Quantifying microbe transmission networks for wild and domestic ungulates in Kenya

Multi-host wildlife pathogens are an increasing concern for both wildlife conservation and livestock husbandry. Here, we combined social network theory with microbial genetics to assess patterns of interspecific pathogen transmission among ten species of wild and domestic ungulates in Kenya. If two individuals shared the same genetic subtype of a genetically diverse microbe, Escherichia coli, then we inferred that these individuals were part of the same transmission chain. Individuals in the same transmission chain were interlinked to create a transmission network. Given interspecific variation in physiology and behavior, some species may function as "super-spreaders" if individuals of that species are consistently central in the transmission network. Pathogen management strategies targeted at key super-spreader species are theoretically more effective at limiting pathogen spread than conventional strategies, and our approach provides a means to identify candidate super-spreaders in wild populations. We found that Grant's gazelle (Gazella granti) typically occupied central network positions and were connected to a large number of other individuals in the network. Zebra (Equus burchelli), in contrast, seemed to function as bridges between regions of the network that would otherwise be poorly connected, and interventions targeted at zebra significantly increased the level of fragmentation in the network. Although not usually pathogenic, E. coli transmission pathways provide insight into transmission dynamics by demonstrating where contact between species is sufficient for transmission to occur and identifying species that are potential super-spreaders. © 2013 Elsevier Ltd

Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis)

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis

Assessing Transmission of Antimicrobial-Resistant Escherichia coli in Wild Giraffe Contact Networks.

There is growing evidence that anthropogenic sources of antibiotics and antimicrobial-resistant bacteria can spill over into natural ecosystems, raising questions about the role wild animals play in the emergence, maintenance, and dispersal of antibiotic resistance genes. In particular, we lack an understanding of how resistance genes circulate within wild animal populations, including whether specific host characteristics, such as social associations, promote interhost transmission of these genes. In this study, we used social network analysis to explore the forces shaping population-level patterns of resistant Escherichia coli in wild giraffe (Giraffa camelopardalis) and assess the relative importance of social contact for the dissemination of resistant E. coli between giraffe. Of 195 giraffe sampled, only 5.1% harbored E. coli isolates resistant to one or more tested antibiotics. Whole-genome sequencing on a subset of resistant isolates revealed a number of acquired resistance genes with linkages to mobile genetic elements. However, we found no evidence that the spread of resistance genes among giraffe was facilitated by interhost associations. Giraffe with lower social degree were more likely to harbor resistant E. coli, but this relationship was likely driven by a correlation between an individual's social connectedness and age. Indeed, resistant E. coli was most frequently detected in socially isolated neonates, indicating that resistant E. coli may have a selective advantage in the gastrointestinal tracts of neonates compared to other age classes. Taken together, these results suggest that the maintenance of antimicrobial-resistant bacteria in wild populations may, in part, be determined by host traits and microbial competition dynamics within the host.IMPORTANCE Antimicrobial resistance represents a significant threat to human health, food security, and the global economy. To fully understand the evolution and dissemination of resistance genes, a complete picture of antimicrobial resistance in all biological compartments, including natural ecosystems, is required. The environment and wild animals may act as reservoirs for anthropogenically derived resistance genes that could be transferrable to clinically relevant bacteria of humans and domestic animals. Our study investigated the possible transmission mechanisms for antimicrobial-resistant bacteria within a wild animal population and, more broadly, contributes to our understanding of how resistance genes are spread and maintained in natural ecosystems

Network structure and prevalence of Cryptosporidium in Belding's ground squirrels

Although pathogen transmission dynamics are profoundly affected by population social and spatial structure, few studies have empirically demonstrated the population-level implications of such structure in wildlife. In particular, epidemiological models predict that the extent to which contact patterns are clustered decreases a pathogen's ability to spread throughout an entire population, but this effect has yet to be demonstrated in a natural population. Here, we use network analysis to examine patterns of transmission of an environmentally transmitted parasite, Cryptosporidium spp., in Belding's ground squirrels (Spermophilus beldingi). We found that the prevalence of Cryptosporidium was negatively correlated with transitivity, a measure of network clustering, and positively correlated with the percentage of juvenile males. Additionally, network transitivity decreased when there were higher percentages of juvenile males; the exploratory behavior demonstrated by juvenile males may have altered the structure of the network by reducing clustering, and low clustering was associated with high prevalence. We suggest that juvenile males are critical in mediating the ability of Cryptosporidium to spread through colonies, and thus may function as "super-spreaders." Our results demonstrate the utility of a network approach in quantifying mechanistically how differences in contact patterns may lead to system-level differences in infection patterns

Development of a Robust Method for Isolation of Shiga Toxin-Positive <i>Escherichia coli</i> (STEC) from Fecal, Plant, Soil and Water Samples from a Leafy Greens Production Region in California

