Phenomics (Cham, Switzerland)最新文献

A metabolically healthy status, whether obese or not, is a transient stage with the potential to develop into metabolic disorders during the course of life. We investigated the incidence of metabolic disorders in 1078 metabolically healthy Chinese adults from the Shanghai Changfeng Study and looked for metabolites that discriminated the participants who would develop metabolic disorders in the future. Participants were divided into metabolically healthy overweight/obesity (MHO) and metabolically healthy normal weight (MHNW) groups according to their body mass index (BMI) and metabolic status. Their serum metabolomic profile was measured using a ¹H nuclear magnetic resonance spectrometer (¹H-NMR). The prevalence of diabetes, hypertriglyceridemia, hypercholesterolemia and metabolic syndrome was similar between the MHNW and MHO participants at baseline. After a median of 4.2 years of follow-up, more MHO participants became metabolically unhealthy than MHNW participants. However, a subgroup of MHO participants who remained metabolically healthy (MHO → MHO) had a similar prevalence of metabolic disorders as the MHNW participants at the follow-up examination, despite a significant reduction in their serum concentrations of high-density lipoprotein (HDL) and an elevation in valine, leucine, alanine and tyrosine. Further correlation analysis indicated that serum intermediate-density lipoprotein (IDL) and very low-density lipoprotein cholesterol (VLDL-CH) might be involved in the transition from metabolically healthy to unhealthy status and could be valuable to identify the MHNW and MHO with increased metabolic risks.

The determinative view of mutation penetrance is a fundamental assumption for the building of molecular evolutionary theory: individuals in the population with the same genotype have the same fitness effect. Since this view has been constantly challenged by experimental evidence, it is desirable to examine to what extent violation of this view could affect our understanding of molecular evolution. To this end, the author formulated a new theory of molecular evolution under a random model of penetrance: for any individual with the same mutational genotype, the coefficient of selection is a random variable. It follows that, in addition to the conventional N _e-genetic drift (N _e is the effective population size), the variance of penetrance among individuals (ε ²) represents a new type of genetic drift, coined by the ε ²-genetic drift. It has been demonstrated that these two genetic drifts together provided new insights on the nearly neutral evolution: the evolutionary rate is inversely related to the log-of-N _e when the ε ²-genetic drift is nontrivial. This log-of-N _e feature of ε ²-genetic drift did explain well why the d _N /d _S ratio (the nonsynonymous rate to the synonymous rate) in humans is only as twofold as that in mice, while the effective population size (N _e) of mice is about two-magnitude larger than that of humans. It was estimated that, for the first time, the variance of random penetrance in mammalian genes was approximately ε ²≈ 5.89 × 10^-3.

Kidney disease is manifested in a wide variety of phenotypes, many of which have an important hereditary component. To delineate the genotypic and phenotypic spectrum of pediatric nephropathy, a multicenter registration system is being implemented based on the Chinese Children Genetic Kidney Disease Database (CCGKDD). In this study, all the patients with kidney and urological diseases were recruited from 2014 to 2020. Genetic analysis was conducted using exome sequencing for families with multiple affected individuals with nephropathy or clinical suspicion of a genetic kidney disease owing to early-onset or extrarenal features. The genetic diagnosis was confirmed in 883 of 2256 (39.1%) patients from 23 provinces in China. Phenotypic profiles showed that the primary diagnosis included steroid-resistant nephrotic syndrome (SRNS, 23.5%), glomerulonephritis (GN, 32.2%), congenital anomalies of the kidney and urinary tract (CAKUT, 21.2%), cystic renal disease (3.9%), renal calcinosis/stone (3.6%), tubulopathy (9.7%), and chronic kidney disease of unknown etiology (CKDu, 5.8%). The pathogenic variants of 105 monogenetic disorders were identified. Ten distinct genomic disorders were identified as pathogenic copy number variants (CNVs) in 11 patients. The diagnostic yield differed by subgroups, and was highest in those with cystic renal disease (66.3%), followed by tubulopathy (58.4%), GN (57.7%), CKDu (43.5%), SRNS (29.2%), renal calcinosis /stone (29.3%) and CAKUT (8.6%). Reverse phenotyping permitted correct identification in 40 cases with clinical reassessment and unexpected genetic conditions. We present the results of the largest cohort of children with kidney disease in China where diagnostic exome sequencing was performed. Our data demonstrate the utility of family-based exome sequencing, and indicate that the combined analysis of genotype and phenotype based on the national patient registry is pivotal to the genetic diagnosis of kidney disease.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00014-1.

