Influence of nitrogen status on fermentation performances of non-Saccharomyces yeasts:a review

Nitrogen,one of the most crucial nutrients present in grapes and musts,plays a key role in yeast activities during alcoholic fermentation.Such influences are imposed on yeast growth and fermentation performances including the formation of secondary metabolites.Saccharomyces cerevisiae,the main yeast responsible for fermentation,has been studied extensively regarding nitrogen impacts.On the other hand,a similar study for non-Saccharomyces yeasts,whose contributions to winemaking have gradually been acknowledged,remains to be fully explored,with a few studies being reported.This review starts by discussing nitrogen impacts on non-Saccharomyces yeast growth and fermentation kinetics in different case scenarios,then proceeds to summarize the nitrogen preferences of individual yeast strains with regulation mechanisms elucidated by recent studies.Detailed discussions on the influences on the production of volatile compounds and proposed pathways therein are made,followed by future work suggested as the final section.In summarizing the nitrogen impacts on non-Saccharomyces yeasts throughout alcoholic fermentation,this review will be helpful in obtaining a more comprehensive view on these non-conventional wine yeasts in terms of nutrient requirements and corresponding volatile production.Research gaps will therefore be elucidated for future research.

Mapping of de novo mutations in primary biliary cholangitis to a disease-specific co-expression network underlying homeostasis and metabolism

Primary biliary cholangitis(PBC) is an autoimmune disease involving dysregulation of a broad array of homeostatic and metabolic processes. Although considerable single-nucleotide polymorphisms have been unveiled, a large fraction of risk factors remains enigmatic. Candidate genes with rare mutations that tend to confer more deleterious effects need to be identified. To help pinpoint cellular and developmental mechanisms beyond common noncoding variants, we integrate whole exome sequencing with integrative network analysis to investigate genes harboring de novo mutations. Prominent convergence has been revealed on a network of disease-specific co-expression comprised of 55 genes associated with homeostasis and metabolism. The transcription factor gene MEF2 D and the DNA repair gene PARP2 are highlighted as hub genes and identified to be up-and down-regulated, respectively, in peripheral blood data set. Enrichment analysis demonstrates that altered expression of MEF2 D and PARP2 may trigger a series of molecular and cellular processes with pivotal roles in PBC pathophysiology. Our study identifies genes with de novo mutations in PBC and suggests that a subset of genes in homeostasis and metabolism tend to act in synergy through converging on co-expression network, providing novel insights into the etiology of PBC and expanding the pool of molecular candidates for discovering clinically actionable biomarkers.

Genetic analysis of transcription factors in dopaminergic neuronal development in Parkinson’s disease

Background:Genetic variants of dopaminergic transcription factor-encoding genes are suggested to be Parkinson’s disease(PD)risk factors;however,no comprehensive analyses of these genes in patients with PD have been undertaken.Therefore,we aimed to genetically analyze 16 dopaminergic transcription factor genes in Chinese patients with PD.Methods:Whole-exome sequencing(WES)was performed using a Chinese cohort comprising 1917 unrelated patients with familial or sporadic early-onset PD and 1652 controls.Additionally,whole-genome sequencing(WGS)was performed using another Chinese cohort comprising 1962 unrelated patients with sporadic late-onset PD and 1279 controls.Results:We detected 308 rare and 208 rare protein-altering variants in the WES and WGS cohorts,respectively.Gene-based association analyses of rare variants suggested that MSX1 is enriched in sporadic late-onset PD.However,the significance did not pass the Bonferroni correction.Meanwhile,72 and 1730 common variants were found in the WES and WGS cohorts,respectively.Unfortunately,single-variant logistic association analyses did not identify significant associations between common variants and PD.Conclusions:Variants of 16 typical dopaminergic transcription factors might not be major genetic risk factors for PD in Chinese patients.However,we highlight the complexity of PD and the need for extensive research elucidating its etiology.

