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Introduction: Autism spectrum disorders (ASDs) are complex neurodevelopmental disorders characterized by restrictive repetitive behavior and impairment in social and communication skills. They are extremely heterogeneous with a strong genetic preponderance. They are clinically highly convoluted, presenting with multiple comorbid conditions and syndromic features. More than 100 genes have been identified to date., Method: Whole exome sequencing (WES) has emerged as a valuable tool in evaluating the genetic underpinnings of ASDs, be it the syndromic or the idiopathic variants. In the current study, we performed WES on a multiplex family of Indian origin to investigate the disease etiology in the siblings (S1 [Female] and S2 [Male]) exhibiting ASD syndromic features, at both clinical and genetic aspects., Results: Exome sequencing identified a missense variant (NM_030665.4:c.5320C>T; p.Arg1774Trp) in S1 resulting in RAI1 haploinsufficiency. Validation by Sanger sequencing confirmed that the variant was true positive and maternally transmitted in the subject. Likewise, we report an inherited missense variant at the same locus (17p11.2) corresponding to the FLII gene (NM_002018.4:c.2030A>C; p.Glu677Ala) in the other sibling, S2. Both the variants were reported in the Smith Magenis syndrome (SMS) critical region justifying their contribution to the presentation of the syndromic SMS features. These WES findings were consistent with the clinical findings that imply SMS features in both siblings., Conclusion: The current study employed WES to provide insights into the genetic complexity associated with syndromic ASD and how that contributes to the disease heterogeneity. Moving forward, combinatorial approaches and findings from syndromic ASDs can potentially act as indicators to understand the genetic and phenotypic variations seen in idiopathic ASD., Competing Interests: The authors have no conflicts of interest to declare., (© 2024 S. Karger AG, Basel.)

Competing Interests: None declared.

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bhardwaj, Sharma, Parambath, Gul, Zhang, Lobie, Qin and Pandey.)

Background: Tuberculosis (TB) is a major infectious disease, where incomplete information about host genetics and immune responses is hindering the development of transformative therapies. This study characterized the immune cell landscape and blood transcriptomic profile of patients with pulmonary TB (PTB) to identify the potential therapeutic biomarkers., Methods: The blood transcriptome profile of patients with PTB and controls were used for fractionating immune cell populations with the CIBERSORT algorithm and then to identify differentially expressed genes (DEGs) with R/Bioconductor packages. Later, systems biology investigations (such as semantic similarity, gene correlation, and graph theory parameters) were implemented to prioritize druggable genes contributing to the immune cell alterations in patients with TB. Finally, real time-PCR (RT-PCR) was used to confirm gene expression levels., Results: Patients with PTB had higher levels of four immune subpopulations like CD8 + T cells ( P = 1.9 × 10 -8 ), natural killer (NK) cells resting ( P = 6.3 × 10 -5 ), monocytes ( P = 6.4 × 10 -6 ), and neutrophils ( P = 1.6 × 10 -7 ). The functional enrichment of 624 DEGs identified in the blood transcriptome of patients with PTB revealed major dysregulation of T cell-related ontologies and pathways (q ≤ 0.05). Of the 96 DEGs shared between transcriptome and immune cell types, 39 overlapped with TB meta-profiling genetic signatures, and their semantic similarity analysis with the remaining 57 genes, yielded 45 new candidate TB markers. This study identified 9 CD8 + T cell-associated genes ( ITK, CD2, CD6, CD247, ZAP70, CD3D, SH2D1A, CD3E , and IL7R ) as potential therapeutic targets of PTB by combining computational druggability and co-expression (r 2 ≥ |0.7|) approaches., Conclusion: The changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alsulaimany, Zabermawi, Almukadi, Parambath, Shetty, Vaidyanathan, Elango, Babanaganapalli and Shaik.)

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1 , and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.