Multiple Cases of Bacterial Sequence Erroneously Incorporated Into Publicly Available Chloroplast Genomes.
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ABSTRACT:
SUBMITTER: Robinson AJ
PROVIDER: S-EPMC8793683 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:It has recently been reported that an NR1H3 missense variant, R415Q, causes a novel familial form of multiple sclerosis (Wang et al., 2016a). This claim is at odds with publicly available data from the Exome Aggregation Consortium (ExAC; http://exac.broadinstitute.org). The allele frequency of R415Q is not significantly higher in cases (0.024%-0.049%) than in ExAC population controls (0.031%), whereas if R415Q conferred even 50% lifetime risk of developing MS, it would be hundreds of times more common in cases than in controls. The upper bound of the 95% confidence interval of penetrance for R415Q can be estimated at 2.2% for women and 1.2% for men, indicating that even if this variant is disease associated, individuals harboring the variant would have a lifetime risk of developing MS no higher than a few percent. ExAC data should be considered when evaluating claims of variant pathogenicity. This Matters Arising paper is in response to Wang et al. (2016a), published in Neuron. See also the related Matters Arising paper by The International Multiple Sclerosis Genetics Consortium (2016) and the response by Wang et al. (2016b), published in this issue.
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Project description:ABSTRACT Clostridium perfringens is a spore-forming anaerobic pathogen responsible for a variety of histotoxic and intestinal infections in humans and animals. High-resolution genotyping aiming to identify bacteria at strain level has become increasingly important in modern microbiology to understand pathogen transmission pathways and to tackle infection sources. This study aimed at establishing a publicly available genome-wide multilocus sequence-typing (MLST) scheme for C. perfringens. A total of 1,431 highly conserved core genes (1.34 megabases; 50% of the reference genome genes) were indexed for a core genome-based MLST (cgMLST) scheme for C. perfringens. The scheme was applied to 282 ecologically and geographically diverse genomes, showing that the genotyping results of cgMLST were highly congruent with the core genome-based single-nucleotide-polymorphism typing in terms of resolution and tree topology. In addition, the cgMLST provided a greater discrimination than classical MLST methods for C. perfringens. The usability of the scheme for outbreak analysis was confirmed by reinvestigating published outbreaks of C. perfringens-associated infections in the United States and the United Kingdom. In summary, a publicly available scheme and an allele nomenclature database for genomic typing of C. perfringens have been established and can be used for broad-based and standardized epidemiological studies. IMPORTANCE Global epidemiological surveillance of bacterial pathogens is enhanced by the availability of standard tools and sharing of typing data. The use of whole-genome sequencing has opened the possibility for high-resolution characterization of bacterial strains down to the clonal and subclonal levels. Core genome multilocus sequence typing is a robust system that uses highly conserved core genes for deep genotyping. The method has been successfully and widely used to describe the epidemiology of various bacterial species. Nevertheless, a cgMLST typing scheme for Clostridium perfringens is currently not publicly available. In this study, we (i) developed a cgMLST typing scheme for C. perfringens, (ii) evaluated the performance of the scheme on different sets of C. perfringens genomes from different hosts and geographic regions as well as from different outbreak situations, and, finally, (iii) made this scheme publicly available supported by an allele nomenclature database for global and standard genomic typing.
