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Identifying Breast Cancer Treatment Biomarkers Using Transcriptomics
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Multi-cancer detection tests like GRAIL (Klein et al., 2021) using DNA-methylation or comprehensive genome profiling using novel immuno-oncology signatures like tumour mutation burden, microsatellite instability, DNA repair and homologous recombination deficiency (HRD) have been gaining momentum. NGS-based cancer tests and therapies have shown increasing adoption with promising clinical benefits. The future of precision medicine in oncology lies in the adoption of whole genome, exome, transcriptome and epigenome-based tests and combining information from these tests using the multiomics approach to get a complete picture at the multi-dimensional scale.
Major Issues Related to Progress in NEC
Published in David J. Hackam, Necrotizing Enterocolitis, 2021
Reductionist approaches involve critically evaluating an individual component that leads to disease, which can be an important part of the pathophysiology, but only when taken in context of a holistic understanding of a myriad of mechanisms that interact with one another. For example, one factor that has received considerable attention in the pathogenesis of NEC is the toll-like receptor 4 (TLR4) pathway (16). Despite convincing evidence that this can be a significant contributor to NEC, it is a mistake to consider a TLR4 pathway aberration as the single pathophysiologic factor. It appears that NEC may have a genetic predisposition, and genetic loci that associate with NEC are being found (17). However, if NEC was simply dependent on a single gene, one protein, one inflammatory pathway, prevention or treatment using modulators of this pathway would provide a simple remedy. This is not and will not be the case. Rather, the concept of “multiomics,” which includes an interplay between host genetics, environmental factors such as resident microbes, host immune and metabolic responses to these microbes, and diet and other environmental factors, is increasingly being integrated to help us understand these complex diseases using multiomic and “systems biology” approaches (13).
Statistical Considerations and Biological Mechanisms Underlying Individual Differences in Adaptations to Exercise Training
Published in Peter M. Tiidus, Rebecca E. K. MacPherson, Paul J. LeBlanc, Andrea R. Josse, The Routledge Handbook on Biochemistry of Exercise, 2020
Jacob T. Bonafiglia, Hashim Islam, Nir Eynon, Brendon J. Gurd
Many experts in the field of exercise “-omics” have emphasized the need for multiomic approaches to better understand the mechanisms underlying variability in individual responses to exercise training (17, 29). In brief, multiomic approaches involve genome-wide (-omic) analysis across multiple levels of biological information, including but not limited to genomics (DNA sequences), transcriptomics (gene expression), epigenomics (epigenetics), proteomics (protein), and metabolomics (metabolites) (22). The enthusiasm underlying multiomic approaches is rooted in the belief that synthesizing data across multiple biological levels provides more information than what can be gleaned from any single “-omic” layer alone (88). Within the context of exercise science, it is believed that combining information in a multiomic fashion can leverage current genomic, transcriptomic, epigenomic, and other “-omic” findings to better identify the mechanisms underlying variability in responses to exercise training.
Mapping of audiometric analysis with microbiological findings in patients with chronic suppurative otitis media (CSOM): a neglected clinical manifestation
Published in Critical Reviews in Clinical Laboratory Sciences, 2023
Shefali Dhingra, Dharam Vir, Jaimanti Bakshi, Praveen Rishi
CSOM, in addition to being one of the most common chronic diseases worldwide, is also a major neglected pediatric disorder. This neglected manifestation puts heavy burden on the medical system globally as no drug has been introduced in over a decade. Currently, the various underlying mechanisms that led to development of CSOM pathology are poorly understood. This emphasizes the urgent need to focus on studies elucidating novel diagnostics and treatment strategies. Establishing novel animal models, targeting biofilms, and introducing new surgical interventions can help to reduce the burden of the disease. Novel animal models that provide direct evidence regarding the progression of AOM to CSOM, as well as give account of the virulence factors of pathogens and host factors, can serve as effective tools to explore the mysteries of CSOM pathology. Further, emerging technologies of molecular and system biology, multiomics, and computational biology can help decode the complexity of this intractable infection. However, it is likely that in addition to clinical research developments, awareness among the general public is the most important factor in mitigating the effect of CSOM. Making people aware of personal hygiene practices and the importance of regular ENT checkups may prove to be very effectual in the battle with this infection. Nonetheless, guiding people not to consider watery ears as “normal” could help to improve detection time for CSOM.
Integrative multiomics analysis reveals host-microbe-metabolite interplays associated with the aging process in Singaporeans
Published in Gut Microbes, 2022
Liwei Chen, Tingting Zheng, Yifan Yang, Prem Prashant Chaudhary, Jean Pui Yi Teh, Bobby K. Cheon, Daniela Moses, Stephan C. Schuster, Joergen Schlundt, Jun Li, Patricia L. Conway
To address this issue, we offer a framework for the integrated analysis of multiomics data, including microbiome, metabolome and various phenotypic parameters. In this study, the crosslinks among different omics datasets were identified based on sPLS-DA. We assessed the robustness of our sPLS-DA results by calculating Spearman correlation of correlation pairs extracted from the gut-oral association analysis and multi-omics integrative analysis. As a result, a highly positively correlated result was observed (Spearman Rho = 0.964, p = 1.307e-70, Supplementary Figure S11), which indicated that our sPLS-DA results are robust to noise from multiple omics data. Moreover, we examined the concordance of the associated pairs identified from the multiomics integrative analysis between Kendall rank correlation coefficient (τ) and Spls-DA correlation. The results indicated that the association strength of Spls-DA and Kendall tau are highly correlated (Spearman Rho = 0.663, p = 4.704e-08, Supplementary Figure S11). Besides, none of the pairs showed an inconsistent direction of association (±) between Kendall rank correlation and Spls-DA correlation. Overall, the evaluation of the consistency between Spls-DA and Kendall’s tau supported that associations identified by Spls-DA analysis were overall robust, verified by the Kendall rank correlation test.
An overview of technologies for MS-based proteomics-centric multi-omics
Published in Expert Review of Proteomics, 2022
Andrew T. Rajczewski, Pratik D. Jagtap, Timothy J. Griffin
Given these now maturing, sensitive, and accessible technologies across the ‘omic domains, the concept of multi-omic analysis has become a viable option for many researchers. Multi-omics seeks to integrate the system-wide information generated by different ‘omic technologies to gain a more comprehensive molecular picture within biological systems. Given the array of ‘omic technologies now available, multi-omics can take on many flavors, depending on the types of information being generated and integrated [18]. Here, we review multi-omic approaches, which rely on MS-based bottom-up proteomics as the centerpiece. We describe some of the recent advances in experimental methods and sample preparation, and MS instrumentation that have helped overcome some of the past limitations of MS-based proteomics, facilitating the generation of deep proteomic information necessary for multi-omic analysis. We also provide an overview of the bioinformatic tools and approaches available for the integration of proteomic data with other ‘omic information. Collectively, this review should help to guide researchers seeking to integrate MS-based proteomics data with other ‘omic information to drive new discoveries across a wide variety of research fields.