Screening candidate genes related to volatile synthesis in shiitake mushrooms and construction of regulatory networks to effectively improve mushroom aroma

2021 ◽  
Author(s):  
Wen Li ◽  
Wan‐Chao Chen ◽  
Jin‐Bin Wang ◽  
Jie Feng ◽  
Di Wu ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Regulatory Networks ◽  
Shiitake Mushrooms

scholarly journals Integrated metabolomic and transcriptomic analysis of the anthocyanin regulatory networks in Salvia miltiorrhiza Bge. flowers

2020 ◽  
Author(s):  
Tao Jiang ◽  
Meide Zhang ◽  
Chunxiu Wen ◽  
Xiaoliang Xie ◽  
Wei Tian ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Regulatory Networks ◽  
Anthocyanin Biosynthesis ◽  
Salvia Miltiorrhiza ◽  
Flower Color ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Anthocyanin Content ◽  
Transcriptomics Data

Abstract Background: The study objectives were to reveal the anthocyanin biosynthesis metabolic pathway in white and purple flowers of Salvia miltiorrhiza using metabolomics and transcriptomics, to identify different anthocyanin metabolites, and to analyze the differentially expressed genes involved in anthocyanin biosynthesis . Results: We analyzed the metabolomics and transcriptomics data of Salvia miltiorrhiza flowers. A total of 1994 differentially expressed genes and 84 flavonoid metabolites were identified between the white and purple flowers of Salvia miltiorrhiza . Integrated analysis of transcriptomic and metabolomics showed that cyanidin 3,5-O-diglucoside, malvidin 3,5-diglucoside, and cyanidin 3-O-galactoside were mainly responsible for the purple flower color of Salvia miltiorrhiza. A total of 100 unigenes encoding 10 enzymes were identified as candidate genes involved in anthocyanin biosynthesis in Salvia miltiorrhiza flowers. The low expression of the ANS gene decreased the anthocyanin content but enhanced the accumulation of flavonoids in Salvia miltiorrhiza flowers. Conclusions: Our results provide valuable information on the anthocyanin metabolites and the candidate genes involved in the anthocyanin biosynthesis pathways in Salvia miltiorrhiza .

Genome-wide association studies reveal the coordinated regulatory networks underlying photosynthesis and wood formation in Populus

2021 ◽  
Author(s):  
Mingyang Quan ◽  
Xin Liu ◽  
Qingzhang Du ◽  
Liang Xiao ◽  
Wenjie Lu ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Regulatory Networks ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Wood Properties ◽  
Wood Formation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Regulatory Pathways ◽  
Genome Wide

Abstract Photosynthesis and wood formation underlie the ability of trees to provide renewable resources and perform ecosystem services; however, the genetic basis and regulatory pathways coordinating these two linked processes remain unclear. Here, we used a systems genetics strategy, integrating genome-wide association study, transcriptomic analyses, and transgenic experiments, to investigate the genetic architecture of photosynthesis and wood properties among 435 unrelated individuals of Populus tomentosa and unravel the coordinated regulatory networks causative of two trait categories. We totally detected 222 significant single-nucleotide polymorphisms, annotated to 177 candidate genes, for 10 traits of photosynthesis and wood properties. Epistasis uncovered 74 epistatic interactions for phenotypes. Strikingly, we deciphered the coordinated regulation patterns of pleiotropic genes underlying phenotypic variations for two trait categories. Furthermore, expression quantitative trait nucleotide mapping and coexpression analysis were integrated to unravel the potential transcriptional regulatory networks of candidate genes coordinating photosynthesis and wood properties. Finally, we heterologously expressed two pleiotropic genes, PtoMYB62 and PtoMYB80, in Arabidopsis thaliana, and demonstrated that they coordinate regulatory networks balancing photosynthesis and stem secondary cell wall components, respectively. Our study provides insight into the regulatory mechanisms coordinating photosynthesis and wood formation in poplar, which will accelerate the genetic breeding in trees via molecular design.

scholarly journals Effect of phyB and phyC loss-of-function mutations on the wheat transcriptome under short and long day photoperiods

2020 ◽  
Author(s):  
Nestor Kippes ◽  
Carl VanGessel ◽  
James Hamilton ◽  
Ani Akpinar ◽  
Hikmet Budak ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Regulatory Networks ◽  
Red Light ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Loss Of Function ◽  
Wild Type ◽  
Long Day ◽  
Alternatively Spliced ◽  
Null Mutants

