1786. An Automated Method to Assess Oligonucleotide Primer and Probe Complementarity to Genomic Targets in Infectious Disease qPCR Assays

Background Success of real-time TaqMan PCR (qPCR) in detecting pathogen targets and quantifying pathogen load is dependent upon frequent assay monitoring. This is due to i) the high degree of complementarity needed between primers / probes and genomic targets for assay accuracy and ii) natural pathogen variation and evolution. Failure to monitor and refine may result in false negativity or under quantification. Here we present a bioinformatics tool to identify potential problems resulting from newly discovered genomic mutations in primer/probe regions. Methods The tool performs an unbiased and automated search of the NCBI database, collects relevant genomic sequences based on user-defined Taxon-ID and executes a Python program to discard synthetic sequences. A profile of primer-probe sequence complementarity to targets is then generated. While the tool can be used for any microbe, here we present results for our laboratory’s cytomegalovirus (CMV) qPCR primer-probe analysis. In addition, our laboratory’s traditional approach utilizing alignment software was performed (download of all CMV sequences (~10,000) followed by iterative alignment building of these against our primers and probes). The amount of time to perform the automated and manual methods was recorded. Results The tool retrieved 8,732 sequences from NCBI and compared these to the CMV qPCR primers and probes. The tool found 2,501 alignments between the primers / probes and the downloaded genomic data (~15 minutes to finish (6 CPUs)). A total of 64% (1,624/2,501) of BLASTn alignments were exact matches between all primers / probes and viral genomic sequences. 17.5% (439/2,501) of alignments had 1 mismatch at either 5’ or 3’ terminus, and 1% (25/2,501) of alignments had two mismatches with the primers / probes. Similar results were found using a primarily manual approach (which took approx. 5 hours computing time and 20 hours of labor). Conclusion This new bioinformatics approach performed indistinguishably vs. a manual approach and did so in minutes rather than days. Both methods led to the conclusion that, by virtue of our design involving overlapping primers and probes, none of the identified mismatches are predicted to lead to false negativity or under quantification in our current CMV qPCR assay. Disclosures All authors: No reported disclosures.

Pathogen genetic control of transcriptome variation in the Arabidopsis thaliana – Botrytis cinerea pathosystem

ABSTRACTDisease symptoms arise from the interaction of the host and pathogen genomes. However, little is known about how genetic variation in the interaction modulates both organisms’ transcriptomes, especially in complex interactions like those between generalist pathogens and their plant hosts. To begin mapping how polygenic pathogen variation influences both organisms’ transcriptomes, we used the Botrytis cinerea - Arabidopsis thaliana pathosystem. We measured the co-transcriptome across a genetically diverse collection of 96 B. cinerea isolates infected on the Arabidopsis wildtype, Col-0. Using the B. cinerea genomic variation, we performed genome-wide association (GWA) for each of 23,947 measurable transcripts in the host, and 9,267 measurable transcripts in the pathogen. Unlike other eGWA studies, there was a relative absence of cis-eQTL that is likely explained by structural variants and allelic heterogeneity within the pathogen’s genome. This analysis identified mostly trans-eQTL in the pathogen with eQTL hotspots dispersed across the pathogen genome that altered the pathogen’s transcripts, the host’s transcripts, or both the pathogen and the host. Gene membership in the trans-eQTL hotspots suggests links to several known and many novel virulence mechanisms in the plant-pathogen interaction. Genes annotated to these hotspots provide potential targets for blocking manipulation of the host response by this ubiquitous generalist pathogen. This shows that genetic control over the co-transcriptome is polygenic, similar to the virulence outcome in the interaction of Botrytis cinerea on Arabidopsis thaliana.

