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Role of AI and ML in empowering and solving problems linked to COVID-19 pandemic
Published in Sanjeeva Srivastava, Multi-Pronged Omics Technologies to Understand COVID-19, 2022
Deeptarup Biswas, Gaurish Loya, Graham Roy Ball
One of the recent proteome studies on the cellular processes and host factors responsible for infection was reported using the SARS-CoV-2-infected cell line model. This part has aided in the further identification and drug target and the development of therapeutic modalities (Bojkova et al. 2020). In addition, Gordon et al. have utilized affinity purification-mass spectrometry to identify viral proteins interacting with host proteins which have further been incorporated in Human Protein Atlas database (Gordon et al. 2020; Uhlen et al. 2010). Furthermore, researchers around the globe have looked at the transcriptome, proteome, and metabolome profiles of clinical samples like blood plasma, nasopharyngeal swab, and urine from COVID-19 patients to identify the underlying biological pathways that get altered due to SARS-CoV-2 infection (Shu et al. 2020; D’Alessandro et al. 2020; Shen et al. 2020). It can be safely concluded from these reports that the disease progression, morbidity, and mortality of COVID-19 is a multifactorial event caused by a diverse set of dysregulated biological pathways: hemostasis, complement cascades, leukocyte migration, regulation of cell adhesion, platelet degranulation, regulation of peptidase activity, coagulation cascades, apoptosis, T-cell signaling, neutrophil degranulation, etc.
Perturbations to metabolic networks
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
Beginning with Recon 1 [57], till the more recent Recon 3D [58], a number of increasingly detailed models of human metabolism have been reconstructed. A large number of studies have used the various available human metabolic models, to derive interesting insights into both health and disease [59, 60]. Nielsen and co-workers built a consensus hepatocyte metabolic model iHepatocytes2322 [61], based on the Human Metabolic Reaction database (HMR version 2.0) and proteomics data in Human Protein Atlas (https://www.proteinatlas.org; [62]). Integrating clinical data from non-alcoholic fatty liver disease patients into the hepatocyte model, they identified key disease-specific biomarkers.
From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2020
Maria Eugenia Gallo Cantafio, Katia Grillone, Daniele Caracciolo, Francesca Scionti, Mariamena Arbitrio, Vito Barbieri, Licia Pensabene, Pietro Hiram Guzzi, Maria Teresa Di Martino
Transcriptomic technologies allow for the production of information on the total transcripts of a genome or a specific cell by the use of two high-throughput methods: (i) microarrays, which allow the simultaneous detection and quantification of thousands of previously identified transcripts by hybridization of targets on high-density array containing complementary probes; (ii) RNA sequencing (RNA-Seq), which uses high-throughput massive parallel sequencing combined with computational methods to detect and quantify the complete set of RNA transcripts. Comparison of transcriptomes in different tissues, conditions, time points, or even at single cell level gives information on how genes are regulated and differentially expressed disclosing details about the biology of the system. Moreover, expression profiles can also help to infer the functions of previously unannotated genes. Thereby, the lowering of the technology costs and increased sensitivity allowed a large amount of studies. Many consortium efforts have produced transcriptomic data sets of (i) cancer cell lines, such as the Encyclopedia of DNA Elements (ENCODE) [46], the Cancer Cell Line Encyclopedia (CCLE) [47], and Genentech [48]; (ii) normal tissues, such as the Genotype-Tissue Expression (GTEx) project [49] and the Human Protein Atlas (HPA) [50]; and (iii) tumor tissues such as TCGA [51] and the Stand Up To Cancer-Prostate Cancer Foundation (SU2C-PCF) project [52]. RNA-seq has become the most robust and comprehensive transcriptome profiling technology, virtually replacing all expression microarrays. An example of the clinical utility of RNA-seq has been demonstrated by several studies disclosing a large number of new actionable genetic events [53] or the real-time management of pediatric tumors [54] as well as the characterization of metastatic tumors [55]. Moreover, the advance in RNA-Seq library preparation methods, resulted in enhanced sensitivity and effectiveness of single-cell in situ RNA-Seq also performed in fixed tissues [56].
Toxicological and pharmacokinetic properties of sucralose-6-acetate and its parent sucralose: in vitro screening assays
Published in Journal of Toxicology and Environmental Health, Part B, 2023
Susan S. Schiffman, Elizabeth H. Scholl, Terrence S. Furey, H. Troy Nagle
There were 464 genes identified as differentially expressed. There was an over-representation in 7 Gene Ontology (Ashburner et al. 2000; The Gene Ontology Consortium 2019) categories listed under “Cellular Component.” In addition, 43 total regulatory motifs from TRANSFAC (Wingender 2008) were significantly over-represented as well as 33 terms from the Human Protein Atlas (The Human Protein Atlas 2023; Uhlén et al. 2015). The Cellular components were cytoplasm, cytosol, integral component of Golgi membrane, intracellular, intracellular membrane-bound organelle, intrinsic component of Golgi membrane, and membrane-bound organelle. The Human Protein Atlas indicated expression in 33 different tissue types originating in the small intestine, bronchus, colon, appendix, duodenum, salivary gland, pancreas, rectum, urinary bladder, stomach, lung, prostate, endometrium and kidneys. Transcription factor binding sites were associated with 266 of the genes. The 23 transcription factors identified were as follows: AP-2gamma: Elk-1, AP-2gamma, BEN, Churchill, E2F–1, E2F–2, E2F–3:HES-7, E2F–3, E2F–4, E2F–7, E2F, ETF, IRX-1, MAZ, MOVO-B, Sp1, TCF-1, TR4, WT1, ZF5, ZIC4, p300, pax-6.