Literature DB >> 31395628

Draft Genome Sequences of Potentially Pathogenic Clostridium perfringens Strains from Environmental Surface Water in the North West Province of South Africa.

Johannes Cornelius Jacobus Fourie1,2, Tomasz J Sanko1, Cornelius Carlos Bezuidenhout3, Charlotte Mienie1, Rasheed Adegbola Adeleke1,2.   

Abstract

Surface water systems in South Africa are experiencing a major decline in quality due to various anthropogenic factors. This poses a possible health risk for humans. Here, we present the draft genome sequences of three Clostridium perfringens isolates obtained from a fecally polluted river system in the North West province of South Africa.
Copyright © 2019 Fourie et al.

Entities:  

Year:  2019        PMID: 31395628      PMCID: PMC6687915          DOI: 10.1128/MRA.00407-19

Source DB:  PubMed          Journal:  Microbiol Resour Announc        ISSN: 2576-098X


ANNOUNCEMENT

Clostridium perfringens is a Gram-positive bacterium that requires strict anaerobic conditions to grow. Its ability to produce endospores ensures its survival under unfavorable conditions, e.g., in aerobic environments. Due to its ubiquitous nature (especially in soil and aquatic systems), C. perfringens could also be a looming clinical problem. This species can cause severe disease in humans (1, 2). C. perfringens strains are classified into seven types (A through G) according to the production of six major toxins (alpha-, beta-, epsilon-, iota-, Clostridium perfringens enterotoxin [CPE], and necrotic enteritis B-like [NetB] toxins) (3). C. perfringens type A strains are known to cause gas gangrene (clostridial myonecrosis) and necrotic enteritis, as well as mild diarrhea, in humans (4). This paper presents the draft genome assemblies of C. perfringens strains derived from river water during a warm rainy season. The water quality is influenced by various anthropogenic activities, including mining (gold and diamonds), agriculture, and, in particular, return flows from wastewater treatment plants (5, 6). Recently, high levels of indicator bacteria showed occurrences of fecal contamination, and various points in this river were designated potential “hot spots” for outbreaks of bacterial diseases (7; http://www.dwa.gov.za/iwqs/microbio/nmmp.asp). Clostridium perfringens were isolated from river water using a modified version of the Fung double-tube method (8). The bacteria were grown in tryptose sulfite cycloserine agar (Oxoid, UK) at 42°C for 6 h. Single colonies were incubated anaerobically overnight in reinforced clostridial medium (Oxoid, UK) and then pelleted. Total genomic DNA was extracted from each pellet with the use of a NucleoSpin tissue kit (Macherey-Nagel). Amplification and sequencing of the 16S rRNA genes confirmed the identities of the three isolates to be C. perfringens. Paired-end sequencing libraries were generated with a Nextera XT DNA library prep kit (Illumina, San Diego, CA, USA), and this was followed by whole-genome sequencing with a MiSeq reagent kit v3 (600 cycles). Quality evaluation and trimming of short (less than 50 bp) or low-quality nucleotides (Q < 15) were performed in Trimmomatic (v.0.36) (9). De novo assembly was conducted in SPAdes (v.3.9.0) (10), followed by gene prediction and annotation using the NCBI Prokaryotic Genome Annotation Pipeline (v.4.3) (11). BLASTx comparison was used to search databases for virulence factors (VF) and antibiotic resistance genes (ARGs) in deepARG (v.2.0) (12, 13). Average nucleotide identity (ANI) was determined by OrthoANI (v.1.4) (14). Default parameters were used for all software unless otherwise specified. In silico analysis of the C. perfringens strains (Table 1) created, on average, 110 to 205 scaffolds, with an overall average genome coverage of 186×. Draft genomes were generated with a total length of between 3.44 Mbp and 3.6 Mbp and an average G+C content of 28.18%.
TABLE 1

Genome characteristics and accession numbers of C. perfringens strains

CharacteristicData for strain:
SC4-C13SC4-C17SC4-C24
No. of reads8,746,6828,055,82211,557,934
Avg read length (bp)233.678238.49244.97
No. of scaffolds205205110
Largest contig size (bp)1,386,2171,397,8331,428,470
N50461,812461,812356,343
G+C content (%)28.1928.1428.21
Gene annotation data
    Genome size (bp)3,604,7703,514,9483,437,837
    No. of CDSa 3,2453,2013,079
    No. of total RNAs125124130
    No. of total rRNAs292934
    No. of total tRNAs929192
    No. of pseudogenes556655
GenBank accession no.RQNR00000000RQNQ00000000RQNP00000000
SRA accession no.SRR8867692SRR8867693SRR8867691

CDS, coding sequences.

