Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications.

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Acute metabolic alterations in the hippocampus are associated with decreased acetylation after blast induced TBI.

INTRODUCTION: Blast induced Traumatic brain injury (BI-TBI) is common among military personnels as well as war affected civilians. In the war zone, people can also encounter repeated exposure of blast wave, which may affect their cognition and metabolic alterations. OBJECTIVE: In this study we assess the metabolic and histological changes in the hippocampus of rats at 24 h post injury. METHOD: Rats were divided into four groups: (i) Sham; (ii) Mild TBI (mi); (iii) Moderate TBI (mo); and (iv) Repetitive mild TBI (rm TBI) and then subjected to different intensities of blast exposure. Hippocampal tissues were collected after 24 h of injury for proton nuclear magnetic resonance spectroscopy (1H NMR spectroscopy) and immunohistochemical (IHC) analysis. RESULTS: The metabolic alterations were found in the hippocampal tissue samples and these alterations showed significant change in glutamate, N-Acetylaspartic acid (NAA), acetate, creatine, phosphoethanolamine (PE), ethanolamine and PC/choline concentrations in rmTBI rats only. IHC studies revealed that AH3 (Acetyl histone) positive cells were decreased in rm TBI tissue samples in comparison to other TBI groups and sham rats. This might reflect an epigenetic alteration due to repeated blast exposure at 24 h post injury. Additionally, astrogliosis was observed in miTBI and moTBI hippocampal tissue while no change was observed in rmTBI tissues. CONCLUSION: The present study reports altered acetylation in the presence of altered metabolism in hippocampal tissue of blast induced rmTBI at 24 h post injury. Mechanistic understanding of these intertwined processes may help in the development of better therapeutic pathways and agents for blast induced TBI in near future.

A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines.

Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to the accumulation of massive cellular transcription data at an astounding resolution of single cells. It provides valuable insights into cells previously unachieved by bulk cell analysis and is proving crucial in uncovering cellular heterogeneity, identifying rare cell populations, distinct cell-lineage trajectories, and mechanisms involved in complex cellular processes. SCT data is highly complex and necessitates advanced statistical and computational methods for analysis. This review provides a comprehensive overview of the steps in a typical SCT workflow, starting from experimental protocol to data analysis, deliberating various pipelines used. We discuss recent trends, challenges, machine learning methods for data analysis, and future prospects. We conclude by listing the multitude of scRNA-seq data applications and how it shall revolutionize our understanding of cellular biology and diseases.

Insights into Structural Modifications of Valproic Acid and Their Pharmacological Profile.

Valproic acid (VPA) is a well-established anticonvulsant drug discovered serendipitously and marketed for the treatment of epilepsy, migraine, bipolar disorder and neuropathic pain. Apart from this, VPA has potential therapeutic applications in other central nervous system (CNS) disorders and in various cancer types. Since the discovery of its anticonvulsant activity, substantial efforts have been made to develop structural analogues and derivatives in an attempt to increase potency and decrease adverse side effects, the most significant being teratogenicity and hepatotoxicity. Most of these compounds have shown reduced toxicity with improved potency. The simple structure of VPA offers a great advantage to its modification. This review briefly discusses the pharmacology and molecular targets of VPA. The article then elaborates on the structural modifications in VPA including amide-derivatives, acid and cyclic analogues, urea derivatives and pro-drugs, and compares their pharmacological profile with that of the parent molecule. The current challenges for the clinical use of these derivatives are also discussed. The review is expected to provide necessary knowledgebase for the further development of VPA-derived compounds.

The Ser/Thr protein kinase PrkC imprints phenotypic memory in Bacillus anthracis spores by phosphorylating the glycolytic enzyme enolase.

Bacillus anthracis is the causative agent of anthrax in humans, bovine, and other animals. B. anthracis pathogenesis requires differentiation of dormant spores into vegetative cells. The spores inherit cellular components as phenotypic memory from the parent cell, and this memory plays a critical role in facilitating the spores' revival. Because metabolism initiates at the beginning of spore germination, here we metabolically reprogrammed B. anthracis cells to understand the role of glycolytic enzymes in this process. We show that increased expression of enolase (Eno) in the sporulating mother cell decreases germination efficiency. Eno is phosphorylated by the conserved Ser/Thr protein kinase PrkC which decreases the catalytic activity of Eno. We found that phosphorylation also regulates Eno expression and localization, thereby controlling the overall spore germination process. Using MS analysis, we identified the sites of phosphorylation in Eno, and substitution(s) of selected phosphorylation sites helped establish the functional correlation between phosphorylation and Eno activity. We propose that PrkC-mediated regulation of Eno may help sporulating B. anthracis cells in adapting to nutrient deprivation. In summary, to the best of our knowledge, our study provides the first evidence that in sporulating B. anthracis, PrkC imprints phenotypic memory that facilitates the germination process.

