The microbiome has been implicated in the development of metabolic conditions which occur at high rates in people with schizophrenia and related psychoses. This exploratory proof-of-concept study aimed to: (i) characterize the gut microbiota in antipsychotic naïve or quasi-naïve people with first-episode psychosis, and people with established schizophrenia receiving clozapine therapy; (ii) test for microbiome changes following a lifestyle intervention which included diet and exercise education and physical activity. Participants were recruited from the Eastern Suburbs Mental Health Service, Sydney, Australia. Anthropometric, lifestyle and gut microbiota data were collected at baseline and following a 12-week lifestyle intervention. Stool samples underwent 16S rRNA sequencing to analyse microbiota diversity and composition. Seventeen people with established schizophrenia and five people with first-episode psychosis were recruited and matched with 22 age-sex, BMI and ethnicity matched controls from a concurrent study for baseline comparisons. There was no difference in α-diversity between groups at baseline, but microbial composition differed by 21 taxa between the established schizophrenia group and controls. In people with established illness pre-post comparison of α-diversity showed significant increases after the 12-week lifestyle intervention. This pilot study adds to the current literature that detail compositional differences in the gut microbiota of people with schizophrenia compared to those without mental illness and suggests that lifestyle interventions may increase gut microbial diversity in patients with established illness. These results show that microbiome studies are feasible in patients with established schizophrenia and larger studies are warranted to validate microbial signatures and understand the relevance of lifestyle change in the development of metabolic conditions in this population.

Dietary intake during pregnancy may influence the antenatal microbiome, which is proposed to impact maternal and infant health during the pregnancy and beyond. The aim of this sub-study was to examine associations between dietary intake and microbiota diversity during pregnancy using whole metagenomic sequencing and examine associations in low-risk versus high-risk pregnancies, as well as complicated versus uncomplicated pregnancies. Pregnancy data were analysed from women participating in the MUMS cohort study in Sydney, Australia (women followed from trimester 1 of pregnancy to 1-year postpartum), who had dietary intake data at either trimester 1 or 3, assessed using the Australian Eating Survey, and a matched stool sample (n = 86). Correlations of microbial alpha diversity with dietary intake data were determined using the repeated-measures correlation, rmcorr, in R. In the combined cohort, no associations were found between diet quality or diet composition and microbial alpha diversity or beta diversity. However, trends in our analysis suggested that dietary intake of specific macro- and micronutrients may influence microbial diversity differently, depending on particular pregnancy conditions. Our findings suggest that dietary intake during pregnancy may have a variable influence on the maternal microbiota, unique to the individual maternal pregnancy phenotype. More research is needed to disentangle these associations.

The microbiome has emerged as a key determinant of human health and reproduction, with recent evidence suggesting a dysbiotic microbiome is implicated in adverse perinatal health outcomes. The existing research has been limited by the sample collection and timing, cohort design, sample design, and lack of data on the preconception microbiome. This prospective, longitudinal cohort study will recruit 2000 Australian women, in order to fully explore the role of the microbiome in the development of adverse perinatal outcomes. Participants are enrolled for a maximum of 7 years, from 1 year preconception, through to 5 years postpartum. Assessment occurs every three months until pregnancy occurs, then during Trimester 1 (5 + 0–12 + 6 weeks gestation), Trimester 2 (20 + 0–24 + 6 weeks gestation), Trimester 3 (32 + 0–36 + 6 weeks gestation), and postpartum at 1 week, 2 months, 6 months, and then annually from 1 to 5 years. At each assessment, maternal participants self-collect oral, skin, vaginal, urine, and stool samples. Oral, skin, urine, and stool samples will be collected from children. Blood samples will be obtained from maternal participants who can access a study collection center. The measurements taken will include anthropometric, blood pressure, heart rate, and serum hormonal and metabolic parameters. Validated self-report questionnaires will be administered to assess diet, physical activity, mental health, and child developmental milestones. Medications, medical, surgical, obstetric history, the impact of COVID-19, living environments, and pregnancy and child health outcomes will be recorded. Multiomic bioinformatic and statistical analyses will assess the association between participants who developed high-risk and low-risk pregnancies, adverse postnatal conditions, and/or childhood disease, and their microbiome for the different sample types.

The success of amplicon sequencing studies of environmental DNA (eDNA) depends on the choice of primer pairs used to select taxa‐specific regions of DNA for amplification then sequencing to characterize sample composition. This paper presents practical methods to visualize the extent to which different primer pairs can differentiate taxonomic groups, enabling researchers to assess which primers might be best suited for a study or environment of interest. These methods can also be used to review taxonomic annotations in genomic reference sequence databases. We apply the concept of DNA barcoding gaps to metabarcoding of multiple species in environmental DNA to leverage reference data on the amplicon sequences of previously characterized specimens. Since reference data sets are large and complex, we provide a simple and intuitive method to navigate subsets of reference sequence data containing conflicting or ambiguous relationships between genomic information and taxonomic classification. We demonstrate how to use gap visualization and taxonomic segmentation in comparing how well different primer pairs discriminate species of interest, and in detecting anomalies in reference sequence annotation. We show how these visualization methods can enable amplicon survey study design and make fundamental molecular resources more accessible to a wider research audience beyond bioinformaticians and data scientists.