Editorial: Omics approach to study the biology and virulence of microorganisms causing zoonotic diseases.
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ABSTRACT:
SUBMITTER: Salmon-Divon M
PROVIDER: S-EPMC9376672 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Editorial: Omics approach to study the biology and virulence of microorganisms causing zoonotic diseases.
Salmon-Divon Mali M He Yongqun Oliver YO Kornspan David D Wen Zezhang Tom ZT
Frontiers in microbiology 20220801
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Project description:Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.
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Project description:This submission corresponds to peptides identified from proteomics analysis of tryptic digests of lung homogenates from C57Bl/6 mice infected with avian influenza (A/VN/1203/04). Mice were infected at 10e2, 10e3, or 10e4 pfu (n = 5 each) and sacrificed at 1, 2, 4, and 7 d post-infection. Aliquots of lung proteins were combined to create two pools corresponding to early (1 and 2 d post-infection) and late (4 and 7 d post-infection) infection and each pool was then subjected to tryptic digestion, followed by strong-cation exchange fractionation, resulting in 24 fractions each. Capillary LC-MS/MS analysis was then performed on each fraction. This submission corresponds to proteomics data collected from capillary LC-MS/MS analysis of the early infection pool. The MS/MS datasets were searched with SEQUEST using no enzyme search rules, and the peptide identification results were filtered using the following cutoffs; XCorr >= 1.6, 2.4, or 3.2 for 1+, 2+, or 3+ for fully tryptic peptides, and >= 4.3 or 4.7 for 2+ or 3+ partially tryptic or non-tryptic protein terminal peptides. Additionally, to reduce the total data size to a reasonable level, each spectrum was filtered to only include the top 100 most abundant ions in each 100 m/z-wide window. This submission is the aggregation of 24 individual LC-MS/MS analyses into one pseudo dataset. More information can be found at the following web sites: https://www.systemsvirology.org/project/home/begin.view? http://omics.pnl.gov
2012-01-17 | PRD000594 | Pride
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Project description:Limitations to biomarker discovery are not only technical or bioinformatic but conceptual as well. In our attempt to offer a solution, we are highlighting three issues that we think are limiting progress in biomarkers discovery. First, the confusion stemming from the imposition of a pathology-type immunohistochemical marker (IHCM) concept on omics data without fully understanding the characteristics and limitations of IHCMs as applied in clinical pathology. Second, the lack of serious consideration for the scope of disease heterogeneity. Third, the refusal of the biomedical community to borrow from other biological disciplines their well established methods for dealing with heterogeneity. Therefore, real progress in biomarker discovery will be attained when we recognize that an omics biomarker cannot be assigned and validated without a priori data modeling and subtyping of the disease itself to reveal the extent of its heterogeneity, and its omics' clonal aberrations (drivers) underlying its subtypes and pathways' diversity. To further support our viewpoints, we are contributing a novel a systems biology method such as parsimony phylogenetic approach for disease modeling prior to biomarker circumscription. As an analytical approach that has been successfully used for a half of a century in other biological disciplines, parsimony phylogenetics simultaneously achieves several objectives: it provides disease modeling in a hierarchical phylogenetic classification, identifies biomarkers as the shared derived expressions or mutations--synapomorphies, constructs the omics profiles of specimens based on the most parsimonious arrangement of their heterogeneous data, and permits network profiling of affected signaling pathways as the biosignature of disease classes.
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Project description:BackgroundChina is vulnerable to zoonotic disease transmission due to a large agricultural work force, sizable domestic livestock population, and a highly biodiverse ecology. To better address this threat, representatives from the human, animal, and environmental health sectors in China held a One Health Zoonotic Disease Prioritization (OHZDP) workshop in May 2019 to develop a list of priority zoonotic diseases for multisectoral, One Health collaboration.MethodsRepresentatives used the OHZDP Process, developed by the US Centers for Disease Control and Prevention (US CDC), to prioritize zoonotic diseases for China. Representatives defined the criteria used for prioritization and determined questions and weights for each individual criterion. A review of English and Chinese literature was conducted prior to the workshop to collect disease specific information on prevalence, morbidity, mortality, and Disability-Adjusted Life Years (DALYs) from China and the Western Pacific Region for zoonotic diseases considered for prioritization.ResultsThirty zoonotic diseases were evaluated for prioritization. Criteria selected included: 1) disease hazard/severity (case fatality rate) in humans, 2) epidemic scale and intensity (in humans and animals) in China, 3) economic impact, 4) prevention and control, and 5) social impact. Disease specific information was obtained from 792 articles (637 in English and 155 in Chinese) and subject matter experts for the prioritization process. Following discussion of the OHZDP Tool output among disease experts, five priority zoonotic diseases were identified for China: avian influenza, echinococcosis, rabies, plague, and brucellosis.ConclusionRepresentatives agreed on a list of five priority zoonotic diseases that can serve as a foundation to strengthen One Health collaboration for disease prevention and control in China; this list was developed prior to the emergence of SARS-CoV-2 and the COVID-19 pandemic. Next steps focused on establishing a multisectoral, One Health coordination mechanism, improving multisectoral linkages in laboratory testing and surveillance platforms, creating multisectoral preparedness and response plans, and increasing workforce capacity.
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