<div><p>During a 2.5-year survey of 33 farms and ranches in a major leafy greens production region in California, 13,650 produce, soil, livestock, wildlife, and water samples were tested for Shiga toxin (<i>stx</i>)-producing <i>Escherichia coli</i> (STEC). Overall, 357 and 1,912 samples were positive for <i>E. coli</i> O157:H7 (2.6%) or non-O157 STEC (14.0%), respectively. Isolates differentiated by O-typing ELISA and multilocus variable number tandem repeat analysis (MLVA) resulted in 697 O157:H7 and 3,256 non-O157 STEC isolates saved for further analysis. Cattle (7.1%), feral swine (4.7%), sediment (4.4%), and water (3.3%) samples were positive for <i>E. coli</i> O157:H7; 7/32 birds, 2/145 coyotes, 3/88 samples from elk also were positive. Non-O157 STEC were at approximately 5-fold higher incidence compared to O157 STEC: cattle (37.9%), feral swine (21.4%), birds (2.4%), small mammals (3.5%), deer or elk (8.3%), water (14.0%), sediment (12.3%), produce (0.3%) and soil adjacent to produce (0.6%). <i>stx1</i>, <i>stx2</i> and <i>stx1/stx2</i> genes were detected in 63%, 74% and 35% of STEC isolates, respectively. Subtilase, intimin and hemolysin genes were present in 28%, 25% and 79% of non-O157 STEC, respectively; 23% were of the “Top 6″ O-types. The initial method was modified twice during the study revealing evidence of culture bias based on differences in virulence and O-antigen profiles. MLVA typing revealed a diverse collection of O157 and non-O157 STEC strains isolated from multiple locations and sources and O157 STEC strains matching outbreak strains. These results emphasize the importance of multiple approaches for isolation of non-O157 STEC, that livestock and wildlife are common sources of potentially virulent STEC, and evidence of STEC persistence and movement in a leafy greens production environment.</p></div

Flow chart for STEC isolation (O157 and non-O157) and examples of typical colony morphologies.

<p>The final isolation protocol (M3) incorporates the M1 and M2 methods and starts with enrichment in TSB and plating anti-O157 magnetic beads on three different media (“IMS”; media A, B, C) and direct plating of <i>stx</i>-positive enrichment broths on C-O157 (“PCR”; medium D). O157 suspect colonies appear as pale and steel blue colonies on SMAC and NT-RA, respectively. Suspect STEC colonies from any media are subcultured on LB and confirmed as either O157 or non-O157 STEC by PCR. Anti-O157 magnetic beads bind other bacteria present in enrichment broths of environmental samples, but, fortuitously, also many non-O157 STEC. Typical non-O157 STEC colonies are shown from enrichments growing on C-O157 (Indicator Media, panel D, blue colonies), NT-RA agar (panel B, pink colonies). Non-O157 STEC colonies expressing beta-galactosidase and hemolysin are indicated by blue colonies with a clearing zone of hemolysis on mSBA (panel C). The parts of the final method for isolating O157 and non-O157 STEC are shown by an orange box (O157), blue box (M1), green box (M2) and red box (M3).</p

Primers and probes.

a<p>Fluorescent probe labels are Q670, Quasar 670; CFO560, CAL Fluor Orange 560; FAM, Carboxyfluorescein and CFR610, CAL Fluor Red 610. Quenchers are BHQ, Black Hole Quencher 1 and 2 (Biosearch, Novato, CA).</p>b<p>Position is relative to the coding region of <i>stx</i>1, <i>stx</i>2A or <i>omp</i>A from EDL933.</p>c<p><i>omp</i>A forward and reverse sequences in <b>BOLD</b> are homologous to <i>omp</i>A.</p

Phylogeny of non-O157 STEC by 7-loci MLVA and <i>omp</i>A sequence analysis.

<p>A minimal spanning tree was constructed of 286 MLVA/<i>ompA</i> types representing the non-O157 STEC strains isolated by M3. Node size indicates the relative number of isolates of that type; i.e. the smallest size node represents a single strain of that type. The nodes are color-coded by farm/ranch site code (Panel A) and by sample source (Panel B). Human clinical isolates RM12844 and RM12856 (OregonPublic Health, 2010) and RM14735 (Germany Fenugreek Outbreak strain, MA Dept. Public Health 2011) are included for comparison only.</p

Venn diagram of samples positive for non-O157 STEC by NT-RA, C-O157 and mSBA.

<p>The enrichment broths for 4,160 samples (sampling period Jan – Oct 2010) were processed by M3 corresponding to plating on C-O157 (PCR method; “C”), NT-RA agar (IMS method; “R”) and mSBA (IMS method; “B”). The figure shows the number of samples that were positive for non-O157 STEC on only 1 of the 3 media (R, C, B) and any combination of the 3 media (BC, RB, RC, RBC).</p