Dermal injury drives a skin to gut axis that disrupts the intestinal microbiome and intestinal immune homeostasis in mice

The composition of the microbial community in the intestine may influence the functions of distant organs such as the brain, lung, and skin. These microbes can promote disease or have beneficial functions, leading to the hypothesis that microbes in the gut explain the co-occurrence of intestinal and skin diseases. Here, we show that the reverse can occur, and that skin directly alters the gut microbiome. Disruption of the dermis by skin wounding or the digestion of dermal hyaluronan results in increased expression in the colon of the host defense genes Reg3 and Muc2, and skin wounding changes the composition and behavior of intestinal bacteria. Enhanced expression Reg3 and Muc2 is induced in vitro by exposure to hyaluronan released by these skin interventions. The change in the colon microbiome after skin wounding is functionally important as these bacteria penetrate the intestinal epithelium and enhance colitis from dextran sodium sulfate (DSS) as seen by the ability to rescue skin associated DSS colitis with oral antibiotics, in germ-free mice, and fecal microbiome transplantation to unwounded mice from mice with skin wounds. These observations provide direct evidence of a skin-gut axis by demonstrating that damage to the skin disrupts homeostasis in intestinal host defense and alters the gut microbiome.

The changing metabolic landscape of bile acids - keys to metabolism and immune regulation

Bile acids regulate nutrient absorption and mitochondrial function, they establish and maintain gut microbial community composition and mediate inflammation, and they serve as signalling molecules that regulate appetite and energy homeostasis. The observation that there are hundreds of bile acids, especially many amidated bile acids, necessitates a revision of many of the classical descriptions of bile acids and bile acid enzyme functions. For example, bile salt hydrolases also have transferase activity. There are now hundreds of known modifications to bile acids and thousands of bile acid-associated genes, especially when including the microbiome, distributed throughout the human body (for example, there are >2,400 bile salt hydrolases alone). The fact that so much of our genetic and small-molecule repertoire, in both amount and diversity, is dedicated to bile acid function highlights the centrality of bile acids as key regulators of metabolism and immune homeostasis, which is, in large part, communicated via the gut microbiome.

Harnessing the power within: engineering the microbiome for enhanced gynecologic health

Graphical abstract

Abstract

Although numerous studies have demonstrated the impact of microbiome manipulation on human health, research on the microbiome's influence on female health remains relatively limited despite substantial disease burden. In light of this, we present a selected review of clinical trials and preclinical studies targeting both the vaginal and gut microbiomes for the prevention or treatment of various gynecologic conditions. Specifically, we explore studies that leverage microbiota transplants, probiotics, prebiotics, diet modifications, and engineered microbial strains. A healthy vaginal microbiome for females of reproductive age consists of lactic acid-producing bacteria predominantly of the Lactobacillus genus, which serves as a protective barrier against pathogens and maintains a balanced ecosystem. The gut microbiota's production of short-chain fatty acids, metabolism of primary bile acids, and modulation of sex steroid levels have significant implications for the interplay between host and microbes throughout the body, ultimately impacting reproductive health. By harnessing interventions that modulate both the vaginal and gut microbiomes, it becomes possible to not only maintain homeostasis but also mitigate pathological conditions. While the field is still working toward making broad clinical recommendations, the current studies demonstrate that manipulating the microbiome holds great potential for addressing diverse gynecologic conditions.

Lay summary

Manipulating the microbiome has recently entered popular culture, with various diets thought to aid the microbes that live within us. These microbes live in different locations of our body and accordingly help us digest food, modulate our immune system, and influence reproductive health. The role of the microbes living in and influencing the female reproductive tract remains understudied despite known roles in common conditions such as vulvovaginal candidiasis (affecting 75% of females in their lifetime), bacterial vaginosis (25% of females in their lifetime), cervical HPV infection (80% of females in their lifetime), endometriosis (6-10% of females of reproductive age), and polycystic ovary syndrome (10-12% of females of reproductive age). Here, we review four different approaches used to manipulate the female reproductive tract and gastrointestinal system microbiomes: microbiota transplants, probiotics, prebiotics, and dietary interventions, and the use of engineered microbial strains. In doing so, we aim to stimulate discussion on new ways to understand and treat female reproductive health conditions.

Robustness of cancer microbiome signals over a broad range of methodological variation

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes

BACKGROUND

Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly the role of gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate gut microbiota and circulating metabolites associated with dietary fiber intake and their relationships with type 2 diabetes (T2D).

