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Genome-wide association (GWA) is gaining popularity as a means to study the architecture of complex quantitative traits, partially due to the improvement of high-throughput low-cost genotyping and phenotyping technologies. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of adaptive quantitative traits. GSL are key anti-herbivory defenses that impart adaptive advantages within field trials. While little is known about how variation in the external or internal environment of an organism may influence the efficiency of GWA, GSL variation is known to be highly dependent upon the external stresses and developmental processes of the plant lending it to be an excellent model for studying conditional GWA. To understand how development and environment can influence GWA, we conducted a study using 96 Arabidopsis thaliana accessions, >40 GSL phenotypes across three conditions (one developmental comparison and one environmental comparison) and ∼230,000 SNPs. Developmental stage had dramatic effects on the outcome of GWA, with each stage identifying different loci associated with GSL traits. Further, while the molecular bases of numerous quantitative trait loci (QTL) controlling GSL traits have been identified, there is currently no estimate of how many additional genes may control natural variation in these traits. We developed a novel co-expression network approach to prioritize the thousands of GWA candidates and successfully validated a large number of these genes as influencing GSL accumulation within A. thaliana using single gene isogenic lines. Together, these results suggest that complex traits imparting environmentally contingent adaptive advantages are likely influenced by up to thousands of loci that are sensitive to fluctuations in the environment or developmental state of the organism. Additionally, while GWA is highly conditional upon genetics, the use of additional genomic information can rapidly identify causal loci en masse.

While R2R3 MYB transcription factors are a large gene family of transcription factors within plants, comprehensive functional data in planta are still scarce. A model for studying R2R3 MYB control of metabolic networks is the glucosinolates (GLSs), secondary metabolites that control plant resistance against insects and pathogens and carry cancer-preventive properties. Three related members of the R2R3 MYB transcription factor family within Arabidopsis (Arabidopsis thaliana), MYB28, MYB29, and MYB76, are the commonly defined regulators of aliphatic GLS biosynthesis. We utilized new genotypes and systems analysis techniques to test the existing regulatory model in which MYB28 is the dominant regulator, MYB29 plays a minor rheostat role, and MYB76 is largely uninvolved. We unequivocally show that MYB76 is not dependent on MYB28 and MYB29 for induction of aliphatic GLSs and that MYB76 plays a role in determining the spatial distribution of aliphatic GLSs within the leaf, pointing at a potential role of MYB76 in transport regulation. Transcriptional profiling of knockout mutants revealed that GLS metabolite levels are uncoupled from the level of transcript accumulation for aliphatic GLS biosynthetic genes. This uncoupling of chemotypes from biosynthetic transcripts suggests revising our view of the regulation of GLS metabolism from a simple linear transcription factor-promoter model to a more modular system in which transcription factors cause similar chemotypes via nonoverlapping regulatory patterns. Similar regulatory networks might exist in other secondary pathways.

Background Women who are pregnant or have recently given birth constitute a distinct life-stage-specific population with elevated risks for mental health problems and well understood risk and protective factors. Perinatal mental health remains a worldwide public health problem that requires local health service reform to improve access and meet need. The aim of this paper was to create a framework for a proposed model of postnatal mental healthcare and understand its application in existing health services in a rural area in Australia. Methods The Theory of Change (ToC) method was used in a three-step procedure: 1) document reviews 2) stakeholder consultations and 3) ToC workshops. A proposed model of postnatal mental healthcare was developed and its application in a local setting was investigated. The model was documented using a ToC framework. Results Drawing on evidence from policy documents, clinical practice guidelines, existing health systems, and practice expertise, a ToC for a proposed ‘stepped model of postnatal mental healthcare’ was developed. Stepped care is a complex intervention intended to deliver tailored care pathways according to severity of individual need. It links four interrelated components: primary prevention, secondary prevention, early intervention and treatment. In the ToC, evidence-based Rationales support each Pathway, and Assumptions describe external conditions that must be satisfied for successful implementation of each Pathway and the overall model. Conclusions The proposed stepped-care model is a valuable investigation of an integrated model of postnatal mental health service reform in a local service environment. The Assumptions generate testable hypotheses for future research to understand the conditions for successful implementation in this or other settings.

With the improvement and decline in cost of high-throughput genotyping and phenotyping technologies, genome-wide association (GWA) studies are fast becoming a preferred approach for dissecting complex quantitative traits. Glucosinolate (GSL) secondary metabolites within Arabidopsis spp. can serve as a model system to understand the genomic architecture of quantitative traits. GSLs are key defenses against insects in the wild and the relatively large number of cloned quantitative trait locus (QTL) controlling GSL traits allows comparison of GWA to previous QTL analyses. To better understand the specieswide genomic architecture controlling plant-insect interactions and the relative strengths of GWA and QTL studies, we conducted a GWA mapping study using 96 A. thaliana accessions, 43 GSL phenotypes, and ∼230,000 SNPs. Our GWA analysis identified the two major polymorphic loci controlling GSL variation (AOP and MAM) in natural populations within large blocks of positive associations encompassing dozens of genes. These blocks of positive associations showed extended linkage disequilibrium (LD) that we hypothesize to have arisen from balancing or fluctuating selective sweeps at both the AOP and MAM loci. These potential sweep blocks are likely linked with the formation of new defensive chemistries that alter plant fitness in natural environments. Interestingly, this GWA analysis did not identify the majority of previously identified QTL even though these polymorphisms were present in the GWA population. This may be partly explained by a nonrandom distribution of phenotypic variation across population subgroups that links population structure and GSL variation, suggesting that natural selection can hinder the detection of phenotype–genotype associations in natural populations.

