Association between baseline abundance of Peptoniphilus, a Gram-positive anaerobic coccus, and wound healing outcomes of DFUs

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

The wound microbiota: microbial mechanisms of impaired wound healing and infection

The skin barrier protects the human body from invasion by exogenous and pathogenic microorganisms. A breach in this barrier exposes the underlying tissue to microbial contamination, which can lead to infection, delayed healing, and further loss of tissue and organ integrity. Delayed wound healing and chronic wounds are associated with comorbidities, including diabetes, advanced age, immunosuppression and autoimmune disease. The wound microbiota can influence each stage of the multi-factorial repair process and influence the likelihood of an infection. Pathogens that commonly infect wounds, such as Staphylococcus aureus and Pseudomonas aeruginosa, express specialized virulence factors that facilitate adherence and invasion. Biofilm formation and other polymicrobial interactions contribute to host immunity evasion and resistance to antimicrobial therapies. Anaerobic organisms, fungal and viral pathogens, and emerging drug-resistant microorganisms present unique challenges for diagnosis and therapy. In this Review, we explore the current understanding of how microorganisms present in wounds impact the process of skin repair and lead to infection through their actions on the host and the other microbial wound inhabitants.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Study Highlights

The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Which biomarkers predict hard-to-heal diabetic foot ulcers? A scoping review

Diabetic foot ulcers (DFUs) often develop into hard-to-heal wounds due to complex factors. Several biomarkers capable of identifying those at risk of delayed wound healing have been reported. Controlling or targeting these biomarkers could prevent the progression of DFUs into hard-to-heal wounds. This scoping review aimed to identify the key biomarkers that can predict hard-to-heal DFUs. Studies that reported biomarkers related to hard-to-heal DFUs, from 1980 to 2023, were mapped. Studies were collected from the following databases: MEDLINE, CINAHL, EMBASE, and ICHUSHI (Japana Centra Revuo Medicina), search terms included "diabetic," "ulcer," "non-healing," and "biomarker." A total of 808 articles were mapped, and 14 (10 human and 4 animal studies) were included in this review. The ulcer characteristics in the clinical studies varied. Most studies focused on either infected wounds or neuropathic wounds, and patients with ischemia were usually excluded. Among the reported biomarkers for the prediction of hard-to-heal DFUs, the pro-inflammatory cytokine CXCL-6 in wound fluid from non-infected and non-ischemic wounds had the highest prediction accuracy (area under the curve: 0.965; sensitivity: 87.27%; specificity: 95.56%). CXCL-6 levels could be a useful predictive biomarker for hard-to-heal DFUs. However, CXCL6, a chemoattractant for neutrophilic granulocytes, elicits its chemotactic effects by combining with the chemokine receptors CXCR1 and CXCR2, and is involved in several diseases. Therefore, it's difficult to use CXCL6 as a prevention or treatment target. Targetable specific biomarkers for hard-to-heal DFUs need to be determined.

Peptoniphilus equinus sp. nov., a novel Gram-stain-positive anaerobic coccus isolated from the faeces of a thoroughbred racehorse

A novel, anaerobic, Gram-stain-positive coccoid strain, CBA3646T, was isolated from the faeces of a thoroughbred racehorse. Phylogenetic analysis based on 16S rRNA gene sequencing yielded results indicative of CBA3646T representing a member of the genus Peptoniphilus, with the species most closely related to it being Peptoniphilus asaccharolyticus DSM 20463T, with a similarity of 94.79 %. DNA-DNA relatedness and average nucleotide identity values between CBA3646T and P. asaccharolyticus DSM 20463T were 21.4 and 67.6 %, respectively. CBA3646T has a circular chromosomal genome of 1 709 189 bp (45.5 mol% DNA G+C content), containing 1652 genes in total, 1584 predicted protein-coding genes, 3 complete rRNA loci and 47 tRNA genes. The cells were non-motile diplococci, catalase-positive and oxidase-negative. Growth of CBA3646T was observed at 20-40 °C (optimal temperature, 35 °C) and in the presence of 0-4 % (w/v) NaCl (optimum concentration, 1 %). The major fatty acids (>10 %) of CBA3646T were C16 : 0, C18 : 1ω9c and C18 : 1ω9c dimethyl acetal, with its major polar lipids being diphosphatidylglycerol and phosphatidylglycerol. The elucidated phylogenetic, physiological, chemotaxonomic and molecular properties are indicative of strain CBA3646T representing a novel species of the genus Peptoniphilus, or which the name Peptoniphilus equinus sp. nov. is proposed. The type strain is CBA3646T (= KACC 22890T = JCM 35845T).

