ResearchPad - consortia https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[<i>Salmonella</i> Typhimurium discreet-invasion of the murine gut absorptive epithelium]]> https://www.researchpad.co/article/elastic_article_14650 Bacterial pathogens can use secreted effector molecules to drive entry into host cells. Studies of the intestinal pathogen S.Tm have been central to uncover the mechanistic basis for the entry process. More than two decades of research have resulted in a detailed model for how S.Tm invades gut epithelial cells through effector triggering of large Rho-GTPase-dependent actin ruffles. However, the evidence for this model comes predominantly from studies in cultured cell lines. These experimental systems lack many of the architectural and signaling features of the intact gut epithelium. Our study surprisingly reveals that in the intact mouse gut, S.Tm invades absorptive epithelial cells through a process that does not require the Rho-GTPase-activating effectors and can proceed in the absence of the prototypical ruffling response. Instead, S.Tm exploits another effector, SipA, to sneak in through discreet entry structures close to cell–cell junctions. Our results challenge the current model for S.Tm epithelial cell entry and emphasizes the need of taking a physiological host cell context into account when studying bacterium–host cell interactions.

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<![CDATA[Instigation of indigenous thermophilic bacterial consortia for enhanced oil recovery from high temperature oil reservoirs]]> https://www.researchpad.co/article/elastic_article_13812 The purpose of the study involves the development of an anaerobic, thermophilic microbial consortium TERIK from the high temperature reservoir of Gujarat for enhance oil recovery. To isolate indigenous microbial consortia, anaerobic baltch media were prepared and inoculated with the formation water; incubated at 65°C for 10 days. Further, the microbial metabolites were analyzed by gas chromatography, FTIR and surface tension. The efficiency of isolated consortia towards enhancing oil recovery was analyzed through core flood assay. The novelty of studied consortia was that, it produces biomass (600 mg/l), bio-surfactant (325 mg/l), and volatile fatty acids (250 mg/l) at 65°C in the span of 10 days, that are adequate to alter the surface tension (70 to 34 mNm -1) and sweep efficiency of zones facilitating the displacement of oil. TERIK was identified as Clostridium sp. The FTIR spectra of biosurfactant indicate the presence of N-H stretch, amides and polysaccharide. A core flooding assay was designed to explore the potential of TERIK towards enhancing oil recovery. The results showed an effective reduction in permeability at residual oil saturation from 2.14 ± 0.1 to 1.39 ± 0.05 mD and 19% incremental oil recovery.

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<![CDATA[Genetic association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci]]> https://www.researchpad.co/article/5c7ee7c7d5eed0c4848f4db2

Cystic Fibrosis (CF) exhibits morbidity in several organs, including progressive lung disease in all patients and intestinal obstruction at birth (meconium ileus) in ~15%. Individuals with the same causal CFTR mutations show variable disease presentation which is partly attributed to modifier genes. With >6,500 participants from the International CF Gene Modifier Consortium, genome-wide association investigation identified a new modifier locus for meconium ileus encompassing ATP12A on chromosome 13 (min p = 3.83x10-10); replicated loci encompassing SLC6A14 on chromosome X and SLC26A9 on chromosome 1, (min p<2.2x10-16, 2.81x10−11, respectively); and replicated a suggestive locus on chromosome 7 near PRSS1 (min p = 2.55x10-7). PRSS1 is exclusively expressed in the exocrine pancreas and was previously associated with non-CF pancreatitis with functional characterization demonstrating impact on PRSS1 gene expression. We thus asked whether the other meconium ileus modifier loci impact gene expression and in which organ. We developed and applied a colocalization framework called the Simple Sum (SS) that integrates regulatory and genetic association information, and also contrasts colocalization evidence across tissues or genes. The associated modifier loci colocalized with expression quantitative trait loci (eQTLs) for ATP12A (p = 3.35x10-8), SLC6A14 (p = 1.12x10-10) and SLC26A9 (p = 4.48x10-5) in the pancreas, even though meconium ileus manifests in the intestine. The meconium ileus susceptibility locus on chromosome X appeared shifted in location from a previously identified locus for CF lung disease severity. Using the SS we integrated the lung disease association locus with eQTLs from nasal epithelia of 63 CF participants and demonstrated evidence of colocalization with airway-specific regulation of SLC6A14 (p = 2.3x10-4). Cystic Fibrosis is realizing the promise of personalized medicine, and identification of the contributing organ and understanding of tissue specificity for a gene modifier is essential for the next phase of personalizing therapeutic strategies.

