The clinical importance of microbiomes to the chronicity of wounds is widely appreciated, yet little is understood about patient-specific processes shaping wound microbiome composition. Here, a two-cohort microbiome-genome wide association study is presented through which patient genomic loci associated with chronic wound microbiome diversity were identified. Further investigation revealed that alternative TLN2 and ZNF521 genotypes explained significant inter-patient variation in relative abundance of two key pathogens, Pseudomonas aeruginosa and Staphylococcus epidermidis. Wound diversity was lowest in Pseudomonas aeruginosa infected wounds, and decreasing wound diversity had a significant negative linear relationship with healing rate. In addition to microbiome characteristics, age, diabetic status, and genetic ancestry all significantly influenced healing. Using structural equation modeling to identify common variance among SNPs, six loci were sufficient to explain 53% of variation in wound microbiome diversity, which was a 10% increase over traditional multiple regression. Focusing on TLN2, genotype at rs8031916 explained expression differences of alternative transcripts that differ in inclusion of important focal adhesion binding domains. Such differences are hypothesized to relate to wound microbiomes and healing through effects on bacterial exploitation of focal adhesions and/or cellular migration. Related, other associated loci were functionally enriched, often with roles in cytoskeletal dynamics. This study, being the first to identify patient genetic determinants for wound microbiomes and healing, implicates genetic variation determining cellular adhesion phenotypes as important drivers of infection type. The identification of predictive biomarkers for chronic wound microbiomes may serve as risk factors and guide treatment by informing patient-specific tendencies of infection. Chronic, or non-healing, wounds represent a costly burden to patients, and bacterial infection of wounds is an important driver of chronicity. A variety of bacterial species often occur in chronic wounds, but it is unknown why certain species are observed in some wound infections and not others. In this study, genetic variation of wound clinic patients was compared to the bacteria observed in their infected wounds. Through these comparisons, genetic variation in the TLN2 and ZNF521 genes was found to be associated with both the number of bacteria observed in wounds and the abundance of common pathogens (primarily Pseudomonas aeruginosa and Staphylococcus epidermidis). Moreover, Pseudomonas infected wounds were found to have fewer species present and wounds with fewer species were slower to heal. Furthermore, patient genes associated with microbiomes commonly encode proteins known to be important for cellular structures important to healing and to which bacteria directly interact. Experimental investigation of one such gene, TLN2, identified genotype-dependent differences in the expression of functionally different versions of TLN2 that is hypothesized to shape differences in cellular adhesion structures. Finally, a new statistical approach is presented in which patient biomarkers are used to predict the number of species observed during infection. Overall, our results describe how patient genetic variation influence the types of bacteria likely to infect an individual as well as influence healing.

Chronic wound infections are increasingly recognized to be dynamic and polymicrobial in nature, necessitating the development of wound models which reflect the complexities of infection in a non-healing wound. Wound slough isolated from human chronic wounds and transferred to mice was recently shown to create polymicrobial infection in mice, and there is potential this tool may be improved by cryogenic preservation. The purpose of this study was to investigate the application of cryogenic preservation to transferring polymicrobial communities, specifically by quantifying the effects of cryopreservation and wound microbiome transplantation. Slough from an established murine polymicrobial surgical excision model and five patients were subjected to three preservation strategies: refrigeration until infection, freezing in liquid nitrogen, or freezing in liquid nitrogen with glycerol solution prior to infection in individual mice. Four days following inoculation onto mice, wound microbiota were quantified using either culture isolation or by 16s rRNA gene community profiling and quantitative PCR. Cryogenic preservation did not significantly reduce bacterial viability. Reestablished microbial communities were significantly associated with patient of origin as well as host context (i.e., originally preserved from a patient versus mouse infection). Whereas preservation treatment did not significantly shape community composition, the transfers of microbiomes from human to mouse were characterized by reduced diversity and compositional changes. These findings indicated that changes should be expected to occur to community structure after colonization, and that compositional change is likely due to the rapid change in infection context as opposed to preservation strategy. Furthermore, species that were present in higher relative abundance in wound inoculate were more likely to colonize subsequent wounds, and wound inoculate with higher bacterial load established wound communities that were more compositionally similar. Results inform expectations for the complementation of chronic wound in vivo modeling with cryogenic preservation archives.

