scholarly journals Evolution of ST-4821 clonal complex hyperinvasive and quinolone-resistant meningococci: the next meningococcal pandemic?

2020 ◽  
Author(s):  
Mingliang Chen ◽  
Odile B. Harrison ◽  
Holly B. Bratcher ◽  
Zhiyan Bo ◽  
Keith A. Jolley ◽  
...  

AbstractThe expansion of quinolone-resistant Neisseria meningitidis clone ChinaCC4821-R1-C/B from ST-4821 clonal complex (cc4821) caused a serogroup shift from serogroup A to C in invasive meningococcal disease (IMD) in China. To establish the relationship among globally distributed cc4821 meningococci, we analysed whole genome sequence data from 173 cc4821 meningococci isolated in four continents from 1972-2019. These meningococci clustered into four sub-lineages (1-4), with sub-lineage 1 primarily comprising serogroup C IMD isolates (82%, 41/50). Most isolates from outside China formed a distinct sub-lineage (81.6%, 40/49, the Europe-USA cluster), with the typical strain designation B:P1.17-6,23:F3-36:ST-3200(cc4821) and harbouring mutations in penicillin-binding protein 2. These data show that the quinolone-resistant clone ChinaCC4821-R1-C/B has expanded to other countries. The increasing global distribution of B:cc4821 meningococci raises concern that cc4821 has the potential to cause a global pandemic and, this would be challenging to control though there is indirect evidence that Trumenba® vaccine might afford some protection.

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Abraham Gihawi ◽  
Ghanasyam Rallapalli ◽  
Rachel Hurst ◽  
Colin S. Cooper ◽  
Richard M. Leggett ◽  
...  

Abstract Background Human tissue is increasingly being whole genome sequenced as we transition into an era of genomic medicine. With this arises the potential to detect sequences originating from microorganisms, including pathogens amid the plethora of human sequencing reads. In cancer research, the tumorigenic ability of pathogens is being recognized, for example, Helicobacter pylori and human papillomavirus in the cases of gastric non-cardia and cervical carcinomas, respectively. As of yet, no benchmark has been carried out on the performance of computational approaches for bacterial and viral detection within host-dominated sequence data. Results We present the results of benchmarking over 70 distinct combinations of tools and parameters on 100 simulated cancer datasets spiked with realistic proportions of bacteria. mOTUs2 and Kraken are the highest performing individual tools achieving median genus-level F1 scores of 0.90 and 0.91, respectively. mOTUs2 demonstrates a high performance in estimating bacterial proportions. Employing Kraken on unassembled sequencing reads produces a good but variable performance depending on post-classification filtering parameters. These approaches are investigated on a selection of cervical and gastric cancer whole genome sequences where Alphapapillomavirus and Helicobacter are detected in addition to a variety of other interesting genera. Conclusions We provide the top-performing pipelines from this benchmark in a unifying tool called SEPATH, which is amenable to high throughput sequencing studies across a range of high-performance computing clusters. SEPATH provides a benchmarked and convenient approach to detect pathogens in tissue sequence data helping to determine the relationship between metagenomics and disease.


2020 ◽  
Vol 9 (6) ◽  
pp. 1615 ◽  
Author(s):  
Rene Kaden

The phylogenetic clustering of 95 SARS-CoV-2 sequences from the first 3 months of the pandemic reveals insights into the early evolution of the virus and gives first indications of how the variants are globally distributed. Variants might become a challenge in terms of diagnostics, immunology, and effectiveness of drugs. All available whole genome sequence data from the NCBI database (March 16, 2020) were phylogenetically analyzed, and gene prediction as well as analysis of selected variants were performed. Antigenic regions and the secondary protein structure were predicted for selected variants. While some clusters are presenting the same variant with 100% identical bases, other SARS-CoV-2 lineages show a beginning diversification and phylogenetic clustering due to base substitutions and deletions in the genomes. First molecular epidemiological investigations are possible with the results by adding metadata as travelling history to the presented data. The advantage of variants in source tracing can be a challenge in terms of virulence, immune response, and immunological memory. Variants of viruses often show differences in virulence or antigenicity. This must also be considered in decisions like herd immunity. Diagnostic methods might not work if the variations or deletions are in target regions for the detection of the pathogen. One base substitution was detected in a primer binding site.


