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Merkel Cell Carcinoma (MCC) is a destructive form of neuroendocrine carcinoma that represents the 2nd highest cause of skin cancer related deaths despite being a relatively rare form of cancer. Different risk factors have been associated with MCC such as advancement age, immunosuppression, and ultraviolet light exposure, polyomavirus infection and a current, concurrent or previous diagnosis of Chronic Lymphocytic Leukaemia (CLL). However, the exact mechanism that leads to MCC and its inherent biology is yet to be fully researched and explored. The current treatments for MCC are a combination of surgery and radiation or chemo-radiation. So far, only two drugs have been approved by FDA for the treatment of MCC, with both drugs being immune checkpoint inhibitors. However, the drug approval from FDA was accelerated based on promising results from a relatively small number of patients, which means that the more research and trials are undergoing currently considering the fact that a very small cohort was used for clinical trials. Presently there are no biomarkers indicative of MCC and additional research is essential. With the advent of bioinformatics and microarray technology, in silico approaches pertaining to ‘omics’ data analysis, shall allow identification of hub genes and miRNA to broaden our understanding of MCC biology and their clinical utility.

Melanoma is a type of skin cancer. Numerous genes and their proteins are strongly associated with melanoma susceptibility. This study aims to use an in silico method to identify genetic variants in the melanoma susceptibility gene. The COSMIC database was queried for genes and cross-referenced with three environment-gene interaction databases (EGP, SeattleSNPs and CTD) to identify shared genes. The majority of approved skin melanoma drugs were found to act on the protein serine/threonine-protein kinase (B-RAF) encoded by the BRAF gene, which was also present in all three referenced databases. Comprehensive computational analysis was performed to predict deleterious genetic variants associated with skin melanoma, and the nsSNPs G469V and G469E were prioritized based on their predicted deleterious effects. Molecular dynamic simulation analysis of the B-RAF protein mutants G469V and G469E reveals that variations in the amino acid conformation at the drug binding site result in inconsistency in drug interaction. Additionally, this analysis showed that the G469V and G469E mutants have lower binding energy for dabrafenib than the wild type. The population with the highest frequency of each deleterious and pathogenic variant has been determined. The study's findings would support the development of more effective treatment strategies for skin melanoma. Communicated by Ramaswamy H. Sarma.

Polycystic ovary syndrome (PCOS) is an endocrine disorder which affects about 6-10% women across the world especially in the reproductive stage. Liraglutide an analog to GLP-1 and metformin which is an AMPK agonist lower blood glucose concentration in overweight or obese PCOS patients and thus used for reducing weight and restoring ovulation. However genetic variability leads to inter-individual differences in effectiveness of these drugs leading to clinical unpredictability which is mainly attributed to single nucleotide polymorphisms (SNPs) in targets of these drugs. This study is aimed at finding drug response for diabetic symptoms in PCOS in certain populations based on SNP data from 1000Genomes and gnomAD projects, thus finding regions of strong and weak respondents to these drugs which will further help clinicians in deciding the dose and type of drug to be given to people in the countries or regions depending on SNP frequency.

The risk for community acquired pneumonia (CAP) is partially driven by genetics. To identify the CAP-associated genetic risk loci, we performed a meta-analysis of clinically diagnosed CAP (3,310 individuals) with 2,655 healthy controls. The findings revealed CYP1A1 variants (rs2606345, rs4646903, rs1048943) associated with pneumonia. We observed rs2606345 [G vs T; OR=1.49(1.29-1.69); p=0.0001; I2= 15.5%], and rs1048943 [T vs G; OR= 1.31(0.90-1.71); p=0.002; I2=19.3%] as risk markers and rs4646903 [T vs C; OR= 0.79(0.62-0.96); p=0.03; I2=0%] as a protective marker for susceptibility to CAP, when compared with healthy controls. Our meta-analysis showed the presence of CYP1A1 SNP alleles contributing significant risk toward pneumonia susceptibility. Interestingly, we observed a striking difference of allele frequency for rs2606345 (CYP1A1) among Europeans, Africans and Asians which may provide a possible link for observed variations in death due to coronavirus disease 2019 (COVID-19), a viral pneumonia. We report, for the first time, a significant positive correlation for the risk allele (T or A) of rs2606345, with a higher COVID-19 mortality rate worldwide and within a genetically heterogeneous nation like India. Mechanistically, the risk allele ‘A’ (rs2606345) is associated with lower expression of CYP1A1 and presumably leads to reduced capacity for xenobiotic detoxification. We note that ambient air pollution, a powerful inducer of CYP1A1 gene expression, is globally associated with lower, not higher mortality, as would normally be predicted. In conclusion, we find that CYP1A1 alleles are associated with CAP mortality, presumably via altered xenobiotic metabolism. We speculate that gene-environment interactions governing CYP1A1 expression may influence COVID-19 mortality.

