Two Boundaries Separate Borrelia burgdorferi Populations in North America

Understanding the spread of infectious diseases is crucial for implementing effective control measures. For this, it is important to obtain information on the contemporary population structure of a disease agent and to infer the evolutionary processes that may have shaped it. Here, we investigate on a continental scale the population structure of Borrelia burgdorferi, the causative agent of Lyme borreliosis (LB), a tick-borne disease, in North America. We test the hypothesis that the observed d population structure is congruent with recent population expansions and that these were preceded by bottlenecks mostly likely caused by the near extirpation in the 1900s of hosts required for sustaining tick populations. Multilocus sequence typing and complementary population analytical tools were used to evaluate B. burgdorferi samples collected in the Northeastern, Upper Midwestern, and Far-Western United States and Canada. The spatial distribution of sequence types (STs) and inferred population boundaries suggest that the current populations are geographically separated. One major population boundary separated western B. burgdorferi populations transmitted by Ixodes pacificus in California from Eastern populations transmitted by I. scapularis; the other divided Midwestern and Northeastern populations. However, populations from all three regions were genetically closely related. Together, our findings suggest that although the contemporary populations of North American B. burgdorferi now com- prise three geographically separated subpopulations with no or limited gene flow among them, they arose from a common ancestral population. A comparative analysis of the B. burgdorferi outer surface protein C (ospC) gene revealed novel linkages and provides additional insights into the genetic characteristics of strains

Epidemic Wave Dynamics Attributable to Urban Community Structure: A Theoretical Characterization of Disease Transmission in a Large Network.

BACKGROUND: Multiple waves of transmission during infectious disease epidemics represent a major public health challenge, but the ecological and behavioral drivers of epidemic resurgence are poorly understood. In theory, community structure—aggregation into highly intraconnected and loosely interconnected social groups—within human populations may lead to punctuated outbreaks as diseases progress from one community to the next. However, this explanation has been largely overlooked in favor of temporal shifts in environmental conditions and human behavior and because of the difficulties associated with estimating large-scale contact patterns. OBJECTIVE: The aim was to characterize naturally arising patterns of human contact that are capable of producing simulated epidemics with multiple wave structures. METHODS: We used an extensive dataset of proximal physical contacts between users of a public Wi-Fi Internet system to evaluate the epidemiological implications of an empirical urban contact network. We characterized the modularity (community structure) of the network and then estimated epidemic dynamics under a percolation-based model of infectious disease spread on the network. We classified simulated epidemics as multiwave using a novel metric and we identified network structures that were critical to the network's ability to produce multiwave epidemics. RESULTS: We identified robust community structure in a large, empirical urban contact network from which multiwave epidemics may emerge naturally. This pattern was fueled by a special kind of insularity in which locally popular individuals were not the ones forging contacts with more distant social groups. CONCLUSIONS: Our results suggest that ordinary contact patterns can produce multiwave epidemics at the scale of a single urban area without the temporal shifts that are usually assumed to be responsible. Understanding the role of community structure in epidemic dynamics allows officials to anticipate epidemic resurgence without having to forecast future changes in hosts, pathogens, or the environment

Human milk-associated bacterial communities associate with the infant gut microbiome over the first year of life

IntroductionMicrobial communities inhabiting the human infant gut are important for immune system development and lifelong health. One critical exposure affecting the bacterial colonization of the infant gut is consumption of human milk, which contains diverse microbial communities and prebiotics. We hypothesized that human milk-associated microbial profiles are associated with those of the infant gut.MethodsMaternal–infant dyads enrolled in the New Hampshire Birth Cohort Study (n = 189 dyads) contributed breast milk and infant stool samples collected approximately at 6 weeks, 4 months, 6 months, 9 months, and 12 months postpartum (n = 572 samples). Microbial DNA was extracted from milk and stool and the V4-V5 region of the bacterial 16S rRNA gene was sequenced.ResultsClustering analysis identified three breast milk microbiome types (BMTs), characterized by differences in Streptococcus, Staphylococcus, Pseudomonas, Acinetobacter, and microbial diversity. Four 6-week infant gut microbiome types (6wIGMTs) were identified, differing in abundances of Bifidobacterium, Bacteroides, Clostridium, Streptococcus, and Escherichia/Shigella, while two 12-month IGMTs (12mIGMTs) differed primarily by Bacteroides presence. At 6 weeks, BMT was associated with 6wIGMT (Fisher’s exact test value of p = 0.039); this association was strongest among infants delivered by Cesarean section (Fisher’s exact test value of p = 0.0028). The strongest correlations between overall breast milk and infant stool microbial community structures were observed when comparing breast milk samples to infant stool samples collected at a subsequent time point, e.g., the 6-week breast milk microbiome associated with the 6-month infant gut microbiome (Mantel test Z-statistic = 0.53, value of p = 0.001). Streptoccous and Veillonella species abundance were correlated in 6-week milk and infant stool, and 4- and 6-month milk Pantoea species were associated with infant stool Lachnospiraceae genera at 9 and 12  months.DiscussionWe identified clusters of human milk and infant stool microbial communities that were associated in maternal–infant dyads at 6 weeks of life and found that milk microbial communities were more strongly associated with infant gut microbial communities in infants delivered operatively and after a lag period. These results suggest that milk microbial communities have a long-term effect on the infant gut microbiome both through sharing of microbes and other molecular mechanisms