AbstractBackgroundPhotoperiod signals provide important cues by which plants regulate their growth and development in response to predictable seasonal changes. Phytochromes, a family of red and far-red light receptors, play critical roles in regulating flowering time in response to changing photoperiods. A previous study showed that loss-of-function mutations in either PHYB or PHYC result in large delays in heading time and in the differential regulation of a large number of genes in wheat plants grown in an inductive long day (LD) photoperiod.ResultsWe found that under non-inductive short-day (SD) photoperiods, phyB-null and phyC-null mutants were taller, had a reduced number of tillers, longer and wider leaves, and headed later than wild-type plants. Unexpectedly, both mutants flowered earlier in SD than LD, the inverse response to that of wild-type plants. We observed a larger number of differentially expressed genes between mutants and wild-type under SD than under LD, and in both cases, the number was larger for phyB than for phyC. We identified subsets of differentially expressed and alternatively spliced genes that were specifically regulated by PHYB and PHYC in either SD or LD photoperiods, and a smaller set of genes that were regulated in both photoperiods. We observed significantly higher transcript levels of the flowering promoting genes VRN-A1, PPD-B1 and GIGANTEA in the phy-null mutants in SD than in LD, which suggests that they could contribute to the earlier flowering of the phy-null mutants in SD than in LD.ConclusionsOur study revealed an unexpected reversion of the wheat LD plants into SD plants in the phyB-null and phyC-null mutants and identified candidate genes potentially involved in this phenomenon. Our RNA-seq data provides insight into light signaling pathways in inductive and non-inductive photoperiods and a set of candidate genes to dissect the underlying developmental regulatory networks in wheat.

scholarly journals Using ontologies and semantic similarity measures for prioritization of gene regulatory networks

2016 ◽  
Author(s):  
Marianna Milano ◽  
Pietro Guzzi ◽  
Mario Cannataro
Keyword(s):  
Candidate Genes ◽  
Semantic Similarity ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
Denv Infection ◽  
Disease Ontology ◽  
Cancer Disease ◽  
Human Phenotype ◽  
Gene Regulatory

Omics sciences are widely used to analyze diseases at a molecular level. Usually, results of omics experiments are sets of candidate genes potentially involved in different diseases. The interpretation of results and the filtering of candidate genes or proteins selected in an experiment is a challenge in some scenarios. This problem is particularly evident in clinical environments in which researchers are interested in the behavior of few molecules related to some specific disease while results may contains thousands of data and have very relevant dimensions. The filtering requires the use of domain-specific knowledge that is usually encoded into ontologies. Consequently, to filter out false positive genes, different approaches for selecting genes have been introduced. Such approaches are often referred to as Gene prioritization methods. They aim to identify the most related genes to a disease among a larger set of candidates genes, through the use of computational methods. We implemented GoD (Gene ranking based On Diseases), an algorithm that ranks a given set of genes based on ontology annotations. The algorithm orders genes by the semantic similarity computed with respect to a disease among the annotations of each gene and those describing the selected disease.The current version of GoD enables the prioritization of a list of input genes for a selected disease. It uses HPO (Human Phenotype Ontology), GO (Gene Ontology), and DO (Disease Ontology) ontologies for the calculation of the ranking. It takes as input a list of genes or gene products annotated with GO Terms, HPO Terms, DO Terms and a selected disease described regarding annotation of GO, HPO or DO (user may also provide novel annotations). It produces as output the ranking of those genes with respect of the input disease. Package consists of three main functions: hpoGoD (for HPO based prioritization), goGoD (for GO based prioritization), and doGoD (for DO based prioritization). We tested GoD on Gene Regulatory Networks (GRNs). Biological network inference aims to reconstruct network of interactions (or associations) among biological genes starting from experimental observations. We selected three expression datasets: Dataset 1 (GDS3285) , related to breast cancer disease; Dataset 2 (GDS5072), related to prostate cancer disease; and Dataset 3 (GDS5093), related to Dengue virus (DENV) infection. Initially, experimental data are given as input to five GRN inference algorithms, i.e. ARACNE, CLR, MRNET, GENIE3 and GGM, to produce 5 inferred GRN networks. For each inferred GRN, GoD receives as input the list of top genes and produces for each gene a semantic similarity value on a selected disease considering one of the previous ontologies (e.g. Disease Ontology). For each GRN, the genes are ranked and reordered on the basis of the computed semantic similarity and are compared allowing to rank each GRN inference method with respect to the initially selected disease.

scholarly journals Genome-Wide Profiling of the Microrna Transcriptome Regulatory Network to Identify Putative Candidate Genes Associated with Backfat Deposition in Pigs

Animals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 313 ◽  
Author(s):  
Xin Liu ◽  
Jianfei Gong ◽  
Ligang Wang ◽  
Xinhua Hou ◽  
Hongmei Gao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Molecular Genetic ◽  
Subcutaneous Fat ◽  
Fat Deposition ◽  
Backfat Thickness ◽  
Feed Conversion ◽  
Protein Coding ◽  
Genes Encoding

