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A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.

BackgroundQuantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data.ResultsUsing a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL.ConclusionsThis proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.

ObjectiveObesity is influenced by genetic and environmental factors. Despite the success of human genome-wide association studies, the specific genes that confer obesity remain largely unknown. The objective of this study was to use outbred rats to identify the genetic loci underlying obesity and related morphometric and metabolic traits.MethodsThis study measured obesity-relevant traits, including body weight, body length, BMI, fasting glucose, and retroperitoneal, epididymal, and parametrial fat pad weight in 3,173 male and female adult N/NIH heterogeneous stock (HS) rats across three institutions, providing data for the largest rat genome-wide association study to date. Genetic loci were identified using a linear mixed model to account for the complex family relationships of the HS and using covariates to account for differences among the three phenotyping centers.ResultsThis study identified 32 independent loci, several of which contained only a single gene (e.g., Epha5, Nrg1, Klhl14) or obvious candidate genes (e.g., Adcy3, Prlhr). There were strong phenotypic and genetic correlations among obesity-related traits, and there was extensive pleiotropy at individual loci.ConclusionsThis study demonstrates the utility of HS rats for investigating the genetics of obesity-related traits across institutions and identify several candidate genes for future functional testing.

Both emotional and social traits interact with genetic factors to influence smoking behavior. We previously established a socially acquired nicotine intravenous self-administration model where social learning of a nicotine-associated odor cue reversed conditioned flavor aversion and promoted nicotine intake. In this study, we first phenotyped ~800 adolescent heterogeneous stock rats in open field, novel object interaction, social interaction, elevated plus maze, and marble burying behaviors. These rats were then phenotyped on socially acquired nicotine self-administration. We found 243 significant correlations between different behavioral tests. Principal component regression analysis found that ~10-20% of the variance in nicotine-related measures, such as intake during the first or the last three fixed-ratio sessions, the progressive ratio session, and reinstatement behavior, can be explained by variations in behavioral traits. Factors corresponding to social behavior and anxiety were among the strongest predictors of nicotine intake and reinstatement of nicotine-seeking behavior. We also found many sex differences in behavioral measures. These data indicated that the genetic diversity of this population, in combination with social behaviour and anxiety, are significant contributors to the divergent nicotine self-administration behavior and indicated a high probability of discovering sex-specific genetic mechanisms for nicotine intake in future genome-wide association studies.

Exosomes are indispensable for intercellular communications. Tumor microenvironment (TME) is the living environment of tumor cells, which is composed of various components, including immune cells. Based on TME, immunotherapy has been recently developed for eradicating cancer cells by reactivating antitumor effect of immune cells. The communications between tumor cells and TME are crucial for tumor development, metastasis, and drug resistance. Exosomes play an important role in mediating these communications and regulating the reprogramming of TME, which affects the sensitivity of immunotherapy. Therefore, it is imperative to investigate the role of exosomes in TME reprogramming and the impact of exosomes on immunotherapy. Here, we review the communication role of exosomes in regulating TME remodeling and the efficacy of immunotherapy, as well as summarize the underlying mechanisms. Furthermore, we also introduce the potential application of the artificially modified exosomes as the delivery systems of antitumor drugs. Further efforts in this field will provide new insights on the roles of exosomes in intercellular communications of TME and cancer progression, thus helping us to uncover effective strategies for cancer treatment. [ABSTRACT FROM AUTHOR]

Soil excessive acidification is one of the main problems affecting the sustainable and healthy development of tea industry. To understand the long-term impact of organic management on soil pH of tea gardens, this study selected both soil samples from organic and conventional tea gardens in 84 tea producing counties of 18 provinces in China. The organic tea garden was managed organically from 1 to 21 years. The results show that the mean soil pH in organic tea gardens was significantly increased by 0.36, compared to the conventional tea gardens. With the increase of the years under organic management, the overall pH of tea garden soil shows a significant rise, and then a stable trend. The soil pH under organic management for 1-5, 6-10, 11-15 and 16-21 a significantly increased by 0.48, 0.23, 0.28 and 0.30, respectively compared to those in conventional tea gardens. The organic management also helped the soil pH towards the direction for most suitable growth and development of tea plants, the proportion of soils with pH 4.5-5.5 was only 41.9% in conventional tea gardens, it was increased to 53.1%,48.9%, 58.7% and 66.7% in tea gardens under organic management for 1-5, 6-10, 11-15 and 16-21 a, respectively. These results indicate that organic management could not only overcome soil over acidification, but towards to the direction of the most suitable soil pH for the growth and development of tea plants. [ABSTRACT FROM AUTHOR]

Shading is an effective agronomic technique to protect tea plants from intense sunlight. However, there are currently very few studies on more effective shading methods to improve the quality of summer tea. In this study, 'Longjing43' plants were grown under four different shading treatments for 14 days, with no shading as the control. Among the four shading treatments, double-layer-net shadings had the most positive impact on the tea quality, resulting in higher levels of amino acids but lower levels of tea polyphenols. Additionally, double-layer-net shadings provided more suitable microenvironments for tea plants. The tea leaves in T4 (double nets 50 cm above the plant canopy) contained 16.13 mg∙g−1 of umami and sweet amino acids, which was significantly higher than in other treatments. T4 had the lowest air temperature and the most suitable and stable soil water content. Interestingly, the ratio of red light to far-red light in T4 was only 1.65, much lower than other treatments, which warrants further study. In conclusion, the microenvironment induced by shading can greatly affect the tea quality, and double-layer-net shading is better for improving the quality of summer tea. [ABSTRACT FROM AUTHOR]

The tea plant is a perennial leaf-used economical crop and cultivated all over the world. Indole-3-acetic acid (IAA) plays key roles in plant development and environmental stress. YUCCA (YUC) flavin monooxygenases are the rate-limiting enzymes of the TAA/YUC pathway, which is the most important IAA biosynthetic pathway in plants. The YUC gene family in tea plants has not been systematically studied so far. A total of 17 CsYUC members were identified from a tea plant genome database and phylogenetically classified into three subfamilies. Phylogenetic analysis showed that the CsYUC gene family is evolutionarily conserved. The physical and chemical properties, gene structures, and conserved domains were analyzed. The expression profiles of CsYUCs were analyzed on the basis of open available RNA-seq data, as well as by RNA-seq and qRT-PCR assays. Combined with previous studies, it can be concluded that YUC10 may play key roles in seed development. The results also showed that CsYUC2.1 may play important roles in the coordinated regulation of the growth of leaf buds and flower buds induced by pruning. Low temperature markedly induced the expression of CsYUC2.2, -11.8, and -11.9. Furthermore, CsYUC genes that might play key roles in the specific development stages and involve enhancing the resistance to drought and NaCl stress were screened, respectively. This study could provide a research basis for deeply studying the gene functions of the CsYUC family in the tea plant. [ABSTRACT FROM AUTHOR]

This paper is devoted to the investigation of optimality conditions and saddle point theorems for robust approximate quasi-weak efficient solutions for a nonsmooth uncertain multiobjective fractional semi-infinite optimization problem (NUMFP). Firstly, a necessary optimality condition is established by using the properties of the Gerstewitz's function. Furthermore, a kind of approximate pseudo/quasi-convex function is defined for the problem (NUMFP), and under its assumption, a sufficient optimality condition is obtained. Finally, we introduce the notion of a robust approximate quasi-weak saddle point to the problem (NUMFP) and prove corresponding saddle point theorems. [ABSTRACT FROM AUTHOR]