With frequent advancements in development of algorithms and need to incorporate them with clinically synthesized medical information is the paramount of modern-day bioinformatics. This aspect of computational study is of great healthcare significance as deduced results could be farfetched to generalize conclusions in regular medicine practice and diagnosis thus fastening up the process of detection. This paper tries to generalize cervical cancer detection approach with random forest regression technique. Unlike other papers which focus on accuracy and precision, this paper emphasizes on recall-based approach and beneficial tenets this approach over former ones. Four diagnostic tests used for early stage detection of Cervical cancer are Hinselmann's test, Schiller's test, Biopsy and Cytology. Each test is studied individually and analysis was made on the basis of confusion matrix, recall score and receiver operator curve (ROC). The basic aim during the entire development was to achieve higher recall scores with reduced false positive values.

With the advent of DNA sequencing technology that led to the human genome project, there was an explosion of big data in the field of biomedical sciences. The successful completion of the human genome project led to the emergence of numerous fields for data-driven discovery like genomics, proteomics, and bioinformatics per se. It was expected that the human genome information would reveal essential insights into the disease biology and revolutionize the field of medical sciences. It has been more than a decade since this important project. However, the impact of fundamental biological discoveries made in the laboratories is not reaching the clinical setting at a pace that was expected. Ever since, scientists have made innumerable attempts to decode the accumulated genetic information but are encountered with new challenges like complex disorders, epigenetics, and multifactorial diseases. So in order to bridge this gap, the focus of biomedical researchers has shifted from trying to decode the human genome in its entirety to “translating” only clinically relevant biological information. The change in focus has redirected the field of biotechnology toward what we call “translational research,” which is the buzzword of the era. By definition, it is “an interdisciplinary branch of the biomedical field supported by three main pillars: bench-side, bedside, and community,” that is, to analyze fundamental biological data with an explicit goal of affecting clinical care and ultimately the community as a whole. Stemming from translational biotechnology came system biology, biological engineering, pharmacogenomics, cheminformatics, molecular medicine, and so on. This chapter begins with a conceptual overview of this field and then introduces some of the key methods for translational research, its different stages implemented in integrative biology, overcoming its challenges with solutions, and the applications of multiomics platforms. Combining such biomedical information from different layers of omics data can ultimately help us in revealing the landscape of molecular mechanisms involved in highly prevalent complex diseases like cancer, cardiovascular, and metabolic diseases.

Melanoma is a highly prevalent dermatological disease and a life-threatening form of skin cancer. Lifestyle factors have been observed to influence melanoma risk. The relationship between increased BMI and melanoma incidence has been investigated using epidemiological, in our previous studies. In order to study relationships between melanoma and other modifiable lifestyle factors such as alcohol consumption, smoking, sunscreen application, and use of tanning devices, a meta-analysis aided by machine learning technique was carried out. PubMed database was searched carefully to sort out literature pertaining to the associations of these lifestyle factors with melanoma risk. Meta-analyses were carried out using a software called Review Manager 5.3. It gave risk ratios and 95% confidence intervals as results. Analysis of these data revealed weak positive relationship between alcohol consumption $(\text{OR} =1.46;\ 95\%\ \text{CI} =1.32-1.62)$ and use of tanning devices $(\text{OR}=1.36;\ 95\%\ \text{CI}=1.20-1.53)$ . Negative associations were found between smoking $(\text{OR}=0.78;\ 95\%\ \text{CI} =0.67-0.92)$ and sunscreen application $(\text{OR}=0.5;\ 95\%\ \text{CI}= 0.44-0.57)$ . These results were matched, and association trends were confirmed with the rule induction results obtained by applying Naive Bayes model to the data of each lifestyle factor. All the data from the five factors were pooled in together to create a master datasheet, and machine learning was performed on it to generate a predictive model for melanoma risk. The results were validated through a test split (ratio 0.7:0.3) and cross-validation as well. The accuracy was observed to be 70.23% and 70.35% + 0.79%. The functioning of the model was tested on an unlabeled dataset.

