Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle

CEBPA-Regulated Expression of SOCS1 Suppresses Milk Protein Synthesis through mTOR and JAK2-STAT5 Signaling Pathways in Buffalo Mammary Epithelial Cells

Milk protein content is a key quality indicator of milk, and therefore elucidating its synthesis mechanism has been the focus of research in recent years. Suppressor of cytokine signaling 1 (SOCS1) is an important inhibitor of cytokine signaling pathways that can inhibit milk protein synthesis in mice. However, it remains elusive whether SOCS1 plays roles in the milk protein synthesis in the buffalo mammary gland. In this study, we found that the mRNA and protein expression levels of SOCS1 in buffalo mammary tissue during the dry-off period was significantly lower than those during lactation. Overexpression and knockdown experiments of SOCS1 showed that it influenced the expression and phosphorylation of multiple key factors in the mTOR and JAK2-STAT5 signaling pathways in buffalo mammary epithelial cells (BuMECs). Consistently, intracellular milk protein content was significantly decreased in cells with SOCS1 overexpression, while it increased significantly in the cells with SOCS1 knockdown. The CCAAT/enhancer binding protein α (CEBPA) could enhance the mRNA and protein expression of SOCS1 and its promoter activity in BuMECs, but this effect was eliminated when CEBPA and NF-κB binding sites were deleted. Therefore, CEBPA was determined to promote SOCS1 transcription via the CEBPA and NF-κB binding sites located in the SOCS1 promoter. Our data indicate that buffalo SOCS1 plays a significant role in affecting milk protein synthesis through the mTOR and JAK2-STAT5 signaling pathways, and its expression is directly regulated by CEBPA. These results improve our understanding of the regulation mechanism of buffalo milk protein synthesis.

An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach

The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.

Inferring causal structures of gut microbiota diversity and feed efficiency traits in poultry using Bayesian learning and genomic structural equation models

Feed and phosphorus (P) efficiency are of increasing importance in poultry breeding. It has been shown recently that these efficiency traits are influenced by the gut microbiota composition of the birds. The efficiency traits and the gut microbiota composition are partly under control of the host genome. Thus, the gut microbiota composition can be seen as a mediator trait between the host genome and the efficiency traits. The present study used data from 749 individuals of a Japanese quail F2 cross. The birds were genotyped for 4k single-nucleotide polymorphism (SNP) and trait recorded for P utilization (PU) and P retention (PR), body weight gain (BWG), and feed per gain ratio (F:G). The gut microbiota composition was characterized by targeted amplicon sequencing. The alpha diversity was calculated as the Pielou's evenness index (J'). A stable Bayesian network was established using a Hill-Climbing learning algorithm. Pielou's evenness index was placed as the most upstream trait and BWG as the most downstream trait, with direct and indirect links via PR, PU, and F:G. The direct and indirect effects between J', PU, and PR were quantified with structural equation models (SEM), which revealed a causal link from J' to PU and from PU to PR. Quantitative trait loci (QTL) linkage mapping revealed three genome-wide significant QTL regions for these traits with in total 49 trait-associated SNP within the QTL regions. SEM association mapping separated the total SNP effect for a trait into a direct effect and indirect effects mediated by upstream traits. Although the indirect effects were in general small, they contributed to the total SNP effect in some cases. This enabled us to detect some shared genetic effects. The method applied allows for the detection of shared genetic architecture of quantitative traits and microbiota compositions.

Application of Bayesian genomic prediction methods to genome-wide association analyses

Background

Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.

Results

By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.

Conclusions

Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.