Urinary microbiome differences between lichen sclerosus induced and non-lichen sclerosus induced urethral stricture disease

OBJECTIVE

To describe differences in the urinary microbiome of patients with pathologically confirmed lichen sclerosus (LS) urethral stricture disease (USD) vs non-lichen sclerosus (non-LS) USD pre- and post-operatively.

METHODS

Patients were pre-operatively identified and prospectively followed, all underwent surgical repair and had tissue samples obtained to make a pathological diagnosis of LS. Pre- and post-operative urine samples were collected. Bacterial genomic DNA was extracted. Alpha and beta diversity measurements were calculated and compared. A zero-inflated negative binomial model was utilized to compare taxa abundances between disease status and surgery status.

RESULTS

Urine samples were obtained from both cohorts, 69 samples in total: 36 samples were obtained pre-operatively and 33 samples were obtained post-operatively. Ten patients provided both a pre-operative and post-operative urine sample. Twenty-six patients had pathological evidence of LS and 33 patients did not. There was a statistically significant difference in alpha diversity between the pre-operative urine samples of patients with non-LS USD and LS USD, (p = 0.01). There was no significant difference in alpha diversity within post-operative urine samples between patients with non-LS USD and LS USD, (p = 0.1). A significant difference was observed in Weighed UniFrac distances with respect to disease and operative status, (p = 0.001 and 0.002).

CONCLUSIONS

LS USD have significant alterations in diversity and differential abundance of urine microbiota compared to non-LS USD controls. These findings could be used to guide further investigations into the role of the urinary microbiome in LS USD pathogenesis, severity of presentation, and stricture recurrence.

Wound microbiota-mediated correction of matrix metalloproteinase expression promotes re-epithelialization of diabetic wounds

Chronic wounds are a common and costly complication of diabetes, where multifactorial defects contribute to dysregulated skin repair, inflammation, tissue damage, and infection. We previously showed that aspects of the diabetic foot ulcer microbiota were correlated with poor healing outcomes, but many microbial species recovered remain uninvestigated with respect to wound healing. Here we focused on Alcaligenes faecalis , a Gram-negative bacterium that is frequently recovered from chronic wounds but rarely causes infection. Treatment of diabetic wounds with A. faecalis accelerated healing during early stages. We investigated the underlying mechanisms and found that A. faecalis treatment promotes re-epithelialization of diabetic keratinocytes, a process which is necessary for healing but deficient in chronic wounds. Overexpression of matrix metalloproteinases in diabetes contributes to failed epithelialization, and we found that A. faecalis treatment balances this overexpression to allow proper healing. This work uncovers a mechanism of bacterial-driven wound repair and provides a foundation for the development of microbiota-based wound interventions.

Colonizing microbiota is associated with clinical outcomes in diabetic wound healing

With the development of society and the improvement of life quality, more than 500 million people are affected by diabetes. More than 10 % of people with diabetes will suffer from diabetic wounds, and 80 % of diabetic wounds will reoccur, so the development of new diabetic wound treatments is of great importance. The development of skin microbe research technology has gradually drawn people's attention to the complex relationship between microbes and diabetic wounds. Many studies have shown that skin microbes are associated with the outcome of diabetic wounds and can even be used as one of the indicators of wound prognosis. Skin microbes have also been found to have the potential to treat diabetic wounds. The wound colonization of different bacteria can exert opposing therapeutic effects. It is necessary to fully understand the skin microbes in diabetic wounds, which can provide valuable guidance for clinical diabetic wound treatment.

Relationship between healing status and microbial dissimilarity in wound and peri-wound skin in pressure injuries

AIM

Wound infection is the most serious cause of delayed healing for patients with pressure injuries. The wound microbiota, which plays a crucial role in delayed healing, forms by bacterial dissemination from the peri-wound skin. To manage the bioburden, wound and peri-wound skin care has been implemented; however, how the microbiota at these sites contribute to delayed healing is unclear. Therefore, we investigated the relationship between healing status and microbial dissimilarity in wound and peri-wound skin.

