Differences in the gut microbiome of young adults with schizophrenia spectrum disorder: using machine learning to distinguish cases from controls.

While an association between the gut microbiome and schizophrenia spectrum disorders (SSD) has been suggested, the existing evidence is still inconclusive. To this end, we analyzed bacteria and bacterial genes in feces from 52 young adult SSD patients and 52 controls using fecal shotgun metagenomic sequencing. Compared to controls, young SSD patients were found to have significantly lower α-diversity and different ß-diversity both regarding bacterial species (i.e., taxonomic diversity) and bacterial genes (i.e., functional diversity). Furthermore, the α-diversity measures 'Pielou's evenness' and 'Shannon' were significantly higher for both bacterial species, bacterial genes encoding enzymes and gut brain modules in young SSD patients on antipsychotic treatment (young SSD not on antipsychotics=9 patients, young SSD on antipsychotics=43 patients). We also applied machine learning classifiers to distinguish between young SSD patients and healthy controls based on their gut microbiome. Results showed that taxonomic and functional data classified young SSD individuals with an accuracy of ≥ 70% and with an area under the receiver operating characteristic curve (AUROC) of ≥ 0.75. Differential abundance analysis on the most important features in the classifier models revealed that most of the species with higher abundance in young SSD patients had their natural habitat in the oral cavity. In addition, many of the modules with higher abundance in young SSD patients were amino acid biosynthesis modules. Moreover, the abundances of gut-brain modules of butyrate synthesis and acetate degradation were lower in the SSD patients compared to controls. Collectively, our findings continue to support the presence of gut microbiome alterations in SSD and provide support for the use of machine learning algorithms to distinguish patients from controls based on gut microbiome profiles.

Bacterial gut microbiome differences in adults with ADHD and in children with ADHD on psychostimulant medication.

Recent evidence suggests that there is a link between neurodevelopmental disorders, such as attention-deficit hyperactivity disorder (ADHD), and the gut microbiome. However, most studies to date have had low sample sizes, have not investigated the impact of psychostimulant medication, and have not adjusted for potential confounders, including body mass index, stool consistency and diet. To this end, we conducted the largest, to our knowledge, fecal shotgun metagenomic sequencing study in ADHD, with 147 well-characterized adult and child patients. For a subset of individuals, plasma levels of inflammatory markers and short-chain fatty acids were also measured. In adult ADHD patients (n = 84), compared to controls (n = 52), we found a significant difference in beta diversity both regarding bacterial strains (taxonomic) and bacterial genes (functional). In children with ADHD (n = 63), we found that those on psychostimulant medication (n = 33 on medication vs. n = 30 not on medication) had (i) significantly different taxonomic beta diversity, (ii) lower functional and taxonomic evenness, (iii) lower abundance of the strain Bacteroides stercoris CL09T03C01 and bacterial genes encoding an enzyme in vitamin B12 synthesis, and (iv) higher plasma levels of vascular inflammatory markers sICAM-1 and sVCAM-1. Our study continues to support a role for the gut microbiome in neurodevelopmental disorders and provides additional insights into the effects of psychostimulant medication. However, additional studies are needed to replicate these findings and examine causal relationships with the disorder.

Discovery of Species-unique Peptide Biomarkers of Bacterial Pathogens by Tandem Mass Spectrometry-based Proteotyping.

Mass spectrometry (MS) and proteomics offer comprehensive characterization and identification of microorganisms and discovery of protein biomarkers that are applicable for diagnostics of infectious diseases. The use of biomarkers for diagnostics is widely applied in the clinic and the use of peptide biomarkers is increasingly being investigated for applications in the clinical laboratory. Respiratory-tract infections are a predominant cause for medical treatment, although, clinical assessments and standard clinical laboratory protocols are time-consuming and often inadequate for reliable diagnoses. Novel methods, preferably applied directly to clinical samples, excluding cultivation steps, are needed to improve diagnostics of infectious diseases, provide adequate treatment and reduce the use of antibiotics and associated development of antibiotic resistance. This study applied nano-liquid chromatography (LC) coupled with tandem MS, with a bioinformatics pipeline and an in-house database of curated high-quality reference genome sequences to identify species-unique peptides as potential biomarkers for four bacterial pathogens commonly found in respiratory tract infections (RTIs): Staphylococcus aureus; Moraxella catarrhalis; Haemophilus influenzae and Streptococcus pneumoniae The species-unique peptides were initially identified in pure cultures of bacterial reference strains, reflecting the genomic variation in the four species and, furthermore, in clinical respiratory tract samples, without prior cultivation, elucidating proteins expressed in clinical conditions of infection. For each of the four bacterial pathogens, the peptide biomarker candidates most predominantly found in clinical samples, are presented. Data are available via ProteomeXchange with identifier PXD014522. As proof-of-principle, the most promising species-unique peptides were applied in targeted tandem MS-analyses of clinical samples and their relevance for identifications of the pathogens, i.e. proteotyping, was validated, thus demonstrating their potential as peptide biomarker candidates for diagnostics of infectious diseases.