With the rapid development of omics technologies during the last several decades, genomics, transcriptomics, and proteomics have been extensively used to characterize gene or protein functions in many organisms at the cell or tissue level. However, metabolomics has not been conducted in reproductive organs, with a focus on meiosis in plants. In this study, we adopted a nuclear magnetic resonance (NMR)-based metabolomics approach to reveal the metabolic profile of inflorescences from two Arabidopsis accessions, Columbia (Col) and Landsberg erecta (Ler), and several sterile mutants caused by meiosis defects. We identified 68 dominant metabolites in the samples. Col and Ler displayed distinct metabolite profiles. Interestingly, mutants with similar meiotic defects, such as Atrad51-3, Atrfc1-2, and Atpol2a-2, exhibited similar alterations in metabolites, including upregulation of energy metabolites and promotion of compounds related to maintenance of genomic stability, cytoplasmic homeostasis, and membrane integrity. The collective data reveal distinct changes in metabolites in Arabidopsis inflorescences between the Col and Ler wild type accessions. NMR-based metabolomics could be an effective tool for molecular phenotyping in studies of aspects of plant reproductive development such as meiosis.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00012-3.

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

Light modulates human brain function through its effect on circadian rhythms, which are related to several human behavioral and physiological processes. Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical neuroimaging technique used for recording brain activation during task performance. This study aimed to investigate the effects of light on cognitive function, particularly in the prefrontal cortex using fNIRS. The effect of light on cognitive modulation was analyzed using the Stroop task, which was performed on 30 participants under three different light conditions (color temperature 4500 K, 2500 K, and none). The behavioral results indicated that light conditions can easily and effectively modulate the performance of tasks based on the feedback, including the response time and accuracy. fNIRS showed hemodynamic changes in the bilateral dorsolateral prefrontal cortices, and the activated brain regions varied under different light conditions. Moreover, light may be regarded as a safe, effective, inexpensive, and accessible tool for modulating human cognitive function.

Altitude acclimatization is a human physiological process of adjusting to the decreased oxygen availability. Since several physiological processes are involved and their correlations are complicated, the analyses of single traits are insufficient in revealing the complex mechanism of high-altitude acclimatization. In this study, we examined these physiological responses as the composite phenotypes that are represented by a linear combination of physiological traits. We developed a strategy that combines both spectral clustering and partial least squares path modeling (PLSPM) to define composite phenotypes based on a cohort study of 883 Chinese Han males. In addition, we captured 14 composite phenotypes from 28 physiological traits of high-altitude acclimatization. Using these composite phenotypes, we applied k-means clustering to reveal hidden population physiological heterogeneity in high-altitude acclimatization. Furthermore, we employed multivariate linear regression to systematically model (Models 1 and 2) oxygen saturation (SpO₂) changes in high-altitude acclimatization and evaluated model fitness performance. Composite phenotypes based on Model 2 fit better than single trait-based Model 1 in all measurement indices. This new strategy of using composite phenotypes may be potentially employed as a general strategy for complex traits research such as genetic loci discovery and analyses of phenomics.

Aligning billions of reads generated by the next-generation sequencing (NGS) to reference sequences, termed "mapping", is the time-consuming and computationally-intensive process in most NGS applications. A Fast, accurate and robust mapping algorithm is highly needed. Therefore, we developed the FANSe3 mapping algorithm, which can map a 30 × human whole-genome sequencing (WGS) dataset within 30 min, a 50 × human whole exome sequencing (WES) dataset within 30 s, and a typical mRNA-seq dataset within seconds in a single-server node without the need for any hardware acceleration feature. Like its predecessor FANSe2, the error rate of FANSe3 can be kept as low as 10^-9 in most cases, this is more robust than the Burrows-Wheeler transform-based algorithms. Error allowance hardly affected the identification of a driver somatic mutation in clinically relevant WGS data and provided robust gene expression profiles regardless of the parameter settings and sequencer used. The novel algorithm, designed for high-performance cloud-computing after infrastructures, will break the bottleneck of speed and accuracy in NGS data analysis and promote NGS applications in various fields. The FANSe3 algorithm can be downloaded from the website: http://www.chi-biotech.com/fanse3/.

A better understanding of the molecular mechanisms that control the UCP1 expression in brown and beige adipocytes is essential for us to modulate adipose cell fate and promote thermogenesis, which may provide a therapeutic view for the treatment of obesity and obesity-related diseases. To systematically identify cis-element(s) that transcriptionally regulates Ucp1, we here took advantage of the high-throughput CRIPSR-Cas9 screening system, and performed an in situ saturating mutagenesis screen, by using a customized sgRNA library targeting the ~ 20 kb genomic region near Ucp1. Through the screening, we have identified several genomic loci that may contain key regulatory element for Ucp1 expression in cultured brown and white adipocytes in vitro, and in inguinal white adipose tissue in vivo. Our study highlights a broadly useful approach for studying cis-regulatory elements in a high-throughput manner.