Spatiotemporal patterns of Lyme disease in North Carolina: 2010-2020.

Background: Lyme disease is the most common vector-borne disease in the United States with the majority of cases occurring in the Northeast, upper Midwest, and mid-Atlantic regions. While historically considered a low incidence state, North Carolina (NC) has reported an increasing number of cases over the past decade. Therefore, the aim of this study was to characterise the spatiotemporal evolution of Lyme disease in NC from 2010 to 2020. Methods: Confirmed and probable cases reported to the NC Division of Public Health without associated travel to high-transmission state were included in the analysis. The study period was divided into four sub-periods and data were aggregated by zip code of residence. The absolute change in incidence was mapped and spatial autocorrelation analyses were performed within each sub-period. Findings: We identified the largest absolute changes in incidence in zip codes located in northwestern NC along the Appalachian Mountains. The spatial distribution of cases became increasingly clustered over the study period (Moran's I of 0.012, p = 0.127 in 2010-2012 vs. 0.403, p < 0.0001 in 2019-2020). Identified clusters included 22 high-incidence zip codes in the 2019-2020 sub-period, largely overlapping with the same areas experiencing the greatest absolute changes in disease incidence. Interpretation: Lyme disease has rapidly emerged in northwestern NC with some zip codes reporting incidence rates similar to historically high incidence regions across the US Northeast, mid-Atlantic, and upper Midwest. Efforts are urgently needed to raise awareness among medical providers to prevent excess morbidity. Funding: Funding was provided by a "Creativity Hub" award from the UNC Office of the Vice Chancellor for Research. Additional support was provided by Southeastern Center of Excellence in Vector Borne Diseases (U01CK000662).

Does Paging Clinicians about Tick-Borne Disease Serological Results Impact Clinical Care? A Retrospective Analysis of 70 Cases in North Carolina.

Delayed treatment of Rocky Mountain spotted fever is associated with increased morbidity and mortality. Because the diagnosis cannot be established from a single serological test, guidelines recommend empirical antibiotic initiation in suspect patients. We evaluated a policy used by UNC Health of paging clinicians when acute testing for Rickettsia returned with a titer ≥1:256. Our objective was to assess the potential effect of paging on routine treatment practices. Notably, we found that a high proportion of cases (N = 28, 40%) were not prescribed antibiotics until the results were available. The vast majority of these cases did not have evidence of compatible symptoms or disease progression. These findings suggest that paging may have prompted unnecessary treatment. Overall, the policy, which has now been discontinued, appears to have had limited benefit. Efforts are urgently needed to improve adherence to testing and treatment guidelines.

A Machine Learning-Based Analytic Pipeline Applied to Clinical and Serum IgG Immunoproteome Data To Predict Chlamydia trachomatis Genital Tract Ascension and Incident Infection in Women.

We developed a reusable and open-source machine learning (ML) pipeline that can provide an analytical framework for rigorous biomarker discovery. We implemented the ML pipeline to determine the predictive potential of clinical and immunoproteome antibody data for outcomes associated with Chlamydia trachomatis (Ct) infection collected from 222 cis-gender females with high Ct exposure. We compared the predictive performance of 4 ML algorithms (naive Bayes, random forest, extreme gradient boosting with linear booster [xgbLinear], and k-nearest neighbors [KNN]), screened from 215 ML methods, in combination with two different feature selection strategies, Boruta and recursive feature elimination. Recursive feature elimination performed better than Boruta in this study. In prediction of Ct ascending infection, naive Bayes yielded a slightly higher median value of are under the receiver operating characteristic curve (AUROC) 0.57 (95% confidence interval [CI], 0.54 to 0.59) than other methods and provided biological interpretability. For prediction of incident infection among women uninfected at enrollment, KNN performed slightly better than other algorithms, with a median AUROC of 0.61 (95% CI, 0.49 to 0.70). In contrast, xgbLinear and random forest had higher predictive performances, with median AUROC of 0.63 (95% CI, 0.58 to 0.67) and 0.62 (95% CI, 0.58 to 0.64), respectively, for women infected at enrollment. Our findings suggest that clinical factors and serum anti-Ct protein IgGs are inadequate biomarkers for ascension or incident Ct infection. Nevertheless, our analysis highlights the utility of a pipeline that searches for biomarkers and evaluates prediction performance and interpretability. IMPORTANCE Biomarker discovery to aid early diagnosis and treatment using machine learning (ML) approaches is a rapidly developing area in host-microbe studies. However, lack of reproducibility and interpretability of ML-driven biomarker analysis hinders selection of robust biomarkers that can be applied in clinical practice. We thus developed a rigorous ML analytical framework and provide recommendations for enhancing reproducibility of biomarkers. We emphasize the importance of robustness in selection of ML methods, evaluation of performance, and interpretability of biomarkers. Our ML pipeline is reusable and open-source and can be used not only to identify host-pathogen interaction biomarkers but also in microbiome studies and ecological and environmental microbiology research.

Reduced Endometrial Ascension and Enhanced Reinfection Associated With Immunoglobulin G Antibodies to Specific Chlamydia trachomatis Proteins in Women at Risk for Chlamydia.

BACKGROUND: Previous research revealed antibodies targeting Chlamydia trachomatis elementary bodies was not associated with reduced endometrial or incident infection in C. trachomatis-exposed women. However, data on the role of C. trachomatis protein-specific antibodies in protection are limited. METHODS: A whole-proteome C. trachomatis array screening serum pools from C. trachomatis-exposed women identified 121 immunoprevalent proteins. Individual serum samples were probed using a focused array. Immunoglobulin (Ig) G antibody frequencies and endometrial or incident infection relationships were examined using Wilcoxon rank sum test. The impact of the breadth and magnitude of protein-specific IgGs on ascension and incident infection were examined using multivariable stepwise logistic regression. Complementary RNA sequencing quantified C. trachomatis gene transcripts in cervical swab samples from infected women. RESULTS: IgG to pGP3 and CT_005 were associated with reduced endometrial infection; anti-CT_443, anti-CT_486, and anti-CT_123 were associated with increased incident infection. Increased breadth of protein recognition did not however predict protection from endometrial or incident infection. Messenger RNAs for immunoprevalent C. trachomatis proteins were highly abundant in the cervix. CONCLUSIONS: Protein-specific C. trachomatis antibodies are not sufficient to protect against ascending or incident infection. However, cervical C. trachomatis gene transcript abundance positively correlates with C. trachomatis protein immunogenicity. These abundant and broadly recognized antigens are viable vaccine candidates.