Background Vitiligo is a common dermatological disorder of chronic depigmentation which is phenotypically characterized by white macules on the skin caused as a result of the systematic destruction of functional melanocytes. This review provides an overview of vitiligo, its etiopathogenesis and disease management, and also discusses the scope of network-interaction studies and polypharmacological studies in understanding vitiligo disease module. Methods A narrative review of the relevant published literatures known to the authors that comprehensively discussed about vitiligo and its implications was conducted. Results Emerging evidence underlines the existing connection between deregulated miRNA function and vitiligo pathogenesis. It has also been linked with autoimmunity for the cause of melanocyte death in susceptible individuals. Alteration of genetic factors involved in immune responses and melanogenesis along with environmental factors are central to disease manifestation. Screening methods as such are not available for vitiligo, and the diagnosis is based on the assessment of the absence of melanocytes from the lesions in the affected area. With the occurrence of vitiligo at any age, most people typically develop it at a young age. Depending on the disease course and duration, clinical management primarily involves disease stabilization either by repigmentation or depigmentation of the skin. Conclusions Several questions remain unsolved which indeed makes vitiligo an excellent model for studying autoimmune and degenerative processes. An understanding of the underlying degenerative mechanisms and unraveling the biological mediators of melanocyte loss will open up avenues for testing novel therapeutic approaches in vitiligo management. Such studies can revolutionize our apprehension of the molecular interconnections that underpin vitiligo pathogenesis.

Background Skin is a complex ecosystem hosting a diverse microbial population as well as distinct environmental niches leading to hundreds of skin conditions that affect humans. There is an evident shift towards the metagenomic analysis from less efficient and strenuous culture-based techniques in biomedical research, thus creating a new dimension for dermatological study. A systematic and comprehensive study of skin microbiome appraises the dynamics between species, their interaction with the immune system, and composition in diseases. Research Metagenomics include research techniques like next-generation sequencing, sequencing of amplicon-based assays, shotgun metagenomics, gene prediction, metatranscriptomics, and statistical and comparative studies allowing us to access the functional and metabolic diversity of the skin microbiome and their role in host health. In disorders like acne, dandruff, seborrheic dermatitis, and bovine digital dermatitis, metagenomics provides information about the organisms present conferring the condition, inter-microbial interactions, and expression profiles of communities. Conclusion We have enriched our understanding of the uncultured world resulting in a better understanding of microbe interaction with each other and their host. Metagenomic analysis provides glimpses into topographical and interpersonal complexity that defines the skin microbiome. It has led to an advanced study of dermatological disorders like acne, dandruff, seborrheic dermatitis, atopic dermatitis, bovine digital dermatitis, and psoriasis, and this knowledge is a breakthrough in dermatology research for creating better therapeutic solutions and personalized treatments.

Background The interaction of genetic variants and their distribution in the genome is firmly believed to contribute to genetic dermatological disorders. For the convenience of clinicians and researchers to explore such genetic variants, we identified and validated the genetic association of human SNPs with dermatological disorders through manual curation and computational analysis. Methods Multiple online resources were investigated for creating a comprehensive list of dermatological disorders followed by identification and manual curation of data description of the SNP–disease relationship. The process for database creation involved extensive review of published literature and relevant articles that probed association of miRNAs and SNPs with vitiligo. Furthermore, computational analysis for deleterious SNPs and polypharmacological studies was performed with an effort to provide potential novel drug candidates for optimal therapeutic interventions. Results We established a dedicated database on dermatological disorders, DermaGene, which proffers a user-friendly interface to enable systematic querying and analysis of SNPs associated with dermatological disorders. Based on our disease network analysis, we further extended our present work to construct another comprehensive database, VitmiRS (Vitiligo associated miRNAs and SNPs), which furnishes detailed information on each miRNA and SNP association that renders susceptibility to vitiligo. Also, our systems analysis approach unraveled potential molecular determinants that may pose as novel drug candidates and can be targeted for efficient therapeutic approaches in vitiligo treatment. Conclusions Both the databases are freely accessible and will serve as a significant resource providing insights into previously undiscovered disease–gene and disease–SNP relationships. Our analysis unveiled significant findings that we believe may furnish a comprehensive understanding of the biological mechanisms that mediate vitiligo disease pathogenesis, thereby, driving the way towards better therapeutic interventions for disease management.