METHODS

This study included up to 11 394 participants from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Diet was assessed with two 24-hour dietary recalls at baseline. We examined associations of dietary fiber intake with gut microbiome measured by shotgun metagenomics (350 species/85 genera and 1958 enzymes; n=2992 at visit 2), serum metabolome measured by untargeted metabolomics (624 metabolites; n=6198 at baseline), and associations between fiber-related gut bacteria and metabolites (n=804 at visit 2). We examined prospective associations of serum microbial-associated metabolites (n=3579 at baseline) with incident T2D over 6 years.

RESULTS

We identified multiple bacterial genera, species, and related enzymes associated with fiber intake. Several bacteria (eg, Butyrivibrio, Faecalibacterium) and enzymes involved in fiber degradation (eg, xylanase EC3.2.1.156) were positively associated with fiber intake, inversely associated with prevalent T2D, and favorably associated with T2D-related metabolic traits. We identified 159 metabolites associated with fiber intake, 47 of which were associated with incident T2D. We identified 18 of these 47 metabolites associated with the identified fiber-related bacteria, including several microbial metabolites (eg, indolepropionate and 3-phenylpropionate) inversely associated with the risk of T2D. Both Butyrivibrio and Faecalibacterium were associated with these favorable metabolites. The associations of fiber-related bacteria, especially Faecalibacterium and Butyrivibrio, with T2D were attenuated after further adjustment for these microbial metabolites.

CONCLUSIONS

Among United States Hispanics/Latinos, dietary fiber intake was associated with favorable profiles of gut microbiota and circulating metabolites for T2D. These findings advance our understanding of the role of gut microbiota and microbial metabolites in the relationship between diet and T2D.

Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Peripheral neuronal activation shapes the microbiome and alters gut physiology

The gastrointestinal (GI) tract is innervated by intrinsic neurons of the enteric nervous system (ENS) and extrinsic neurons of the central nervous system and peripheral ganglia. The GI tract also harbors a diverse microbiome, but interactions between the ENS and the microbiome remain poorly understood. Here, we activate choline acetyltransferase (ChAT)-expressing or tyrosine hydroxylase (TH)-expressing gut-associated neurons in mice to determine effects on intestinal microbial communities and their metabolites as well as on host physiology. The resulting multi-omics datasets support broad roles for discrete peripheral neuronal subtypes in shaping microbiome structure, including modulating bile acid profiles and fungal colonization. Physiologically, activation of either ChAT+ or TH+ neurons increases fecal output, while only ChAT+ activation results in increased colonic contractility and diarrhea-like fluid secretion. These findings suggest that specific subsets of peripherally activated neurons differentially regulate the gut microbiome and GI physiology in mice without involvement of signals from the brain.

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Transforming Big Data into AI-ready data for nutrition and obesity research

OBJECTIVE

Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.

METHODS

We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed.

RESULTS

Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.

CONCLUSIONS

Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Tryptophan metabolism, gut microbiota, and carotid artery plaque in women with and without HIV infection

OBJECTIVE

The perturbation of tryptophan (TRP) metabolism has been linked with HIV infection and cardiovascular disease (CVD), but the interrelationship among TRP metabolites, gut microbiota, and atherosclerosis remain unclear in the context of HIV infection.

METHODS

We included 361 women (241 HIV+, 120 HIV-) with carotid artery plaque assessments from the Women's Interagency HIV Study, measured 10 plasma TRP metabolites and profiled fecal gut microbiome. TRP metabolite-related gut bacteria were selected through the Analysis of Compositions of Microbiomes with Bias Correction method. Associations of TRP metabolites and related microbial features with plaque were examined using multivariable logistic regression.

RESULTS

Although plasma kynurenic acid (KYNA) [odds ratio (OR) = 1.93, 95% confidence interval (CI): 1.12-3.32 per one SD increase; P  = 0.02) and KYNA/TRP [OR = 1.83 (95% CI 1.08-3.09), P  = 0.02] were positively associated with plaque, indole-3-propionate (IPA) [OR = 0.62 (95% CI 0.40-0.98), P  = 0.03] and IPA/KYNA [OR = 0.51 (95% CI 0.33-0.80), P  < 0.01] were inversely associated with plaque. Five gut bacterial genera and many affiliated species were positively associated with IPA (FDR-q < 0.25), including Roseburia spp ., Eubacterium spp., Lachnospira spp., and Coprobacter spp.; but no bacterial genera were found to be associated with KYNA. Furthermore, an IPA-associated-bacteria score was inversely associated with plaque [OR = 0.47 (95% CI 0.28-0.79), P  < 0.01]. But no significant effect modification by HIV serostatus was observed in these associations.