Effects of multiple sources of genetic drift on pathogen variation within hosts

Changes in pathogen genetic variation within hosts alter the severity and spread of infectious diseases, with important implications for clinical disease and public health. Genetic drift may play a strong role in shaping pathogen variation, but analyses of drift in pathogens have oversimplified pathogen population dynamics, either by considering dynamics only at a single scale (within hosts, between hosts), or by making drastic simplifying assumptions (host immune systems can be ignored, transmission bottlenecks are complete). Moreover, previous studies used genetic data to infer the strength of genetic drift, whereas we test whether the genetic drift imposed by pathogen population processes can be used to explain genetic data. We first constructed and parameterized a mathematical model of gypsy moth baculovirus dynamics that allows genetic drift to act within and between hosts. We then quantified the genome-wide diversity of baculovirus populations within each of 143 field-collected gypsy moth larvae using Illumina sequencing. Finally, we determined whether the genetic drift imposed by host-pathogen population dynamics in our model explains the levels of pathogen diversity in our data. We found that when the model allows drift to act at multiple scales, including within hosts, between hosts, and between years, it can accurately reproduce the data, but when the effects of drift are simplified by neglecting transmission bottlenecks and stochastic variation in virus replication within hosts, the model fails. Ade novomutation model and a purifying selection model similarly fail to explain the data. Our results show that genetic drift can play a strong role in determining pathogen variation, and that mathematical models that account for pathogen population growth at multiple scales of biological organization can be used to explain this variation.

Can immunological principles and cross-disciplinary science illuminate the path to vaccines for HIV and other global health challenges?

Vaccines are one of the most impactful and cost-effective public health measures of the twentieth century. However, there remain great unmet needs to develop vaccines for globally burdensome infectious diseases and to allow more timely responses to emerging infectious disease threats. Recent advances in the understanding of immunological principles operative not just in model systems but in humans in concert with the development and application of powerful new tools for profiling human immune responses, in our understanding of pathogen variation and evolution, and in the elucidation of the structural aspects of antibody–pathogen interactions, have illuminated pathways by which these unmet needs might be addressed. Using these advances as foundation, we herein present a conceptual framework by which the discovery, development and iterative improvement of effective vaccines for HIV, malaria and other globally important infectious diseases might be accelerated.

Large-scale whole genome sequencing of M. tuberculosis provides insights into transmission in a high prevalence area

To improve understanding of the factors influencing tuberculosis transmission and the role of pathogen variation, we sequenced all available specimens from patients diagnosed over 15 years in a whole district in Malawi. Mycobacterium tuberculosis lineages were assigned and transmission networks constructed, allowing ≤10 single nucleotide polymorphisms (SNPs) difference. We defined disease as due to recent infection if the network-determined source was within 5 years, and assessed transmissibility from forward transmissions resulting in disease. High-quality sequences were available for 1687 disease episodes (72% of all culture-positive episodes): 66% of patients linked to at least one other patient. The between-patient mutation rate was 0.26 SNPs/year (95% CI 0.21–0.31). We showed striking differences by lineage in the proportion of disease due to recent transmission and in transmissibility (highest for lineage-2 and lowest for lineage-1) that were not confounded by immigration, HIV status or drug resistance. Transmissions resulting in disease decreased markedly over time.

Pathogen variation across time and space: sequencing to characterize Mannheimia haemolytica diversity

Bovine respiratory disease complex (BRDC) is a major animal health and economic issue that affects cattle industries worldwide. Within the USA, the beef cattle industry loses up to an estimated 1 billion dollars a year due to BRDC. There are many contributors to BRDC, including environmental stressors and viral and/or bacterial infections. One species of bacteria in particular, Mannheimia haemolytica, is recognized as the major cause of severe fibrinonecrotic pneumonia in cattle. M. haemolytica is an opportunistic pathogen that normally populates the upper respiratory tract of cattle, and invades the lower respiratory tract in stressed and/or virally infected cattle by mechanisms that are not completely understood. However, not all M. haemolytica appear to be equally pathogenic to cattle. Thus, a test could be developed to distinguish M. haemolytica genetic subtypes by their propensity to cause respiratory disease, allowing isolation and/or treatment of cattle harboring strains with an increased propensity to cause disease. To that end, the genomes of over 300 M. haemolytica strains are being sequenced.