Genome characteristics and accession numbers of C. perfringens strains CDS, coding sequences. The draft genomes described here were also analyzed for the presence of VF and ARGs. This revealed 35 genes that encode VF such as hemolysins, enterotoxins, sialidase, collagenase, perfringolysin O, and alpha-clostripain. The genome assembly also revealed the presence of four hyaluronidase genes, as well as two members of the double-component VirR/VirS regulon. The ARG analysis revealed the presence of macrolide-lincosamide-streptogramin, β-lactam, trimethoprim, tetracycline, kasugamycin, and bacitracin genes. They also harbored the vanRI and vanRG genes, which encode glycopeptides, and vgaB, arlR, and MepA, which are responsible for multidrug-resistant efflux pumps. Genomic comparison with the well-characterized C. perfringens strain 13 (GenBank accession number BA000016) resulted in values of between 98.50% and 98.52%. Therefore, our three C. perfringens strains can be classified as type A strains, which are human pathogens.

Data availability.

These draft genome assemblies have been deposited at GenBank under the accession numbers RQNR00000000 (Clostridium perfringens SC4-C13), RQNQ00000000 (Clostridium perfringens SC4-C17), and RQNP00000000 (Clostridium perfringens SC4-C24). The Sequence Read Archive accession numbers are SRR8867692, SRR8867693, and SRR8867691, respectively.
  11 in total

1.  SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

Authors:  Anton Bankevich; Sergey Nurk; Dmitry Antipov; Alexey A Gurevich; Mikhail Dvorkin; Alexander S Kulikov; Valery M Lesin; Sergey I Nikolenko; Son Pham; Andrey D Prjibelski; Alexey V Pyshkin; Alexander V Sirotkin; Nikolay Vyahhi; Glenn Tesler; Max A Alekseyev; Pavel A Pevzner
Journal:  J Comput Biol       Date:  2012-04-16       Impact factor: 1.479

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Authors:  Imchang Lee; Yeong Ouk Kim; Sang-Cheol Park; Jongsik Chun
Journal:  Int J Syst Evol Microbiol       Date:  2015-11-09       Impact factor: 2.747

3.  Antibiotic resistance, efflux pump genes and virulence determinants in Enterococcus spp. from surface water systems.

Authors:  L G Molale; Cornelius Carlos Bezuidenhout
Journal:  Environ Sci Pollut Res Int       Date:  2016-08-11       Impact factor: 4.223

Review 4.  Inactivation Strategies for Clostridium perfringens Spores and Vegetative Cells.

Authors:  Prabhat K Talukdar; Pathima Udompijitkul; Ashfaque Hossain; Mahfuzur R Sarker
Journal:  Appl Environ Microbiol       Date:  2016-12-15       Impact factor: 4.792

5.  Complete genome sequence of Clostridium perfringens, an anaerobic flesh-eater.

Authors:  Tohru Shimizu; Kaori Ohtani; Hideki Hirakawa; Kenshiro Ohshima; Atsushi Yamashita; Tadayoshi Shiba; Naotake Ogasawara; Masahira Hattori; Satoru Kuhara; Hideo Hayashi
Journal:  Proc Natl Acad Sci U S A       Date:  2002-01-15       Impact factor: 11.205

6.  Genetic diversity among toxigenic clostridia isolated from soil, water, meat and associated polluted sites in South India.

Authors:  S Sathish; K Swaminathan
Journal:  Indian J Med Microbiol       Date:  2009 Oct-Dec       Impact factor: 0.985

7.  Trimmomatic: a flexible trimmer for Illumina sequence data.

Authors:  Anthony M Bolger; Marc Lohse; Bjoern Usadel
Journal:  Bioinformatics       Date:  2014-04-01       Impact factor: 6.937

8.  NCBI prokaryotic genome annotation pipeline.

Authors:  Tatiana Tatusova; Michael DiCuccio; Azat Badretdin; Vyacheslav Chetvernin; Eric P Nawrocki; Leonid Zaslavsky; Alexandre Lomsadze; Kim D Pruitt; Mark Borodovsky; James Ostell
Journal:  Nucleic Acids Res       Date:  2016-06-24       Impact factor: 16.971

9.  VFDB 2016: hierarchical and refined dataset for big data analysis--10 years on.

Authors:  Lihong Chen; Dandan Zheng; Bo Liu; Jian Yang; Qi Jin
Journal:  Nucleic Acids Res       Date:  2015-11-17       Impact factor: 16.971

10.  DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Authors:  Gustavo Arango-Argoty; Emily Garner; Amy Pruden; Lenwood S Heath; Peter Vikesland; Liqing Zhang
Journal:  Microbiome       Date:  2018-02-01       Impact factor: 14.650

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