2018 ◽  
Author(s):  
Aaron P. Ragsdale ◽  
Claudia Moreau ◽  
Simon Gravel

AbstractEvolutionary, biological, and demographic processes combine to shape the variation observed in populations. Understanding how these processes are expected to influence variation allows us to infer past demographic events and the nature of selection in human populations. Forward models such as the diffusion approximation provide a powerful tool for analyzing the distribution of allele frequencies in contemporary populations due to their computational tractability and model flexibility. Here, we discuss recent computational developments and their application to reconstructing human demographic history and patterns of selection at new mutations. We also reexamine how some classical assumptions that are still commonly used in inference studies fare when applied to modern data. We use whole-genome sequence data for 797 French Canadian individuals to examine the neutrality of synonymous sites. We find that selection can lead to strong biases in the inferred demography, mutation rate, and distributions of fitness effects. We use these distributions of fitness effects together with demographic and phenotype-fitness models to predict the relationship between effect size and allele frequency, and contrast those predictions to commonly used models in statistical genetics. Thus the simple evolutionary models investigated by Kimura and Ohta still provide important insight into modern genetic research.


Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online


Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106416
Author(s):  
Asset Daniyarov ◽  
Askhat Molkenov ◽  
Saule Rakhimova ◽  
Ainur Akhmetova ◽  
Zhannur Nurkina ◽  
...  

Author(s):  
Pamela Wiener ◽  
Christelle Robert ◽  
Abulgasim Ahbara ◽  
Mazdak Salavati ◽  
Ayele Abebe ◽  
...  

Abstract Great progress has been made over recent years in the identification of selection signatures in the genomes of livestock species. This work has primarily been carried out in commercial breeds for which the dominant selection pressures, are associated with artificial selection. As agriculture and food security are likely to be strongly affected by climate change, a better understanding of environment-imposed selection on agricultural species is warranted. Ethiopia is an ideal setting to investigate environmental adaptation in livestock due to its wide variation in geo-climatic characteristics and the extensive genetic and phenotypic variation of its livestock. Here, we identified over three million single nucleotide variants across 12 Ethiopian sheep populations and applied landscape genomics approaches to investigate the association between these variants and environmental variables. Our results suggest that environmental adaptation for precipitation-related variables is stronger than that related to altitude or temperature, consistent with large-scale meta-analyses of selection pressure across species. The set of genes showing association with environmental variables was enriched for genes highly expressed in human blood and nerve tissues. There was also evidence of enrichment for genes associated with high-altitude adaptation although no strong association was identified with hypoxia-inducible-factor (HIF) genes. One of the strongest altitude-related signals was for a collagen gene, consistent with previous studies of high-altitude adaptation. Several altitude-associated genes also showed evidence of adaptation with temperature, suggesting a relationship between responses to these environmental factors. These results provide a foundation to investigate further the effects of climatic variables on small ruminant populations.


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lynsey K. Whitacre ◽  
Jesse L. Hoff ◽  
Robert D. Schnabel ◽  
Sara Albarella ◽  
Francesca Ciotola ◽  
...  

Author(s):  
Viola Kurm ◽  
Ilse Houwers ◽  
Claudia E. Coipan ◽  
Peter Bonants ◽  
Cees Waalwijk ◽  
...  

AbstractIdentification and classification of members of the Ralstonia solanacearum species complex (RSSC) is challenging due to the heterogeneity of this complex. Whole genome sequence data of 225 strains were used to classify strains based on average nucleotide identity (ANI) and multilocus sequence analysis (MLSA). Based on the ANI score (>95%), 191 out of 192(99.5%) RSSC strains could be grouped into the three species R. solanacearum, R. pseudosolanacearum, and R. syzygii, and into the four phylotypes within the RSSC (I,II, III, and IV). R. solanacearum phylotype II could be split in two groups (IIA and IIB), from which IIB clustered in three subgroups (IIBa, IIBb and IIBc). This division by ANI was in accordance with MLSA. The IIB subgroups found by ANI and MLSA also differed in the number of SNPs in the primer and probe sites of various assays. An in-silico analysis of eight TaqMan and 11 conventional PCR assays was performed using the whole genome sequences. Based on this analysis several cases of potential false positives or false negatives can be expected upon the use of these assays for their intended target organisms. Two TaqMan assays and two PCR assays targeting the 16S rDNA sequence should be able to detect all phylotypes of the RSSC. We conclude that the increasing availability of whole genome sequences is not only useful for classification of strains, but also shows potential for selection and evaluation of clade specific nucleic acid-based amplification methods within the RSSC.


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