Baldini, F. | Search bioRxiv

bioRxiv2018

Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 12 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-four-day-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes mid-infrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

bioinformatics↗

bioRxiv2018

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

MotivationThe application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.\n\nResultsTo address this shortage, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox.\n\nAvailabilityThe Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

systems biology↗

bioRxiv2017

Personalized modeling of the human gut microbiome reveals distinct bile acid deconjugation and biotransformation potential in healthy and IBD individuals

The human gut microbiome performs important functions human health and disease. Intestinal microbes are capable of deconjugation and biotransformation of human primary bile acids to secondary bile acids. Alterations of the bile acid pool as a result of microbial dysbiosis have been linked to multifactorial diseases, such as inflammatory bowel disease (IBD).\n\nConstraint-based modeling is a powerful approach for the mechanistic, systems-level analysis of metabolic interactions in microbial communities. Recently, we constructed a resource of 773 curated genome-scale reconstructions of human gut microbes, AGORA. Here, we performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes to expand these AGORA reconstructions accordingly (available at http://vmh.life).\n\nTo elucidate the metabolic potential of individual microbiomes, publicly available metagenomic data from a cohort of healthy Western individuals, as well as two cohorts of IBD patients and healthy controls, were mapped onto the reference set of AGORA genomes. We constructed for each individual a large-scale personalized microbial community model that take strain-level abundances into account. Using flux balance analysis, we found that distinct potential to deconjugate and tranform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric IBD patients were significantly depleted in their bile acid production potential compared with controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between IBD patients and controls. IBD microbiomes were depleted in contributions of Bacteroidetes strains but enriched in contributions of Proteobacteria. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. For ursodeoxycholate, the abundance of strains producing the precursor rather than of strains directly producing this secondary bile acid was synthesis-limiting in certain microbiomes.\n\nIn summary, we integrated for the first-time metagenomics data with large-scale personalized metabolic modeling of microbial communities. We provided mechanistic insight into the link between dysbiosis and metabolic potential in IBD microbiomes. This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between human host and gut microbiomes in health and diseases states.

systems biology↗