High Amounts of SARS-CoV-2 Precede Sickness Among Asymptomatic Health Care Workers.

BACKGROUND: Whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity among asymptomatic subjects reflects past or future disease may be difficult to ascertain. METHODS: We tested 9449 employees at Karolinska University Hospital, Stockholm, Sweden for SARS-CoV-2 RNA and antibodies, linked the results to sick leave records, and determined associations with past or future sick leave using multinomial logistic regression. RESULTS: Subjects with high amounts of SARS-CoV-2 virus, indicated by polymerase chain reaction (PCR) cycle threshold (Ct) value, had the highest risk for sick leave in the 2 weeks after testing (odds ratio [OR], 11.97; 95% confidence interval [CI], 6.29-22.80) whereas subjects with low amounts of virus had the highest risk for sick leave in the 3 weeks before testing (OR, 6.31; 95% CI, 4.38-9.08). Only 2.5% of employees were SARS-CoV-2 positive while 10.5% were positive by serology and 1.2% were positive in both tests. Serology-positive subjects were not at excess risk for future sick leave (OR, 1.06; 95% CI, .71-1.57). CONCLUSIONS: High amounts of SARS-CoV-2 virus, as determined using PCR Ct values, was associated with development of sickness in the next few weeks. Results support the concept that PCR Ct may be informative when testing for SARS-CoV-2. Clinical Trials Registration. NCT04411576.

No evidence for a placental microbiome in human pregnancies at term.

BACKGROUND: The placenta plays an important role in the modulation of pregnancy immunity; however, there is no consensus regarding the existence of a placental microbiome in healthy full-term pregnancies. OBJECTIVE: This study aimed to investigate the existence and origin of a placental microbiome. STUDY DESIGN: A cross-sectional study comparing samples (3 layers of placental tissue, amniotic fluid, vernix caseosa, and saliva, vaginal, and rectal samples) from 2 groups of full-term births: 50 women not in labor with elective cesarean deliveries and 26 with vaginal deliveries. The comparisons were performed using polymerase chain reaction amplification and DNA sequencing techniques and bacterial culture experiments. RESULTS: There were no significant differences regarding background characteristics between women who delivered by elective cesarean and those who delivered vaginally. Quantitative measurements of bacterial content in all 3 placental layers (quantitative polymerase chain reaction of the 16S ribosomal RNA gene) did not show any significant difference among any of the sample types and the negative controls. Here, 16S ribosomal RNA gene sequencing of the maternal side of the placenta could not differentiate between bacteria in the placental tissue and contamination of the laboratory reagents with bacterial DNA. Probe-specific quantitative polymerase chain reaction for bacterial taxa suspected to be present in the placenta could not detect any statistically significant difference between the 2 groups. In bacterial cultures, substantially more bacteria were observed in the placenta layers from vaginal deliveries than those from cesarean deliveries. In addition, 16S ribosomal RNA gene sequencing of bacterial colonies revealed that most of the bacteria that grew on the plates were genera typically found in human skin; moreover, it revealed that placentas delivered vaginally contained a high prevalence of common vaginal bacteria. Bacterial growth inhibition experiments indicated that placental tissue may facilitate the inhibition of bacterial growth. CONCLUSION: We found no evidence to support the existence of a placental microbiome in our study of 76 term pregnancies, which used polymerase chain reaction amplification and sequencing techniques and bacterial culture experiments. Incidental findings of bacterial species could be due to contamination or to low-grade bacterial presence in some locations; such bacteria do not represent a placental microbiome per se.

Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics.

Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary for accurate diagnosis and efficient treatment of infectious diseases. Bottom-up tandem mass spectrometry (MS) proteomics enables rapid characterization of large parts of the expressed genes of microorganisms. However, the generated data are highly fragmented, making downstream analyses complex. Here we present TCUP, a new computational method for typing and characterizing bacteria using proteomics data from bottom-up tandem MS. TCUP compares the generated protein sequence data to reference databases and automatically finds peptides suitable for characterization of taxonomic composition and identification of expressed antimicrobial resistance genes. TCUP was evaluated using several clinically relevant bacterial species (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae), using both simulated data generated by in silico peptide digestion and experimental proteomics data generated by liquid chromatography-tandem mass spectrometry (MS/MS). The results showed that TCUP performs correct peptide classifications at rates between 90.3 and 98.5% at the species level. The method was also able to estimate the relative abundances of individual species in mixed cultures. Furthermore, TCUP could identify expressed ß-lactamases in an extended spectrum ß-lactamase-producing (ESBL) E. coli strain, even when the strain was cultivated in the absence of antibiotics. Finally, TCUP is computationally efficient, easy to integrate in existing bioinformatics workflows, and freely available under an open source license for both Windows and Linux environments.

Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets.

BACKGROUND: Fluoroquinolones are broad-spectrum antibiotics used to prevent and treat a wide range of bacterial infections. Plasmid-mediated qnr genes provide resistance to fluoroquinolones in many bacterial species and are increasingly encountered in clinical settings. Over the last decade, several families of qnr genes have been discovered and characterized, but their true prevalence and diversity still remain unclear. In particular, environmental and host-associated bacterial communities have been hypothesized to maintain a large and unknown collection of qnr genes that could be mobilized into pathogens. RESULTS: In this study we used computational methods to screen genomes and metagenomes for novel qnr genes. In contrast to previous studies, we analyzed an almost 20-fold larger dataset comprising almost 13 terabases of sequence data. In total, 362,843 potential qnr gene fragments were identified, from which 611 putative qnr genes were reconstructed. These gene sequences included all previously described plasmid-mediated qnr gene families. Fifty-two of the 611 identified qnr genes were reconstructed from metagenomes, and 20 of these were previously undescribed. All of the novel qnr genes were assembled from metagenomes associated with aquatic environments. Nine of the novel genes were selected for validation, and six of the tested genes conferred consistently decreased susceptibility to ciprofloxacin when expressed in Escherichia coli. CONCLUSIONS: The results presented in this study provide additional evidence for the ubiquitous presence of qnr genes in environmental microbial communities, expand the number of known qnr gene variants and further elucidate the diversity of this class of resistance genes. This study also strengthens the hypothesis that environmental bacterial communities act as sources of previously uncharacterized qnr genes.

Strategies to improve usability and preserve accuracy in biological sequence databases.

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

In vitro assessment of platelet concentrates with multiple electrode aggregometry.

Storage impairs platelet function. It was hypothesized that multiple electrode aggregometry in vitro could be used to follow aggregability in platelet concentrates over time and that the results predict the efficacy of platelet transfusion in an ex vivo transfusion model. In vitro platelet aggregability was assessed in apheresis and pooled buffy coat platelet concentrates (BCs) (n = 13 each) using multiple electrode aggregometry with different agonists 1, 3, 5 and 7 days after preparation. In the ex vivo transfusion model, whole blood samples from nine healthy volunteers were collected every second day. The samples were supplemented with stored platelets (+146 × 10(9) × l(-1)) from the same unit 1, 3, 5 and 7 days after preparation. Platelet aggregability was assessed in the concentrate and in the whole blood samples before and after platelet supplementation. There was a continuous reduction in in vitro platelet aggregability over time in both apheresis and pooled BCs. The same pattern was observed after ex vivo addition of apheresis and pooled BCs to whole blood samples. The best correlation between in vitro aggregability and changes in aggregation after addition was achieved with collagen as agonist (r = 0.67, p < 0.001). In conclusion, multiple electrode aggregometry can be used to follow aggregability in platelet concentrates in vitro, and the results predict with moderate accuracy changes in aggregation after addition of platelet concentrate to whole blood samples.

