Identification Of Marker Genes Research Articles

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Identification of marker genes associated with N6-methyladenosine and autophagy in ulcerative colitis.

Both N6-methyladenosine (m6A) methylation and autophagy are considered relevant to the pathogenesis of ulcerative colitis (UC). However, a systematic exploration of the role of the com-bination of m6A methylation and autophagy in UC remains to be performed. To elucidate the autophagy-related genes of m6A with a diagnostic value for UC. The correlation between m6A-related genes and autophagy-related genes (ARGs) was analyzed. Finally, gene set enrichment analysis (GSEA) was performed on the characteristic genes. Additionally, the expression levels of four characteristic genes were verified in dextran sulfate sodium (DSS)-induced colitis in mice. GSEA indicated that BAG3, P4HB and TP53INP2 were involved in the inflammatory response and TNF-α signalling via nuclear factor kappa-B. Furthermore, polymerase chain reaction results showed significantly higher mRNA levels of BAG3 and P4HB and lower mRNA levels of FMR1 and TP53INP2 in the DSS group compared to the control group. This study identified four m6A-ARGs that predict the occurrence of UC, thus providing a scientific reference for further studies on the pathogenesis of UC.

Tumor cell type and gene marker identification by single layer perceptron neural network on single-cell RNA sequence data

Tumor cell type and gene marker identification by single layer perceptron neural network on single-cell RNA sequence data

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

Identification of marker genes for spinal cord injury.

Spinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients' quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society. In this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses. We identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments. In conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis

BackgroundFeature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.ResultsIn this work, we explore the utility of various deep learning-based feature selection methods for scRNA-seq data analysis. We sample from Tabula Muris and Tabula Sapiens atlases to create scRNA-seq datasets with a range of data properties and evaluate the performance of traditional and deep learning-based feature selection methods for cell type classification, feature selection reproducibility and diversity, and computational time.ConclusionsOur study provides a reference for future development and application of deep learning-based feature selection methods for single-cell omics data analyses.

A multi-level approach reveals key physiological and molecular traits in the response of two rice genotypes subjected to water deficit at the reproductive stage.

Rice is more vulnerable to drought than maize, wheat, and sorghum because its water requirements remain high throughout the rice life cycle. The effects of drought vary depending on the timing, intensity, and duration of the events, as well as on the rice genotype and developmental stage. It can affect all levels of organization, from genes to the cells, tissues, and/or organs. In this study, a moderate water deficit was applied to two contrasting rice genotypes, IAC 25 and CIRAD 409, during their reproductive stage. Multi-level transcriptomic, metabolomic, physiological, and morphological analyses were performed to investigate the complex traits involved in their response to drought. Weighted gene network correlation analysis was used to identify the specific molecular mechanisms regulated by each genotype, and the correlations between gene networks and phenotypic traits. A holistic analysis of all the data provided a deeper understanding of the specific mechanisms regulated by each genotype, and enabled the identification of gene markers. Under non-limiting water conditions, CIRAD 409 had a denser shoot, but shoot growth was slower despite better photosynthetic performance. Under water deficit, CIRAD 409 was weakly affected regardless of the plant level analyzed. In contrast, IAC 25 had reduced growth and reproductive development. It regulated transcriptomic and metabolic activities at a high level, and activated a complex gene regulatory network involved in growth-limiting processes. By comparing two contrasting genotypes, the present study identified the regulation of some fundamental processes and gene markers, that drive rice development, and influence its response to water deficit, in particular, the importance of the biosynthetic and regulatory pathways for cell wall metabolism. These key processes determine the biological and mechanical properties of the cell wall and thus influence plant development, organ expansion, and turgor maintenance under water deficit. Our results also question the genericity of the antagonism between morphogenesis and organogenesis observed in the two genotypes.

Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing.

Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, the common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choosing can greatly alter clustering results and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which can be problematic for identifying cells of extremely low abundance due to their subtle contributions toward overall patterns of gene expression. Here, we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within broad cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by a multi-step semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of high specificity to the cell type. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. SCISSORS, including source code and vignettes, are freely available at https://github.com/jr-leary7/SCISSORS.

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes.

As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.

A new gene-scoring method for uncovering novel glaucoma-related genes using non-negative matrix factorization based on RNA-seq data.

