Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Effects of Mosquito Microbiota on the Survival Cost and Development Success of Avian Plasmodium.

Both intrinsic and extrinsic factors affect the capacity of mosquitoes for the transmission of vector-borne pathogens. Among them, mosquito microbiota may play a key role determining the development of pathogens in mosquitoes and the cost of infections. Here, we used a wild avian malaria-mosquito assemblage model to experimentally test the role of vector microbiota on the cost of infection and their consequences for parasite development. To do so, a cohort of Culex pipiens mosquitoes were treated with antibiotics, including gentamicin sulfate and penicillin-streptomycin, to alter their microbiota, and other cohort was treated with sterilized water as controls. Subsequently, both cohorts were allowed to feed on Plasmodium infected or uninfected house sparrows (Passer domesticus). The antibiotic treatment significantly increased the survival rate of mosquitoes fed on infected birds while this was not the case of mosquitoes fed on uninfected birds. Additionally, a higher prevalence of Plasmodium in the saliva of mosquitoes was found in antibiotic treated mosquitoes than in mosquitoes of the control group at 20 days post exposure (dpe). Analyses of the microbiota of a subsample of mosquitoes at 20 dpe suggest that although the microbiota diversity did not differ between individuals of the two treatments, microbiota in control mosquitoes had a higher number of unique features and enriched in biochemical pathways related to the immune system than antibiotic treated ones. In sum, this study provides support for the role of mosquito microbiota on mosquito survival and the presence of parasite DNA in their saliva.