Team III Comparative Genomics Group

Team 3: Comparative Genomics

• File:Comparative Genomics - Team 3 Background and Strategy.pptx.pdf
• File:Comparative Genomics - Team 3 Results.pptx.pdf

Team Members: Swetha Singu, Ruize Yang, Deepali Kundnani, Gulay Bengu Ulukaya, Yuhua Zhang, Jie Zhou

Introduction

Background

Comparative genomics is a field of biological research in which different organisms are compared for their genomic features like the DNA sequence, genes, order of genes and other genomic structural landmarks. This comparative analysis can reveal many insights, such as disease outbreak strains, sporadic strains, evolutionary lineages and genetic variations to name a few. Our group was given 50 different isolates for which genome assembly, gene prediction and functional annotation was performed and the species was identified as Listeria Monocytogenes.

L.monocytogenes belongs to Clade 1 of the Genus Listeria. L.monocytogenes includes 14 serotypes of which 95% of human illnesses are caused by the serotypes 1/2a, 1/2b and 4b. The serotype 4b is most commonly associated with outbreaks. L.monocytogenes is ubiquitous in nature is a hardy organism that can withstand a wide range of conditions including freezing, drying, heat, and relatively high levels of acid, salinity, and alcohol, which makes it a particular problem in ready-to-eat foods that are not cooked before eating. An outbreak is a sudden or violent start of something unwelcome. When two or more people experience a similar illness from same source, eating the same food, it is called an outbreak. It usually takes about 2-10 weeks to determine if a person is part of a Listeria outbreak. When a person is sick, the laboratory tests are performed on samples from patients, foods and environment for related strains of bacteria. And clusters of closely related listeria isolates are identified by comparing the sequences of a patients sample with that of other Listeria infected patients, and the cluster of samples with same bacterial DNA fingerprint are identified as possible outbreak strains. Whole genome sequencing (WGS) has proven to be a powerful sub-typing tool for food borne pathogenic bacteria even L. monocytogenes, since 2013. Based on the knowledge that bacteria with the same DNA fingerprint are more likely to be from the same source, this cluster data is used by the Epidemiologists to find matches to food isolates [ using GenomeTrakr] to identify common food/outbreak source and trace back is where the identified food is recalled and people are warned.

Listeriosis causes miscarriage, stillbirth in pregnant women, death in newborn, sepsis, meningitis in older patients in humans and it has a high fatality of upto 20% in high risk groups like old, immune compromised people and pregnant women. There are about 1600 reported invasive infections annually and 1 in 5 of them, die of listeriosis.

Objectives

Identify the outbreak strains from the sporadic strains for the given isolates Analyze the source of the outbreak using the epidemiological data provided. Determine the virulence and antibiotic resistance profiles of the outbreak isolates. Recommendation for outbreak response and classes of antibiotics to prescribe and to avoid.

Information at hand

Data we have?

Pipeline

pipeline

Approaches

We use five different bioinformatic tools from different levels to compare the 50 isolates.

ANI

introduction of ANI, what level is the method, and the tools used, input and output, general results

MLST

StringMLST, ChewBBACA

SNP-based Typing

kSNP

Pan-genome analysis

Roary, BPGA

References

Filliol I, et al. Global phylogeny of Mycobacterium tuberculosis based on single nucleotide polymorphism (SNP) analysis: insights into tuberculosis evolution, phylogenetic accuracy of other DNA fingerprinting systems, and recommendations for a minimal standard SNP set. J. Bacteriol. 2006;188:759–772. doi: 10.1128/JB.188.2.759-772.2006.

Adam D. Leaché1 and Jamie R. Oaks2, The Utility of Single Nucleotide Polymorphism (SNP) Data in Phylogenetics. Annual Review of Ecology, Evolution, and Systematics. 2017; Vol. 48:69-84. https://doi.org/10.1146/annurev-ecolsys-110316-022645

Shea N Gardner, Tom Slezak, Barry G. Hall, kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome, Bioinformatics, Volume 31, Issue 17, 1 September 2015, Pages 2877–2878, https://doi.org/10.1093/bioinformatics/btv271

Maiden, Martin C J. “Multilocus Sequence Typing of Bacteria.” Annual Review of Microbiology, U.S. National Library of Medicine, 2006, www.ncbi.nlm.nih.gov/pubmed/16774461.

Silva, Mickael, et al. “ChewBBACA: A Complete Suite for Gene-by-Gene Schema Creation and Strain Identification.” Microbial Genomics, Microbiology Society, Mar. 2018, www.ncbi.nlm.nih.gov/pmc/articles/PMC5885018/.

“MentaLiST.” OmicX, omictools.com/mentalist-tool. Feijao, Pedro, et al. “MentaLiST – A Fast MLST Caller for Large MLST Schemes.” BioRxiv, Cold Spring Harbor Laboratory, 1 Jan. 2017, www.biorxiv.org/content/10.1101/172858v2.

Kim, Yeji, et al. “Current Status of Pan-Genome Analysis for Pathogenic Bacteria.” Current Opinion in Biotechnology, vol. 63, 2020, pp. 54–62., doi:10.1016/j.copbio.2019.12.001.

Page, Andrew J., et al. “Roary: Rapid Large-Scale Prokaryote Pan Genome Analysis.” Bioinformatics, vol. 31, no. 22, 2015, pp. 3691–3693., doi:10.1093/bioinformatics/btv421.

Chaudhari, Narendrakumar M., et al. “BPGA- an Ultra-Fast Pan-Genome Analysis Pipeline.” Scientific Reports, vol. 6, no. 1, 2016, doi:10.1038/srep24373.

Valentina Galata, Tobias Fehlmann, Christina Backes, Andreas Keller, PLSDB: a resource of complete bacterial plasmids, Nucleic Acids Research, Volume 47, Issue D1, 08 January 2019, Pages D195–D202

Hunt, Martin et al. “ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads.” Microbial genomics vol. 3,10 e000131. 4 Sep. 2017, doi:10.1099/mgen.0.000131 Annaleise Wilson et al. “Phenotypic and Genotypic

Analysis of Antimicrobial Resistance among Listeria monocytogenes Isolated from Australian Food Production Chains”. Feb 9, 2018. Genes  doi: 10.3390/genes9020080

Clementine Henri et al “An Assessment of Different Genomic Approaches for Inferring Phylogeny of Listeria monocytogenes”Front. Microbiol., 29 November 2017 | https://doi.org/10.3389/fmicb.2017.02351

Yi Chen et al “Core Genome Multilocus Sequence Typing for Identification of Globally Distributed Clonal Groups and Differentiation of Outbreak Strains of Listeria monocytogenes” Appl Environ Microbiology, 2016 Oct 15 doi: 10.1128/AEM.01532-16

"Identification of acquired antimicrobial resistance genes", Zankari et al 2012, PMID: 22782487