METHODS

A prospective cohort study was conducted at a long-term care hospital. The outcome was healing status assessed using the DESIGN-R® tool, a wound assessment tool to monitor the wound healing process. Bacterial DNA was extracted from the wound and peri-wound swabs, and microbiota composition was analyzed using 16S rRNA gene analysis. To evaluate microbial similarity, the weighted UniFrac dissimilarity index between wound and peri-wound microbiota was calculated.

RESULTS

Twenty-two pressure injuries (7 deep and 15 superficial wounds) were included in the study. For deep wounds, the predominant bacteria in wound and peri-wound skin were the same in the healing wounds, whereas they were different in all cases of hard-to-heal wounds. Analysis based on the weighted UniFrac dissimilarity index, there was no significant difference for healing wounds (p = 0.639), while a significant difference was found for hard-to-heal wounds (p = 0.047).

CONCLUSIONS

Delayed healing is possibly associated with formation of wound microbiota that is different in composition from that of the skin commensal microbiota. This study provides a new perspective for assessing wound bioburden.

Analysis of Neutrophil and Monocyte Inflammation Markers in Response to Gram-Positive Anaerobic Cocci

The impact of anaerobic bacteria on the human host is sparsely investigated due to cultivation challenges. Nonetheless, in the last decade increasing research demonstrated the importance of paying attention to these overlooked pathogens. In this chapter, we provide an overview of analyzing surface and intracellular inflammation markers of neutrophils and monocytes in response to Gram-positive anaerobic cocci (GPAC) species Peptoniphilus (P.) harei.

Ankle brachial indices and anaerobes: is peripheral arterial disease associated with anaerobic bacteria in diabetic foot ulcers?

Background

Lower extremity amputations from diabetic foot ulcers (DFUs) are rebounding, and new biomarkers that predict wound healing are urgently needed. Anaerobic bacteria have been associated with persistent ulcers and may be a promising biomarker beyond currently recommended vascular assessments. It is unknown whether anaerobic markers are simply a downstream outcome of peripheral arterial disease (PAD) and ischemia, however. Here, we evaluate associations between two measures of anaerobic bacteria-abundance and metabolic activity-and PAD.

Methods

We built a prospective cohort of 37 patients with baseline ankle brachial index (ABI) results. Anaerobic bacteria were measured in two ways: DNA-based total anaerobic abundance using 16S rRNA gene amplicon sequencing and resulting summed relative abundance, and RNA-based metabolic activity based on bacterial read annotation of metatranscriptomic sequencing. PAD was defined three ways: PAD diagnosis, ABI results, and a dichotomous definition of mild ischemia (versus normal) based on ABI values. Statistical associations between anaerobes and PAD were evaluated using univariate odds ratios (ORs) or Spearman's correlations.

Results

Total anaerobe abundance was not significantly associated with PAD diagnosis, ABI results, or mild ischemia (ORPAD = 0.47, 95% CI = 0.023-7.23, p = 0.60; Spearman's correlation coefficientABI = 0.24, p = 0.17; ORmild ischemia = 0.25, 95% CI = 0.005-5.86, p = 0.42). Anaerobic metabolic activity was not significantly associated with PAD diagnosis, ABI results, or mild ischemia (ORPAD = 1.99, 95% CI = 0.17-21.44, p = 0.57; Spearman's correlation coefficientABI = 0.12, p = 0.52; ORmild ischemia = 0.90, 95% CI = 0.03-15.16, p = 0.94).

Conclusion

Neither anaerobic abundance nor metabolic activity was strongly associated with our three definitions of PAD. Therefore, anaerobic bacteria may offer additional prognostic value when assessing wound healing potential and should be investigated as potential molecular biomarkers for DFU outcomes.