CONCLUSION

In a cohort of women living with and without HIV infection, plasma IPA levels and related gut bacteria were inversely associated with carotid artery plaque, suggesting a potential beneficial role of IPA and its gut bacterial producers in atherosclerosis and CVD.

Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort

BACKGROUND

Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated.

METHODS

We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting.

RESULTS

Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (β=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (β=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates.

CONCLUSIONS

Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Reverse metabolomics for the discovery of chemical structures from humans

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

Evaluation of the safety and efficacy of fecal microbiota transplantations in bottlenose dolphins (Tursiops truncatus) using metagenomic sequencing

AIMS

Gastrointestinal disease is a leading cause of morbidity in bottlenose dolphins (Tursiops truncatus) under managed care. Fecal microbiota transplantation (FMT) holds promise as a therapeutic tool to restore gut microbiota without antibiotic use. This prospective clinical study aimed to develop a screening protocol for FMT donors to ensure safety, determine an effective FMT administration protocol for managed dolphins, and evaluate the efficacy of FMTs in four recipient dolphins.

METHODS AND RESULTS

Comprehensive health monitoring was performed on donor and recipient dolphins. Fecal samples were collected before, during, and after FMT therapy. Screening of donor and recipient fecal samples was accomplished by in-house and reference lab diagnostic tests. Shotgun metagenomics was used for sequencing. Following FMT treatment, all four recipient communities experienced engraftment of novel microbial species from donor communities. Engraftment coincided with resolution of clinical signs and a sustained increase in alpha diversity.

CONCLUSION

The donor screening protocol proved to be safe in this study and no adverse effects were observed in four recipient dolphins. Treatment coincided with improvement in clinical signs.

Sex Hormones, the Stool Microbiome, and Subclinical Atherosclerosis in Women With and Without HIV

CONTEXT

Cardioprotective roles of endogenous estrogens may be particularly important in women with HIV, who have reduced estrogen exposure and elevated cardiovascular disease risk. The gut microbiome metabolically interacts with sex hormones, but little is known regarding possible impact on cardiovascular risk.

OBJECTIVE

To analyze potential interplay of sex hormones and gut microbiome in cardiovascular risk.

METHODS

Among 197 postmenopausal women in the Women's Interagency HIV Study, we measured 15 sex hormones in serum and assessed the gut microbiome in stool. Presence of carotid artery plaque was determined (B-mode ultrasound) in a subset (n = 134). We examined associations of (i) sex hormones and stool microbiome, (ii) sex hormones and plaque, and (iii) sex hormone-related stool microbiota and plaque, adjusting for potential confounders.

RESULTS

Participant median age was 58 years and the majority were living with HIV (81%). Sex hormones (estrogens, androgens, and adrenal precursors) were associated with stool microbiome diversity and specific species, similarly in women with and without HIV. Estrogens were associated with higher diversity, higher abundance of species from Alistipes, Collinsella, Erysipelotrichia, and Clostridia, and higher abundance of microbial β-glucuronidase and aryl-sulfatase orthologs, which are involved in hormone metabolism. Several hormones were associated with lower odds of carotid artery plaque, including dihydrotestosterone, 3α-diol-17G, estradiol, and estrone. Exploratory mediation analysis suggested that estrone-related species, particularly from Collinsella, may mediate the protective association of estrone with plaque.

CONCLUSION

Serum sex hormones are significant predictors of stool microbiome diversity and composition. The gut microbiome may play a role in estrogen-related cardiovascular protection.

Integrated Genomic and Social Network Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in the Healthcare Setting

BACKGROUND

Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.

METHODS

We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.

RESULTS

Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).

CONCLUSIONS

IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

A multi-study analysis enables identification of potential microbial features associated with skin aging signs

Introduction: During adulthood, the skin microbiota can be relatively stable if environmental conditions are also stable, yet physiological changes of the skin with age may affect the skin microbiome and its function. The microbiome is an important factor to consider in aging since it constitutes most of the genes that are expressed on the human body. However, severity of specific aging signs (one of the parameters used to measure "apparent" age) and skin surface quality (e.g., texture, hydration, pH, sebum, etc.) may not be indicative of chronological age. For example, older individuals can have young looking skin (young apparent age) and young individuals can be of older apparent age. Methods: Here we aim to identify microbial taxa of interest associated to skin quality/aging signs using a multi-study analysis of 13 microbiome datasets consisting of 16S rRNA amplicon sequence data and paired skin clinical data from the face. Results: We show that there is a negative relationship between microbiome diversity and transepidermal water loss, and a positive association between microbiome diversity and age. Aligned with a tight link between age and wrinkles, we report a global positive association between microbiome diversity and Crow's feet wrinkles, but with this relationship varying significantly by sub-study. Finally, we identify taxa potentially associated with wrinkles, TEWL and corneometer measures. Discussion: These findings represent a key step towards understanding the implication of the skin microbiota in skin aging signs.