Backfat deposition is strongly related to carcass traits, growth rate, feed conversion rate, and reproductive performance in pig production. To understand the molecular mechanisms underlying porcine backfat thickness phenotypes, transcriptome and miRNA profiling of backfat from high-backfat thickness and low-backfat thickness pigs were performed by RNA sequencing. Twenty genes encoding for miRNAs and 126 genes encoding for protein-coding genes were found to be differentially expressed between the two libraries. After integrative analysis of DEMs targets and DEGs, a total of 33 mRNA‒miRNA interaction pairs were identified, and the regulatory networks of these pairs were determined. Among these genes, five (AQP9, DKK3, GLYCTK, GLIPR1, and DUSP2) related to fat deposition were found to be strong candidate genes, and mir-31-5p/AQP9 and mir-31-5p/GLIPR1 may play important roles in fat deposition. Additionally, potential adipogenesis-related genes and miRNAs were identified. These findings improve the current understanding of the molecular genetic mechanisms of subcutaneous fat deposition in pigs and provide a foundation for further studies.

scholarly journals Phenotypic and expression QTL integration allows the refinement of loci associated with pig carcass and meat quality traits

2021 ◽  
Author(s):  
Frédéric Hérault ◽  
Annie Vincent ◽  
Ando Yoanne Randriamanantena ◽  
Marie Damon ◽  
Pierre Cherel ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Meat Quality ◽  
Calcium Homeostasis ◽  
Complex Traits ◽  
Regulatory Networks ◽  
Muscle Development ◽  
Quality Trait ◽  
Quality Traits ◽  
Meat Quality Traits ◽  
Genome Wide

Abstract Background: Many quantitative trait loci (QTLs) affecting pig meat and carcass quality traits have been reported. However, in most cases, the length of these phenotypic QTLs (pQTLs) is large. Hence, the identification of candidate genes and causative polymorphisms hidden behind those pQTLs remains a difficult task. Combining gene expression, phenotype and genotype data in an integrative genomics approach may help to identify regulatory networks and pathways underlying such complex traits. In the present study, we used genome-wide association study (GWAS) and linkage disequilibrium linkage analysis (LDLA) approaches to identify longissimus muscle (LM) and semimembranosus muscle (SM) expression QTLs (eQTLs). The locations of these eQTLs were compared to those of pQTLs previously mapped in the same population of commercial-type pigs. Colocalized eQTLs/pQTLs could help to identify candidate genes and pathways involved in pig carcass and meat quality trait determination. Results: Both approaches led us to identify 1,253 and 1,109 genome-wide significant eQTLs for LM and SM, respectively. We identified only one common eQTL between the two muscles and a few significant common eQTLs between methodologies : 16 in SM and 1 in LM. A total of 192 overlapping locations were identified between eQTLs and pQTLs. Colocalization highlighted some genes involved in muscle development, adipogenic processes or ion calcium homeostasis. These eQTLs allowed us to refine previously identified pQTLs related to carcass and meat quality traits. However, in most cases, the refined loci were still large and contained several coding and noncoding genes. Conclusions: Our results shed light on the muscle-specific genetic control governing mRNA expression and hence controlling the development of pig carcass and meat quality traits. Moreover, colocations between eQTLs and pQTLs implicated genes potentially involved in muscle development, adipogenic processes or ion calcium homeostasis in the pathways governing these traits. Finally, our results allowed us to refine QTLs controlling meat quality traits and to highlight the possible involvement of long noncoding RNAs in the architecture of regulatory networks governing complex traits such as pig carcass and meat quality traits.

scholarly journals Analytical Strategy to Prioritize Alzheimer’s Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

2017 ◽  
Vol 59 (4) ◽  
pp. 1237-1254 ◽  
Author(s):  
Shweta Bagewadi Kawalia ◽  
Tamara Raschka ◽  
Mufassra Naz ◽  
Ricardo de Matos Simoes ◽  
Philipp Senger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Analytical Strategy ◽  
Public Expression ◽  
Gene Regulatory

Integration of multi-omics data to mine cancer-related gene modules

2019 ◽  
Vol 17 (06) ◽  
pp. 1950038
Author(s):  
Peng Li ◽  
Maozu Guo ◽  
Bo Sun
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Regulatory Networks ◽  
Feature Selection Method ◽  
Lung Squamous Cell Carcinoma ◽  
Diagnosis And Treatment ◽  
Omics Data ◽  
Related Gene ◽  
Cancer Related Gene ◽  
Gene Modules

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.

scholarly journals Using ontologies and semantic similarity measures for prioritization of gene regulatory networks