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<![CDATA[Integrating predicted transcriptome from multiple tissues improves association detection]]> https://www.researchpad.co/article/5c50c43bd5eed0c4845e8359

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

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<![CDATA[Faculty perceptions and knowledge of career development of trainees in biomedical science: What do we (think we) know?]]> https://www.researchpad.co/article/5c5b52cbd5eed0c4842bd03f

The Broadening Experiences in Scientific Training (BEST) program is an NIH-funded effort testing the impact of career development interventions (e.g. internships, workshops, classes) on biomedical trainees (graduate students and postdoctoral fellows). BEST Programs seek to increase trainees’ knowledge, skills and confidence to explore and pursue expanded career options, as well as to increase training in new skills that enable multiple career pathways. Faculty mentors are vital to a trainee’s professional development, but data about how faculty members of biomedical trainees view the value of, and the time spent on, career development are lacking. Seven BEST institutions investigated this issue by conducting faculty surveys during their BEST experiment. The survey intent was to understand faculty perceptions around professional and career development for their trainees. Two different, complementary surveys were employed, one designed by Michigan State University (MSU) and the other by Vanderbilt University. Faculty (592) across five institutions responded to the MSU survey; 225 faculty members from two institutions responded to the Vanderbilt University survey. Participating faculty were largely tenure track and male; approximately 1/3 had spent time in a professional position outside of academia. Respondents felt a sense of urgency in introducing broad career activities for trainees given a recognized shortage of tenure track positions. They reported believing career development needs are different between a graduate student and postdoctoral fellow, and they indicated that they actively mentor trainees in career development. However, faculty were uncertain as to whether they actually have the knowledge or training to do so effectively. Faculty perceived that trainees themselves lack a knowledge base of skills that are of interest to non-academic employers. Thus, there is a need for exposure and training in such skills. Faculty stated unequivocally that institutional support for career development is important and needed. BEST Programs were considered beneficial to trainees, but the awareness of local BEST Programs and the national BEST Consortium was low at the time surveys were employed at some institutions. It is our hope that the work presented here will increase the awareness of the BEST national effort and the need for further career development for biomedical trainees.

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<![CDATA[BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2]]> https://www.researchpad.co/article/5c2d2eb3d5eed0c484d9b2c0

The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.

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<![CDATA[Reply to “Far away from the lamppost”]]> https://www.researchpad.co/article/5c196692d5eed0c484b5244e

In this Formal Comment, the authors of the recent publication "Large-scale investigation of the reasons why potentially important genes are ignored" maintain that it can be read as an opportunity to explore the unknown.

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<![CDATA[Far away from the lamppost]]> https://www.researchpad.co/article/5c196694d5eed0c484b524af

This Formal Comment responds to a recent Meta-Research Article by identifying initiatives that are already in place for funding risky exploratory research that illuminate mysteries of the dark genome.

