Activity against Mycobacterium tuberculosis of a new class of spirooxindolopyrrolidine embedded chromanone hybrid heterocycles.

A new class of structurally intriguing heterocycles embedded with spiropyrrolidine, oxindole and chromanones was prepared by regio- and stereoselectively in quantitative yields using an intermolecular tandem cycloaddition protocol. The compounds synthesized were assayed for their anti-mycobacterial activity against Mycobacterium tuberculosis (Mtb) H37Rv and isoniazid-resistant (katG and inhA promoter mutations) clinical Mtb isolates. Four compounds exhibited significant antimycobacterial activity against Mtb strains tested. In particular, a compound possessing a fluorine substituted derivative displayed potent activity at 0.39 µg mL-1 against H37Rv, while it showed 0.09 µg mL-1 and 0.19 µg mL-1 activity against inhA promoter and katG mutation isolates, respectively. A molecular docking study was conducted with the potent compound, which showed results that were consistent with the in vitro experiments.

Integrated Multi-Omics Analysis and Validation in Yeast Model of Amyotrophic Lateral Sclerosis.

Transcriptomics is a complex process that involves raw data extraction, normalization, differential gene expression, and analysis. The Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) is a repository of experimental datasets. Amyotrophic lateral sclerosis (ALS) datasets are deposited by various scientists and research investigators to expand the horizon of scientific knowledge. R-statistical tools are the most common ways for conducting these kinds of studies. The first step is the identification of appropriate datasets. Since the raw data is available in a variety of formats, a large array of software is used for extraction and analysis. Normalization is conducted for the datasets using NetworkAnalyst. Differential analysis is further conducted on the normalized data to identify significantly enriched genes. The significant genes are then grouped into pathways. The results were validated using yeast model of ALS in which the yeast is transformed with ALS plasmids encoding genes associated with ALS. The resulting GFP-tagged protein aggregates are imaged using fluorescence microscopy and subsequently validated using filter retardation assay and quantified using ImageJ software. Functional role of different genes is studied using metabolite treatment and knockout studies.

Integrated Omic Analysis Delineates Pathways Modulating Toxic TDP-43 Protein Aggregates in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a multi-systemic, incurable, amyloid disease affecting the motor neurons, resulting in the death of patients. The disease is either sporadic or familial with SOD1, C9orf72, FUS, and TDP-43 constituting the majority of familial ALS. Multi-omics studies on patients and model systems like mice and yeast have helped in understanding the association of various signaling and metabolic pathways with the disease. The yeast model system has played a pivotal role in elucidating the gene amyloid interactions. We carried out an integrated transcriptomic and metabolomic analysis of the TDP-43 expressing yeast model to elucidate deregulated pathways associated with the disease. The analysis shows the deregulation of the TCA cycle, single carbon metabolism, glutathione metabolism, and fatty acid metabolism. Transcriptomic analysis of GEO datasets of TDP-43 expressing motor neurons from mice models of ALS and ALS patients shows considerable overlap with experimental results. Furthermore, a yeast model was used to validate the obtained results using metabolite addition and gene knock-out experiments. Taken together, our result shows a potential role for the TCA cycle, cellular redox pathway, NAD metabolism, and fatty acid metabolism in disease. Supplementation of reduced glutathione, nicotinate, and the keto diet might help to manage the disease.

Integrated multi-omics analysis of Huntington disease identifies pathways that modulate protein aggregation.

Huntington disease (HD) is a neurodegenerative disease associated with polyglutamine expansion in the protein huntingtin (HTT). Although the length of the polyglutamine repeat correlates with age at disease onset and severity, psychological, cognitive and behavioral complications point to the existence of disease modifiers. Mitochondrial dysfunction and metabolic deregulation are both associated with the HD but, despite multi-omics characterization of patients and model systems, their mechanisms have remained elusive. Systems analysis of multi-omics data and its validation by using a yeast model could help to elucidate pathways that modulate protein aggregation. Metabolomics analysis of HD patients and of a yeast model of HD was, therefore, carried out. Our analysis showed a considerable overlap of deregulated metabolic pathways. Further, the multi-omics analysis showed deregulated pathways common in human, mice and yeast model systems, and those that are unique to them. The deregulated pathways include metabolic pathways of various amino acids, glutathione metabolism, longevity, autophagy and mitophagy. The addition of certain metabolites as well as gene knockouts targeting the deregulated metabolic and autophagy pathways in the yeast model system showed that these pathways do modulate protein aggregation. Taken together, our results showed that the modulation of deregulated pathways influences protein aggregation in HD, and has implications for progression and prognosis. This article has an associated First Person interview with the first author of the paper.