Genetic Landscape of Common Epilepsies: Advancing towards Precision in Treatment.

Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.

Gut microbiome contributes to impairment of immunity in pulmonary tuberculosis patients by alteration of butyrate and propionate producers.

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.

Transcriptional responses of attractin gene in the mosquito Anopheles culicifacies: A synergistic neuro-olfactory regulation.

BACKGROUND & OBJECTIVES: Attractin, is a large multi-domain protein which has regulatory functions in multiple physiological processes and thus have strong therapeutic potential. In invertebrates, it was first identified as a water-borne protein pheromone that plays important role in chemical communication and coordinates reproductive activities. But its role in mosquitoes/insects remains unknown. Our unexpected discovery of attractin homolog from the olfactory tissue of Anopheles culicifacies mosquito prompted us to investigate the possible role of Ac-attractin (Ac-atrn) in diverse behavioural responses, e.g. feeding, mating and other non-genetic stresses. METHODS: A homology search analysis was performed to identify the full length attractin (Ac-atrn) gene of Anopheles culicifacies mosquito. To unravel its molecular function during external and internal stresses, extensive real-time PCR was performed in the neuro-olfactory tissues of the adult mosquitoes as well as in the larval stages. Further, a behavioural assay was conducted to elucidate its role in mosquitoes mating behaviour. RESULTS: The results indicated that Ac-atrn is a 3942 bp long transcript which encodes a 1313 amino acid protein, having multiple domains including CUB, EGF, Keltch, etc, with 80-90% homology to other insect/mosquito homologs. Ac-atrn gene was dominantly expressed in the young larvae and its expression was elevated in response to the fresh food supply in the starved larvae. Cold stress temporarily arrested the expression of Ac-atrn gene. In case of adult mosquitoes, olfactory and brain tissue showed relatively higher expression of Ac-atrn than reproductive organs. Although, starvation did not yield significant changes in olfactory tissues, but aging and nutritional stress modulated Ac-atrn expression in the brain tissue. Furthermore, a circadian rhythm dependent change in the expression of Ac-atrn of virgin and mated mosquitoes (both sexes), indicates that Ac-atrn might also have a pheromone guided role during swarm formation and mating behaviour. INTERPRETATION & CONCLUSION: The relative expression profiling of Ac-atrn gene in the larvae during nutritional and cold stress suggested its possible role in mediating chemical communication towards the food source and in thermal regulation of young larvae. Similarly, it might have crucial regulatory role in the stress management and survival of adult mosquitoes. The results revealed that Ac-atrn gene is a global regulator of many physiological processes in mosquitoes including stress response and mating behaviour and thus might be a potential target to design novel intervention strategy against mosquitoes.

Effect of Homocysteine on Biofilm Formation by Mycobacteria.

Mycobacteria show peculiar aggregated outgrowth like biofilm on the surface of solid or liquid media. Biofilms harbor antibiotic resistant bacteria in a self-produced extracellular matrix that signifies the bacterial fate to sedentary existence. Despite years of research, very little is known about the mechanisms that contribute to biofilm formation. LuxS has been previously known to play a role in biofilm formation in Autoinducer-2 dependent manner. We here show the effect of LuxS product-homocysteine, on the biofilm forming ability of non-tuberculous mycobacteria, Mycobacterium smegmatis and Mycobacterium bovis BCG showing AI-2 independent phenotypic effect of LuxS. Exogenous supplementation of homocysteine in the culture media leads to aberrant cording, pellicle outgrowth, and biofilm formation. Thus, our study contributes to the better understanding of the mechanism of mycobacterial biofilm formation and sheds light on the role of LuxS product homocysteine. In addition, we highlight the contribution of activated methyl cycle in bacterial quorum sensing.

GroEL Mediates Folding of Bacillus anthracis Serine/Threonine Protein Kinase, PrkC.

