Network properties of salmonella epidemics

We examine non-typhoidal Salmonella (S. Typhimurium or STM) epidemics as complex systems, driven by evolution and interactions of diverse microbial strains, and focus on emergence of successful strains. Our findings challenge the established view that seasonal epidemics are associated with random sets of co-circulating STM genotypes. We use high-resolution molecular genotyping data comprising 17,107 STM isolates representing nine consecutive seasonal epidemics in Australia, genotyped by multiple-locus variable-number tandem-repeats analysis (MLVA). From these data, we infer weighted undirected networks based on distances between the MLVA profiles, depicting epidemics as networks of individual bacterial strains. The network analysis demonstrated dichotomy in STM populations which split into two distinct genetic branches, with markedly different prevalences. This distinction revealed the emergence of dominant STM strains defined by their local network topological properties, such as centrality, while correlating the development of new epidemics with global network features, such as small-world propensity.

[1]  V. Sintchenko,et al.  Novel Salmonella enterica Serovar Typhimurium Genotype Levels as Herald of Seasonal Salmonellosis Epidemics , 2018, Emerging infectious diseases.

[2]  Luay Nakhleh,et al.  Phylogenetic networks , 2004 .

[3]  S. Cobey.  Pathogen evolution and the immunological niche , 2014, Annals of the New York Academy of Sciences.

[4]  Oliver M. Cliff,et al.  Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: Results of a census-calibrated model , 2018, Science Advances.

[5]  Robin Davies,et al.  Improving emergency preparedness and response in the Asia-Pacific , 2019, BMJ Global Health.

[6]  Doolittle Wf.  Phylogenetic Classification and the Universal Tree , 1999 .

[7]  Paul J. Laurienti,et al.  The Ubiquity of Small-World Networks , 2011, Brain Connect..

[8]  A. Szilágyi,et al.  Limiting similarity and niche theory for structured populations. , 2009, Journal of theoretical biology.

[9]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[10]  Gemma C. Langridge,et al.  Distinct Salmonella Enteritidis lineages associated with enterocolitis in high-income 1 settings and invasive disease in low-income settings , 2016 .

[11]  L. Orgel,et al.  Phylogenetic Classification and the Universal Tree , 1999 .

[12]  Sarah Feldt Muldoon,et al.  Small-World Propensity and Weighted Brain Networks , 2016, Scientific Reports.

[13]  M. A. Suchard,et al.  Distinguishable Epidemics of Multidrug-Resistant Salmonella Typhimurium DT104 in Different Hosts , 2013, Science.

[14]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[15]  M. Prokopenko,et al.  Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks , 2013, PloS one.

[16]  E. Nielsen,et al.  Tandem Repeat Analysis for Surveillance of Human Salmonella Typhimurium Infections , 2007, Emerging infectious diseases.

[17]  K. Kaski,et al.  Intensity and coherence of motifs in weighted complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Albert Y. Zomaya,et al.  Assortative mixing in directed biological networks , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[19]  J. Crump,et al.  Global Burden of Invasive Nontyphoidal Salmonella Disease, 2010 , 2015, Emerging infectious diseases.

[20]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[21]  X. Rosalind Wang,et al.  Fisher Information at the Edge of Chaos in Random Boolean Networks , 2011, Artificial Life.

[22]  Rustom Antia,et al.  The role of evolution in the emergence of infectious diseases , 2003, Nature.

[23]  J. Hiller,et al.  Heatwaves differentially affect risk of Salmonella serotypes. , 2016, The Journal of infection.

[24]  G. Kapperud,et al.  Multiple-locus variable-number tandem-repeats analysis of Salmonella enterica subsp. enterica serovar Typhimurium using PCR multiplexing and multicolor capillary electrophoresis. , 2004, Journal of microbiological methods.

[25]  Tine Hald,et al.  World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis , 2015, PLoS medicine.

[26]  Mikhail Prokopenko,et al.  Minimising the Kullback–Leibler Divergence for Model Selection in Distributed Nonlinear Systems , 2018, Entropy.

[27]  Aamir Fazil,et al.  The global burden of nontyphoidal Salmonella gastroenteritis. , 2010, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[28]  Shi Zhou,et al.  The rich-club phenomenon in the Internet topology , 2003, IEEE Communications Letters.

[29]  S. Octavia,et al.  Population structure, origins and evolution of major Salmonella enterica clones. , 2009, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[30]  S. Carpenter,et al.  Anticipating Critical Transitions , 2012, Science.

[31]  Falk Schreiber,et al.  Analysis of Biological Networks , 2008 .

[32]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[33]  Edward T. Bullmore,et al.  A Unifying Framework for Measuring Weighted Rich Clubs , 2014, Scientific Reports.

[34]  Mikhail Prokopenko,et al.  Thermodynamic efficiency of contagions: a statistical mechanical analysis of the SIS epidemic model , 2018, Interface Focus.

[35]  Gemma C. Langridge,et al.  Patterns of genome evolution that have accompanied host adaptation in Salmonella , 2014, Proceedings of the National Academy of Sciences.

[36]  Mikhail Prokopenko,et al.  Investigating Spatiotemporal Dynamics and Synchrony of Influenza Epidemics in Australia: An Agent-Based Modelling Approach , 2018, Simul. Model. Pract. Theory.

[37]  Albert Y. Zomaya,et al.  Assortativeness and information in scale-free networks , 2009 .

[38]  E. Nielsen,et al.  Development of a new nomenclature for Salmonella typhimurium multilocus variable number of tandem repeats analysis (MLVA). , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[39]  J. Wain,et al.  Intra-continental spread of human invasive Salmonella Typhimurium pathovariants in sub-Saharan Africa , 2012, Nature Genetics.