With the given diversity and abundance of several targets of miRNAs, they functionally appear to interact with several elements of the multiple cellular networks to maintain physiologic homeostasis. They can function as tumor suppressors or oncogenes, whose under or overexpression has both diagnostic and prognostic significance in various cancers while being implicated as prospective regulators of age-related disorders (ARD) as well. Establishing a concatenate between ARD and cancers by looking into the insights of the shared miRNAs may have a practical relevance. In the present work, we performed network analysis of miRNA-disease association and miRNA-target gene interaction to prioritize miRNAs that play significant roles in the manifestation of cancer as well as ARD. Also, we developed a repository that stores miRNAs common to both ARD and cancers along with their target genes. We have comprehensively curated all miRNAs that we found to be shared in both the diseases in the human genome and established a database, miRACA (Database for microRNAs Associated with Cancers and ARD) that currently houses information of 1648 miRNAs that are significantly associated with 38 variants supported with pertinent data. It has been made available online at http://genomeinformatics.dtu.ac.in/miraca/ for easy retrieval and utilization of data by the scientific community. To the best of our knowledge, our database is the first attempt at compilation of such data. We believe this work may serve as a significant resource and facilitate the analysis of miRNA regulatory mechanisms shared between cancers and ARD to apprehend disease etiology.

Tuberculosis (TB) is primarily associated with decline in immune health status. As gut microbiome (GM) is implicated in the regulation of host immunity and metabolism, here we investigate GM alteration in TB patients by 16S rRNA gene and whole-genome shotgun sequencing. The study group constituted of patients with pulmonary TB and their healthy household contacts as controls (HCs). Significant alteration of microbial taxonomic and functional capacity was observed in patients with active TB as compared to the HCs. We observed that Prevotella and Bifidobacterium abundance were associated with HCs, whereas butyrate and propionate-producing bacteria like Faecalibacterium, Roseburia, Eubacterium and Phascolarctobacterium were significantly enriched in TB patients. Functional analysis showed reduced biosynthesis of vitamins and amino acids in favour of enriched metabolism of butyrate and propionate in TB subjects. The TB subjects were also investigated during the course of treatment, to analyse the variation of GM. Although perturbation in microbial composition was still evident after a month's administration of anti-TB drugs, significant changes were observed in metagenome gene pool that pointed towards recovery in functional capacity. Therefore, the findings from this pilot study suggest that microbial dysbiosis may contribute to pathophysiology of TB by enhancing the anti-inflammatory milieu in the host.

Display Omitted Age related disorders are responsible for troublesome ageing and increasing mortality.An online database, dbAARD, of human genetic variants and associated genes of ARDs.Gene prediction tool, AGP, based on machine learning classifier Rotation Forest.Overall accuracy in terms of precision 75%, recall 76%, F-measure 76% and AUC 0.85.Prospects in healthcare, personalized medicine and biomedical sciences. The atrocious behavioral and physiological shift with aging accelerate occurrence of deleterious disorders. Contemporary research is focused at uncovering the role of genetic associations in age-related disorders (ARDs). While the completion of the Human Genome Project and the HapMap project has generated huge amount of data on genetic variations; Genome-Wide Association Studies (GWAS) have identified genetic variations, essentially SNPs associated with several disorders including ARDs. However, a repository that houses all such ARD associations is lacking. The present work is aimed at filling this void. A database, dbAARD (database of Aging and Age Related Disorders) has been developed which hosts information on more than 3000 genetic variations significantly (p-value

The modern-day society is increasingly dependent on computer-aided tools and techniques. Digital imaging techniques have a tremendous impact on our day-to-day lives. Image processing is a vital component in the field of biological sciences and has the potential to drastically change the computer-human interface. Image processing refers to the conversion of an image into a digital form followed by enhancement of the image in order to extract useful information from it that are indiscernible by human ocular perceivers. Rapid advances in image processing, computerized reconstruction of an image and allied advancements in image analysis algorithms and the application of artificial intelligence has spurred a revolution in the field of medical and diagnostic imaging. Deep learning, a type of Artificial Neural Network (Machine Learning), is resurfacing as a powerful tool for its utilization in big healthcare data. The integration of deep learning techniques to image processing has the potential to add momentum to the dermatological imaging and promote early and accurate diagnosis of skin lesions. This review attempts to discuss the fundamentals of image processing, its importance, various clinical imaging modalities in use in the field of dermatology and application of deep learning algorithms in dermatological imaging, accentuating the inadequacies and future research prospects.