Early diagnosis and treatment of glaucoma are challenging. The discovery of glaucoma biomarkers based on gene expression data could potentially provide new insights for early diagnosis, monitoring, and treatment options of glaucoma. Non-negative Matrix Factorization (NMF) has been widely used in numerous transcriptome data analyses in order to identify subtypes and biomarkers of different diseases; however, its application in glaucoma biomarker discovery has not been previously reported. Our study applied NMF to extract latent representations of RNA-seq data from BXD mouse strains and sorted the genes based on a novel gene scoring method. The enrichment ratio of the glaucoma-reference genes, extracted from multiple relevant resources, was compared using both the classical differentially expressed gene (DEG) analysis and NMF methods. The complete pipeline was validated using an independent RNA-seq dataset. Findings showed our NMF method significantly improved the enrichment detection of glaucoma genes. The application of NMF with the scoring method showed great promise in the identification of marker genes for glaucoma.

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification.

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.

Abstract 2075: Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC

Abstract Single-cell (sc) transcriptomics has revolutionized our understanding of the biological characteristics and dynamics of cancer development. It can help us identify rare cell subpopulations and understand mechanisms associated with tumor genesis, progression, and response to therapy. The most important step in the analyses of any scRNA-seq dataset is subpopulation identification, usually performed via unsupervised clustering, followed by gene marker identification. We created a highly customizable workflow for sc data analysis, implemented in Common Workflow Language (CWL) on the Cancer Genomics Cloud (CGC) platform. The NCI-funded CGC platform, powered by Seven Bridges, provides a collaborative cloud base computation infrastructure that collocates computation, over 750 bioinformatics workflows, and 3+ PB data to researchers, making the analysis of large datasets accessible from any environment. The “Multi-Sample Clustering and Gene Marker Identification with Seurat 4.1.0” workflow comprises the following steps: Loading scRNA-seq Expression Datasets, Quality Control and Preprocessing, and Clustering and Identification of Gene Markers. Our solution supports gene-cell count matrices generated by several commonly used quantifiers (for example, Cell Ranger counts, Salmon Alevin, Kallisto BUStools, STAR) from single or multiple sc datasets from different batches, as well as single or multiple single-cell samples combined in a single SingleCellExperiment object. The versatility of the pipeline is obtained using several implemented options in each of the steps. Quality control can be performed manually or automatically using several options for normalization (LogNormalize, Deconvolution, SCnorm and Linnorm) and for batch effect correction (Seurat and Harmony). For clustering, the pipeline uses Seurat's graph-based approach, with options for different clustering resolutions. After performing identification of gene markers for each cluster, a researcher can test differential expression using various packages including wilcox, bimod, roc, and DESeq2. Here, we demonstrate the application of this workflow to a typical sc analysis, by processing an open access dataset of 61k cells isolated from embryonal mouse pons and forebrain, two major brain tumor locations. We used different clustering resolutions to achieve different degrees of granularity and identified cluster-specific marker genes used to identify vulnerable cell populations. To enable researchers to use this analysis as a guideline, we made this analysis available as a public project. Further development of single-cell sequencing techniques will undoubtedly improve our understanding of tumor biology and highlight promising drug targets. CGC’s cloud base computation infrastructure, along with numerous available cancer datasets and easy-to-use single-cell data processing workflows, among others, will be instrumental in this process. Citation Format: Nevena Vukojicic, Aleksandar Danicic, Zelia Worman, Rowan Beck, Dalibor Veljkovic, Marko Matic, Jack DiGiovanna, Brandi Davis-Dusenbery. Highly customizable multi-sample single cell RNA-Seq pipeline on the CGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2075.

Identification of gene markers to predict proanthocyanidins biosynthesis in cell culture of Croton lechleri

Croton lechleri Müll. Arg. is a traditional medicinal tree growing in South America, which produces a deep red sap known as Dragon’s Blood Sap (DBS) used for its many therapeutic benefits. DBS bioactivity is mainly due to the high content of proanthocyanidins (PAs), important plant secondary metabolites involved in plant defence, but also utilised as antioxidant for human health. Since the process to directly obtain DBS from the C. lechleri tree is expensive, time-consuming and strongly dependent on the environmental conditions, the use of plant cell cultures for DBS production seems to represent a suitable alternative. The aptitude of plant cell cultures to synthesize specific secondary metabolites can be elicited by different types of abiotic stresses. In this study, the C. lechleri main genes putatively involved in the PAs biosynthetic pathway were identified and their expression was evaluated in both calli in solid medium and cell culture suspensions to identify the most suitable predictive gene markers for PAs biosynthesis. C. lechleri cell cultures were grown and elicited by N-starvation to induce the production of PAs and were then monitored by assessing the expression of previously identified gene markers, by quantifying the content of PAs and measuring few phenotypical parameters. The results show a new perspective on how PA gene transcript abundance could be used to predict the biosynthesis of useful secondary metabolites in plant cell cultures and to speed up the screening of putative elicitors.

