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.

To understand the post-transcriptional molecular mechanisms attributing to oleaginousness in microalgae challenged with nitrogen starvation (N-starvation), the longitudinal proteome dynamics of Chlorella sp. FC2 IITG was investigated using multipronged quantitative proteomics and multiple reaction monitoring assays. Physiological data suggested a remarkably enhanced lipid accumulation with concomitant reduction in carbon flux towards carbohydrate, protein and chlorophyll biosynthesis. The proteomics-based investigations identified the down-regulation of enzymes involved in chlorophyll biosynthesis (porphobilinogen deaminase) and photosynthetic carbon fixation (sedoheptulose-1,7 bisphosphate and phosphoribulokinase). Profound up-regulation of hydroxyacyl-ACP dehydrogenase and enoyl-ACP reductase ascertained lipid accumulation. The carbon skeletons to be integrated into lipid precursors were regenerated by glycolysis, β-oxidation and TCA cycle. The enhanced expression of glycolysis and pentose phosphate pathway enzymes indicates heightened energy needs of FC2 cells for the sustenance of N-starvation. FC2 cells strategically reserved nitrogen by incorporating it into the TCA-cycle intermediates to form amino acids; particularly the enzymes involved in the biosynthesis of glutamate, aspartate and arginine were up-regulated. Regulation of arginine, superoxide dismutase, thioredoxin-peroxiredoxin, lipocalin, serine-hydroxymethyltransferase, cysteine synthase, and octanoyltransferase play a critical role in maintaining cellular homeostasis during N-starvation. These findings may provide a rationale for genetic engineering of microalgae, which may enable synchronized biomass and lipid synthesis.

Summary: To prevent vaccine‐associated paralytic poliomyelitis, WHO recommended withdrawal of Oral Polio Vaccine (Serotype‐2) and a single dose of Inactivated Poliovirus Vaccine (IPV). IPV however is expensive, requires cold chain, injections and offers limited intestinal mucosal immunity, essential to prevent polio reinfection in countries with open sewer system. To date, there is no virus‐free and cold chain‐free polio vaccine capable of inducing robust mucosal immunity. We report here a novel low‐cost, cold chain/poliovirus‐free, booster vaccine using poliovirus capsid protein (VP1, conserved in all serotypes) fused with cholera non‐toxic B subunit (CTB) expressed in lettuce chloroplasts. PCR using unique primer sets confirmed site‐specific integration of CTB‐VP1 transgene cassettes. Absence of the native chloroplast genome in Southern blots confirmed homoplasmy. Codon optimization of the VP1 coding sequence enhanced its expression 9–15‐fold in chloroplasts. GM1‐ganglioside receptor‐binding ELISA confirmed pentamer assembly of CTB‐VP1 fusion protein, fulfilling a key requirement for oral antigen delivery through gut epithelium. Transmission Electron Microscope images and hydrodynamic radius analysis confirmed VP1‐VLPs of 22.3 nm size. Mice primed with IPV and boosted three times with lyophilized plant cells expressing CTB‐VP1co, formulated with plant‐derived oral adjuvants, enhanced VP1‐specific IgG1, VP1‐IgA titres and neutralization (80%–100% seropositivity of Sabin‐1, 2, 3). In contrast, IPV single dose resulted in <50% VP1‐IgG1 and negligible VP1‐IgA titres, poor neutralization and seropositivity (<20%, <40% Sabin 1,2). Mice orally boosted with CTB‐VP1co, without IPV priming, failed to produce any protective neutralizing antibody. Because global population is receiving IPV single dose, booster vaccine free of poliovirus or cold chain offers a timely low‐cost solution to eradicate polio.

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.

Current momentum of microalgal research rests extensively in tapping the potential of multi-omics methodologies in regard to sustainable biofuels. Microalgal biomass is fermented to bioethanol; while lipids, particularly triacylglycerides (TAGs), are transesterified to biodiesels. Biodiesel has emerged as an ideal biofuel candidate; hence, its commercialization and use are increasingly being emphasized. Abiotic stresses exaggerate TAG accumulation, but the precise mechanisms are yet to be known. More recently, comprehensive multi-omics studies in microalgae have emerged from the biofuel perspective. Genomics and transcriptomics of microalgae have provided crucial leads and basic understanding toward lipid biosynthesis. Proteomics and metabolomics are now complementing “algal omics” and offer precise functional insights into the attendant static and dynamic physiological contexts. Indeed, the field has progressed from shotgun to targeted approaches. Notably, targeted proteomics studies in microalga are not yet reported. Several multi-omics tools and technologies that may be used to dig deeper into the microalgal physiology are examined and highlighted in this review. The article therefore aims to both introduce various available high-throughput biotechnologies and applications of “omics” in microalgae, and enlists a compendium of the emerging cutting edge literature. We suggest that a strategic and thoughtful combination of data streams from different omics platforms can provide a system-wide overview. The algal omics warrants closer attention in the future, with a view to technical, economic, and societal impacts that are anticipated in the current postgenomics era.

The gene Par-4 (Prostate Apoptosis Response 4) was originally identified in prostate cancer cells undergoing apoptosis and its product Par-4 showed cancer specific pro-apoptotic activity. Particularly, the SAC domain of Par-4 (SAC-Par-4) selectively kills cancer cells leaving normal cells unaffected. The therapeutic significance of bioactive SAC-Par-4 is enormous in cancer biology; however, its large scale production is still a matter of concern. Here we report the production of SAC-Par-4-GFP fusion protein coupled to translational enhancer sequence (5′ AMV) and apoplast signal peptide (aTP) in transgenic Nicotiana tabacum cv. Samsun NN plants under the control of a unique recombinant promoter M24. Transgene integration was confirmed by genomic DNA PCR, Southern and Northern blotting, Real-time PCR and Nuclear run-on assays. Results of Western blot analysis and ELISA confirmed expression of recombinant SAC-Par-4-GFP protein and it was as high as 0.15% of total soluble protein. In addition, we found that targeting of plant recombinant SAC-Par-4-GFP to the apoplast and endoplasmic reticulum (ER) was essential for the stability of plant recombinant protein in comparison to the bacterial derived SAC-Par-4. Deglycosylation analysis demonstrated that ER-targeted SAC-Par-4-GFP-SEKDEL undergoes O-linked glycosylation unlike apoplast-targeted SAC-Par-4-GFP. Furthermore, various in vitro studies like mammalian cells proliferation assay (MTT), apoptosis induction assays, and NF-κB suppression suggested the cytotoxic and apoptotic properties of plant-derived SAC-Par-4-GFP against multiple prostate cancer cell lines. Additionally, pre-treatment of MAT-LyLu prostate cancer cells with purified SAC-Par-4-GFP significantly delayed the onset of tumor in a syngeneic rat prostate cancer model. Taken altogether, we proclaim that plant made SAC-Par-4 may become a useful alternate therapy for effectively alleviating cancer in the new era.