Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis

ABSTRACTAssociations between leguminous plants and symbiotic nitrogen fixing bacteria (rhizobia) are a classical example of mutualism between a eukaryotic host and a specific group of prokaryotic microbes. Though being in part species-specific, different strains may colonize the same plant symbiotic structure (nodule). It is known that some rhizobial strains are better competitor than others, but detailed analyses aimed to predict from the rhizobial genome its competitive abilities are still scarce. Here we performed a bacterial genome wide association (GWAS) analysis to define the genomic determinants related to the competitive capabilities in the model rhizobial species Sinorhizobium meliloti. Thirteen tester strains were GFP-tagged and assayed against three reference competitor strains RFP-tagged (Rm1021, AK83 and BL225C) in a Medicago sativa nodule occupancy test. Competition data in combination with strains genomic sequences were used to build-up a model for GWAS based on k-mers. The model was then trained and applied for competition capabilities prediction. The model was able to well predict the competition abilities against two partners, BL225C, Rm1021 with coefficient of determination of 0.96 and 0.84, respectively. Four strains showing the highest competition phenotypes (> 60% single strain nodule occupancy; GR4, KH35c, KH46 and SM11) versus BL225C were used to identify k-mers associated with competition. The k-mers with highest scores mapped on the symbiosis-related megaplasmid pSymA and on genes coding for transporters, proteins involved in the biosynthesis of cofactors and proteins related to metabolism (i.e. glycerol, fatty acids) suggesting that competition abilities reside in multiple genetic determinants comprising several cellular components.IMPORTANCEDecoding the competitive pattern that occurs in the rhizosphere is challenging in the study of bacterial social interaction strategies. To date, single-gene approach has been mainly used to uncover the bases of nodulation, but there is still a gap about the main features that a priori turn out rhizobial strains able to outcompete indigenous rhizobia. Therefore, tracking down which traits make different rhizobial strains able to win the competition for plant infection over other indigenous rhizobia will allow ameliorating strain selection and consequently plant yield in sustainable agricultural production systems. We have proven that a k-mer based GWAS approach can effectively predict the competition abilities of a panel of strains, which were analyzed for their plant tissue occupancy by using double fluorescent labeling. The reported strategy could be used for detailed studies on the genomic aspects of the evolution of bacterial symbiosis and for an extensive evaluation of rhizobial inoculants.

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Competitiveness for Nodule Colonization in Sinorhizobium meliloti: Combined In Vitro -Tagged Strain Competition and Genome-Wide Association Analysis

mSystems ◽

10.1128/msystems.00550-21 ◽

2021 ◽

Author(s):

Agnese Bellabarba ◽

Giovanni Bacci ◽

Francesca Decorosi ◽

Erki Aun ◽

Elisa Azzarello ◽

...

Keyword(s):

Social Interaction ◽

Association Analysis ◽

Sinorhizobium Meliloti ◽

Single Gene ◽

A Priori ◽

Genome Wide Association ◽

Strain Competition ◽

Genome Wide ◽

Competitive Pattern

Decoding the competitive pattern that occurs in the rhizosphere is challenging in the study of bacterial social interaction strategies. To date, the single-gene approach has mainly been used to uncover the bases of nodulation, but there is still a knowledge gap regarding the main features that a priori characterize rhizobial strains able to outcompete indigenous rhizobia.

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Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle

Animals ◽

10.3390/ani11010192 ◽

2021 ◽

Vol 11 (1) ◽

pp. 192

Author(s):

Xinghai Duan ◽

Bingxing An ◽

Lili Du ◽

Tianpeng Chang ◽

Mang Liang ◽

...

Keyword(s):

Beef Cattle ◽

Candidate Genes ◽

Growth Curve ◽

Growth And Development ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Coefficient Of Determination ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

A Genome

The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight–age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R2 = 0.954). The parameters’ mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

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Genetic Data Analysis

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning - Advances in Medical Diagnosis, Treatment, and Care ◽

10.4018/978-1-7998-2742-9.ch017 ◽

2021 ◽

pp. 358-372

Author(s):

M. Shamila ◽

Amit Kumar Tyagi

Keyword(s):

Data Analysis ◽

Association Studies ◽

Single Gene ◽

Genetic Data ◽

Building Blocks ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Human Beings ◽

Genome Wide ◽

Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

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Genome-wide association analysis reveals extensive genetic overlap between mood instability and psychiatric disorders but divergent patterns of genetic effects

10.1101/2021.07.16.21260608 ◽

2021 ◽

Author(s):

Guy Hindley ◽

Kevin S O'Connell ◽

Zillur Rahman ◽

Oleksandr Frei ◽

Shahram Bahrami ◽

...

Keyword(s):

Psychiatric Disorders ◽

Genetic Correlation ◽

Association Studies ◽

Single Gene ◽

Genetic Effects ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Mood Instability ◽

Genome Wide

Mood instability (MOOD) is a transdiagnostic phenomenon with a prominent neurobiological basis. Recent genome-wide association studies found significant positive genetic correlation between MOOD and major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. Summary statistics for schizophrenia (SCZ, n=105,318), bipolar disorder (BIP, n=413,466), DEP (n=450,619), attention-deficit hyperactivity disorder (ADHD, n=53,293) and MOOD (n=363,705), were analysed using the bivariate causal mixture model and conjunctional false discovery rate methods to estimate the proportion of shared variants influencing MOOD and each disorder, and identify jointly associated genomic loci. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg=0.10-0.62). Of 10.4K genomic variants influencing MOOD, 4K-9.4K were estimated to influence psychiatric disorders. MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25, with consistent genetic effects in independent samples. Fifty-three jointly associated loci were overlapping across two or more disorders (transdiagnostic), seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, synapse organization. The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions of shared loci suggest divergent effects on corresponding neurobiological mechanisms which may relate to differences in the core clinical features of each disorder.