Refining the Global Spatial Limits of Dengue Virus Transmission by Evidence-Based Consensus

Background: Dengue is a growing problem both in its geographical spread and in its intensity, and yet current global distribution remains highly uncertain. Challenges in diagnosis and diagnostic methods as well as highly variable national health systems mean no single data source can reliably estimate the distribution of this disease. As such, there is a lack of agreement on national dengue status among international health organisations. Here we bring together all available information on dengue occurrence using a novel approach to produce an evidence consensus map of the disease range that highlights nations with an uncertain dengue status. Methods/Principle Findings: A baseline methodology was used to assess a range of evidence for each country. In regions where dengue status was uncertain, additional evidence types were included to either clarify dengue status or confirm that it is unknown at this time. An algorithm was developed that assesses evidence quality and consistency, giving each country an evidence consensus score. Using this approach, we were able to generate a contemporary global map of national-level dengue status that assigns a relative measure of certainty and identifies gaps in the available evidence

MedZIM: Mediation analysis for Zero-Inflated Mediators with applications to microbiome data

The human microbiome can contribute to the pathogenesis of many complex diseases such as cancer and Alzheimer's disease by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate in the context of microbiome data due to the excessive number of zero values in the data. Zero-valued sequencing reads, commonly observed in microbiome studies, arise for technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inflated mediators are still lacking largely because of challenges raised by the zero-inflated data structure: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel mediation analysis method under the potential-outcomes framework to fill this gap. We show that the mediation effect of the microbiome can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. The first component corresponds to the mediation effect attributable to a unit-change over the positive relative abundance and the second component corresponds to the mediation effect attributable to discrete binary change of the mediator from zero to a non-zero state. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the applications in two real microbiome studies demonstrate that our approach outperforms existing mediation analysis approaches.Comment: Corresponding: Zhigang L

Electronic Event–based Surveillance for Monitoring Dengue, Latin America

Dengue, a potentially fatal disease, is spreading around the world. An estimated 2.5 billion people in tropical and subtropical regions are at risk. Early detection of outbreaks is crucial to prevention and control of dengue virus and other viruses. Case reporting may often take weeks or months. Therefore, researchers explored whether electronic sources of real-time information (such as Internet news outlets, health expert mailing lists, social media sites, and queries to online search engines) might be faster, and they were. Although information from unofficial sources should be interpreted with caution, when used in conjunction with traditional case reporting, real-time electronic surveillance can help public health authorities allocate resources in time to avert full-blown epidemics

Association of cesarean delivery and formula supplementation with the intestinal microbiome of 6-week-old infants

Author Posting. © American Medical Association, 2016. This article is posted here by permission of American Medical Association for personal use, not for redistribution. The definitive version was published in JAMA Pediatrics 170 (2016): 212-219, doi:10.1001/jamapediatrics.2015.3732.The intestinal microbiome plays a critical role in infant development, and delivery mode and feeding method (breast milk vs formula) are determinants of its composition. However, the importance of delivery mode beyond the first days of life is unknown, and studies of associations between infant feeding and microbiome composition have been generally limited to comparisons between exclusively breastfed and formula-fed infants, with little consideration given to combination feeding of both breast milk and formula.This work was supported in part by grants from the National Institutes of Health (grants NIEHS P01ES022832, NIEHS P20ES018175, NIGMS P20GM104416, NLM K01LM011985, NLM R01LM009012, and NLM R01 LM010098) and the US Environmental Protection Agency (grants RD83459901 and RD83544201).2017-01-1