Quantity of questing black-legged ticks and associated micro-scale environmental data collected from four Suburban Parks near New York City.

During 2017 and 2018, we collected the quantity of questing black-legged ticks (Ixodes scapularis), also known as deer ticks, in 124 sampling sites of 5m by 5m in four state parks-Caumsett State Historic Park, Connetquot River State Park, Rockefeller State Park, and Fire Island National Seashore-around New York City. The black-legged tick is the primary vector for the spirochete Borrelia burgdorferi, the pathogen of Lyme disease, in Northeastern United States. Using the flagging method, we collected and counted the numbers of adult and nymphal black-legged ticks at each stie. Along with these quantities, we also recorded the geographic coordinates, ambient temperature, and relative humidity at the sampling sites. Using high-resolution aerial imagery and LiDAR data, we further derived land cover composition, ecotone boundary length, normalized difference vegetation index (NDVI), elevation, solar radiation, and other environmental factors. The data could be used to conduct longitudinal analysis at the same sampling sites as well as comparison with other sites. Ecologists and environmental scientists can use the data for spatiotemporal and statistical analyses of tick ecology at the local scale.

Maximum antigen diversification in a lyme bacterial population and evolutionary strategies to overcome pathogen diversity.

Natural populations of pathogens and their hosts are engaged in an arms race in which the pathogens diversify to escape host immunity while the hosts evolve novel immunity. This co-evolutionary process poses a fundamental challenge to the development of broadly effective vaccines and diagnostics against a diversifying pathogen. Based on surveys of natural allele frequencies and experimental immunization of mice, we show high antigenic specificities of natural variants of the outer surface protein C (OspC), a dominant antigen of a Lyme Disease-causing bacterium (Borrelia burgdorferi). To overcome the challenge of OspC antigenic diversity to clinical development of preventive measures, we implemented a number of evolution-informed strategies to broaden OspC antigenic reactivity. In particular, the centroid algorithm-a genetic algorithm to generate sequences that minimize amino-acid differences with natural variants-generated synthetic OspC analogs with the greatest promise as diagnostic and vaccine candidates against diverse Lyme pathogen strains co-existing in the Northeast United States. Mechanistically, we propose a model of maximum antigen diversification (MAD) mediated by amino-acid variations distributed across the hypervariable regions on the OspC molecule. Under the MAD hypothesis, evolutionary centroids display broad cross-reactivity by occupying the central void in the antigenic space excavated by diversifying natural variants. In contrast to vaccine designs based on concatenated epitopes, the evolutionary algorithms generate analogs of natural antigens and are automated. The novel centroid algorithm and the evolutionary antigen designs based on consensus and ancestral sequences have broad implications for combating diversifying pathogens driven by pathogen-host co-evolution.

Genetic codes optimized as a traveling salesman problem.

The Standard Genetic Code (SGC) is robust to mutational errors such that frequently occurring mutations minimally alter the physio-chemistry of amino acids. The apparent correlation between the evolutionary distances among codons and the physio-chemical distances among their cognate amino acids suggests an early co-diversification between the codons and amino acids. Here we formulated the co-minimization of evolutionary distances between codons and physio-chemical distances between amino acids as a Traveling Salesman Problem (TSP) and solved it with a Hopfield neural network. In this unsupervised learning algorithm, macromolecules (e.g., tRNAs and aminoacyl-tRNA synthetases) associating codons with amino acids were considered biological analogs of Hopfield neurons associating "tour cities" with "tour positions". The Hopfield network efficiently yielded an abundance of genetic codes that were more error-minimizing than SGC and could thus be used to design artificial genetic codes. We further argue that as a self-optimization algorithm, the Hopfield neural network provides a model of origin of SGC and other adaptive molecular systems through evolutionary learning.