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Panton-Valentine leucocidin is the key determinant of Staphylococcus aureus pyomyositis in a bacterial genome-wide association study

10.1101/430538 ◽

2018 ◽

Author(s):

Bernadette C Young ◽

Sarah G Earle ◽

Sona Soeng ◽

Poda Sar ◽

Varun Kumar ◽

...

Keyword(s):

Staphylococcus Aureus ◽

Association Study ◽

Genome Wide Association Study ◽

Association Studies ◽

Bacterial Genome ◽

Strong Relationship ◽

Genome Wide Association ◽

Tropical Regions ◽

Genome Wide ◽

A Genome

AbstractPyomyositis is a severe bacterial infection of skeletal muscle, commonly affecting children in tropical regions and predominantly caused by Staphylococcus aureus. To understand the contribution of bacterial genomic factors to pyomyositis, we conducted a genome-wide association study of S. aureus cultured from 101 children with pyomyositis and 417 children with asymptomatic nasal carriage attending the Angkor Hospital for Children in Cambodia. We found a strong relationship between bacterial genetic variation and pyomyositis, with estimated heritability 63.8% (95% CI 49.2-78.4%). The presence of the Panton-Valentine leucocidin (PVL) locus increased the odds of pyomyositis 130-fold (p =10-17.9). The signal of association mapped both to the PVL-coding sequence and the sequence immediately upstream. Together these regions explained > 99.9% of heritability. Our results establish staphylococcal pyomyositis, like tetanus and diphtheria, as critically dependent on expression of a single toxin and demonstrate the potential for association studies to identify specific bacterial genes promoting severe human disease.

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Bacterial Genome wide association studies (bGWAS) and transcriptomics identifies cryptic antimicrobial resistance mechanisms in Acinetobacter baumannii

10.1101/864462 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chandler Roe ◽

Charles H.D. Williamson ◽

Adam J. Vazquez ◽

Kristen Kyger ◽

Michael Valentine ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Acinetobacter Baumannii ◽

Genome Wide Association Study ◽

Association Studies ◽

Bacterial Genome ◽

Genome Wide Association ◽

Multiple Drug ◽

Genome Wide Association Studies ◽

Genome Wide ◽

A Genome

AbstractAntimicrobial resistance (AMR) in the nosocomial pathogen, Acinetobacter baumannii, is becoming a serious public health threat. While some mechanisms of AMR have been reported, understanding novel mechanisms of resistance is critical for identifying emerging resistance. One of the first steps in identifying novel AMR mechanisms is performing genotype/phenotype association studies. However, performing genotype/phenotype association studies is complicated by the plastic nature of the A. baumannii pan-genome. In this study, we compared the antibiograms of 12 antimicrobials associated with multiple drug families for 84 A. baumannii isolates, many isolated in Arizona, USA. in silico screening of these genomes for known AMR mechanisms failed to identify clear correlations for most drugs. We then performed a genome wide association study (GWAS) looking for associations between all possible 21-mers; this approach generally failed to identify mechanisms that explained the resistance phenotype. In order to decrease the genomic noise associated with population stratification, we compared four phylogenetically-related pairs of isolates with differing susceptibility profiles. RNA-Sequencing (RNA-Seq) was performed on paired isolates and differentially expressed genes were identified. In these isolate pairs, we identified four different potential mechanisms, highlighting the difficulty of broad AMR surveillance in this species. To verify and validate differential expression, amplicon sequencing was performed. These results suggest that a diagnostic platform based on gene expression rather than genomics alone may be beneficial in certain surveillance efforts. The implementation of such advanced diagnostics coupled with increased AMR surveillance will potentially improve A. baumannii infection treatment and patient outcomes.

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Genome-wide association mapping of Sclerotinia sclerotiorum resistance in soybean using whole-genome resequencing data

10.21203/rs.2.14709/v2 ◽

2019 ◽

Author(s):

Chiheb Boudhrioua ◽

Maxime Bastien ◽

Davoud Torkamaneh ◽

François Belzile

Keyword(s):

Association Mapping ◽

Sclerotinia Sclerotiorum ◽

Single Gene ◽

Genome Wide Association ◽

Chromosome 1 ◽

Whole Genome ◽

Sclerotinia Stem Rot ◽

Genome Wide ◽

A Genome ◽

The Impact

Abstract Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (Lib.) de Bary, is an important cause of yield loss in soybean. Although many papers have reported different loci contributing to partial resistance, few of these were proved to reproduce the same phenotypic impact in different populations. In this study, we identified a major quantitative trait loci (QTL) associated with resistance to SSR progression on the main stem by using a genome-wide association mapping (GWAM). A population of 127 soybean accessions was genotyped with 1.5M SNPs derived from genotyping-by-sequencing (GBS) and whole-genome sequencing (WGS) ensuring an extensive genome coverage and phenotyped for SSR resistance. SNP-trait association led to discovery of a new QTL on chromosome 1 (Chr01) where resistant lines had shorter lesions on the stem by 29 mm . A single gene (Glyma.01g048000) resided in the same LD block as the peak SNP, but it is of unknown function. The impact of this QTL was even more significant in the descendants of a cross between two lines carrying contrasted alleles for Chr01. Individuals carrying the resistance allele developed lesions almost 50% shorter than those bearing the sensitivity allele. These results suggest that this region harbors a promising resistance QTL to SSR that can be used in soybean breeding program.

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