Bacterial Interactions in the Context of Chronic Wound Biofilm: A Review

Chronic wounds, defined by their resistance to care after four weeks, are a major concern, affecting millions of patients every year. They can be divided into three types of lesions: diabetic foot ulcers (DFU), pressure ulcers (PU), and venous/arterial ulcers. Once established, the classical treatment for chronic wounds includes tissue debridement at regular intervals to decrease biofilm mass constituted by microorganisms physiologically colonizing the wound. This particular niche hosts a dynamic bacterial population constituting the bed of interaction between the various microorganisms. The temporal reshuffle of biofilm relies on an organized architecture. Microbial community turnover is mainly associated with debridement (allowing transitioning from one major representant to another), but also with microbial competition and/or collaboration within wounds. This complex network of species and interactions has the potential, through diversity in antagonist and/or synergistic crosstalk, to accelerate, delay, or worsen wound healing. Understanding these interactions between microorganisms encountered in this clinical situation is essential to improve the management of chronic wounds.

Machine Learning Models for Predicting the Risk of Hard-to-Heal Diabetic Foot Ulcers in a Chinese Population

BACKGROUND

Early detection of hard-to-heal diabetic foot ulcers (DFUs) is vital to prevent a poor prognosis. The purpose of this work was to employ clinical characteristics to create an optimal predictive model of hard-to-heal DFUs (failing to decrease by >50% at 4 weeks) based on machine learning algorithms.

METHODS

A total of 362 DFU patients hospitalized in two tertiary hospitals in eastern China were enrolled in this study. The training dataset and validation dataset were split at a ratio of 7:3. Univariate logistic analysis and clinical experience were utilized to screen clinical characteristics as predictive features. The following six machine learning algorithms were used to build prediction models for differentiating hard-to-heal DFUs: support vector machine, the naïve Bayesian (NB) model, k-nearest neighbor, general linear regression, adaptive boosting, and random forest. Five cross-validations were employed to realize the model's parameters. Accuracy, precision, recall, F1-scores, and AUCs were utilized to compare and evaluate the models' efficacy. On the basis of the best model identified, the significance of each characteristic was evaluated, and then an online calculator was developed.

RESULTS

Independent predictors for model establishment included sex, insulin use, random blood glucose, wound area, diabetic retinopathy, peripheral arterial disease, smoking history, serum albumin, serum creatinine, and C-reactive protein. After evaluation, the NB model was identified as the most generalizable model, with an AUC of 0.864, a recall of 0.907, and an F1-score of 0.744. Random blood glucose, C-reactive protein, and wound area were determined to be the three most important influencing factors. A corresponding online calculator was created (https://predicthardtoheal.azurewebsites.net/).

CONCLUSION

Based on clinical characteristics, machine learning algorithms can achieve acceptable predictions of hard-to-heal DFUs, with the NB model performing the best. Our online calculator can assist doctors in identifying the possibility of hard-to-heal DFUs at the time of admission to reduce the likelihood of a dismal prognosis.

The dilemma of identifying Peptoniphilus species by using two MALDI-TOF MS systems

Two commercial MALDI-TOF MS systems were used to identify 18 isolates, belonging to the Peptoniphilus genus; also the 16S rRNA sequencing identity was compared against the MALDI-TOF MS system results. Bruker Biotyper system provided higher accuracy than Vitek MS system, however, adding spectra could allow a more reliable species level identification.

Priority effects dictate community structure and alter virulence of fungal-bacterial biofilms

Polymicrobial biofilms are a hallmark of chronic wound infection. The forces governing assembly and maturation of these microbial ecosystems are largely unexplored but the consequences on host response and clinical outcome can be significant. In the context of wound healing, formation of a biofilm and a stable microbial community structure is associated with impaired tissue repair resulting in a non-healing chronic wound. These types of wounds can persist for years simmering below the threshold of classically defined clinical infection (which includes heat, pain, redness, and swelling) and cycling through phases of recurrent infection. In the most severe outcome, amputation of lower extremities may occur if spreading infection ensues. Here we take an ecological perspective to study priority effects and competitive exclusion on overall biofilm community structure in a three-membered community comprised of strains of Staphylococcus aureus, Citrobacter freundii, and Candida albicans derived from a chronic wound. We show that both priority effects and inter-bacterial competition for binding to C. albicans biofilms significantly shape community structure on both abiotic and biotic substrates, such as ex vivo human skin wounds. We further show attachment of C. freundii to C. albicans is mediated by mannose-binding lectins. Co-cultures of C. freundii and C. albicans trigger the yeast-to-hyphae transition, resulting in a significant increase in neutrophil death and inflammation compared to either species alone. Collectively, the results presented here facilitate our understanding of fungal-bacterial interactions and their effects on host-microbe interactions, pathogenesis, and ultimately, wound healing.