Effects of variation in sample storage conditions and swab order on 16S vaginal microbiome analyses

IMPORTANCE

The composition of the human vaginal microbiome has been linked to a variety of medical conditions including yeast infection, bacterial vaginosis, and sexually transmitted infection. The vaginal microbiome is becoming increasingly acknowledged as a key factor in personal health, and it is essential to establish methods to collect and process accurate samples with self-collection techniques to allow large, population-based studies. In this study, we investigate if using AssayAssure Genelock, a nucleic acid preservative, introduces microbial biases in self-collected vaginal samples. To our knowledge, we also contribute some of the first evidence regarding the impacts of multiple swabs taken at one time point. Vaginal samples have relatively low biomass, so the ability to collect multiple swabs from a unique participant at a single time would greatly improve the replicability and data available for future studies. This will hopefully lay the groundwork to gain a more complete and accurate understanding of the vaginal microbiome.

Variant of the lactase LCT gene explains association between milk intake and incident type 2 diabetes

Cow's milk is frequently included in the human diet, but the relationship between milk intake and type 2 diabetes (T2D) remains controversial. Here, using data from the Hispanic Community Health Study/Study of Latinos, we show that in both sexes, higher milk intake is associated with lower risk of T2D in lactase non-persistent (LNP) individuals (determined by a variant of the lactase LCT gene, single nucleotide polymorphism rs4988235 ) but not in lactase persistent individuals. We validate this finding in the UK Biobank. Further analyses reveal that among LNP individuals, higher milk intake is associated with alterations in gut microbiota (for example, enriched Bifidobacterium and reduced Prevotella) and circulating metabolites (for example, increased indolepropionate and reduced branched-chain amino acid metabolites). Many of these metabolites are related to the identified milk-associated bacteria and partially mediate the association between milk intake and T2D in LNP individuals. Our study demonstrates a protective association between milk intake and T2D among LNP individuals and a potential involvement of gut microbiota and blood metabolites in this association.

Pangenome comparison of Bacteroides fragilis genomospecies unveil genetic diversity and ecological insights

Bacteroides fragilis is a Gram-negative commensal bacterium commonly found in the human colon that differentiates into two genomospecies termed division I and II. We leverage a comprehensive collection of 694 B. fragilis whole genome sequences and report differential gene abundance to further support the recent proposal that divisions I and II represent separate species. In division I strains, we identify an increased abundance of genes related to complex carbohydrate degradation, colonization, and host niche occupancy, confirming the role of division I strains as gut commensals. In contrast, division II strains display an increased prevalence of plant cell wall degradation genes and exhibit a distinct geographic distribution, primarily originating from Asian countries, suggesting dietary influences. Notably, division II strains have an increased abundance of genes linked to virulence, survival in toxic conditions, and antimicrobial resistance, consistent with a higher incidence of these strains in bloodstream infections. This study provides new evidence supporting a recent proposal for classifying divisions I and II B. fragilis strains as distinct species, and our comparative genomic analysis reveals their niche-specific roles.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Jumbo phages are active against extensively drug-resistant eyedrop-associated Pseudomonas aeruginosa infections

Antibiotic-resistant bacteria present an emerging challenge to human health. Their prevalence has been increasing across the globe due in part to the liberal use of antibiotics that has pressured them to develop resistance. Those bacteria that acquire mobile genetic elements are especially concerning because those plasmids may be shared readily with other microbes that can then also become antibiotic resistant. Serious infections have recently been related to the contamination of preservative-free eyedrops with extensively drug-resistant (XDR) isolates of Pseudomonas aeruginosa, already resulting in three deaths. These drug-resistant isolates cannot be managed with most conventional antibiotics. We sought to identify alternatives to conventional antibiotics for the lysis of these XDR isolates and identified multiple bacteriophages (viruses that attack bacteria) that killed them efficiently. We found both jumbo phages (>200 kb in genome size) and non-jumbo phages that were active against these isolates, the former killing more efficiently. Jumbo phages effectively killed the three separate XDR P. aeruginosa isolates both on solid and liquid medium. Given the ongoing nature of the XDR P. aeruginosa eyedrop outbreak, the identification of phages active against them provides physicians with several novel potential alternatives for treatment.

