We evaluated both the GBD-002/GBD-003 pair and an analogous structural isomer pair using bioluminescence resonance energy transfer (BRET) assays to assess key CB1 and CB2 transducer pathways. Site-directed mutagenesis characterized contributions of non-conserved residues between CB1 and CB2 and aromatic residues on CB1 transmembrane helix 2 (TM2). This study provided insights into the molecular determinants of cannabinoid receptor selectivity and efficacy.

pharmacology and toxicology↗

bioRxiv2023

A mechanistic study on the tolerance of PAM distal end mismatch by SpCas9

CRISPR-Cas9 is the newest technology available for targeted genome editing. It is very efficient and cheap compared to other genome editing techniques. However, its therapeutic application is limited due to its off-target activity. To have a better understanding of this off-target effect, we concentrated our efforts on its mismatch-prone PAM distal end. Current off-target prediction algorithms use RNA-DNA complementation derived energy as a major factor in predicting off-target effect. RNA-DNA complementation derived energy drives Cas9 conformational change, which in turn drives its functional activity. In the case of lower RNA-DNA complementarity, a partial conformational change occurs resulting in a slower reaction rate and partial activity. However, extensive mismatches are often tolerated despite lower complementation derived energy available from RNA: DNA duplex formation. Thus, the off-target activity of Cas9 depends directly on the nature of mismatches which in turn result in deviation of the active site of the enzyme due to structural instability in the duplex strand. In order to test the hypothesis, we have designed an array of mismatched target sites and performed in vitro and cell line-based experiments to assess the effects of PAM distal mismatches in Cas9 activity. For further mechanistic validation, Molecular dynamics simulation was performed and it revealed that certain mismatch mutations induced pronounced conformational instability within the RNA-DNA duplex, leading to elevated root mean square deviation (RMSD) values. We found that, target sites having mismatches in the 18th to 16th position upstream of the PAM showed no to little activity.

genomics↗

bioRxiv2022

omicsGAT: Graph Attention Network for Cancer Subtype Analyses

MotivationThe use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of studied cancer mechanisms compared to traditional approaches. Graph-based learning models have been proposed to learn important hidden representations from gene expression data and network structure to improve cancer outcome prediction, patient stratification, and cell clustering. However, these graph-based methods cannot rank the importance of the different neighbors for a particular sample in the downstream cancer subtype analyses. In this study, we introduce omicsGAT, a graph attention network (GAT) model to integrate graph-based learning with an attention mechanism for RNA-seq data analysis. The multi-head attention mechanism in omicsGAT can more effectively secure information of a particular sample by assigning different attention coefficients to its neighbors. ResultsComprehensive experiments on The Cancer Genome Atlas (TCGA) breast cancer and bladder cancer bulk RNA-seq data, and primary diffuse gliomas single-cell RNA-seq data validate that (1) the proposed model can effectively integrate neighborhood information of a sample and learn an embedding vector to improve disease phenotype prediction, cancer patient stratification, and cell clustering of the sample. (2) The attention matrix generated from the multi-head attention coefficients provides more useful information compared to the sample correlation-based adjacency matrix. From the results, we can conclude that some neighbors play a more important role than others in cancer subtype analyses of a particular sample based on the attention coefficient. Availability and implementationSource code is available at: https://github.com/CompbioLabUCF/omicsGAT Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗

bioRxiv2021

Multi-omics Data Integration by Generative Adversarial Network

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network (GAN) model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. Large-scale experiments on The Cancer Genome Atlas (TCGA) breast cancer, lung cancer, and ovarian cancer datasets validate that (1) the model can effectively integrate two omics data (e.g., mRNA and microRNA expression data) and their interaction network (e.g., microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (2) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals.

bioinformatics↗

bioRxiv2020

An Advanced Framework for Time-lapse Microscopy Image Analysis

Time-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients. Availability and ImplementationSource code is available at: https://github.com/CompbioLabUCF/CellTrack ACM Reference FormatQibing Jiang, Praneeth Sudalagunta, Mark B. Meads, Khandakar Tanvir Ahmed, Tara Rutkowski, Ken Shain, Ariosto S. Silva, and Wei Zhang. 2020. An Advanced Framework for Time-lapse Microscopy Image Analysis. In Proceedings of BioKDD: 19th International Workshop on Data Mining In Bioinformatics (BioKDD). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2020

APA-Scan: Detection and Visualization of 3'-UTR APA with RNA-seq and 3'-end-seq Data

BackgroundThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3-untranslated region (3-UTR) of mRNA produces transcripts with shorter or longer 3-UTR. Often, 3-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. MethodsAPA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3-UTR annotation and read coverage on the 3-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive users manual are freely available at https://github.com/compbiolabucf/APA-Scan. ResultAPA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3 -UTR APA events and improve genome annotation. ConclusionAPA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3-UTR APA events. The pipeline integrates both RNA-seq and 3-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

bioinformatics↗

bioRxiv2017

Is the 25 year hepatitis C marathon coming to an end to declare victory?

Hepatitis C virus (HCV) which was originally recognized as posttransfusion non-A, non-B hepatitis has been a major global health problem affecting 3% of the world population. Interferon/peginterferon and ribavirin combination therapy was the backbone of chronic HCV therapy for two decades of the journey. However, the interferon based treatment success rate was around 50% with many side effects. Many chronic HCV patients with psychiatric diseases, or even cytopenias, were ineligible for HCV treatment. Now, we no longer need any injectable medicine. New direct-acting antiviral agents against HCV allowed the advance of interferon-free and ribavirin-free oral regimens with high rates of response and tolerability. The cost of the medications should not be a barrier to their access in certain parts of the world. While we are getting closer, we should still focus on preventing the spread of the disease, screening and delivering the cure globally to those in need. In the near future, development of an effective vaccine against HCV would make it possible to eradicate HCV infection worldwide completely.

microbiology↗