Взаимосвязь воспроизводительных и продуктивных признаков в популяции овец породы манычский меринос с аллельным состоянием генов GH, GDF9

The problem of preserving, improving, and ra‑ tional use of the gene pool of domestic breeds of farm animals, including sheep, is always relevant. To date, the priority in solving the problems of intensification of the sheep and goat breeding industry is the introduction of modern methods of genetic diagnostics - the identification and identification of marker genes of economically valuable traits. This article presents the results of studies of the relationship of productivity traits in the population of sheep breeds мanych merino with the alel state of the genes of growth hormone (GH), differential growth factor (GDF9).

Identification of marker genes to monitor residual iPSCs in iPSC-derived products

BackgroundEngineered tissues and cell therapies based on human induced pluripotent stem cells (iPSCs) represent a promising approach for novel medicines. However, iPSC-derived cells and tissues may contain residual undifferentiated iPSCs that could lead to teratoma formation after implantation into patients. As a consequence, highly sensitive and specific methods for detecting residual undifferentiated iPSCs are indispensable for safety evaluations of iPSC-based therapies. The present study provides an approach for identifying potential marker genes for iPSC impurities in iPSC-derived cells using RNA sequencing data from iPSCs and various differentiated cell types. MethodsIdentifying iPSC marker genes for each cell type individually provided a larger and more specific set of potential marker genes than considering all cell types in the analysis. Thus, the authors focused on identifying markers for iPSC impurities in iPSC-derived cardiomyocytes (iCMs) and validated the selected genes by reverse transcription quantitative polymerase chain reaction. The sensitivity of the candidate genes was determined by spiking different amounts of iPSCs into iCMs and their performance was compared with the previously suggested marker lin-28 homolog A (LIN28A). ResultsEmbryonic stem cell-related gene (ESRG), long intergenic non-protein coding RNA 678 (LINC00678), CaM kinase-like vesicle-associated (CAMKV), indoleamine 2,3-dioxygenase 1 (IDO1), chondromodulin (CNMD), LINE1-type transposase domain containing 1 (L1DT1), LIN28A, lymphocyte-specific protein tyrosine kinase (LCK), vertebrae development-associated (VRTN) and zinc finger and SCAN domain containing 10 (ZSCAN10) detected contaminant iPSCs among iCMs with a limit of detection that ranged from 0.001% to 0.1% depending on the gene and iCM batch used. ConclusionsUsing the example of iCMs, the authors provide a strategy for identifying a set of highly specific and sensitive markers that can be used for quality assessment of iPSC-derived products.

GE-Impute: graph embedding-based imputation for single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) has been widely used to depict gene expression profiles at the single-cell resolution. However, its relatively high dropout rate often results in artificial zero expressions of genes and therefore compromised reliability of results. To overcome such unwanted sparsity of scRNA-seq data, several imputation algorithms have been developed to recover the single-cell expression profiles. Here, we propose a novel approach, GE-Impute, to impute the dropout zeros in scRNA-seq data with graph embedding-based neural network model. GE-Impute learns the neural graph representation for each cell and reconstructs the cell-cell similarity network accordingly, which enables better imputation of dropout zeros based on the more accurately allocated neighbors in the similarity network. Gene expression correlation analysis between true expression data and simulated dropout data suggests significantly better performance of GE-Impute on recovering dropout zeros for both droplet- and plated-based scRNA-seq data. GE-Impute also outperforms other imputation methods in identifying differentially expressed genes and improving the unsupervised clustering on datasets from various scRNA-seq techniques. Moreover, GE-Impute enhances the identification of marker genes, facilitating the cell type assignment of clusters. In trajectory analysis, GE-Impute improves time-course scRNA-seq data analysis and reconstructing differentiation trajectory. The above results together demonstrate that GE-Impute could be a useful method to recover the single-cell expression profiles, thus enabling better biological interpretation of scRNA-seq data. GE-Impute is implemented in Python and is freely available at https://github.com/wxbCaterpillar/GE-Impute.

