@article{rai_patel_muthuraj_gandhi_das_srivastava_2021, title={Systematic metabolome profiling and multi-omics analysis of the nitrogen-limited non-model oleaginous algae for biorefining}, volume={8}, ISSN={["2292-8782"]}, DOI={10.18331/BRJ2021.8.1.4}, abstractNote={Oleaginous microalga Chlorella is a promising microbial cell factory for producing lipids, transesterifiable to biodiesel, and phytochemical, high-value molecules (HVMs). To better understand the stress-induced oleaginous mechanism of Chlorella sp. FC2 IITG that closely matches with Chlorella sorokiniana based on 18s rRNA gene sequence, we performed 'Algomics' by systematically integrating metabolomics and proteomics data in response to time-resolved nitrogen-limitation: 40 h (mild), 88 h (moderate), and 120 h (severe). Ten HVMs belonging to four biological classes: triacylglycerol (TAG), polyunsaturated fatty acids (PUFA), phytosterols, and terpenoids were annotated using untargeted metabolomics and MS/MS fragmentation pattern match to the spectral library. In particular, the study evidenced 4× and 6× increased accumulation of two different PUFA: 9(S)HpOTrE and dihomo-gamma-linolenic acid, respectively, in nitrogen-limitation conditions. Co-extraction of TAG and PUFA could lower the biodiesel production cost for feasible commercialization. The investigation found a maximum accumulation of TAG 59:10 at 40 h, while that for TAG 54:4 was recorded at 88 h, which suggests different TAG species could be induced by regulating nitrogen-limitation severity. Elevated ꞵ-oxidation, glycolysis, and tricarboxylic acid (TCA) cycle identified in proteomics analysis could provide the substrates, phosphoenolpyruvate, pyruvate, and acetyl CoA, for different phytochemical accumulation in response to nitrogen limitation. The multiomics data unraveled a metabolic tug-of-war ongoing between biomass and storage lipid (TAG) accumulation during nitrogen-limitation, which involved multiple processes including hindered CO2 fixation, the supply of energy, reductants, and carbon reallocation from proteins and membrane lipids. These findings provide distinct oleaginous mechanisms in non-model microalgae, Chlorella sp. FC2 IITG, and engineerable targets for microalgal trait improvements.}, number={1}, journal={BIOFUEL RESEARCH JOURNAL-BRJ}, author={Rai, Vineeta and Patel, Sandip Kumar and Muthuraj, Muthusivaramapandian and Gandhi, Mayuri N. and Das, Debasish and Srivastava, Sanjeeva}, year={2021}, pages={1330-+} }
 @misc{patel_george_rai_2020, title={Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology}, volume={11}, ISSN={["1663-9812"]}, DOI={10.3389/fphar.2020.01177}, abstractNote={The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient’s response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from “big” data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.}, journal={FRONTIERS IN PHARMACOLOGY}, author={Patel, Sandip Kumar and George, Bhawana and Rai, Vineeta}, year={2020}, month={Aug} }
 @article{rai_fisher_duckworth_baars_2020, title={Extraction and Detection of Structurally Diverse Siderophores in Soil}, volume={11}, ISSN={["1664-302X"]}, DOI={10.3389/fmicb.2020.581508}, abstractNote={Although the biochemistry of bacterial and fungal siderophores has been intensively studied in laboratory cultures, their distribution and impacts on nutrient cycling and microbial communities in soils remain poorly understood. The detection of siderophores in soil is an analytical challenge because of the complexity of the soil matrix and their structural diversity. Liquid chromatography-mass spectrometry (LC-MS) is a suitable method for the sensitive analysis of siderophores in complex samples; however, siderophore extraction into liquid phases for analysis by LC-MS is problematic because of their adsorption to soil particles and organic matter. To determine extraction efficiencies of structurally diverse siderophores, spike-recovery experiments were set up with standards representing the three main siderophore classes: the hydroxamate desferrioxamine B (DFOB), the α-hydroxycarboxylate rhizoferrin, and the catecholate protochelin. Previously used solvent extractions with water or methanol recovered only a small fraction (< 35%) of siderophores, including < 5% for rhizoferrin and protochelin. We designed combinatorial chemical extractions (22 total solutions) to target siderophores associated with different soil components. A combination of calcium chloride and ascorbate achieved high and, for some soils, quantitative extraction of DFOB and rhizoferrin. Protochelin analysis was complicated by potential fast oxidation and interactions with colloidal soil components. Using the optimized extraction method, we detected α-hydroxycarboxylate type siderophores (viz. rhizoferrin, vibrioferrin, and aerobactin) in soil for the first time. Concentrations reached 461 pmol g–1, exceeding previously reported concentrations of siderophores in soil and suggesting a yet unrecognized importance of α-hydroxycarboxylate siderophores for biological interactions and biogeochemical processes in soil.}, journal={FRONTIERS IN MICROBIOLOGY}, author={Rai, Vineeta and Fisher, Nathaniel and Duckworth, Owen W. and Baars, Oliver}, year={2020}, month={Sep} }
 @misc{kothari_singh_nath_kumar_rai_kaushal_omar_pandey_jain_2020, title={Immune Dysfunction and Multiple Treatment Modalities for the SARS-CoV-2 Pandemic: Races of Uncontrolled Running Sweat?}, volume={9}, ISSN={["2079-7737"]}, DOI={10.3390/biology9090243}, abstractNote={Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic threat with more than 11.8 million confirmed cases and more than 0.5 million deaths as of 3 July 2020. Given the lack of definitive pharmaceutical interventions against SARS-CoV-2, multiple therapeutic strategies and personal protective applications are being used to reduce the risk of high mortality and community spread of this infection. Currently, more than a hundred vaccines and/or alternative therapeutic regimens are in clinical trials, and some of them have shown promising results in improving the immune cell environment and controlling the infection. In this review, we discussed high-performance multi-directory strategies describing the uncontrolled deregulation of the host immune landscape associated with coronavirus disease (COVID-19) and treatment strategies using an anti-neoplastic regimen. We also followed selected current treatment plans and the most important on-going clinical trials and their respective outcomes for blocking SARS-CoV-2 pathogenesis through regenerative medicine, such as stem cell therapy, chimeric antigen receptors, natural killer (NK) cells, extracellular vesicular-based therapy, and others including immunomodulatory regimens, anti-neoplastic therapy, and current clinical vaccine therapy.}, number={9}, journal={BIOLOGY-BASEL}, author={Kothari, Ashish and Singh, Vanya and Nath, Uttam Kumar and Kumar, Sandeep and Rai, Vineeta and Kaushal, Karanvir and Omar, Balram Ji and Pandey, Atul and Jain, Neeraj}, year={2020}, month={Sep} }
 @misc{phapale_rai_mohanty_srivastava_2020, title={Untargeted Metabolomics Workshop Report: Quality Control Considerations from Sample Preparation to Data Analysis}, volume={31}, ISSN={["1879-1123"]}, DOI={10.1021/jasms.0c00224}, abstractNote={The Proteomics Society, India (PSI), hosted the Metabolomics workshop on experimental and data analysis training for untargeted metabolomics in December 2019. The workshop included six tutorial lectures and hands-on data analysis training sessions presented by seven speakers from across the globe. The tutorials and hands-on data analysis sessions focused on workflows for liquid chromatography-mass spectrometry (LC-MS) based on untargeted metabolomics. We review here three main topics from the workshop, which were uniquely identified as bottlenecks for new researchers: (a) experimental design, (b) quality controls during sample preparation and instrumental analysis, and (c) data quality evaluation using open source tools. Our objective here is to present common challenges faced by novice researchers and present guidelines to address them. We provide resources and good practices for researchers who are at the initial stage of setting up metabolomics workflows in their laboratories.}, number={9}, journal={JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY}, author={Phapale, Prasad and Rai, Vineeta and Mohanty, Ashok Kumar and Srivastava, Sanjeeva}, year={2020}, month={Sep}, pages={2006–2010} }