2016 ◽  
Author(s):  
Marianna Milano ◽  
Pietro Guzzi ◽  
Mario Cannataro
Keyword(s):  
Candidate Genes ◽  
Semantic Similarity ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
Denv Infection ◽  
Disease Ontology ◽  
Cancer Disease ◽  
Human Phenotype ◽  
Gene Regulatory

Omics sciences are widely used to analyze diseases at a molecular level. Usually, results of omics experiments are sets of candidate genes potentially involved in different diseases. The interpretation of results and the filtering of candidate genes or proteins selected in an experiment is a challenge in some scenarios. This problem is particularly evident in clinical environments in which researchers are interested in the behavior of few molecules related to some specific disease while results may contains thousands of data and have very relevant dimensions. The filtering requires the use of domain-specific knowledge that is usually encoded into ontologies. Consequently, to filter out false positive genes, different approaches for selecting genes have been introduced. Such approaches are often referred to as Gene prioritization methods. They aim to identify the most related genes to a disease among a larger set of candidates genes, through the use of computational methods. We implemented GoD (Gene ranking based On Diseases), an algorithm that ranks a given set of genes based on ontology annotations. The algorithm orders genes by the semantic similarity computed with respect to a disease among the annotations of each gene and those describing the selected disease.The current version of GoD enables the prioritization of a list of input genes for a selected disease. It uses HPO (Human Phenotype Ontology), GO (Gene Ontology), and DO (Disease Ontology) ontologies for the calculation of the ranking. It takes as input a list of genes or gene products annotated with GO Terms, HPO Terms, DO Terms and a selected disease described regarding annotation of GO, HPO or DO (user may also provide novel annotations). It produces as output the ranking of those genes with respect of the input disease. Package consists of three main functions: hpoGoD (for HPO based prioritization), goGoD (for GO based prioritization), and doGoD (for DO based prioritization). We tested GoD on Gene Regulatory Networks (GRNs). Biological network inference aims to reconstruct network of interactions (or associations) among biological genes starting from experimental observations. We selected three expression datasets: Dataset 1 (GDS3285) , related to breast cancer disease; Dataset 2 (GDS5072), related to prostate cancer disease; and Dataset 3 (GDS5093), related to Dengue virus (DENV) infection. Initially, experimental data are given as input to five GRN inference algorithms, i.e. ARACNE, CLR, MRNET, GENIE3 and GGM, to produce 5 inferred GRN networks. For each inferred GRN, GoD receives as input the list of top genes and produces for each gene a semantic similarity value on a selected disease considering one of the previous ontologies (e.g. Disease Ontology). For each GRN, the genes are ranked and reordered on the basis of the computed semantic similarity and are compared allowing to rank each GRN inference method with respect to the initially selected disease.

scholarly journals Modularity of genes involved in local adaptation to climate despite physical linkage

2017 ◽  
Author(s):  
Katie E. Lotterhos ◽  
Sam Yeaman ◽  
Jon Degner ◽  
Sally Aitken ◽  
Kathryn A. Hodgins
Keyword(s):  
Candidate Genes ◽  
Local Adaptation ◽  
Evolutionary Biology ◽  
Pinus Contorta ◽  
Regulatory Networks ◽  
Pleiotropic Effects ◽  
Recombination Rates ◽  
Selection Pressures ◽  
Physical Linkage ◽  
Peer Community

AbstractThis preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (https://doi.org/10.24072/pci.evolbiol.100050)BackgroundLinkage among genes experiencing different selection pressures can make natural selection less efficient. Theory predicts that when local adaptation is driven by complex and non-covarying stresses, increased linkage is favoured for alleles with similar pleiotropic effects, with increased recombination favoured among alleles with contrasting pleiotropic effects. Here, we introduce a framework to test these predictions with a co-association network analysis, which clusters loci based on differing associations. We use this framework to study the genetic architecture of local adaptation to climate in lodgepole pine (Pinus contorta), based on associations with environments.ResultsWe identified many clusters of candidate genes and SNPs associated with distinct environments (aspects of aridity, freezing, etc.), and discovered low recombination rates among some candidate genes in different clusters. Only a few genes contained SNPs with effects on more than one distinct aspect of climate. There was limited correspondence between co-association networks and gene regulatory networks. We further showed how associations with environmental principal components can lead to misinterpretation. Finally, simulations illustrated both benefits and caveats of co-association networks.ConclusionsOur results supported the prediction that different selection pressures favored the evolution of distinct groups of genes, each associating with a different aspect of climate. But our results went against the prediction that loci experiencing different sources of selection would have high recombination among them. These results give new insight into evolutionary debates about the extent of modularity, pleiotropy, and linkage in the evolution of genetic architectures.

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