The protective role of commensal gut microbes and their metabolites against bacterial pathogens.

Multidrug-resistant microorganisms have become a major public health concern around the world. The gut microbiome is a gold mine for bioactive compounds that protect the human body from pathogens. We used a multi-omics approach that integrated whole-genome sequencing (WGS) of 74 commensal gut microbiome isolates with metabolome analysis to discover their metabolic interaction with Salmonella and other antibiotic-resistant pathogens. We evaluated differences in the functional potential of these selected isolates based on WGS annotation profiles. Furthermore, the top altered metabolites in co-culture supernatants of selected commensal gut microbiome isolates were identified including a series of dipeptides and examined for their ability to prevent the growth of various antibiotic-resistant bacteria. Our results provide compelling evidence that the gut microbiome produces metabolites, including the compound class of dipeptides that can potentially be applied for anti-infection medication, especially against antibiotic-resistant pathogens. Our established pipeline for the discovery and validation of bioactive metabolites from the gut microbiome as novel candidates for multidrug-resistant infections represents a new avenue for the discovery of antimicrobial lead structures.

Functional verification of computationally predicted qnr genes.

BACKGROUND: The quinolone resistance (qnr) genes are widely distributed among bacteria. We recently developed and applied probabilistic models to identify tentative novel qnr genes in large public collections of DNA sequence data including fragmented metagenomes. FINDINGS: By using inducible recombinant expressions systems the functionality of four identified qnr candidates were evaluated in Escherichia coli. Expression of several known qnr genes as well as two novel candidates provided fluoroquinolone resistance that increased with elevated inducer concentrations. The two novel, functionally verified qnr genes are termed Vfuqnr and assembled qnr 1. Co-expression of two qnr genes suggested non-synergistic action. CONCLUSION: The combination of a computational model and recombinant expression systems provides opportunities to explore and identify novel antibiotic resistance genes in both genomic and metagenomic datasets.

A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences.

BACKGROUND: Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. RESULTS: In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. CONCLUSIONS: The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/.

Novel strain of Pseudoruminococcus massiliensis possesses traits important in gut adaptation and host-microbe interactions.

Fecal microbiota transplantation (FMT) is an efficient treatment for recurrent Clostridioides difficile infection and currently investigated as a treatment for other intestinal and systemic diseases. Better understanding of the species potentially transferred in FMT is needed. We isolated from a healthy fecal donor a novel strain E10-96H of Pseudoruminococcus massiliensis, a recently described strictly anaerobic species currently represented only by the type strain. The whole genome sequence of E10-96H had over 98% similarity with the type strain. E10-96H carries 20 glycoside hydrolase encoding genes, degrades starch in vitro and thus may contribute to fiber degradation, cross-feeding of other species and butyrate production in the intestinal ecosystem. The strain carries pilus-like structures, harbors pilin genes in its genome and adheres to enterocytes in vitro but does not provoke a proinflammatory response. P. massiliensis seems to have commensal behavior with the host epithelium, and its role in intestinal ecology should be studied further.

The microbiome in reproductive health: protocol for a systems biology approach using a prospective, observational study design.