Diagnostic accuracy of diffusion-weighted imaging in variant Creutzfeldt-Jakob disease

PURPOSE

This study sought to investigate the diagnostic sensitivity of diffusion-weighted imaging (DWI) in variant Creutzfeldt-Jakob disease (vCJD), a prion disease with significant public health implications on account of its transmissibility. The importance of this research stemmed from the first neuropathologically confirmed vCJD case in a PRNP heterozygous individual in 2016, which displayed DWI features typical of sporadic CJD (sCJD). The case was classified as 'probable' sCJD in life, predominantly based on these imaging findings. While DWI has proven valuable in diagnosing sCJD, its utility in vCJD diagnosis remains unclear.

METHODS

DWI and Fluid-attenuated inversion recovery (FLAIR) images from probable and definite vCJD cases referred to the National CJD Research and Surveillance Unit (NCJDRSU) were independently analysed by an expert neuroradiologist. Scans were reviewed within a mixed cohort of CJD cases including definite sCJD and non-CJD controls.

RESULTS

FLAIR sequences demonstrated greater sensitivity in identifying the pulvinar sign in vCJD compared to DWI (73% vs 41%, p-value <0.001). Basal ganglia hyperintensities were more prevalent in DWI (84%) than FLAIR (64%), and cortical hyperintensities were exclusive to DWI (24%). The pulvinar sign showed a specificity of 98% for vCJD and was rare in sCJD.

CONCLUSION

DWI showed reduced sensitivity compared to FLAIR imaging in detecting the pulvinar sign in vCJD. Conversely, DWI can more distinctively identify basal ganglia and cortical hyperintensities, thus leading to imaging patterns more characteristic of sCJD. Therefore, DWI should be cautiously interpreted in vCJD diagnosis, with axial FLAIR potentially providing a more precise evaluation of the pulvinar sign.

A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models

During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.

Effects of a ketogenic and low-fat diet on the human metabolome, microbiome, and foodome in adults at risk for Alzheimer's disease

INTRODUCTION

The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimer's disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD-mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low-fat-diet intervention.

METHODS

Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low-fat American Heart Association diet or high-fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6-week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals' microbiome and metabolome throughout the dietary interventions.

RESULTS

Participants with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila. MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase-containing microbes and an altered bile acid pool, suggesting reduced gut motility.

DISCUSSION

Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut-transit time.

Microbiome-based risk prediction in incident heart failure: a community challenge

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

Association of Subjective and Objective Measures of Sleep With Gut Microbiota Composition and Diversity in Older Men: The Osteoporotic Fractures in Men Study

BACKGROUND

Growing evidence suggests bidirectional links between gut microbiota and sleep quality as shared contributors to health. Little is known about the relationship between microbiota and sleep among older persons.

METHODS

We used 16S rRNA sequencing to characterize stool microbiota among men (n = 606, mean [standard deviation] age = 83.9 [3.8]) enrolled in the Osteoporotic Fractures in Men (MrOS) study from 2014 to 2016. Sleep was assessed concurrently by a questionnaire (Pittsburgh Sleep Quality index [PSQI]), and activity monitor to examine timing (acrophase) and regularity of patterns (F-statistic). Alpha diversity was measured using Faith's phylogenetic diversity (PD). Beta diversity was calculated with robust Aitchison distance with matrix completion (RPCA) and phylogenetic-RPCA (PRPCA). Their association with sleep variables was tested with partial distance-based redundancy analysis (dbRDA). Predictive-ratio biomarkers associated with sleep measurements were identified with CoDaCoRe.

RESULTS

In unadjusted analyses, men with poor sleep (PSQI >5) tended to have lower alpha diversity compared to men with normal sleep (Faith's PD, beta = -0.15; 95% confidence interval [CI]: -0.30 to 0.01, p = .06). Sleep regularity was significantly associated with RPCA and PRPCA, even after adjusting for site, batch, age, ethnicity, body mass index, diabetes, antidepressant and sleep medication use, and health behaviors (RPCA/PRPCA dbRDA; p = .033/.002). In taxonomic analysis, ratios of 7:6 bacteria for better regularity (p = .0004) and 4:7 for worse self-reported sleep (p = .005) were differentially abundant: some butyrate-producing bacteria were associated with better sleep characteristics.

CONCLUSIONS

Subjective and objective indicators of sleep quality suggest that older men with better sleep patterns are more likely to harbor butyrate-producing bacteria associated with better health.

