Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments.

Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering.

Characterization of caffeine response regulatory variants in vascular endothelial cells.

Genetic variants in gene regulatory sequences can modify gene expression and mediate the molecular response to environmental stimuli. In addition, genotype-environment interactions (GxE) contribute to complex traits such as cardiovascular disease. Caffeine is the most widely consumed stimulant and is known to produce a vascular response.

Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks.

Panicle architecture is a key determinant of rice grain yield and is mainly determined at the 1-2 mm young panicle stage. Here, we investigated the transcriptome of the 1-2 mm young panicles from 275 rice varieties and identified thousands of genes whose expression levels were associated with panicle traits. Multimodel association studies suggested that many small-effect genetic loci determine spikelet per panicle (SPP) by regulating the expression of genes associated with panicle traits.

Single nucleus transcriptomics of ventral midbrain identifies glial activation associated with chronic opioid use disorder.

Dynamic interactions of neurons and glia in the ventral midbrain mediate reward and addiction behavior. We studied gene expression in 212,713 ventral midbrain single nuclei from 95 individuals with history of opioid misuse, and individuals without drug exposure. Chronic exposure to opioids was not associated with change in proportions of glial and neuronal subtypes, however glial transcriptomes were broadly altered, involving 9.

Integrated of multi-omics and molecular docking reveal PHGDH, PSAT1 and PSPH in the serine synthetic pathway as potential targets of T-2 toxin exposure in pig intestinal tract.

T-2 toxin (T-2) with a molecular weight of 466.52 g/mol is an inevitable mycotoxin in food products and feeds, posing a significant threat to human and animal health. However, the underlying molecular mechanisms of the cytotoxic effects of T-2 exposure on porcine intestinal epithelial cells (IPEC-J2) remain unclear.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children.

Single Nucleus Transcriptomics Reveals Pervasive Glial Activation in Opioid Overdose Cases.

Dynamic interactions of neurons and glia in the ventral midbrain (VM) mediate reward and addiction behavior. We studied gene expression in 212,713 VM single nuclei from 95 human opioid overdose cases and drug-free controls. Chronic exposure to opioids left numerical proportions of VM glial and neuronal subtypes unaltered, while broadly affecting glial transcriptomes, involving 9.

Photocatalytic Microbial Fuel Cells and Performance Applications: A Review.

In recent years, photocatalytic microbial fuel cells have gradually become a hot research topic in pollutant treatment, using either or indirectly the oxidation of organic pollutants by catalytic materials under light and the biodegradation and mineralization of various components in wastewater by microorganisms, or through the generation of electricity by the microbial fuel cell (MFC) system to promote the photogeneration and separation of electrons and holes by the catalytic materials of the photocatalytic cell (PC) system. This study aims to provide new ideas for the development of environmentally friendly wastewater treatment technologies by investigating the use of photocatalytic cells for the efficient degradation and resource utilization of target pollutants. This study aims to raise awareness of the use of photocatalytic microbial fuel cells for pollutant degradation by providing an overview of the practical status of photocatalytic microbial fuel cells.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution.

Synthetic glucocorticoids, such as dexamethasone, have been used as treatment for many immune conditions, such as asthma and more recently severe COVID-19. Single cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children.

Leveraging Single-Cell RNA-seq Data to Uncover the Association Between Cell Type and Chronic Liver Diseases.

Background: Components of liver microenvironment is complex, which makes it difficult to clarify pathogenesis of chronic liver diseases (CLD). Genome-wide association studies (GWASs) have greatly revealed the role of host genetic background in CLD pathogenesis and prognosis, while single-cell RNA sequencing (scRNA-seq) enables interrogation of the cellular diversity and function of liver tissue at unprecedented resolution. Here, we made integrative analysis on the GWAS and scRNA-seq data of CLD to uncover CLD-related cell types and provide clues for understanding on the pathogenesis.

Extended application of genomic selection to screen multiomics data for prognostic signatures of prostate cancer.

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis.

Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes.

Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved.

Analysis of trait heritability in functionally partitioned rice genomes.

Knowledge of the genetic architecture of importantly agronomical traits can speed up genetic improvement in cultivated rice (Oryza sativa L.). Many recent investigations have leveraged genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs), associated with agronomic traits in various rice populations.

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies.

Linear mixed model (LMM) is an efficient method for GWAS. There are numerous forms of LMM-based GWAS methods. However, improving statistical power and computing efficiency have always been the research hotspots of the LMM-based GWAS methods.

Efficient multivariate analysis algorithms for longitudinal genome-wide association studies.

Motivation: Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.

Results: We herein developed efficient genome-wide multivariate association algorithms for longitudinal data.

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods.

Genomic selection using principal component regression.

Many statistical methods are available for genomic selection (GS) through which genetic values of quantitative traits are predicted for plants and animals using whole-genome SNP data. A large number of predictors with much fewer subjects become a major computational challenge in GS. Principal components regression (PCR) and its derivative, i.

miR-22 regulates C2C12 myoblast proliferation and differentiation by targeting TGFBR1.

Recently, miR-22 was found to be differentially expressed in different skeletal muscle growth period, indicated that it might have function in skeletal muscle myogenesis. In this study, we found that the expression of miR-22 was the most in skeletal muscle and was gradually up-regulated during mouse myoblast cell (C2C12 myoblast cell line) differentiation. Overexpression of miR-22 repressed C2C12 myoblast proliferation and promoted myoblast differentiation into myotubes, whereas inhibition of miR-22 showed the opposite results.

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA and mRNA data in GDC.

Motivation: The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies.

Results: We have developed a user-friendly R/Bioconductor package, named GDCRNATools, for downloading, organizing and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs regulatory network in cancers.

Metabolome-wide association studies for agronomic traits of rice.

Identification of trait-associated metabolites will advance the knowledge and understanding of the biosynthetic and catabolic pathways that are relevant to the complex traits of interest. In the past, the association between metabolites (treated as quantitative traits) and genetic variants (e.g.

MiR-696 Regulates C2C12 Cell Proliferation and Differentiation by Targeting CNTFRα.

Micro-696 (miR-696) has been previously known as an exercise related miRNA, which has a profound role in fatty acid oxidation and mitochondrial biogenesis of skeletal muscle. However, its role in skeletal myoblast proliferation and differentiation is still unclear. In this study, we found that miR-696 expressed highly in skeletal muscle and reduced during C2C12 myoblasts differentiation.

A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations.

Most standard QTL mapping procedures apply to populations derived from the cross of two parents. QTL detected from such biparental populations are rarely relevant to breeding programs because of the narrow genetic basis: only two alleles are involved per locus. To improve the generality and applicability of mapping results, QTL should be detected using populations initiated from multiple parents, such as the multiparent advanced generation intercross (MAGIC) populations.

A genome scan for selection signatures in pigs.

Identifying signatures of selection can provide a straightforward insight into the mechanism of artificial selection and further uncover the causal genes related to the phenotypic variation. Based on Illumina Porcine60KSNP chip data, four complementary methods, Long-Range Haplotype (LRH), Tajima's D, Cross Population Extend Haplotype Homozygosity Test (XPEHH) and FST, were implemented in this study to detect the selection signatures in the whole genome of one typical Chinese indigenous breed, Rongchang, one Chinese cultivated breed, Songliao, and two western breeds, Landrace and Yorkshire. False Discovery Rate (FDR) was implemented to control the false positive rates.

Differential gene expression in uterine endometrium during implantation in pigs.

Embryonic mortality during the implantation period strongly affects litter size in pigs. To analyze the differentially expressed genes (DEGs) in the endometrium during implantation and further to identify candidate genes for litter size, tissues of endometrial attachment sites and intersites were collected from nine pregnant sows on Days 13, 18, and 24 of pregnancy. Endometrium tissue was also collected from another three nonpregnant sows.