Genomic landscapes of Chinese sporadic autism spectrum disordersrevealed by whole-genome sequencing

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical and genetic heterogeneity.In this study,we identified all classes of genomic variants from whole-genome sequencing (WGS) dataset of 32 Chinese trios with ASD,including de novo mutations,inherited variants,copy number variants (CNVs) and genomic structural variants.A higher mutation rate (Poisson test,P<2.2×10^(-16)) in exonic (1.37×10^(-8)) and 3'-UTR regions (1.42×10^(-8)) was revealed in comparison with that of whole genome (1.05×10^(-8)).Using an integrated model,we identified 87 potentially risk genes (P<0.01) from 4832 genes harboring various rare deleterious variants,including CHD8 and NRXN2,implying that the disorders may be in favor to multiple-hit.In particular,frequent rare inherited mutations of several microcephaly-associated genes (ASPM,WDR62,and ZNF335)were found in ASD.In chromosomal structure analyses,we found four de novo CNVs and one de novo chromosomal rearrangement event,including a de novo duplication of UBE3A-containing region at 15q11.2-q13.1,which causes Angelman syndrome and microcephaly,and a disrupted TNR due to de novo chromosomal translocation t (1;5) (q25.1;q33.2).Taken together,our results suggest that abnormalities of centrosomal function and chromatin remodeling of the microcephaly-associated genes may be implicated in pathogenesis of ASD.Adoption of WGS as a new yet efficient technique to illustrate the full genetic spectrum in complex disorders,such as ASD,could provide novel insights into pathogenesis,diagnosis and treatment.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects.Hence,an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences.However,it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods.To solve this issue,we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets:(1)rare germline variants from clinical relevant sequence variants(ClinVar),(2)rare somatic variants from Catalogue Of Somatic Mutations In Cancer(COSMIC),(3)common regulatory variants from curated expression quantitative trait locus(eQTL)data,and(4)disease-associated common variants from curated genomewide association studies(GWAS).All 24 tested methods performed differently under various conditions,indicating varying strengths and weaknesses under different scenarios.Importantly,the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve(AUROC)of 0.4481–0.8033 and poor for rare somatic variants from COSMIC(AUROC=0.4984–0.7131),common regulatory variants from curated eQTL data(AUROC=0.4837–0.6472),and disease-associated common variants from curated GWAS(AUROC=0.4766–0.5188).We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder,and found that the combined annotation-dependent depletion(CADD)and context-dependent tolerance score(CDTS)methods showed better performance.Summarily,we assessed the performance of 24 computational methods under diverse scenarios,providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Targeted sequencing and integrative analysis of 3,195 Chinese patients with neurodevelopmental disorders prioritized 26 novel candidate genes

Neurodevelopmental disorders(NDDs)are a set of complex disorders characterized by diverse and cooccurring clinical symptoms.The genetic contribution in patients with NDDs remains largely unknown.Here,we sequence 519 NDD-related genes in 3,195 Chinese probands with neurodevelopmental phenotypes and identify 2,522 putative functional mutations consisting of 137 de novo mutations(DNMs)in 86 genes and 2,385 rare inherited mutations(RIMs)with 22 X-linked hemizygotes in 13 genes,2 homozygous mutations in 2 genes and 23 compound heterozygous mutations in 10 genes.Furthermore,the DNMs of16,807 probands with NDDs are retrieved from public datasets and combine in an integrated analysis with the mutation data of our Chinese NDD probands by taking 3,582 in-house controls of Chinese origin as background.We prioritize 26 novel candidate genes.Notably,six of these genes d ITSN1,UBR3,CADM1,RYR3,FLNA,and PLXNA3 d preferably contribute to autism spectrum disorders(ASDs),as demonstrated by high co-expression and/or interaction with ASD genes confirmed via rescue experiments in a mouse model.Importantly,these genes are differentially expressed in the ASD cortex in a significant manner and involved in ASD-associated networks.Together,our study expands the genetic spectrum of Chinese NDDs,further facilitating both basic and translational research.