Psychosocial impact of recurrent urogenital infections: a review.

Recurrent urogenital infections such as bacterial vaginosis, vulvovaginal candidiasis, and urinary tract infections have a high prevalence and pronounced psychosocial impact. However, no review has compared the psychosocial impacts across infection types. This narrative review discusses the impact of common recurrent urogenital infections on psychosocial aspects, including quality of life, stress, mental health, sexual health, work productivity, race and ethnicity, and satisfaction of medical care. Validated questionnaires show that women with recurrent vulvovaginal candidiasis and urinary tract infections have decreased scores on all aspects of quality of life. Those with recurrent vulvovaginal candidiasis and urinary tract infections show lower mental health scores compared to the general population, with increased risk of anxiety and depression. Recurrent urogenital infections affect sexual relationships and intimacy, including avoidance due to symptoms or as a method of prevention. Recurrent infections also increase medical cost and negatively affect work productivity, leading to a combined estimated cost of over US$13 billion per year. There are clear effects of racial inequality involving minority populations that affect diagnosis, treatment, prevalence, and reporting of recurrent urogenital infections. Satisfactory medical treatment improves quality of life and mental health in those suffering from these conditions. Research evaluating psychosocial aspects of recurrent urogenital infections is variable and is not comparable across vulvovaginal conditions. Even so, psychosocial factors are important in understanding contribution and consequence of urogenital infections. Education, awareness, normalization, community support, and access to care can help to alleviate the negative implications of recurrent urogenital infections.

A narrative review discussing the psychosocial impact of common recurrent urogenital infections and highlights areas where further research is needed to improve clinical care.

Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets.

Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment.

Gardnerella is the primary pathogenic bacterial genus present in the polymicrobial condition known as bacterial vaginosis (BV). Despite BV's high prevalence and associated chronic and acute women's health impacts, the Gardnerella pangenome is largely uncharacterized at both the genetic and functional metabolic levels. Here, we used genome-scale metabolic models to characterize in silico the Gardnerella pangenome metabolic content. We also assessed the metabolic functional capacity in a BV-positive cervicovaginal fluid context. The metabolic capacity varied widely across the pangenome, with 38.15% of all reactions being core to the genus, compared to 49.60% of reactions identified as being unique to a smaller subset of species. We identified 57 essential genes across the pangenome via in silico gene essentiality screens within two simulated vaginal metabolic environments. Four genes, gpsA, fas, suhB, and psd, were identified as core essential genes critical for the metabolic function of all analyzed bacterial species of the Gardnerella genus. Further understanding these core essential metabolic functions could inform novel therapeutic strategies to treat BV. Machine learning applied to simulated metabolic network flux distributions showed limited clustering based on the sample isolation source, which further supports the presence of extensive core metabolic functionality across this genus. These data represent the first metabolic modeling of the Gardnerella pangenome and illustrate strain-specific interactions with the vaginal metabolic environment across the pangenome. IMPORTANCE Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women's health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. In this paper, we use metabolic models, which are a computational representation of the possible functional metabolism of an organism, to investigate metabolic conservation, gene essentiality, and pathway utilization across 110 Gardnerella strains. These models allow us to investigate in silico how strains may differ with respect to their metabolic interactions with the vaginal-host environment.

New perspectives into the vaginal microbiome with systems biology.

The vaginal microbiome (VMB) is critical to female reproductive health; however, the mechanisms associated with optimal and non-optimal states remain poorly understood due to the complex community structure and dynamic nature. Quantitative systems biology techniques applied to the VMB have improved understanding of community composition and function using primarily statistical methods. In contrast, fewer mechanistic models that use a priori knowledge of VMB features to develop predictive models have been implemented despite their use for microbiomes at other sites, including the gastrointestinal tract. Here, we explore systems biology approaches that have been applied in the VMB, highlighting successful techniques and discussing new directions that hold promise for improving understanding of health and disease.

Mechanistic models of microbial community metabolism.

Microbial communities affect many facets of human health and well-being. Naturally occurring bacteria, whether in nature or the human body, rarely exist in isolation. A deeper understanding of the metabolic functions of these communities is now possible with emerging computational models. In this review, we summarize frameworks for constructing mechanistic models of microbial community metabolism and discuss available algorithms for model analysis. We highlight essential decision points that greatly influence algorithm selection, as well as model analysis. Polymicrobial metabolic models can be utilized to gain insights into host-pathogen interactions, bacterial engineering, and many more translational applications.