Biology subjects

Nair, A. S.

Publications and source records attributed to Nair, A. S..

5 recordsLinked to original sources

bioRxiv2026

Host Metabolic Reprogramming Defines Macrophage Antifungal Activity Against Cryptococcus neoformans

Innate immune cells, including macrophages and DCs, are critical for controlling infection by Cryptococcus neoformans. Although C. neoformans is phagocytosed equally by each pulmonary phagocyte subset, CD11b+ DCs support fungal growth, whereas Ly6c- monocyte-like macrophages are antifungal. The metabolic programs underlying these contrasting responses remain incompletely understood, but our previous work points to increased glycolysis in antifungal cells. Therefore, we hypothesized that metabolic reprogramming in phagocytes following cryptococcal exposure leads to different outcomes. We examined ten metabolic pathways using flow cytometry, metabolic inhibition, antifungal assays, and pulmonary infection. Cryptococcal exposure increased GLUT-1, HK, HIF-1, and lactate production in J774A.1 macrophages, consistent with enhanced glycolytic metabolism in antifungal cells. In contrast, ATP5a and IDH2 were reduced, suggesting diminished mitochondrial metabolism. Cryptococcus also decreased G6PD, ACAC, ASS1, and PRDX2, indicating alterations in PPP activity, fatty acid synthesis, arginine metabolism, and antioxidant defense. Pharmacologic inhibition of PPP further reduced G6PD expression and significantly decreased intracellular cryptococcal burden, supporting a role for glucose-derived metabolic pathways in regulating fungal survival. In vivo, infection increased GLUT-1 and HK expression in antifungal female Ly6c- monocyte-like macrophages but not in permissive CD11b+ DCs, whereas other metabolic markers showed limited or subset-specific changes. Collectively, these findings demonstrate that antifungal cells use a predominantly glycolytic program accompanied by reduced mitochondrial, anabolic, and antioxidant-associated protein expression following interaction with C. neoformans. This phagocyte subset-dependent metabolic response during pulmonary infection may influence antifungal activity and identify metabolic pathways that could be targeted to strengthen host defense.

immunology↗

bioRxiv2024

Discovery of Novel R-Selective Aminotransferase Motifs through Computational Screening

Transaminases, enzymes facilitating amino group transfers, are divided into four subfamilies: D-alanine transaminase (DATA), L-selective Branched chain aminotransferase (BCAT), 4-amino-4-deoxychorismate lyase (ADCL), and R-selective aminotransferase (RATA). RATA enzymes are particularly valuable in biocatalysis for synthesizing chiral amines and resolving racemic mixtures, yet their identification in sequence databases is challenging due to the lack of robust motif-based screening methods. By constructing a transaminase sequence dataset and categorizing them into subfamilies, we re-screened conserved motifs and explored novel ones. Phylogenetic clustering and structural localization of these motifs on Alphafold-predicted protein models validated their importance. For ADCL, BCAT, DATA, and RATA datasets, we discovered 5, 7, 10, and 2 novel motifs, respectively. Additionally, unique residue patterns were identified, underscoring their structural significance. This motif-based computational approach promises to unveil novel RATA enzymes for biocatalytic applications.

bioinformatics↗

bioRxiv2024

Chitin synthase targeting antifungal agents for Mucormycosis (Black fungus disease) caused by Rhizopus delemar - An in-silico study

Mucormycosis, a severe fungal infection caused by Mucorales fungi, particularly Rhizopus delemar, has prompted the development of new and more potent antifungal drugs due to the emergence of drug-resistant strains. Currently used drugs are known to be toxic to human cells also, which is a major drawback in their administration. Antifungal drugs commercially under use and their structurally modified analogues along with 30 plants reported with antifungal activity from IMPPAT database, totalling 229 compounds, were screened against the best modelled structure of Chitin Synthase, a key target enzyme of Rhizopus delemar by in-silico molecular docking studies. Computerized methods to assess absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles was employed to further filter key lead drug candidates from among the docked ligands that could act as potential antifungal medications. Modified structures of Posaconazole, Nikkomycin and Isavuconazole were found to give better docking results than compared to the original drugs, though they failed to comply with ADMET parameters. Among the phytochemicals, 1-nonacosanol displayed binding affinity (highest LibDock score) greater than the synthetic drugs and their derivatives, though it too failed to comply with ADMET parameters. The phytochemical Dihydrocapsaicin was found to be the best compound that satisfied all the parameters such as ADMET, TOPKAT, Lipinskis and Vebers rule along with good binding affinity. All in all, this novel study paves a way to investigate the interaction of Chitin Synthase inhibitors at molecular level to achieve an alternative solution with less toxic effects to treat Mucormycosis.

bioinformatics↗

bioRxiv2023

Integrated ACMG approved genes and ICD codes for the translational research and precision medicine

Timely understanding of biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data is developing at an unmatched pace and levels to foster translational research and precision medicine. Over ten million genomics datasets have been produced and publicly shared in the year 2022. Diverse and high-volume genomics and clinical data have the potential to broaden the scope of biological discoveries and insights by extracting, analyzing, and interpreting the hidden information. However, the current and still unresolved challenges include the integration of genomic profiles of the patients with their medical records. The disease definition in genomics medicine is simplified, when in the clinical world, diseases are classified, identified, and adopted with their International Classification of Diseases (ICD) codes, which are maintained by the World Health Organization (WHO). Several biological databases have been produced, which includes information about human genes and related diseases. However, still, there is no database exists, which can precisely link clinical codes with relevant genes and variants to support genomic and clinical data integration for clinical and translation medicine. In this project, we are focused on the development of an annotated gene-disease-code database, which is accessible through an online, cross-platform, and user-friendly application i.e., PAS-GDC. However, our scope is limited to the integration of ICD-9 and ICD-10 codes with the list of genes approved by the American College of Medical Genetics and Genomics (ACMG). Results include over seventeen thousand diseases and four thousand ICD codes, and over eleven thousand gene-disease-code combinations.

bioinformatics↗

bioRxiv2017

HYPO: A database of hypothetical human proteins

All annotated genes were once hypothetical or uncharacterized. Keeping this as an epilogue, we have enhanced our former database of hypothetical proteins (HP) in human (HypoDB) with added annotation, application programming interfaces and descriptive features. The database hosts 1000+ manually curated records of the known unknown regions in the human genome. The new updated version of HypoDB with functionalities (Blast, Match) is freely accessible at http://www.bioclues.org/hypo2.

bioinformatics↗