22 research outputs found

    Minimal genetic change in Vibrio cholerae in Mozambique over time: Multilocus variable number tandem repeat analysis and whole genome sequencing.

    Get PDF
    Although cholera is a major public health concern in Mozambique, its transmission patterns remain unknown. We surveyed the genetic relatedness of 75 Vibrio cholerae isolates from patients at Manhiça District Hospital between 2002-2012 and 3 isolates from river using multilocus variable-number tandem-repeat analysis (MLVA) and whole genome sequencing (WGS). MLVA revealed 22 genotypes in two clonal complexes and four unrelated genotypes. WGS revealed i) the presence of recombination, ii) 67 isolates descended monophyletically from a single source connected to Wave 3 of the Seventh Pandemic, and iii) four clinical isolates lacking the cholera toxin gene. This Wave 3 strain persisted for at least eight years in either an environmental reservoir or circulating within the human population. Our data raises important questions related to where these isolates persist and how identical isolates can be collected years apart despite our understanding of high change rate of MLVA loci and the V. cholerae molecular clock

    Microbiota that affect risk for shigellosis in children in low-income countries

    Get PDF
    Pathogens in the gastrointestinal tract exist within a vast population of microbes. We examined associations between pathogens and composition of gut microbiota as they relate to Shigella spp./enteroinvasive Escherichia coli infection. We analyzed 3,035 stool specimens (1,735 nondiarrheal and 1,300 moderate-to-severe diarrheal) from the Global Enteric Multicenter Study for 9 enteropathogens. Diarrheal specimens had a higher number of enteropathogens (diarrheal mean 1.4, nondiarrheal mean 0.95; p<0.0001). Rotavirus showed a negative association with Shigella spp. in cases of diarrhea (odds ratio 0.31, 95% CI 0.17–0.55) and had a large combined effect on moderate-to-severe diarrhea (odds ratio 29, 95% CI 3.8–220). In 4 Lactobacillus taxa identified by 16S rRNA gene sequencing, the association between pathogen and disease was decreased, which is consistent with the possibility that Lactobacillus spp. are protective against Shigella spp.–induced diarrhea. Bacterial diversity of gut microbiota was associated with diarrhea status, not high levels of the Shigella spp. ipaH gene.publishedVersio

    Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke

    Get PDF
    Background and Objectives Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke. Methods We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18-59 years, using individual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS. Results We observed genome-wide significant associations of EOS with 2 variants in ABO, a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85-0.91) in EOS vs 0.96 (95% CI: 0.92-1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11-1.21) for EOS vs 1.05 (0.99-1.11) in LOS; p-values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS (p = 0.008). Discussion The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.Peer reviewe

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    Get PDF
    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

    Get PDF
    AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec

    Stroke genetics informs drug discovery and risk prediction across ancestries

    Get PDF
    Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

    Diagram displaying only variable nucleotides among the 67 genomic sequences from Wave 3 of the 7<sup>th</sup> pandemic from Manhiça and B33 (Genbank:ACHZ00000000), a Wave 2 isolate from Beira, Mozambique in 2004.

    No full text
    <p>Only variable nucleotides 80 to 120 are shown. Each row contains sequence from a single isolate. Two examples in which two loci with alleles occur in all four possible combinations are identified by boxes around the four chromosomes. These examples are consistent with recombination events.</p

    Genetic relatedness between <i>V</i>. <i>cholerae</i> genotypes by MLVA, Mozambique, 2002–2012.

    No full text
    <p>Each box with five numbers represents a genotype; each connecting line, an allelic change at a single locus. The solid boxes are around genotypes found in both the river and in patients, dotted boxes are for genotypes found only in patients and dashed boxes for genotypes found only in river isolates (R). The numbers outside the boxes indicate the year or years isolates with that genotype were found. There are twenty genotypes in CC1, two genotypes in CC2 and four singleton genotypes unrelated to any other genotypes using eBURST. An alternative analysis using Network revealed the following differences: i) a connection in eBURST, but not in Network, was represented by a dotted line (single locus change), ii) a connection in Network, but not in eBURST, by a dashed line (single locus change), iii) in Network, but not in eBURST, CC2 is connected to CC1, we have represented this by a dot and dashed line (representing changes at two loci), finally, iv) in Network, but not in eBURST, all three singleton genotypes are connected to the founder genotype regardless of whether the singleton genotype differs at three or four loci from the founder.</p

    Phylogeny of 7<sup>th</sup> pandemic isolates of <i>V</i>. <i>cholerae</i>.

    No full text
    <p>The branch lengths are proportional to the number of variable nucleotides. All of the isolates from Manhiça cluster together and are most closely related to isolates from India, Nepal, Kenya, Haiti and Bangladesh. Other isolates from Mozambique and Kenya are more distantly related.</p
    corecore