STUDY QUESTION: What is the microbiome profile across different body sites in relation to the normal menstrual cycle (with and without hormonal contraception), recurrent pregnancy loss (RPL) (before and during pregnancy, pregnancy loss or birth) and endometriosis (before, during and after surgery)? How do these profiles interact with genetics, environmental exposures, immunological and endocrine biomarkers? WHAT IS KNOWN ALREADY: The microbiome is a key factor influencing human health and disease in areas as diverse as immune functioning, gastrointestinal disease and mental and metabolic disorders. There is mounting evidence to suggest that the reproductive microbiome may be influential in general and reproductive health, fertility and pregnancy outcomes. STUDY DESIGN SIZE DURATION: This is a prospective, longitudinal, observational study using a systems biology approach in three cohorts totalling 920 participants. Since microbiome profiles by shot-gun sequencing have never been investigated in healthy controls during varying phases of the menstrual cycle, patients with RPL and patients with endometriosis, no formal sample size calculation can be performed. The study period is from 2017 to 2024 and allows for longitudinal profiling of study participants to enable deeper understanding of the role of the microbiome and of host-microbe interactions in reproductive health. PARTICIPANTS/MATERIALS SETTING METHODS: Participants in each cohort are as follows: Part 1 MiMens-150 healthy women with or without hormonal contraception; Part 2 MiRPL-200 couples with RPL, 50 healthy couples with prior uncomplicated pregnancy and 150 newborns; Part 3 MiEndo-120 patients with endometriosis requiring surgery with or without hormonal treatment. Microbiome profiles from saliva, faeces, rectal mucosa, vaginal fluid and endometrium will be studied, as well as the Omics profile, endocrine disrupting chemicals and endocrine and immune factors in blood, hair, saliva and urine. Pregnancy loss products, seminal microbiome, HLA types, endometriotic tissue and genetic risk and comprehensive questionnaire data will also be studied, where appropriate. Correlations with mental and physical health will be evaluated. STUDY FUNDING/COMPETING INTERESTS: This work is supported by funding from Ferring Pharmaceuticals ([#MiHSN01] to H.S.N., M.C.K., M.E.M., L.E.V., L.E., I.S.-K., F.B., L.W.H., E.F. and M.H.), Rigshospitalet's Research Funds ([#E-22614-01 and #E-22614-02] to M.C.K. and [#E-22222-06] to S.B.), Niels and Desiree Yde's Foundation (S.B., endocrine analyses [#2015-2784]), the Musikforlæggerne Agnes and Knut Mørk's Foundation (S.B., endocrine and immune analyses [#35108-001]) and Oda and Hans Svenningsen's Foundation ([#F-22614-08] to H.S.N.). Medical writing assistance with this manuscript was provided by Caroline Loat, PhD, and funded by Ferring Pharmaceuticals. H.S.N. reports personal fees from Ferring Pharmaceuticals, Merck Denmark A/S, Ibsa Nordic, Astra Zeneca and Cook Medical outside the submitted work. K.W. is a full-time employee of Ferring Pharmaceuticals. No other conflicts are reported. TRIAL REGISTRATION NUMBER: N/A. TRIAL REGISTRATION DATE: N/A. DATE OF FIRST PATIENT'S ENROLMENT: N/A.

Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden.

Temporal dynamics of the gut microbiota potentially limit the identification of microbial features associated with health status. Here, we used whole-genome metagenomic and 16S rRNA gene sequencing to characterize the intra- and inter-individual variations of gut microbiota composition and functional potential of a disease-free Swedish population (n = 75) over one year. We found that 23% of the total compositional variance was explained by intra-individual variation. The degree of intra-individual compositional variability was negatively associated with the abundance of Faecalibacterium prausnitzii (a butyrate producer) and two Bifidobacterium species. By contrast, the abundance of facultative anaerobes and aerotolerant bacteria such as Escherichia coli and Lactobacillus acidophilus varied extensively, independent of compositional stability. The contribution of intra-individual variance to the total variance was greater for functional pathways than for microbial species. Thus, reliable quantification of microbial features requires repeated samples to address the issue of intra-individual variations of the gut microbiota.

Cohort profile: the Swedish Maternal Microbiome project (SweMaMi) - assessing the dynamic associations between the microbiome and maternal and neonatal adverse events.

PURPOSE: The Swedish Maternal Microbiome (SweMaMi) project was initiated to better understand the dynamics of the microbiome in pregnancy, with longitudinal microbiome sampling, shotgun metagenomics, extensive questionnaires and health registry linkage. PARTICIPANTS: Pregnant women were recruited before the 20th gestational week during 2017-2021 in Sweden. In total, 5439 pregnancies (5193 unique women) were included. For 3973 pregnancies (73%), samples were provided at baseline, and for 3141 (58%) at all three timepoints (second and third trimester and postpartum). In total, 38 591 maternal microbiome samples (vaginal, faecal and saliva) and 3109 infant faecal samples were collected. Questionnaires were used to collect information on general, reproductive and mental health, diet and lifestyle, complemented by linkage to the nationwide health registries, also used to follow up the health of the offspring (up to age 10). FINDINGS TO DATE: The cohort is fairly representative for the total Swedish pregnant population (data from 2019), with 41% first-time mothers. Women with university level education, born in Sweden, with normal body mass index, not using tobacco-products and aged 30-34 years were slightly over-represented. FUTURE PLANS: The sample and data collection were finalised in November 2021. The next steps are the characterisation of the microbial DNA and linkage to the health and demographic information from the questionnaires and registries. The role of the microbiome on maternal and neonatal outcomes and early-childhood diseases will be explored (including preterm birth, miscarriage) and the role and interaction of other risk factors and confounders (including endometriosis, polycystic ovarian syndrome, diet, drug use). This is currently among the largest pregnancy cohorts in the world with longitudinal design and detailed and standardised microbiome sampling enabling follow-up of both mothers and children. The findings are expected to contribute greatly to the field of reproductive health focusing on pregnancy and neonatal outcomes.

