scholarly journals Small RNAs from the plant pathogenic fungus Sclerotinia sclerotiorum highlight candidate host target genes associated with quantitative disease resistance

2018 ◽  
Author(s):  
Mark C Derbyshire ◽  
Malick Mbengue ◽  
Marielle Barascud ◽  
Olivier Navaud ◽  
Sylvain Raffaele
Keyword(s):  
Disease Resistance ◽  
Sclerotinia Sclerotiorum ◽  
Host Species ◽  
Small Rnas ◽  
Target Genes ◽  
Association Studies ◽  
Pathogenic Fungus ◽  
Effector Proteins ◽  
Genome Wide Association Studies ◽  
Quantitative Disease Resistance

ABSTRACTPlant pathogenic fungi secrete effector proteins and secondary metabolites to cause disease. Additionally, some produce small RNAs (sRNAs) that silence transcripts of host immunity genes through RNA interference. The fungus Sclerotinia sclerotiorum infects over 600 plant species, but little is known about its molecular interactions with its hosts. In particular, the role of sRNAs in S. sclerotiorum pathogenicity has not been determined. By sequencing sRNAs in vitro and during infection of two host species, we found that S. sclerotiorum produces at least 374 highly abundant sRNAs. These sRNAs mostly originated from polymorphic repeat-rich genomic regions. Predicted gene targets of these sRNAs, from 10 different host species, were enriched for immunity-related functional domains. Predicted A. thaliana gene targets of S. sclerotiorum sRNAs were significantly more down-regulated during infection than other genes. A. thaliana gene targets were also more likely to contain single nucleotide polymorphisms (SNPs) associated with quantitative disease resistance. In conclusion, sRNAs produced by S. sclerotiorum are likely capable of silencing immunity components in multiple hosts. Prediction of fungal sRNA targets in host plant genomes can be combined with other global approaches, such as genome wide association studies and transcriptomics, to assist identification of plant genes involved in disease resistance.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

scholarly journals A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257265
Author(s):  
Seung-Soo Kim ◽  
Adam D. Hudgins ◽  
Jiping Yang ◽  
Yizhou Zhu ◽  
Zhidong Tu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Target Genes ◽  
Association Studies ◽  
Regulatory Elements ◽  
Immune Dysregulation ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Gwas Analysis ◽  
Regulatory Variants

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

scholarly journals Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

2021 ◽  
Vol 12 ◽  
Author(s):  
Martina Rauner ◽  
Ines Foessl ◽  
Melissa M. Formosa ◽  
Erika Kague ◽  
Vid Prijatelj ◽  
...  
Keyword(s):  
Resource Sharing ◽  
Complex Traits ◽  
Cellular Localization ◽  
Target Genes ◽  
Mission Statement ◽  
Association Studies ◽  
Repetitive Sequences ◽  
Genome Wide Association Studies ◽  
Causal Genes

The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits (“endophenotypes”), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.

scholarly journals Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits.

2021 ◽  
Author(s):  
Nancy Y.A Sey ◽  
Benxia Hu ◽  
Marina Iskhakova ◽  
Huaigu Sun ◽  
Neda Shokrian ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alcohol Use ◽  
Cigarette Smoking ◽  
Genetic Risk ◽  
Target Genes ◽  
Association Studies ◽  
Critical Role ◽  
Genetic Risk Factors ◽  
Genome Wide Association Studies ◽  
Risk Genes

Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ~26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture refines neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

scholarly journals Meta-Analysis Reveals Consensus Genomic Regions Associated with Multiple Disease Resistance in Wheat ( Triticum Aestivum L.)