Stem Cell Biomarkers and Tumorigenesis in Gastric Cancer.

Stomach cancer has a high mortality, which is partially caused by an absence of suitable biomarkers to allow detection of the initiation stages of cancer progression. Thus, identification of critical biomarkers associated with gastric cancer (GC) is required to advance its clinical diagnoses and treatment. Recent studies using tracing models for lineage analysis of GC stem cells indicate that the cell fate decision of the gastric stem cells might be an important issue for stem cell plasticity. They include leucine-rich repeat-containing G-protein-coupled receptor 5 (Lgr5+), Cholecystokinin receptor 2 (Cckr2+), and axis inhibition protein 2 (Axin2+) as the stem cell markers in the antrum, Trefoil Factor 2 (TFF2+), Mist1+ stem cells, and Troy+ chief cells in the corpus. By contrast, Estrogen receptor 1 (eR1), Leucine-rich repeats and immunoglobulin-like domains 1 (Lrig1), SRY (sex determining region Y)-box 2 (Sox2), and B lymphoma Mo-MLV insertion region 1 homolog (Bmi1) are rich in both the antrum and corpus regions. These markers might help to identify the cell-lineage identity and analyze the plasticity of each stem cell population. Thus, identification of marker genes for the development of GC and its environment is critical for the clinical application of cancer stem cells in the prevention of stomach cancers.

Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors.

Analyzing single-cell transcriptomes promises to decipher the plasticity, heterogeneity, and rapid switches in developmental cellular state transitions. Such analyses require the identification of gene markers for semi-stable transition states. However, there are nontrivial challenges such as unexplainable stochasticity, variable population sizes, and alternative trajectory constructions. By advancing current tipping-point theory-based models with feature selection, network decomposition, accurate estimation of correlations, and optimization, we developed BioTIP to overcome these challenges. BioTIP identifies a small group of genes, called critical transition signal (CTS), to characterize regulated stochasticity during semi-stable transitions. Although methods rooted in different theories converged at the same transition events in two benchmark datasets, BioTIP is unique in inferring lineage-determining transcription factors governing critical transition. Applying BioTIP to mouse gastrulation data, we identify multiple CTSs from one dataset and validated their significance in another independent dataset. We detect the established regulator Etv2 whose expression change drives the haemato-endothelial bifurcation, and its targets together in CTS across three datasets. After comparing to three current methods using six datasets, we show that BioTIP is accurate, user-friendly, independent of pseudo-temporal trajectory, and captures significantly interconnected and reproducible CTSs. We expect BioTIP to provide great insight into dynamic regulations of lineage-determining factors.

Liver transcriptome of rabbits supplemented with oral Moringa oleifera leaf powder under heat stress is associated with modulation of lipid metabolism and up-regulation of genes for thermo-tolerance, antioxidation, and immunity

Heat stress resulting from global warming is a serious threat to livestock and humans and can cause impaired metabolism, dysregulated immune functions, and even death. Liver transcriptome of the heat-stressed rabbits supplemented with oral Moringa oleifera leaf powder (MOLP) was performed with the hypothesis that antioxidative properties of MOLP might help to maintain homeostasis under heat stress environment. A total of 21 rabbits were divided into 3 groups (n = 7/group); control (CON, 25°C), heat stress (HS, 35°C for 7 hours daily), and HS supplemented with MOLP (HSM, 35°C) at 200 mg/kg body weight daily for 28 days. Serum analysis indicated that dietary MOLP (HSM) reduced glucose, total cholesterol, triglycerides, and low-density lipoprotein cholesterol contents compared with the HS group (P < .05). The HS group showed increased mRNA expression of interleukin (IL)-1α and IL-1β (P < .05), whereas enhanced expression of Nrf2 was observed in HSM compared with the HS group indicates antioxidative capacity of MOLP. Up-regulated genes PCK1 and ANGPTL4 as indicated by transcriptome analysis can explain increased serum glucose and lipid levels in the HS group. Up-regulation of antiapoptotic gene BCL2A1 by MOLP may suggest protection from heat stress induced apoptosis. In HSM, the up-regulated IL-6 family genes dictate their importance for immune and survival response, whereas genes PIK3R5 and TLR-2 are vital in thermo-tolerance. Further, identification of marker genes elucidates stress regulation response. In conclusion, findings of current study reveal beneficial aspects of dietary MOLP on liver function in heat-stressed rabbits.