scholarly journals Understanding species - level primate diversity in Madagascar

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
I Tattersall
Keyword(s):  
Species Level ◽  
Primate Diversity

ERRORS ASSOCIATED WITH HERBAGE DISSECTION ANALYSES 2. Problems of species identification

1976 ◽  
pp. 213-225
Author(s):  
I.M. Ritchie ◽  
C.C. Boswell ◽  
A.M. Badland
Keyword(s):  
New Zealand ◽  
Dry Weight ◽  
Analytical Tool ◽  
Species Level ◽  
Weight Basis ◽  
Plant Identification ◽  
Leaf Fragment ◽  
Research Division ◽  
Or Groups ◽  
Botanical Analysis

HERBACE DISSECTION is the process in which samples of herbage cut from trials are separated by hand into component species. Heavy reliance is placed on herbage dissection as an analytical tool ,in New Zealand, and in the four botanical analysis laboratories in the Research Division of the Ministry of Agriculture and Fisheries about 20 000 samples are analysed each year. In the laboratory a representative subsample is taken by a rigorous quartering procedure until approximately 400 pieces of herbage remain. Each leaf fragment is then identified to species level or groups of these as appropriate. The fractions are then dried and the composition calculated on a percentage dry weight basis. The accuracy of the analyses of these laboratories has been monitored by a system of interchanging herbage dissection samples between them. From this, the need to separate subsampling errors from problems of plant identification was, appreciated and some of this work is described here.

scholarly journals An analysis of the population of Cryptococcus neoformans strains isolated from animals in Poland, in the years 2015–2019

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Magdalena Florek ◽  
Urszula Nawrot ◽  
Agnieszka Korzeniowska-Kowal ◽  
Katarzyna Włodarczyk ◽  
Anna Wzorek ◽  
...  
Keyword(s):  
Cryptococcus Neoformans ◽  
Species Level ◽  
Molecular Type ◽  
Animal Group ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Severe Infections ◽  
Resistant Strains ◽  
World Distribution ◽  
Tof Ms

AbstractFungi belonging to the Cryptococcus neoformans/C. gattii species complex (CNGSC) are pathogens causing severe infections in humans and animals, that for humans may result in a mortality rate ranging up to 70%. The CNGSC is divided into eight major molecular types, that may differ in their virulence and susceptibility. In order to fully understand the epidemiology of cryptococcosis, it is important to study the world distribution and population structure of these pathogens. The present study is the first presenting a population of strains isolated in Poland and one of the few using a multi-species animal group as a source of the specimen. The pathogen was present in 2.375% of the tested animals. The URA5-RFLP and MALDI-TOF MS analyses have revealed that the population consisted exclusively of C. neoformans strains, with a predominance of major molecular type VNIV (C. neoformans var. neoformans). The MALDI-TOF MS was used to perform the CNGSC strains identification on both the species and sub-species level. Despite the fact that the animals providing the specimens were not treated with 5-fluorocytosine, around 10% of the tested population presented MIC values exceeding 64 mg/L, indicating the existence of the 5-fluorocytosine-resistant strains in the environment.

scholarly journals Evaluating Metagenomic Analysis for Pathogen Transmission in Healthcare Settings

2020 ◽  
Vol 41 (S1) ◽  
pp. s224-s224
Author(s):  
Curt Hewitt ◽  
Katharina Weber ◽  
Danielle LeSassier ◽  
Anthony Kappell ◽  
Kathleen Schulte ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Dna Analysis ◽  
Pathogen Transmission ◽  
Species Level ◽  
Metagenomic Analysis ◽  
Skin Microbiome ◽  
Limited Information ◽  
Culture Methods ◽  
Data Set ◽  
Skin Contact

Background: The prevalence of healthcare-acquired infections (HAIs) and rising levels of antimicrobial resistance place a significant burden on modern healthcare systems. Cultures are typically used to track HAIs; however, culture methods provide limited information and are not applicable to all pathogens. Next-generation sequencing (NGS) can detect and characterize pathogens present within a sample, but few research studies have explored how NGS could be used to detect pathogen transmission events under HAI-relevant scenarios. The objective of this CDC-funded project was to evaluate and correlate sequencing approaches for pathogen transmission with standard culture-based analysis. Methods: We modeled pathogen transfer via hand contact using synthetic skin. These skin coupons were seeded with a community of commensal organisms to mimic the human skin microbiome. Pathogens were added at physiologically relevant high or low levels prior to skin-to-skin contact. The ESKAPE pathogens: E. faecium, S. aureus, K. pneumoniae, A. baumannii, P. aeruginosa, and Enterobacter spp plus C. difficile were employed because they are the most common antibiotic resistant HAIs. Pathogen transfer between skin coupons was measured following direct skin contact and fomite surface transmission. The effects of handwashing or fomite decontamination were also evaluated. Transferred pathogens were enumerated via culture to establish a robust data set against which DNA and RNA sequence analyses of the same samples could be compared. These data also provide a quantitative assessment of individual ESKAPE+C pathogen transfer rates in skin contact scenarios. Results: Metagenomic and metatranscriptomic analysis using custom analysis pipelines and reference databases successfully identified the commensal and pathogenic organisms present in each sample at the species level. This analysis also identified antibiotic resistance genes and plasmids. Metatranscriptomic analysis permitted not only gene identification but also confirmation of gene expression, a critical factor in the evaluation of antibiotic resistance. DNA analysis does not require cell viability, a key differentiator between sequencing and culturing reflected in simulated handwashing data. Sensitivity remains a key limitation of metagenomic analysis, as shown by the poor species identification and gene content characterization of pathogens present at low abundance within the simulated microbial community. Species level identification typically failed as ratios fell below 1:1,000 pathogen CFU:total community CFU. Conclusions: These findings demonstrate the strengths and weaknesses of NGS for molecular epidemiology. The data sets produced for this study are publicly available so they can be employed for future metagenomic benchmarking studies.Funding: NoneDisclosures: None

scholarly journals panRGP: a pangenome-based method to predict genomic islands and explore their diversity

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i651-i658 ◽  
Author(s):  
Adelme Bazin ◽  
Guillaume Gautreau ◽  
Claudine Médigue ◽  
David Vallenet ◽  
Alexandra Calteau
Keyword(s):  
Escherichia Coli ◽  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Species Level ◽  
Genomic Islands ◽  
Genome Plasticity ◽  
Reference Dataset ◽  
Prokaryotic Genomes ◽  
Ideal Approach ◽  
Genomic Regions

Abstract Motivation Horizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of genome plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to genomic islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date, no methods are available to identify GIs using hundreds of genomes to explore their diversity. Results We present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on metagenome assembled genomes by redefining the borders of the leuX tRNA hotspot, a well-studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies. Availability and implementation The methods presented in the current work are available through the following software: https://github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https://github.com/axbazin/panrgp_supdata.

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