Background Endometriosis is a chronic condition, requiring long-term care as there is no cure. Self-management is the active participation of a person in managing their chronic condition and has been associated with improved knowledge, self-efficacy, performance of self-management tasks and some aspects of health status in interventions for other chronic diseases. The aim was to review the available evidence about the impact of self-management on condition-specific health among women with endometriosis. Methods The Medline, PsycINFO, CinahlPlus, Web of Science and Scopus databases were searched and PRISMA guidelines were followed. Search terms were entered both as keywords and mapped to individual database subject headings. Inclusion criteria were: papers that reported investigations of any approach to self-management; among women (at least 18 years) diagnosed with endometriosis and published in English in a peer-reviewed journal. All study designs using quantitative or qualitative methods were eligible for inclusion. Two reviewers independently examined the quality of studies using standard criteria. The systematic review was registered with Prospero (CRD42016042028). Results A total of 1164 records were identified (after duplicates were removed), and 27 papers, reporting 19 studies met inclusion criteria. Two papers reported findings from RCTs of complementary therapies, seven reported survey data and 18 qualitative studies. No study had investigated all elements of self-management. Women with endometriosis utilise a range of self-care activities and complementary therapies to assist them to manage their symptoms. Women reported both positive and negative experiences with health care providers. Conclusions There is some evidence that self-care activities, complementary therapies and positive patient–healthcare provider relationships are important components of self-management for endometriosis. Self-management among women with endometriosis is an emerging field of research and no investigations of all elements of self-management, informed by a comprehensive definition and theoretical framework are available. Health and wellbeing outcomes and barriers and facilitators to self-management for women with endometriosis require further investigation.

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype-metabolite associations distributed non-randomly within the genome. These clusters of genotype-metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype-metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.

While R2R3 MYB transcription factors are a large gene family of transcription factors within plants, comprehensive functional data in planta are still scarce. A model for studying R2R3 MYB control of metabolic networks is the glucosinolates (GLSs), secondary metabolites that control plant resistance against insects and pathogens and carry cancer-preventive properties. Three related members of the R2R3 MYB transcription factor family within Arabidopsis (Arabidopsis thaliana), MYB28, MYB29, and MYB76, are the commonly defined regulators of aliphatic GLS biosynthesis. We utilized new genotypes and systems analysis techniques to test the existing regulatory model in which MYB28 is the dominant regulator, MYB29 plays a minor rheostat role, and MYB76 is largely uninvolved. We unequivocally show that MYB76 is not dependent on MYB28 and MYB29 for induction of aliphatic GLSs and that MYB76 plays a role in determining the spatial distribution of aliphatic GLSs within the leaf, pointing at a potential role of MYB76 in transport regulation. Transcriptional profiling of knockout mutants revealed that GLS metabolite levels are uncoupled from the level of transcript accumulation for aliphatic GLS biosynthetic genes. This uncoupling of chemotypes from biosynthetic transcripts suggests revising our view of the regulation of GLS metabolism from a simple linear transcription factor-promoter model to a more modular system in which transcription factors cause similar chemotypes via nonoverlapping regulatory patterns. Similar regulatory networks might exist in other secondary pathways.

Genomic approaches have accelerated the study of the quantitative genetics that underlie phenotypic variation. These approaches associate genome-scale analyses such as transcript profiling with targeted phenotypes such as measurements of specific metabolites. Additionally, these approaches can help identify uncharacterized networks or pathways. However, little is known about the genomic architecture underlying data sets such as metabolomics or the potential of such data sets to reveal networks. To describe the genetic regulation of variation in the Arabidopsis thaliana metabolome and test our ability to integrate unknown metabolites into biochemical networks, we conducted a replicated metabolomic analysis on 210 lines of an Arabidopsis population that was previously used for targeted metabolite quantitative trait locus (QTL) and global expression QTL analysis. Metabolic traits were less heritable than the average transcript trait, suggesting that there are differences in the power to detect QTLs between transcript and metabolite traits. We used statistical analysis to identify a large number of metabolite QTLs with moderate phenotypic effects and found frequent epistatic interactions controlling a majority of the variation. The distribution of metabolite QTLs across the genome included 11 QTL clusters; 8 of these clusters were associated in an epistatic network that regulated plant central metabolism. We also generated two de novo biochemical network models from the available data, one of unknown function and the other associated with central plant metabolism.

Despite the described central role of jasmonate signaling in plant defense against necrotrophic pathogens, the existence of intraspecific variation in pathogen capacity to activate or evade plant jasmonate-mediated defenses is rarely considered. Experimental infection of jasmonate-deficient and jasmonate-insensitive Arabidopsis thaliana with diverse isolates of the necrotrophic fungal pathogen Botrytis cinerea revealed pathogen variation for virulence inhibition by jasmonate-mediated plant defenses and induction of plant defense metabolites. Comparison of the transcriptional effects of infection by two distinct B. cinerea isolates showed only minor differences in transcriptional responses of wild-type plants, but notable isolate-specific transcript differences in jasmonate-insensitive plants. These transcriptional differences suggest B. cinerea activation of plant defenses that require plant jasmonate signaling for activity in response to only one of the two B. cinerea isolates tested. Thus, similar infection phenotypes observed in wild-type plants result from different signaling interactions with the plant that are likely integrated by jasmonate signaling.