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Pan-Omics in Sheep: Unveiling Genetic Landscapes.

ABSTRACT: Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

SUBMITTER: Li M

PROVIDER: S-EPMC10812798 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Pan-Omics in Sheep: Unveiling Genetic Landscapes.

Li Mengfei M Lu Ying Y Gao Zhendong Z Yue Dan D Hong Jieyun J Wu Jiao J Xi Dongmei D Deng Weidong W Chong Yuqing Y

Animals : an open access journal from MDPI 20240115 2

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This ...[more]

PMID: 38254442

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Project description:BackgroundSchizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia.MethodsLiquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial α-diversity and β-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software.ResultsThe findings of the study indicated a lack of substantial evidence supporting significant disparities in α-diversity and β-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system.ConclusionOur results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia.

Project description:The dynamic protein distribution within and across the plasma membrane is pivotal in regulating cell communications. Yet, methods facilitating rapid labeling, applicable across diverse cell types, and amenable to multiplexed live imaging are scarce. We discovered that live mammalian cell surface proteins can be labeled using N-hydroxysuccinimide (NHS)-ester-based amine crosslinking of fluorescent dyes. Using mouse DC2.4 dendritic cells, human Jurkat T cells, and mouse primary lymphoma T cells as model systems, we revealed the membrane topology and membrane-associated cell communications previously challenging to capture by fluorescence microscopy techniques. In DC2.4 cells, we observed transient membrane protein accumulation at cell-cell contacts and their bidirectional migration patterns guided by membrane fibers. We further visualized the involvement of membrane fibers in the transfer of membrane-derived particles and ovalbumin, as well as caveolin-dependent endocytosis and subsequent transport of membrane labels in late endosomes and lysosomes. In addition, we demonstrate caveolin-1 phosphorylation-dependent insulin receptor endocytosis in HEK 293T cells. Volumetric structured illumination microscopy revealed changes in the membrane topology of Jurkat T cells in response to the activating surface, generation of membrane-derived microvesicles, and phagocytosis of CD3/CD28 Dynabeads on the glass surface. Finally, we demonstrate that the membrane label remains stable in the in vivo environment and evidence of intercellular membrane protein transfer from splenocytes from the donor T cell lymphoma SNF5 mouse to the healthy splenocytes in the C57/BL6 mouse 24 and 48-h after cell transfer. The ultrahigh membrane labeling density, coupled with high multiplexity and rapid labeling, enables innovative fluorescence microscopy applications to dissect membrane and membrane-derived structures in intercellular and intracellular communication both in vitro and in vivo.

Project description:Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein-protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

Dataset Information

Pan-Omics in Sheep: Unveiling Genetic Landscapes.

Publications

Pan-Omics in Sheep: Unveiling Genetic Landscapes.

Similar Datasets

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