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<![CDATA[Rhinovirus infections change DNA methylation and mRNA expression in children with asthma]]> https://www.researchpad.co/article/5c23ff95d5eed0c484092a2d

Human rhinovirus infection (HRVI) plays an important role in asthma exacerbations and is thought to be involved in asthma development during early childhood. We hypothesized that HRVI causes differential DNA methylation and subsequently differential mRNA expression in epithelial cells of children with asthma. Primary nasal epithelial cells from children with (n = 10) and without (n = 10) asthma were cultivated up to passage two and infected with Rhinovirus-16 (RV-16). HRVI-induced genome-wide differences of DNA methylation in asthmatics (vs. controls) and resulting mRNA expression were analyzed by the HumanMethylation450 BeadChip Kit (Illumina) and RNA sequencing. These results were further verified by pyrosequencing and quantitative PCR, respectively. 471 CpGs belonging to 268 genes were identified to have HRVI-induced asthma-specifically modified DNA methylation and mRNA expression. A minimum-change criteria was applied to restrict assessment of genes with changes in DNA methylation and mRNA expression of at least 3% and least 0.1 reads/kb per million mapped reads, respectively. Using this approach we identified 16 CpGs, including HLA-B-associated transcript 3 (BAT3) and Neuraminidase 1 (NEU1), involved in host immune response against HRVI. HRVI in nasal epithelial cells leads to specific modifications of DNA methylation with altered mRNA expression in children with asthma. The HRVI-induced alterations in DNA methylation occurred in genes involved in the host immune response against viral infections and asthma pathogenesis. The findings of our pilot study may partially explain how HRVI contribute to the persistence and progression of asthma, and aid to identify possible new therapeutic targets. The promising findings of this pilot study would benefit from replication in a larger cohort.

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<![CDATA[ISCB’s Initial Reaction to The New England Journal of Medicine Editorial on Data Sharing]]> https://www.researchpad.co/article/5989d9d5ab0ee8fa60b657d6 ]]> <![CDATA[Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities]]> https://www.researchpad.co/article/5989db52ab0ee8fa60bdc8fe

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

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<![CDATA[Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia]]> https://www.researchpad.co/article/5989da6eab0ee8fa60b93d96

Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine. Notwithstanding, a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design. Thus, future success of these devices depends on obtaining circuits with scalable complexity and reusable parts. Here we show how to build complex computational devices using multicellular consortia and space as key computational elements. This spatial modular design grants scalability since its general architecture is independent of the circuit’s complexity, minimizes wiring requirements and allows component reusability with minimal genetic engineering. The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs, thus demonstrating the scalability and flexibility of this method. The potential implications of our results are outlined.

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<![CDATA[Validation of a commercially available test that enables the quantification of the numbers of CGG trinucleotide repeat expansion in FMR1 gene]]> https://www.researchpad.co/article/5989db52ab0ee8fa60bdc56e

In the present study, we evaluated a commercially available TP-PCR-based assay, the FastFraXTM FMR1 Sizing kit, as a test in quantifying the number of CGG repeats in the FMR1 gene. Based on testing with well characterized DNA samples from Coriell, the kit yielded size results within 3 repeats of those obtained by common consensus (n = 14), with the exception of one allele. Furthermore, based on data obtained using all Coriell samples with or without common consensus (n = 29), the Sizing kit was 97.5% in agreement with existing approaches. Additionally, the kit generated consistent size information in repeatability and reproducibility studies (CV 0.39% to 3.42%). Clinical performance was established with 198 archived clinical samples, yielding results of 100% sensitivity (95% CI, 91.03% to 100%) and 100% specificity (95% CI, 97.64% to 100%) in categorizing patient samples into the respective normal, intermediate, premutation and full mutation genotypes.