Predictors and moderators of the response of adults with intellectual disabilities and depression to behavioural activation and guided self-help therapies

BACKGROUND

No previous studies have reported predictors and moderators of outcome of psychological therapies for depression experienced by adults with intellectual disabilities (IDs). We investigated baseline variables as outcome predictors and moderators based on a randomised controlled trial where behavioural activation was compared with guided self-help.

METHODS

This study was an exploratory secondary data analysis of data collected during a randomised clinical trial. Participants (n = 161) were randomised to behavioural activation or guided self-help and followed up for 12 months. Pre-treatment variables were included if they have previously been shown to be associated with an increased risk of having depression in adults with IDs or have been reported as a potential predictor or moderator of outcome of treatment for depression with psychological therapies. The primary outcome measure, the Glasgow Depression Scale for Adults with Learning Disabilities (GDS-LD), was used as the dependant variable in mixed effects regression analyses testing for predictors and moderators of outcome, with baseline GDS-LD, treatment group, study centre and antidepressant use as fixed effects, and therapist as a random effect.

RESULTS

Higher baseline anxiety (mean difference in outcome associated with a 1 point increase in anxiety 0.164, 95% confidence interval [CI] 0.031, 0.297; P = 0.016), lower performance intelligence quotient (IQ) (mean difference in outcome associated with a 1 point increase in IQ 0.145, 95% CI 0.009, 0.280; P = 0.037) and hearing impairment (mean difference 3.449, 95% CI 0.466, 6.432; P = 0.024) were predictors of poorer outcomes, whilst greater severity of depressive symptoms at baseline (mean difference in outcome associated with 1 point increase in depression -0.160, 95% CI -0.806, -0.414; P < 0.001), higher expectation of change (mean difference in outcome associated with a 1 point increase in expectation of change -1.013, 95% CI -1.711, -0.314; p 0.005) and greater percentage of therapy sessions attended (mean difference in outcome with 1 point increase in percentage of sessions attended -0.058, 95% CI -0.099, -0.016; P = 0.007) were predictors of more positive outcomes for treatment after adjusting for randomised group allocation. The final model included severity of depressive and anxiety symptoms, lower WASI performance IQ subscale, hearing impairment, higher expectation of change and percentage of therapy sessions attended and explained 35.3% of the variance in the total GDS-LD score at 12 months (R2  = 0.353, F4, 128  = 17.24, P < 0.001). There is no evidence that baseline variables had a moderating effect on outcome for treatment with behavioural activation or guided self-help.

CONCLUSIONS

Our results suggest that baseline variables may be useful predictors of outcomes of psychological therapies for adults with IDs. Further research is required to examine the value of these potential predictors. However, our findings suggest that therapists consider how baseline variables may enable them to tailor their therapeutic approach when using psychological therapies to treat depression experienced by adults with IDs.

Oral fungal profiling and risk of nasopharyngeal carcinoma: a population-based case-control study

BACKGROUND

Dysbiosis of the oral mycobiome has been linked to some diseases, including cancers. However, the role of oral fungal communities in nasopharyngeal carcinoma (NPC) carcinogenesis has not previously been investigated.

METHODS

We characterized the oral salivary fungal mycobiome in 476 untreated incident NPC patients and 537 population-based controls using fungal internal transcribed spacer (ITS)-2 sequencing. The relationship between oral fungal mycobiome and the risk of NPC was assessed through bioinformatic and biostatistical analyses.

FINDINGS

We found that lower fungal alpha diversity was associated with an increased odds of NPC [lower vs. higher: observed features (adjusted odds ratio [OR] = 5.81, 95% confidence interval [CI] = 3.60-9.38); Simpson diversity (1.53, 1.03-2.29); Shannon diversity (2.03, 1.35-3.04)]. We also observed a significant difference in global fungal community patterns between cases and controls based on Bray-Curtis dissimilarity (P < 0.001). Carriage of oral fungal species, specifically, Saccharomyces cerevisiae, Candida tropicalis, Lodderomyces elongisporus, Candida albicans, and Fusarium poae, was associated with significantly higher odds of NPC, with ORs ranging from 1.56 to 4.66. Individuals with both low fungal and low bacterial alpha diversity had a profoundly elevated risk of NPC.

INTERPRETATION

Our results suggest that dysbiosis in the oral mycobiome, characterized by a loss of fungal community diversity and overgrowth of several fungal organisms, is associated with a substantially increased risk of NPC.

FUNDING

This work was funded by the US National Institutes of Health, the Swedish Research Council, the High-level Talents Research Start-up Project of Fujian Medical University, and the China Scholarship Council.