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Project description:BACKGROUND:Mutations in parkin and PTEN-induced protein kinase (PINK1) represent the two most common causes of autosomal recessive parkinsonism. The possibility that heterozygous mutations in these genes also predispose to disease or lower the age of disease onset has been suggested, but currently there is insufficient data to verify this hypothesis conclusively. OBJECTIVE:To study the frequency and spectrum of parkin and PINK1 gene mutations and to investigate the role of heterozygous mutations as a risk factor for early-onset Parkinson's disease (PD). METHODS:All exons and exon-intron boundaries of PINK1 and parkin were sequenced in 250 patients with early-onset PD and 276 normal controls. Gene dosage measurements were also performed, using high-density single-nucleotide polymorphism arrays. RESULTS:In total 41 variants were found, of which 8 have not been previously described (parkin: p.A38VfsX6, p.C166Y, p.Q171X, p.D243N, p.M458L; PINK1: p.P52L, p.T420T, p.A427E). 1.60% of patients were homozygous or compound heterozygous for pathogenic mutations. Heterozygosity for pathogenic parkin or PINK1 mutations was over-represented in patients compared with healthy controls (4.00% vs. 1.81%) but the difference was not significant (p = 0.13). The mean age at disease onset was significantly lower in patients with homozygous or compound heterozygous mutations than in patients with heterozygous mutations (mean difference 11 years, 95% CI 1.4 to 20.6, p = 0.03). There was no significant difference in the mean age at disease onset in heterozygous patients compared with patients without a mutation in parkin or PINK1 (mean difference 2 years, 95% CI -3.7 to 7.0, p = 0.54). CONCLUSIONS:Our data support a trend towards a higher frequency of heterozygosity for pathogenic parkin or PINK1 mutations in patients compared with normal controls, but this effect was small and did not reach significance in our cohort of 250 cases and 276 controls.
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Project description:ST11-KL64 is an internationally distributed lineage of carbapenem-resistant Klebsiella pneumoniae and is the most common type in China. The international and interprovincial (in China) transmission of ST11-KL64 CRKP remains to be elucidated. We used both static clusters defined based on a fixed cutoff of ≤21 pairwise single-nucleotide polymorphisms and dynamic groups defined by modeling the likelihood to be linked by a transmission threshold to investigate the transmission of ST11-KL64 strains based on genome sequences mining. We analyzed all publicly available genomes (n = 730) of ST11-KL64 strains, almost all of which had known carbapenemase genes with KPC-2 being dominant. We identified 4 clusters of international transmission and 14 clusters of interprovincial transmission across China of ST11-KL64 strains. We found that dynamic grouping could provide further resolution for determining clonal relatedness in addition to the widely adopted static clustering and therefore increases the confidence for inferring transmission.IMPORTANCECarbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious challenge for clinical management and is prone to spread in and between healthcare settings. ST11-KL64 is the dominant CRKP type in China with a worldwide distribution. Here, we used two different methods, the widely used clustering based on a fixed single-nucleotide polymorphism (SNP) cutoff and the recently developed grouping by modeling transmission likelihood, to mine all 730 publicly available ST11-KL64 genomes. We identified international transmission of several strains and interprovincial transmission in China of a few, which warrants further investigations to uncover the mechanisms for their spread. We found that static clustering based on ≤21 fixed SNPs is sensitive to detect transmission and dynamic grouping has higher resolutions to provide complementary information. We suggest the use of the two methods in combination for analyzing transmission of bacterial strains. Our findings highlight the need of coordinated actions at both international and interprovincial levels for tackling multi-drug resistant organisms.
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Project description:UnlabelledBackgroundIdentification of prognostic biomarkers is hallmark of cancer genomics. Since miRNAs regulate expression of multiple genes, they act as potent biomarkers in several cancers. Identification of miRNAs that are prognostically important has been done sporadically, but no resource is available till date that allows users to study prognostics of miRNAs of interest, utilizing the wealth of available data, in major cancer types.DescriptionIn this paper, we present a web based tool that allows users to study prognostic properties of miRNAs in several cancer types, using publicly available data. We have compiled data from Gene Expression Omnibus (GEO), and recently developed "The Cancer Genome Atlas (TCGA)", to create this tool. The tool is called "PROGmiR" and it is available at http://www.compbio.iupui.edu/progmir. Currently, our tool can be used to study overall survival implications for approximately 1050 human miRNAs in 16 major cancer types.ConclusionsWe believe this resource, as a hypothesis generation tool, will be helpful for researchers to link miRNA expression with cancer outcome and to design mechanistic studies. We studied performance of our tool using identified miRNA biomarkers from published studies. The prognostic plots created using our tool for specific miRNAs in specific cancer types corroborated with the findings in the studies.
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Project description:Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.
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