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<![CDATA[Identifying systematic heterogeneity patterns in genetic association meta-analysis studies]]> https://www.researchpad.co/article/5989db5cab0ee8fa60be0230

Progress in mapping loci associated with common complex diseases or quantitative inherited traits has been expedited by large-scale meta-analyses combining information across multiple studies, assembled through collaborative networks of researchers. Participating studies will usually have been independently designed and implemented in unique settings that are potential sources of phenotype, ancestry or other variability that could introduce between-study heterogeneity into a meta-analysis. Heterogeneity tests based on individual genetic variants (e.g. Q, I2) are not suited to identifying locus-specific from more systematic multi-locus or genome-wide patterns of heterogeneity. We have developed and evaluated an aggregate heterogeneity M statistic that combines between-study heterogeneity information across multiple genetic variants, to reveal systematic patterns of heterogeneity that elude conventional single variant analysis. Application to a GWAS meta-analysis of coronary disease with 48 contributing studies uncovered substantial systematic between-study heterogeneity, which could be partly explained by age-of-disease onset, family-history of disease and ancestry. Future meta-analyses of diseases and traits with multiple known genetic associations can use this approach to identify outlier studies and thereby optimize power to detect novel genetic associations.

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<![CDATA[Association between Adult Height and Risk of Colorectal, Lung, and Prostate Cancer: Results from Meta-analyses of Prospective Studies and Mendelian Randomization Analyses]]> https://www.researchpad.co/article/5989da26ab0ee8fa60b80c95

Background

Observational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers.

Methods and Findings

A systematic review of prospective studies was conducted using the PubMed, Embase, and Web of Science databases. Using meta-analyses, results obtained from 62 studies were summarized for the association of a 10-cm increase in height with cancer risk. Mendelian randomization analyses were conducted using summary statistics obtained for 423 genetic variants identified from a recent GWAS of adult height and from a cancer genetics consortium study of multiple cancers that included 47,800 cases and 81,353 controls. For a 10-cm increase in height, the summary relative risks derived from the meta-analyses of prospective studies were 1.12 (95% CI 1.10, 1.15), 1.07 (95% CI 1.05, 1.10), and 1.06 (95% CI 1.02, 1.11) for colorectal, prostate, and lung cancers, respectively. Mendelian randomization analyses showed increased risks of colorectal (odds ratio [OR] = 1.58, 95% CI 1.14, 2.18) and lung cancer (OR = 1.10, 95% CI 1.00, 1.22) associated with each 10-cm increase in genetically predicted height. No association was observed for prostate cancer (OR = 1.03, 95% CI 0.92, 1.15). Our meta-analysis was limited to published studies. The sample size for the Mendelian randomization analysis of colorectal cancer was relatively small, thus affecting the precision of the point estimate.

Conclusions

Our study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.

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<![CDATA[Extending the use of GWAS data by combining data from different genetic platforms]]> https://www.researchpad.co/article/5989db53ab0ee8fa60bdcbc9

Background

In the past decade many Genome-wide Association Studies (GWAS) were performed that discovered new associations between single-nucleotide polymorphisms (SNPs) and various phenotypes. Imputation methods are widely used in GWAS. They facilitate the phenotype association with variants that are not directly genotyped. Imputation methods can also be used to combine and analyse data genotyped on different genotyping arrays. In this study we investigated the imputation quality and efficiency of two different approaches of combining GWAS data from different genotyping platforms. We investigated whether combining data from different platforms before the actual imputation performs better than combining the data from different platforms after imputation.

Methods

In total 979 unique individuals from the AMC-PAS cohort were genotyped on 3 different platforms. A total of 706 individuals were genotyped on the MetaboChip, a total of 757 individuals were genotyped on the 50K gene-centric Human CVD BeadChip, and a total of 955 individuals were genotyped on the HumanExome chip. A total of 397 individuals were genotyped on all 3 individual platforms. After pre-imputation quality control (QC), Minimac in combination with MaCH was used for the imputation of all samples with the 1,000 genomes reference panel. All imputed markers with an r2 value of <0.3 were excluded in our post-imputation QC.

Results

A total of 397 individuals were genotyped on all three platforms. All three datasets were carefully matched on strand, SNP ID and genomic coordinates. This resulted in a dataset of 979 unique individuals and a total of 258,925 unique markers. A total of 4,117,036 SNPs were available when imputation was performed before merging the three datasets. A total of 3,933,494 SNPs were available when imputation was done on the combined set. Our results suggest that imputation of individual datasets before merging performs slightly better than after combining the different datasets.

