Biology subjects

Ahsan, M. A.

Publications and source records attributed to Ahsan, M. A..

4 recordsLinked to original sources

bioRxiv2025

DeepPathway: Predicting Pathway Expression from Histopathology Images

Spatial transcriptomics (ST) technologies provide spatially resolved gene expression along with image data, allowing the integrative analysis of complex tissue microenvironments. Despite their potential, the widespread adoption of ST remains limited due to high costs, and methodological challenges in data acquisition. Thus, there have been recent efforts to develop deep learning methods capable of inferring spatial gene expression from the much cheaper and easily available haematoxylin and eosin (H&E) images. These methods demonstrate promising results in reconstructing transcriptomic landscapes within tissue sections. While existing approaches predominantly focus on gene-level predictions, biological processes are often regulated at the pathway level through coordinated activity among functionally related genes. We present DeepPathway, a contrastive learning-based approach trained on ST data to predict pathway expression from H&E-stained sections. We compute input pathway expression by summarizing the expression of constituent genes using established pathway definitions. We evaluate the performance of our method on two prostate cancer datasets and validate our approach on the H&E images acquired from The Cancer Genome Atlas (TCGA) clearly differentiating between normal and tumour tissues. Finally, we apply our method to predict hypoxia signatures using H&Es of brain tumour samples where hypoxia staining with pimonidazole was available as ground truth. Implementation code for DeepPathway is available at https://github.com/aahsan045/DeepPathway.

bioinformatics↗

bioRxiv2022

Temporally integrated transcriptome analysis reveals ASFV pathology and host response dynamics

African swine fever virus (ASFV) causes a lethal swine hemorrhagic disease and is currently responsible for widespread damage to the pig industry. The molecular mechanisms of ASFV pathogenicity and its interaction with host responses remain poorly understood. In this study, we profiled the temporal viral and host transcriptomes in porcine alveolar macrophages (PAMs) infected at 6, 12, 24 and 48 hours with highly virulent (SY18) and low virulent (HuB20) ASFV strains. We first identified profound differences in the virus expression programs between SY18 and HuB20, while the transcriptome dynamics in host cells were dominated by infection time. Through integrated computational analysis and experimental validation, we identified differentially expressed genes and related biological processes, and elaborated differential usage of the NF-kappaB related pathways by the two virus strains. In addition, we observed that compared to the highly virulent SY18 strain, HuB20 infection quickly activates expression of receptors, sensors, regulators, as well as downstream effectors, including cGAS, STAT1/2, IRF9, MX1/2, suggesting rapid induction of a strong immune response. Lastly, we constructed a host-virus coexpression network, which shed light on pathogenic functions of several ASFV genes. Taken together, these results will provide a basis for further mechanistic studies on the functions of both viral and cellular genes that are involved in different responses. Author SummarySince it was first described in Kenya in 1921, ASF has spread across sub-Saharan Africa, the Caribbean, the Western Europe, the Trans-Caucasus region, and the Russian Federation. Recent outbreaks have also been reported in Asia, which has devastated the pig industry, resulting in an approximately 40% reduction in pork worldwide. In the absence of effective vaccine or treatment, the mortality for infections with highly virulent strains approaches 100%, while low virulent strains causing less mortality spreads fast recently. Nevertheless, the mechanisms of ASFV pathogenicity, especially the differences between highly and low virulent strains remain poorly understood. Here, we used RNA-seq to analyze the viral and host transcriptome changes in PAMs infected with a virulent strain (SY18) or an attenuated strain (HuB20) at different stages. We found that the presence of ASFV significantly affected the cellular transcriptome profile. In addition, we did temporal and described the dynamic expression programs induced in the host cells by ASFV infection of different virulence strains. In particular, we identified differential gene expression patterns in host innate immune responses and expressed cytokines and chemokines between ASFV strains of different virulence. Our study provides new insights into ASFV pathogenicity research and novel drug or vaccine targets.

microbiology↗

bioRxiv2020

Bioinformatic analysis based genome-wide identification, characterization, diversification and regulatory transcription components of RNA silencing machinery genes in wheat (Triticum aestivum L.)

Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families are known as RNA silencing machinery genes or RNAi genes. They have important activities at post-transcriptional and chromatin modification levels. They regulate gene expression relating to different stresses, growth, and development in eukaryotes. A complete cycle of gene silencing is occurred by the collaboration of these three families. However, these gene families are not yet rigorously studied in the economically important wheat genome. Our bioinformatic analysis based genome-wide identification, characterization, diversification and regulatory components of these gene families identified 7 TaDCL, 39 TaAGO and 16 TaRDR genes from wheat genome against RNAi genes of Arabidopsis thaliana. Phylogenetic analysis of wheat genome with Arabidopsis and rice RNAi genes showed that TaDCL, TaAGO and TaRDR proteins are clustered into four, eight and four subgroups respectively. Domain, motif and exon-intron structure analyses showed that the TaDCL, TaAGO and TaRDR proteins conserve identical characteristics within groups while retain diverse differences between groups. GO annotations implied that a number of biological and molecular pathways are linked to RNAi mechanism in wheat. Gene networking between transcription factors and RNAi proteins indicates that ERF is the leading family linked to maximum RNAi genes followed by MIKC-MADS, C2H2, BBR-BPC, MYB, and Dof. Cis-regulatory elements associated to RNAi genes are predicted to act as regulatory components against various environmental conditions. Expressed sequence tag analysis showed that larger numbers of RNAi genes are expressed in different tissues and organs predicted to play roles for healthy plants and grains. Expression analysis of 7 TaDCL genes using qRT-PCR showed that only TaDCL3a and TaDCL3b had root specific significant expression (p-value<0.05) with no expression in leaf validated EST results. Besides, TaDCL3b and TaDCL4 significantly prompted in drought condition indicating their potential role in drought stress tolerance. Overall results would however help researchers for in-depth biological investigation of these RNAi genes in wheat crop improvement.

genomics↗

bioRxiv2020

In Silico Identification, Characterization and Diversity Analysis of RNAi Genes and their Associated Regulatory Elements in Sweet Orange (Citrus sinensis L)

RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which involved in stress responses, development and maintenance of genome integrity during developmental stages. The whole mechanism of RNAi pathway is directly involved with the gene-silencing process by the interaction of Dicer-Like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) gene families. However, the genes of these three RNAi families are largely unknown yet in sweet orange (Citrus sinensis), though it is an economically important fruit plant all over the world. Therefore, a comprehensive investigation for genome-wide identification, characterization and diversity analysis of RNA silencing genes in C. sinensis was conducted and identified 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi genes. To characterize and validate the predicted genes of RNAi families, various bioinformatics analysis was conducted. Phylogenetic analysis clustered the predicted CsDCLs, CsAGOs and CsRDRs genes into four, six and four subgroups with the relevant genes of Arabidopsis respectively. The domain and motif composition analysis, the gene structure for all three-gene families exhibited almost homogeneity within the same group members while showed significant differences in between groups. The GO enrichment analysis results clearly indicated that the predicted genes have direct involvement into the RNAi process as expected in C. sinensis. Moreover, Cis-regulatory elements and regulatory transcription factor analysis of the reported RNAi genes demonstrated the diverse connection to the huge biological functions and regulatory pathways. The expressed sequence tag (EST) analysis showed that these genes are highly expressed in fruit and leaves which indicate that these reported genes have great involvement in C. sinensis food, flowering and fruit production. The expression analysis of the reported RNAi genes might be more useful to explore the most effective RNAi genes in C. sinensis for further biotechnological application.

genomics↗