2021 ◽  
Author(s):  
Dinesh Kumar Saini ◽  
Amneek Chahal ◽  
Neeraj Pal ◽  
Puja Srivast ◽  
Pushpendra Kumar Gupta
Keyword(s):  
Disease Resistance ◽  
Physical Map ◽  
Association Studies ◽  
Meta Analysis ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Karnal Bunt ◽  
Genome Wide Association Studies ◽  
Head Blight ◽  
Multiple Disease Resistance

Abstract In wheat, meta-QTLs (MQTLs), and candidate genes (CGs) were identified for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the five diseases. As many as 493 QTLs were available from these studies, which were distributed in five diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103; fusarium head blight (FHB), 184; karnal bunt (KB), 66, and loose smut (LS), 14. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of initial QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were verified using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identified which were further investigated for differential expression using data from five transcriptome studies, resulting in 194 differentially expressed genes (DEGs). Among the DEGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for fine mapping of MDR genes and marker-assisted breeding.

scholarly journals High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 374 ◽  
Author(s):  
Anna Dziewulska ◽  
Aneta Dobosz ◽  
Agnieszka Dobrzyn
Keyword(s):  
Type 2 Diabetes ◽  
Pancreatic Islets ◽  
High Throughput ◽  
Target Genes ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genomic Landscape ◽  
A Genome ◽  
Next Generation Sequencing Ngs

Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.

BnaMPK6 is a determinant of quantitative disease resistance against Sclerotinia sclerotiorum in oilseed rape

Plant Science ◽  
2020 ◽  
Vol 291 ◽  
pp. 110362
Author(s):  
Zheng Wang ◽  
Feng-Yun Zhao ◽  
Min-Qiang Tang ◽  
Ting Chen ◽  
Ling-Li Bao ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Oilseed Rape ◽  
Sclerotinia Sclerotiorum ◽  
Quantitative Disease Resistance

scholarly journals DOP54 Integrated network analysis using patient-specific single-nucleotide polymorphism profiles uncovers new pathways involved in ulcerative colitis pathogenesis

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S092-S092
Author(s):  
D Modos ◽  
J Brooks ◽  
P Sudhakar ◽  
B Verstockt ◽  
B Alexander-Dann ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Network Analysis ◽  
Tight Junctions ◽  
In Silico ◽  
Target Genes ◽  
Association Studies ◽  
Patient Specific ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Network Propagation

Abstract Background Genome-wide association studies have deciphered the single nucleotide polymorphisms (SNPs) which are responsible for ulcerative colitis (UC) susceptibility. However, to understand how these SNPs are involved in UC, additional methods are necessary. One such approach is in silico network propagation modelling, which can discover how the effects of SNPs in UC can affect the whole cell. A complementary approach is weighted gene co-expression network analysis (WGCNA), where co-regulated genes are identified using transcriptomic data. Integrating these two methods can shed light on how SNPs are affecting the transcriptome of UC patients. Methods We used immunochip profiles of 941 UC patients and focussed on UC-associated SNPs altering regulatory regions. Based on these regions, we identified affected genes. To understand how their corresponding proteins rewire transcriptional regulation, we predicted the path between these proteins and relevant transcription factors (TF) using the OmniPath signalling network (http://omnipathdb.org). From the TFs, we propagated the signal further to target genes using TFlink (https://tflink.net) and GTRD (http://gtrd.biouml.org). To evaluate the predicted network propagation signal, we conducted WGCNA with transcriptomics data from 46 matching patients’ (GEO ID: GSE48959). To interpret the results, we used Gene Ontology Biological Process annotations of the target genes, and we compared the function and regulation of affected genes and the determined WGCNA modules. Results We found 9 predominant signalling pathways, some already known from other studies to be involved in UC pathogenesis, including NFkB signalling, chemokine signalling, Notch pathway, JAK/STAT signalling. Downstream of these pathways we identified potential key TFs regulate the UC phenotype, for example NFKB1, GATA3, GTF2I. The targets of these TFs were enriched in the WGCNA modules of the patients. The WGCNA modules and the transcriptionally affected genes had enriched processes including cell migration, TGF-β signalling, exocytosis, adaptive T- and B-cell-specific immune responses and tight junctions. We also found myogenetic development specific TFs affected transcriptionally such as MyoD, MEF2A, MEF2D. We are currently validating these results through patient-specific biopsies. Conclusion In silico methods bring us closer to understanding UC pathogenesis. Our results suggest that in a well-defined set of patients, weakened tight junctions and insufficient immune response can lead to dysfunctional epithelial barrier, resulting in poor wound healing in UC. We hope the developed workflow will provide novel diagnostic and therapeutic options in UC.

Sign in / Sign up

Export Citation Format

Share Document