Coating surfaces with a copper-silver alloy in clinical settings can be an alternative or complementary antibacterial strategy to other existing technologies and disinfection interventions. A newly developed copper-silver alloy coating has a high antibacterial efficacy against common pathogenic bacteria in laboratory setups, and the purpose of this study is to determine the antibacterial efficacy of this copper-silvery alloy in real-world clinical settings. Two field trials were carried out at a private clinic and a wound care center. Door handles coated with the copper-silver alloy had a lower total aerobic plate count (1.3 ± 0.4 Log CFU/cm² and 0.8 ± 0.3 Log CFU/cm², CFU stands for Colony Forming Units) than the reference uncoated material on-site (2.4 ± 0.4 Log CFU/cm² for the stainless steel and 1.7 ± 0.4 Log CFU/cm² for the satin brass). The copper-silver alloy did not selectively reduce specific bacterial species. This study points to the possibility of a successful long-term implementation of the copper-silver alloy coating as an antibacterial strategy.

Vaginitis is a widespread issue for women worldwide, yet current diagnostic tools are lacking. Bacterial vaginosis (BV) is the most prevalent type of vaginitis, found in 10–50% of reproductive-aged women. Current diagnostic methods for BV rely on clinical criteria, microscopy, or the detection of a few microbes by qPCR. However, many vaginal infections lack a single etiological agent and are characterized by changes in the vaginal microbiome community structure (e.g., BV is defined as a loss of protective lactobacilli resulting in an overgrowth of anaerobic bacteria). Shotgun metagenomic sequencing provides a comprehensive view of all the organisms present in the vaginal microbiome (VMB), allowing for a better understanding of all potential etiologies. Here, we describe a robust VMB metagenomics sequencing test with a sensitivity of 93.1%, a specificity of 90%, a negative predictive value of 93.4%, and a positive predictive value of 89.6% certified by Clinical Laboratory Improvement Amendments (CLIA), the College of American Pathologist (CAP), and the Clinical Laboratory Evaluation Program (CLEP). We sequenced over 7000 human vaginal samples with this pipeline and described general findings and comparisons to US census data.

A unique phenotype of S. aureus called S. aureus small-colony variants (SA-SCVs) are a consequential contributor to multiple infectious processes. SA-SCVs are distinguishable from wild-type S. aureus (WT-SA) by their small size, slowed growth rate, and altered biochemical reactions; these changes make SA-SCV more difficult to detect from clinical specimens using routine diagnostics. While the clinical environment of chronic wound infections has the potential to stimulate the production of SA-SCVs, studies investigating detection of SA-SCVs in chronic wounds have not been previously conducted. Chronic wound specimens found to harbor S. aureus via qPCR screening, and screened for recent aminoglycoside treatment and/or co-infected with Pseudomonas aeruginosa, were collected from a specialty wound care clinic in April 2019. In-house enrichment methods alongside culture-dependent and independent diagnostics were utilized to recover and identify SA-SCVs from these chronic wounds. Our investigation determined difficulties in recovering and identifying SA-SCVs during routine diagnostic procedures, and the potential clinical impact of wounds harboring SA-SCVs related to antimicrobial susceptibility.

To develop and validate a non-invasive, blood-based diagnostic assay for endometriosis that performs accurately across menstrual cycl phases and complements existing imaging modalities. Multi-center case-control study with machine learning classification and independent cohort validation. Clinical and search settings involving symptomatic women evaluated for suspected endometriosis. A total of 298 reproductive-age women were included. The training cohort comprised 218 participants (137 endometriosis-positive and 81 controls). A modest, independent and retrospective validation cohort included 80 participants (40 endometriosis-positive and 40 controls). Peripheral blood sampling with quantification of three microRNAs via qPCR, three protein biomarkers, one steroid hormone using immunoassay, as well as the participant's age and body mass index. Biomarker data were integrated using a random forest machine learning model to classify disease status. In the independent validation cohort, the assay achieved an area under the curve (AUC) of 0.944, sensitivity of 0.80, and specificity of 0.975. Subgroup analysis by menstrual cycle phase demonstrated consistent performance: proliferative-phase samples achieved an AUC of 0.935, sensitivity of 0.767, and specificity of 0.962, while secretory-phase samples achieved an AUC of 0.993, sensitivity of 0.90, and specificity of 1.00. Compared with transvaginal ultrasound and/or MRI, the blood-based assay identified 61.5% histologically confirmed endometriosis cases that were missed by the imaging modalities. A minimally invasive, multi-omic blood-based assay integrating molecular biomarkers with machine learning can accurately detect endometriosis across menstrual cycle phases and provides complementary diagnostic values. This approach has the potential to improve early detection, and guide timely clinical intervention. A prospective validation is ongoing in geographically and ethnically diverse populations to further assess its broad clinical utility.