Breast cancer is one of the most common cancers among women and increased expression of some polymorphic genes, which is rare within families, enhances the risk of breast cancer incidence. The correct identification of the functional SNPs of such genes is important for characterizing the functional aspect of these SNPs which can be assessed by evaluating their significant influence on the structure and function of proteins. Since the presence of SNPs in these genes affects the quality of life of a breast cancer patient, thus, the associated diagnostic markers have a reliable potential for assessing the prognosis of breast cancer. ATP-binding cassette (ABC) genes have been shown to obstruct the treatment of breast cancer by providing resistance to malignant cells from anti-cancer drugs. Some allelic variants of ABCG2 and ABCB1 are also associated with occurrence of skin toxicity during the treatment of breast cancer with anti-cancer drugs. The present study has incorporated comprehensive bioinformatics analysis to explore the possible disease-associated mutations of ABCB1 gene, a gene that resulted from gene-environment interaction study, and understand their consequential effect on the structural and functional behavior of P-glycoprotein. Two gene variants (R538S and M701R) of P-glycoprotein were selected as potentially detrimental point mutations, and these variants were modeled. Molecular dynamic simulation (MDS) studies unraveled the atomic interactions and motion trajectories of the native as well as the two mutant (R538S and M701R) structures and were predicted to have a deleterious effect on breast cancer associated P-gp. Thus, the present study may broaden the way to design novel potent drugs for overcoming the problems associated with multidrug resistance (MDR) resulting from a change in protein conformation due to a mutation in ABCB1 gene.

Personalized medicine is a model that aims at customizing healthcare and tailoring medicine according to an individual`s genetic makeup. It classifies individuals that differ in their susceptibility to a particular disease or response to a particular treatment into subpopulations based on individual's unique genetic and clinical information along with environmental factors. The completion of Human Genome Project and the advent of high-throughput genome analysis tools has helped in building and strengthening this model. There lies a huge potential in the implementation of personalized medicine to significantly improve the clinical outcomes; however, its implementation into clinical practice remains slow and is a matter of concern. This chapter aims at acquainting readers with the underlying concepts and components of personalized medicine supplemented with some disease-based case studies, discussing challenges and recent advancements in the implementation of the model of personalized medicine.

The miRNAs are 22–46 nucleotidelong, non-protein coding RNAsthat act as oncogenes. Their altered expression can cause diseases, includingcancer. Lung cancer is causing death of million peopleworldwide. Yet only few genetic biomarkers are known for detecting lung cancer. miRNAs are those non-coding RNAs that act as such biomarkers. They act as oncogenes and regulate many biological processesand cellular pathways. The miRNAs regulate gene expression of protein coding genes by generating either translation repression or RNAdegradation. When they bind with 3' UTR of newly formed miRNA transcripts along with other helper proteins, they inhibit their expression and function. This translation inhibition, affects tumor suppressor genes and leads to cancer.miRNAexpression deregulation found in various cancers such as Prostrate, Breast, Colonialand Lung canceretc. In this study insilico approach has been used to find out functional genetic variation in miRNA binding sites of genes responsible for causing lung cancer. As a result of this study we have found 7 SNPs involved in such cancers-rs6772, rs1036672, rs739442, rs1050700, rs3185695, rs12723035, rs3787030. These SNPs can act as candidate biomarkers for lung cancer.

Malaria, a life-threatening blood disease, has been a major concern in the field of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specific molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein-protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. The interacting proteins were filtered according to their biological significance and functional roles. α-tubulin was identified as a potential protein target and inhibitors were designed against it by modification of amiprophos methyl. Docking and binding affinity analysis showed two modified inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding affinity of −83.80 kJ/mol and −98.16 kJ/mol with the target. These inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug.