Conjugative transfer of multi-drug resistance IncN plasmids from environmental waterborne bacteria to Escherichia coli.

Watersheds contaminated with municipal, hospital, and agricultural residues are recognized as reservoirs for bacteria carrying antibiotic resistance genes (ARGs). The objective of this study was to determine the potential of environmental bacterial communities from the highly contaminated La Paz River basin in Bolivia to transfer ARGs to an Escherichia coli lab strain used as the recipient. Additionally, we tested ZnSO4 and CuSO4 at sub-inhibitory concentrations as stressors and analyzed transfer frequencies (TFs), diversity, richness, and acquired resistance profiles. The bacterial communities were collected from surface water in an urban site close to a hospital and near an agricultural area. High transfer potentials of a large set of resistance factors to E. coli were observed at both sites. Whole-genome sequencing revealed that putative plasmids belonging to the incompatibility group N (IncN, IncN2, and IncN3) were predominant among the transconjugants. All IncN variants were verified to be mobile by a second conjugation step. The plasmid backbones were similar to other IncN plasmids isolated worldwide and carried a wide range of ARGs extensively corroborated by phenotypic resistance patterns. Interestingly, all transconjugants also acquired the class 1 integron intl1, which is commonly known as a proxy for anthropogenic pollution. The addition of ZnSO4 and CuSO4 at sub-inhibitory concentrations did not affect the transfer rate. Metal resistance genes were absent from most transconjugants, suggesting a minor role, if any, of metals in the spread of multidrug-resistant plasmids at the investigated sites.

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

Dysbiosis of the Human Oral Microbiome During the Menstrual Cycle and Vulnerability to the External Exposures of Smoking and Dietary Sugar.

Physiological hormonal fluctuations exert endogenous pressures on the structure and function of the human microbiome. As such, the menstrual cycle may selectively disrupt the homeostasis of the resident oral microbiome, thus compromising oral health. Hence, the aim of the present study was to structurally and functionally profile the salivary microbiome of 103 women in reproductive age with regular menstrual cycle, while evaluating the modifying influences of hormonal contraceptives, sex hormones, diet, and smoking. Whole saliva was sampled during the menstrual, follicular, and luteal phases (n = 309) of the cycle, and the participants reported questionnaire-based data concerning their life habits and oral or systemic health. No significant differences in alpha-diversity or phase-specific clustering of the overall microbiome were observed. Nevertheless, the salivary abundances of genera Campylobacter, Haemophilus, Prevotella, and Oribacterium varied throughout the cycle, and a higher species-richness was observed during the luteal phase. While the overall community structure maintained relatively intact, its functional properties were drastically affected. In particular, 11 functional modules were differentially abundant throughout the menstrual cycle, including pentose phosphate metabolism, and biosynthesis of cobalamin and neurotransmitter gamma-aminobutyric acid. The menstrual cycle phase, but not oral contraceptive usage, was accountable for greater variations in the metabolic pathways of the salivary microbiome. Further co-risk factor analysis demonstrated that Prevotella and Veillonella were increased in current smokers, whereas high dietary sugar consumption modified the richness and diversity of the microbiome during the cycle. This is the first large study to systematically address dysbiotic variations of the oral microbiome during the course of menstrual cycle, and document the additive effect of smoking and sugar consumption as environmental risk factors. It reveals the structural resilience and functional adaptability of the oral microbiome to the endogenous hormonal pressures of the menstrual cycle, while revealing its vulnerability to the exogenous exposures of diet and smoking.