Microbiome signature of posttraumatic stress disorder and resilience in youth.

Objective: Identifying biomarkers that can distinguish trauma-exposed youth at risk for developing posttraumatic pathology from resilient individuals is essential for targeted interventions. As trauma can alter the microbiome with lasting effects on the host, our longitudinal, multimeasure, cross-species study aimed to identify the microbial signature of posttraumatic stress disorder (PTSD).

Method: We followed children exposed to war-related trauma and matched controls from early childhood ( = 2.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships.

Gut microbiome-metabolome interactions predict host condition.

Background: The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

Recipient-independent, high-accuracy FMT-response prediction and optimization in mice and humans.

Background: Some microbiota compositions are associated with negative outcomes, including among others, obesity, allergies, and the failure to respond to treatment. Microbiota manipulation or supplementation can restore a community associated with a healthy condition. Such interventions are typically probiotics or fecal microbiota transplantation (FMT).

Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy.

The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics.

Sampling bias minimization in disease frequency estimates.

An accurate estimate of the number of infected individuals in any disease is crucial. Current estimates are mainly based on the fraction of positive samples or the total number of positive samples. However, both methods are biased and sensitive to the sampling depth.