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

Whole-body modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilized whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalized models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution

Next-generation sequencing technologies have enabled many advances across diverse areas of biology, with many benefiting from increased sample size. Although the cost of running next-generation sequencing instruments has dropped substantially over time, the cost of sample preparation methods has lagged behind. To counter this, researchers have adapted library miniaturization protocols and large sample pools to maximize the number of samples that can be prepared by a certain amount of reagents and sequenced in a single run. However, due to high variability of sample quality, over and underrepresentation of samples in a sequencing run has become a major issue in high-throughput sequencing. This leads to misinterpretation of results due to increased noise, and additional time and cost rerunning underrepresented samples. To overcome this problem, we present a normalization method that uses shallow iSeq sequencing to accurately inform pooling volumes based on read distribution. This method is superior to the widely used fluorometry methods, which cannot specifically target adapter-ligated molecules that contribute to sequencing output. Our normalization method not only quantifies adapter-ligated molecules but also allows normalization of feature space; for example, we can normalize to reads of interest such as non-ribosomal reads. As a result, this normalization method improves the efficiency of high-throughput next-generation sequencing by reducing noise and producing higher average reads per sample with more even sequencing depth. IMPORTANCE High-throughput next generation sequencing (NGS) has significantly contributed to the field of genomics; however, further improvements can maximize the potential of this important tool. Uneven sequencing of samples in a multiplexed run is a common issue that leads to unexpected extra costs or low-quality data. To mitigate this problem, we introduce a normalization method based on read counts rather than library concentration. This method allows for an even distribution of features of interest across samples, improving the statistical power of data sets and preventing the financial loss associated with resequencing libraries. This method optimizes NGS, which already has huge importance across many areas of biology.

Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

Associations of Circulating Estrogens and Estrogen Metabolites with Fecal and Oral Microbiome in Postmenopausal Women in the Ghana Breast Health Study

The human fecal and oral microbiome may play a role in the etiology of breast cancer through modulation of endogenous estrogen metabolism. This study aimed to investigate associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. A total of 117 women with fecal (N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing, and estrogens and estrogen metabolites data measured by liquid chromatography tandem mass spectrometry were included. The outcomes were measures of the microbiome and the independent variables were the estrogens and estrogen metabolites. Estrogens and estrogen metabolites were associated with the fecal microbial Shannon index (global P < 0.01). In particular, higher levels of estrone (β = 0.36, P = 0.03), 2-hydroxyestradiol (β = 0.30, P = 0.02), 4-methoxyestrone (β = 0.51, P = 0.01), and estriol (β = 0.36, P = 0.04) were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone (β = -0.57, P < 0.01) was inversely associated with the Shannon index as indicated by linear regression. Conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac as indicated by MiRKAT (P < 0.01) and PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other estrogens or estrogen metabolites were associated with any other beta diversity measures. The presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, were associated with several estrogens and estrogen metabolites as indicated by zero-inflated negative binomial regression. Overall, we found several associations of specific estrogens and estrogen metabolites and the fecal and oral microbiome. IMPORTANCE Several epidemiologic studies have found associations of urinary estrogens and estrogen metabolites with the fecal microbiome. However, urinary estrogen concentrations are not strongly correlated with serum estrogens, a known risk factor for breast cancer. To better understand whether the human fecal and oral microbiome were associated with breast cancer risk via the regulation of estrogen metabolism, we conducted this study to investigate the associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. We found several associations of parent estrogens and several estrogen metabolites with the microbial communities, and multiple individual associations of estrogens and estrogen metabolites with the presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen metabolizing properties. Future large, longitudinal studies to investigate the dynamic changes of the fecal and oral microbiome and estrogen relationship are needed.

Stem cells and extracellular vesicles to improve preclinical orofacial soft tissue healing

Orofacial soft tissue wounds caused by surgery for congenital defects, trauma, or disease frequently occur leading to complications affecting patients' quality of life. Scarring and fibrosis prevent proper skin, mucosa and muscle regeneration during wound repair. This may hamper maxillofacial growth and speech development. To promote the regeneration of injured orofacial soft tissue and attenuate scarring and fibrosis, intraoral and extraoral stem cells have been studied for their properties of facilitating maintenance and repair processes. In addition, the administration of stem cell-derived extracellular vesicles (EVs) may prevent fibrosis and promote the regeneration of orofacial soft tissues. Applying stem cells and EVs to treat orofacial defects forms a challenging but promising strategy to optimize treatment. This review provides an overview of the putative pitfalls, promises and the future of stem cells and EV therapy, focused on orofacial soft tissue regeneration.