Bacillus anthracis causes anthrax in human and animals. Both, signaling system such as two component system and endogenous chaperone system such as GroEL-GroES help bacteria to cope with the environmental challenges. Such molecular chaperones are the stress induced proteins that help bacteria to override unfavorable conditions by their moonlighting functions. Previous reports showed that PrkC and PrpC, the Ser/Thr kinase-phosphatase pair in B. anthracis, control phosphorylation of GroEL and regulate biofilm formation. In this study, we show that GroEL is involved in the folding of PrkC to active form. The proteins (GroEL, PrkC and PrpC) were expressed and purified by affinity chromatography. Purified GroEL was used for refolding of denatured PrkC and PrpC and observed that GroEL refolds PrkC but not PrpC as measured by their enzymatic activity. We also observed that purification of GroEL with six histidine tag using Cobalt-Agarose resin yielded superior quality GroEL protein with negligible contamination of non-specific proteins. Thus, cobalt resin can be a better choice for purification of many histidine tagged proteins, where Ni-NTA does not work very well.

An integrative system biology approach to unravel potential drug candidates for multiple age related disorders.

Aging, though an inevitable part of life, is becoming a worldwide social and economic problem. Healthy aging is usually marked by low probability of age related disorders. Good therapeutic approaches are still in need to cure age related disorders. Occurrence of more than one ARD in an individual, expresses the need of discovery of such target proteins, which can affect multiple ARDs. Advanced scientific and medical research technologies throughout last three decades have arrived to the point where lots of key molecular determinants affect human disorders can be examined thoroughly. In this study, we designed and executed an approach to prioritize drugs that may target multiple age related disorders. Our methodology, focused on the analysis of biological pathways and protein protein interaction networks that may contribute to the pharmacology of age related disorders, included various steps such as retrieval and analysis of data, protein-protein interaction network analysis, and statistical and comparative analysis of topological coefficients, pathway, and functional enrichment analysis, and identification of drug-target proteins. We assume that the identified molecular determinants may be prioritized for further screening as novel drug targets to cure multiple ARDs. Based on the analysis, an online tool named as 'ARDnet' has been developed to construct and demonstrate ARD interactions at the level of PPI, ARDs and ARDs protein interaction, ARDs pathway interaction and drug-target interaction. The tool is freely made available at http://genomeinformatics.dtu.ac.in/ARDNet/Index.html.

dbAARD & AGP: A computational pipeline for the prediction of genes associated with age related disorders.

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 <0.05) associated with 51 ARDs. Furthermore, a machine learning based gene prediction tool AGP (Age Related Disorders Gene Prediction) has been constructed by employing rotation forest algorithm, to prioritize genes associated with ARDs. The tool achieved an overall accuracy in terms of precision 75%, recall 76%, F-measure 76% and AUC 0.85. Both the web resources have been made available online at http://genomeinformatics.dce.edu/dbAARD/ and http://genomeinformatics.dce.edu/AGP/ respectively for easy retrieval and usage by the scientific community. We believe that this work may facilitate the analysis of plethora of variants associated with ARDs and provide cues for deciphering the biology of aging.

mit-o-matic: a comprehensive computational pipeline for clinical evaluation of mitochondrial variations from next-generation sequencing datasets.

The human mitochondrial genome has been reported to have a very high mutation rate as compared with the nuclear genome. A large number of mitochondrial mutations show significant phenotypic association and are involved in a broad spectrum of diseases. In recent years, there has been a remarkable progress in the understanding of mitochondrial genetics. The availability of next-generation sequencing (NGS) technologies have not only reduced sequencing cost by orders of magnitude but has also provided us good quality mitochondrial genome sequences with high coverage, thereby enabling decoding of a number of human mitochondrial diseases. In this study, we report a computational and experimental pipeline to decipher the human mitochondrial DNA variations and examine them for their clinical correlation. As a proof of principle, we also present a clinical study of a patient with Leigh disease and confirmed maternal inheritance of the causative allele. The pipeline is made available as a user-friendly online tool to annotate variants and find haplogroup, disease association, and heteroplasmic sites. The "mit-o-matic" computational pipeline represents a comprehensive cloud-based tool for clinical evaluation of mitochondrial genomic variations from NGS datasets. The tool is freely available at http://genome.igib.res.in/mitomatic/.

Explicate molecular landscape of combined pulmonary fibrosis and emphysema through explainable artificial intelligence: a comprehensive analysis of ILD and COPD interactions using RNA from whole lung homogenates.