Conclusions

Imputation of datasets genotyped by different platforms before merging generates more SNPs than imputation after putting the datasets together.

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<![CDATA[Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing]]> https://www.researchpad.co/article/5989db54ab0ee8fa60bdcff5 ]]> <![CDATA[Establishing a reliable framework for harnessing the creative power of the scientific crowd]]> https://www.researchpad.co/article/5989db53ab0ee8fa60bdcea2

Discovering new medicines is difficult and increasingly expensive. The pharmaceutical industry has responded to this challenge by embracing open innovation to access external ideas. Historically, partnerships were usually bilateral, and the drug discovery process was shrouded in secrecy. This model is rapidly changing. With the advent of the Internet, drug discovery has become more decentralised, bottom-up, and scalable than ever before. The term open innovation is now accepted as just one of many terms that capture different but overlapping levels of openness in the drug discovery process. Many pharmaceutical companies recognise the advantages of revealing some proprietary information in the form of results, chemical tools, or unsolved problems in return for valuable insights and ideas. For example, such selective revealing can take the form of openly shared chemical tools to explore new biological mechanisms or by publicly admitting what is not known in the form of an open call. The essential ingredient for addressing these problems is access to the wider scientific crowd. The business of crowdsourcing, a form of outsourcing in which individuals or organisations solicit contributions from Internet users to obtain ideas or desired services, has grown significantly to fill this need and takes many forms today. Here, we posit that open-innovation approaches are more successful when they establish a reliable framework for converting creative ideas of the scientific crowd into practice with actionable plans.

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<![CDATA[Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score]]> https://www.researchpad.co/article/5989db53ab0ee8fa60bdcf31

Background

Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction.

Methods and findings

Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10−5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer’s Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer’s Disease Center [NIA ADC], and Alzheimer’s Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62–4.24, p = 1.0 × 10−22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10−26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran–Armitage trend test, p = 1.5 × 10−10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10−6, and Consortium to Establish a Registry for Alzheimer’s Disease score for neuritic plaques, p = 6.8 × 10−6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10−6, and hippocampus, p = 7.9 × 10−5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use.

Conclusions

We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

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<![CDATA[Parallel Mutations Result in a Wide Range of Cooperation and Community Consequences in a Two-Species Bacterial Consortium]]> https://www.researchpad.co/article/5989daaaab0ee8fa60ba9016

Multi-species microbial communities play a critical role in human health, industry, and waste remediation. Recently, the evolution of synthetic consortia in the laboratory has enabled adaptation to be addressed in the context of interacting species. Using an engineered bacterial consortium, we repeatedly evolved cooperative genotypes and examined both the predictability of evolution and the phenotypes that determine community dynamics. Eight Salmonella enterica serovar Typhimurium strains evolved methionine excretion sufficient to support growth of an Escherichia coli methionine auxotroph, from whom they required excreted growth substrates. Non-synonymous mutations in metA, encoding homoserine trans-succinylase (HTS), were detected in each evolved S. enterica methionine cooperator and were shown to be necessary for cooperative consortia growth. Molecular modeling was used to predict that most of the non-synonymous mutations slightly increase the binding affinity for HTS homodimer formation. Despite this genetic parallelism and trend of increasing protein binding stability, these metA alleles gave rise to a wide range of phenotypic diversity in terms of individual versus group benefit. The cooperators with the highest methionine excretion permitted nearly two-fold faster consortia growth and supported the highest fraction of E. coli, yet also had the slowest individual growth rates compared to less cooperative strains. Thus, although the genetic basis of adaptation was quite similar across independent origins of cooperative phenotypes, quantitative measurements of metabolite production were required to predict either the individual-level growth consequences or how these propagate to community-level behavior.

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