Peptoniphilus faecalis sp. nov., isolated from swine faeces

An obligately anaerobic, Gram-positive, non-motile, coccus-shaped bacterial strain designated AGMB00490^T was isolated from swine faeces. 16S rRNA gene sequence-based phylogenetic analysis indicated that the isolate belongs to the genus Peptoniphilus and that the most closely related species is Peptoniphilus gorbachii WAL 10418^T (=KCTC 5947^T, 97.22 % 16S rRNA gene sequence similarity). Whole genome sequence analysis determined that the DNA G+C content of strain AGMB00490^T was 31.2 mol% and moreover that the genome size and numbers of tRNA and rRNA genes were 2 129 517 bp, 34 and 10, respectively. Strain AGMB00490^T was negative for oxidase and urease; positive for catalase, indole production, arginine arylamidase, leucine arylamidase, tyrosine arylamidase and histidine arylamidase; and weakly positive for phenylalanine arylamidase and glycine arylamidase. The major cellular fatty acids (>10 %) of the isolate were determined to be C_16 : 0 and C_18 : 1 ω9c. Strain AGMB00490^T produced acetic acid as a major end product of metabolism. Accordingly, phylogenetic, physiologic and chemotaxonomic analyses revealed that strain AGMB00490^T represents a novel species for which the name Peptoniphilus faecalis sp. nov. is proposed. The type strain is AGMB00490^T (=KCTC 15944^T=NBRC 114159^T).

Factors related to the composition and diversity of wound microbiota investigated using culture-independent molecular methods: a scoping review

All open wounds are often colonized by commensal microbes as a loss of skin can provide a ready portal of entry for microorganisms. Although the wound microbiota is known to be associated with wound infection and with delayed healing, the factors related to the formations of wound microbiota contributing to such poor clinical outcomes are not clear and have not led to effective infection prevention interventions. This review aimed to scope the factors related to the composition and diversity of wound microbiota that have been investigated using culture-independent molecular methods. Original articles on wound microbiota published from January 1986 to February 2020 were included in this review. Thirty-one articles met the inclusion criteria and were grouped according to wound types: chronic, acute, and animal model wounds. The factors identified were categorized according to patient characteristics, wound characteristics, treatment, and sampling. Although some studies reported the effect size of the factors, the values were small. No studies elucidated the mechanism of wound microbiota formation. The results of this scoping review highlight that the factors associated with the diversity of wound microbiota are poorly understood and that further studies are needed.

Biofilm-Innate Immune Interface: Contribution to Chronic Wound Formation

Delayed wound healing can cause significant issues for immobile and ageing individuals as well as those living with co-morbid conditions such as diabetes, cardiovascular disease, and cancer. These delays increase a patient’s risk for infection and, in severe cases, can result in the formation of chronic, non-healing ulcers (e.g., diabetic foot ulcers, surgical site infections, pressure ulcers and venous leg ulcers). Chronic wounds are very difficult and expensive to treat and there is an urgent need to develop more effective therapeutics that restore healing processes. Sustained innate immune activation and inflammation are common features observed across most chronic wound types. However, the factors driving this activation remain incompletely understood. Emerging evidence suggests that the composition and structure of the wound microbiome may play a central role in driving this dysregulated activation but the cellular and molecular mechanisms underlying these processes require further investigation. In this review, we will discuss the current literature on: 1) how bacterial populations and biofilms contribute to chronic wound formation, 2) the role of bacteria and biofilms in driving dysfunctional innate immune responses in chronic wounds, and 3) therapeutics currently available (or underdevelopment) that target bacteria-innate immune interactions to improve healing. We will also discuss potential issues in studying the complexity of immune-biofilm interactions in chronic wounds and explore future areas of investigation for the field.

Patient genetics is linked to chronic wound microbiome composition and healing

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.