Engineered bacteria detect tumor DNA

Synthetic biology has developed sophisticated cellular biosensors to detect and respond to human disease. However, biosensors have not yet been engineered to detect specific extracellular DNA sequences and mutations. Here, we engineered naturally competent Acinetobacter baylyi to detect donor DNA from the genomes of colorectal cancer (CRC) cells, organoids, and tumors. We characterized the functionality of the biosensors in vitro with coculture assays and then validated them in vivo with sensor bacteria delivered to mice harboring colorectal tumors. We observed horizontal gene transfer from the tumor to the sensor bacteria in our mouse model of CRC. This cellular assay for targeted, CRISPR-discriminated horizontal gene transfer (CATCH) enables the biodetection of specific cell-free DNA.

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data

MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms' role in ecology and human health.

Generation of accurate, expandable phylogenomic trees with uDance

Phylogenetic trees provide a framework for organizing evolutionary histories across the tree of life and aid downstream comparative analyses such as metagenomic identification. Methods that rely on single-marker genes such as 16S rRNA have produced trees of limited accuracy with hundreds of thousands of organisms, whereas methods that use genome-wide data are not scalable to large numbers of genomes. We introduce updating trees using divide-and-conquer (uDance), a method that enables updatable genome-wide inference using a divide-and-conquer strategy that refines different parts of the tree independently and can build off of existing trees, with high accuracy and scalability. With uDance, we infer a species tree of roughly 200,000 genomes using 387 marker genes, totaling 42.5 billion amino acid residues.

Greengenes2 unifies microbial data in a single reference tree

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

Dietary weight loss-induced improvements in metabolic function are enhanced by exercise in people with obesity and prediabetes

The additional therapeutic effects of regular exercise during a dietary weight loss program in people with obesity and prediabetes are unclear. Here, we show that whole-body (primarily muscle) insulin sensitivity (primary outcome) was 2-fold greater (P = 0.006) after 10% weight loss induced by calorie restriction plus exercise training (Diet+EX; n = 8, 6 women) than 10% weight loss induced by calorie restriction alone (Diet-ONLY; n = 8, 4 women) in participants in two concurrent studies. The greater improvement in insulin sensitivity was accompanied by increased muscle expression of genes involved in mitochondrial biogenesis, energy metabolism and angiogenesis (secondary outcomes) in the Diet+EX group. There were no differences between groups in plasma branched-chain amino acids or markers of inflammation, and both interventions caused similar changes in the gut microbiome. Few adverse events were reported. These results demonstrate that regular exercise during a diet-induced weight loss program has profound additional metabolic benefits in people with obesity and prediabetes.Trial Registration: ClinicalTrials.gov (NCT02706262 and NCT02706288).

Diet Quality and the Fecal Microbiota in Adults in the American Gut Project

BACKGROUND

The Dietary Guidelines for Americans advises on dietary intake to meet nutritional needs, promote health, and prevent diseases. Diet affects the intestinal microbiota and is increasingly linked to health. It is vital to investigate the relationships between diet quality and the microbiota to better understand the impact of nutrition on human health.

OBJECTIVES

This study aimed to investigate the differences in fecal microbiota composition in adults from the American Gut Project based on their adherence to the Dietary Guidelines for Americans.

METHODS

This study was a cross-sectional analysis of the 16S sequencing and food frequency data of a subset of adults (n = 432; age = 18-60 y; 65% female, 89% white) participating in the crowdsourced American Gut Project. The Healthy Eating Index-2015 assessed the compliance with Dietary Guideline recommendations. The cohort was divided into tertiles based on Healthy Eating Index-2015 scores, and differences in taxonomic abundances and diversity were compared between high and low scorers.

RESULTS

The mean Total Score for low-scoring adults (58.1 ± 5.4) was comparable with the reported score of the average American adult (56.7). High scorers for the Total Score and components related to vegetables, grains, and dairy had greater alpha diversity than low scorers. High scorers in the fatty acid component had a lower alpha diversity than low scorers (95% CI: 0.35, 1.85). A positive log-fold difference in abundance of plant carbohydrate-metabolizing taxa in the families Lachnospiraceae and Ruminococcaceae was observed in high-scoring tertiles for Total Score, vegetable, fruit, and grain components (Benjamini-Hochberg; q < 0.05).

CONCLUSIONS

Adults with greater compliance to the Dietary Guidelines demonstrated higher diversity in their fecal microbiota and greater abundance of bacteria capable of metabolizing complex carbohydrates, providing evidence on how Dietary Guidelines support the gut microbiota.