Combined pulmonary fibrosis and emphysema (CPFE) presents a unique challenge in respiratory disorders, merging features of interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD). Using the random forest algorithm, our study thoroughly examines the molecular details of CPFE. Analyzing gene expression datasets from GSE47460 (ILD: 254, COPD: 220, control: 108), we identify key genes namely ADRB2, CDH3, IRS2, MATN3, CD38, PDIA4, VEGFC, and among twenty others, crucial in airway regulation, lung function, and apoptosis, shaping the complex pathogenesis of CPFE. Additionally, miRNAs (hsa-mir-101-3p, hsa-mir-1343-3p, hsa-mir-27a-3p, and miR-16-5p) showcase regulatory impacts on CPFE-related molecular pathways. Our machine learning model unveils these intricate interactions, offering a comprehensive insight into CPFE's molecular mechanisms. This research not only pinpoints potential therapeutic targets and biomarkers but also opens avenues for innovative approaches in managing CPFE, linking ILD and COPD within this complex respiratory condition.

Identifying the mitochondrial metabolism network by integration of machine learning and explainable artificial intelligence in skeletal muscle in type 2 diabetes.

Imbalance in glucose metabolism and insulin resistance are two primary features of type 2 diabetes/diabetes mellitus. Its etiology is linked to mitochondrial dysfunction in skeletal muscle tissue. The mitochondria are vital organelles involved in ATP synthesis and metabolism. The underlying biological pathways leading to mitochondrial dysfunction in type 2 diabetes can help us understand the pathophysiology of the disease. In this study, the mitochondrial gene expression dataset were retrieved from the GSE22309, GSE25462, and GSE18732 using Mitocarta 3.0, focusing specifically on genes that are associated with mitochondrial function in type 2 disease. Feature selection on the expression dataset of skeletal muscle tissue from 107 control patients and 70 type 2 diabetes patients using the XGBoost algorithm having the highest accuracy. For interpretation and analysis of results linked to the disease by examining the feature importance deduced from the model was done using SHAP (SHapley Additive exPlanations). Next, to comprehend the biological connections, study of protein-protien and mRNA-miRNA networks was conducted using String and Mienturnet respectively. The analysis revealed BDH1, YARS2, AKAP10, RARS2, MRPS31, were potential mitochondrial target genes among the other twenty genes. These genes are mainly involved in the transport and organization of mitochondria, regulation of its membrane potential, and intrinsic apoptotic signaling etc. mRNA-miRNA interaction network revealed a significant role of miR-375; miR-30a-5p; miR-16-5p; miR-129-5p; miR-1229-3p; and miR-1224-3p; in the regulation of mitochondrial function exhibited strong associations with type 2 diabetes. These results might aid in the creation of novel targets for therapy and type 2 diabetes biomarkers.

Acute and sub-acute metabolic change in different brain regions induced by moderate blunt traumatic brain injury.

The objective of the study was to observe the effect of moderate closed-head injury on hippocampal, thalamic, and striatal tissue metabolism with time. Closed head injury is responsible for metabolic changes. These changes can be permanent or temporary, depending on the injury's impact. For the experiment, 20 rats were randomly divided into four groups, each containing five animals. Animals were subjected to injury using a modified Marmarou's weight drop device; hippocampal, thalamic, and striatal tissue samples were collected after 1 day, 3 days, and 7 days of injury. NMR spectra were acquired following sample processing. Changes in myo-inositol, creatine, glutamate, succinate, lactate, and N-acetyl aspartic acid in hippocampal tissues were observed at day 3 PI. The tyrosine level in the hippocampus was altered at day 7 PI. While thalamic and striatal tissue samples showed altered levels of branched-chain amino acids and myo-inositol at day 1PI. Taurine, gamma amino butyric acid (GABA), choline, and alpha keto-glutarate levels were found to be significantly altered in striatal tissues at days 1 and 3PI. Acetate and GABA levels were altered in the thalamus on day 1 PI. The choline level in the thalamus was found to alter at all-time points after injury. The alteration in these metabolites may be due to the alteration in their respective pathways. Neurotransmitter and energy metabolism pathways were found to be altered in all three brain regions after TBI. This study may help better understand the effect of injury on the metabolic balance of a specific brain region and recovery.

Rare deleterious mutations in Bruton's tyrosine kinase as biomarkers for ibrutinib-based therapy: an in silico insight.