Vitiligo is characterized by the occurrence of depigmented patches on the skin caused as a result of progressive loss of functional melanocytes. It is a polygenic disorder entailing both genetic and non-genetic factors intricately and as such is not yet clearly understood. Hence, a comprehensive understanding of the entire spectrum of disease susceptibility and pathogenesis remains a challenge. Emerging evidence over the decades underlines the cardinal role of miRNAs in disease development and having a significant role in melanocyte development and survival. Also, identifying the susceptible genes and their variants that influence disease onset is fundamental to unravel the rationale for disease susceptibility. We, therefore, applied a systems biology approach to identify potential miRNAs and their target genes to construct a miRNA-target gene network revealing essential miRNAs that might be significantly related to vitiligo. We further investigated the susceptible genetic variants associated with vitiligo and prioritized a few proteins in our protein-protein interaction network as significant hub proteins. Network-based polypharmacological studies of such hub proteins are helpful in analyzing a large set of disease-associated proteins which might initiate better diagnosis with the feasibility of personalized treatment for vitiligo patients in the future. Our polypharmacogenomic systems analysis highlighted novel drug candidates and drug repositioning candidates for vitiligo. Furthermore, we investigated the pathogenic effect of the plausible single nucleotide polymorphisms (SNPs) using computational platforms and carried out preliminary protein modeling to implicate the role of SNPs in disease pathogenesis. Thus, our analysis unveiled significant findings which may provide an insight of the mechanisms of vitiligo development and progression, thereby, driving the way towards improved therapeutic and diagnostic interventions for vitiligo management.

Tuberculosis (TB) is an infectious disease caused by fastidious pathogen Mycobacterium tuberculosis. TB has emerged as one of the major causes of mortality in the developing world. Role of host genetic factors that modulate disease susceptibility have not been studied widely. Recent studies have reported few genetic loci that provide impetus to this area of research. The availability of tools has enabled genome-wide scans for disease susceptibility loci associated with infectious diseases. Till now, information on human genetic variations and their associated genes that modulate TB susceptibility have not been systematically compiled. In this work, we have created a resource: HGV&TB, which hosts genetic variations reported to be associated with TB susceptibility in humans. It currently houses information on 307 variations in 98 genes. In total, 101 of these variations are exonic, whereas 78 fall in intronic regions. We also analysed the pathogenicity of the genetic variations, their phenotypic consequences and ethnic origin. Using various computational analyses, 30 variations of the 101 exonic variations were predicted to be pathogenic. The resource is freely available at http://genome.igib.res.in/hgvtb/index.html. Using integrative analysis, we have shown that the disease associated variants are selectively enriched in the immune signalling pathways which are crucial in the pathophysiology of TB. Database URL: http://genome.igib.res.in/hgvtb/index.html

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative `Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed `3interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

Objective: Alpha-ketoglutarate (α-KG) is a cellular intermediary metabolite of Krebs cycle, involved in energy metabolism, amino acid synthesis, and nitrogen transport. It is available over-the-counter and marketed as a nutritional supplement. There is a growing body of evidence to suggest that dietary α-KG has the potential to maintain cellular redox status and thus can protect various oxidative stress induced disease states. The aim of the present study was to investigate the hepatoprotective role of α-KG in acetaminophen (APAP) induced toxicity in rats. Materials and Methods: Animals were divided into three groups of six animals each. Group I (Vehicle control): Normal Saline, Group II (APAP): A single intraperitoneal injection of 0.6 g/kg, Group III (APAP + α-KG): APAP as in Group II with α-KG treatment at a dose of 2 g/kg, orally for 5 days. Then the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) with oxidative stress markers including malondialdehyde (MDA), reduced glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and histopathology were analyzed. Results: The results indicate that APAP caused significant elevations in ALT, AST, ALP, and MDA levels, while GSH, SOD, and CAT were significantly depleted while co-administration of α-KG showed a significant (P < 0.05) reduction in the severity of these damages. Histologically, the liver showed inflammation and necrosis after APAP treatment, which were significantly restored with co-administration of α-KG. Conclusion: These results indicate the possible therapeutic potential of α-KG in protecting liver damage by APAP in rats.