CONTEXT: Squamous cell carcinoma (SCC) is the second most common type of skin cancer caused by malignant keratinocytes. Multiple studies have shown that protein mutations have a significant impact on the development and progression of cancer, including SCC. We attempted to decode the effect of single amino acid mutations in the Bruton's tyrosine kinase (BTK) protein in this study. Molecular dynamic (MD) simulations were performed on selected deleterious mutations of the BTK protein, revealing that the variants adversely affect the protein, indicating that they may contribute to the prognosis of SCC by making the protein unstable. Then, we investigated the interaction between the protein and its mutants with ibrutinib, a drug designed to treat SCC. Even though the mutations have deleterious effects on protein structure, they bind to ibrutinib similarly to their wild type counterpart. This study demonstrates that the effect of detected missense mutations is unfavorable and can result in function loss, which is severe for SCC, but that ibrutinib-based therapy can still be effective on them, and the mutations can be used as biomarkers for Ibrutinib-based treatment. METHODS: Seven different computational techniques were used to compute the effect of SAVs in accordance with the experimental requirements of this study. To understand the differences in protein and mutant dynamics, MD simulation and trajectory analysis, including RMSD, RMSF, PCA, and contact analysis, were performed. The free binding energy and its decomposition for each protein-drug complex were determined using docking, MM-GBSA, MM-PBSA, and interaction analysis (wild and mutants).

A cystatin C similar protein from Musa acuminata that inhibits cathepsin B involved in rheumatoid arthritis using in silico approach and in vitro cathepsin B inhibition by protein extract.

Rheumatoid arthritis (RA) is an auto-immune disease that affects the synovial lining of the joints, causes synovitis and culminates to joint destruction. Cathepsin B is responsible for digesting unwanted proteins in extracellular matrix but its hyper expression could implicate in pathological diseases like RA. Available treatments for RA are classified into non-steroidal anti-inflammatory drugs (NSAIDs), disease-modifying anti-rheumatic drugs (DMARDs), and steroids, but the severe side effects associated with these drugs is one of concerns and cannot be ignored. Thus, any alternative therapy with minimum or no side effects would be a cornerstone. In our in silico studies a cystatin C similar protein (CCSP) has been identified from Musa acuminata that could effectively inhibit the cathepsin B activity. In silico and molecular dynamics studies showed that the identified CCSP and cathepsin B complex has binding energy -66.89 kcal/mol as compared to cystatin C - cathepsin B complex with binding energy of -23.38 kcal/mol. These results indicate that CCSP from Musa acuminata has better affinity towards cathepsin B as compared to its natural inhibitor cystatin C. Hence, CCSP may be suggested as an alternative therapeutic in combating RA by inhibiting its one of the key proteases cathepsin B. Further, in vitro experiments with fractionated protein extracts from Musa sp. peel inhibited cathepsin B to 98.30% at 300 µg protein concentration and its IC50 was found to be 45.92 µg indicating the presence of cathepsin B inhibitor(s) in protein extract of peel which was further confirmed by reverse zymography.Communicated by Ramaswamy H. Sarma.

Systematic curation and analysis of genomic variations and their potential functional consequences in snoRNA loci.

Small nucleolar RNAs (snoRNAs) are a class of noncoding functional RNAs which are involved in RNA modifications, like methylation and pseudouridylation of other RNAs. The snoRNA species of RNAs are characterized by conserved structural motifs they harbor which are also intricately related to their functionality. Though there have been reports of the involvement of snoRNAs in disease processes and anecdotal reports of genomic variations in snoRNA loci and their effects in modulating snoRNA function, there has been no systematic collection and analysis of variations in snoRNA loci. In this manuscript, we present the most comprehensive curation of genomic single nucleotide variations in human snoRNA loci, and their systematic computational analysis to reveal potential single nucleotide variations which could have functional effects. We show six single nucleotide variations in snoRNA loci could significantly alter snoRNA structure and could have potential implications in their functions. The compilation is available at the snoRNA locus specific variation database: http://genome.igib.res.in/snolovd conforming to the HGVS standards for nomenclature of genomic variants.

Application of explainable artificial intelligence in the identification of Squamous Cell Carcinoma biomarkers.

Non-melanoma skin cancers (NMSCs) are the fifth most common type of cancer worldwide, affecting both men and women. Each year, more than a million new occurrences of NMSC are estimated, with Squamous Cell Carcinoma (SCC) representing approximately 20% of all skin malignancies. The purpose of this study was to find potential diagnostic biomarkers for SCC by application of eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on binary classification datasets comprising the expression data of 40 SCC, 38 AK, and 46 normal healthy skin samples. After successfully incorporating SHAP values into the ML models, 23 significant genes were identified and were found to be associated with the progression of SCC. These identified genes may serve as diagnostic and prognostic biomarkers in patients with SCC.