Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Maintaining beneficial alga-associated bacterial communities under heat stress: insights from controlled co-culture experiments using antibiotic-resistant bacterial strains

Brown algae, like many eukaryotes, possess diverse microbial communities. Ectocarpus-a model brown alga-relies on these communities for essential processes, such as growth development. Controlled laboratory systems are needed for functional studies of these algal-bacterial interactions. We selected bacterial strains based on their metabolic networks to provide optimal completion of the algal metabolism, rendered them resistant to two antibiotics, and inoculate them to establish controlled co-cultures with Ectocarpus under continuous antibiotic treatment. We then monitored the stability of the resulting associations under control conditions and heat stress using 16S metabarcoding. Antibiotics strongly reduced bacterial diversity both in terms of taxonomy and predicted metabolic functions. In the inoculated sample, 63%-69% of reads corresponded to the inoculated strains, and the communities remained stable during temperature stress. They also partially restored the predicted metabolic functions of the natural community. Overall, the development of antibiotic-resistant helper cultures offers a promising route to fully controlled laboratory experiments with algae and microbiota and thus represents an important step towards generating experimental evidence for specific host-microbe interactions in the systems studied. Further work will be required to achieve full control and progressively expand our repertoire of helper strains including those currently 'unculturable'.

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

Analysis of the Propionate Metabolism in Bacillus subtilis during 3-Indolacetic Production

The genera Bacillus belongs to the group of microorganisms that are known as plant growth-promoting bacteria, their metabolism has evolved to produce molecules that benefit the growth of the plant, and the production of 3-indole acetic acid (IAA) is part of its secondary metabolism. In this work, Bacillus subtilis was cultivated in a bioreactor to produce IAA using propionate and glucose as carbon sources in an M9-modified media; in both cases, tryptophan was added as a co-substrate. The yield of IAA using propionate is 17% higher compared to glucose. After 48 h of cultivation, the final concentration was 310 mg IAA/L using propionate and 230 mg IAA/L using glucose, with a concentration of 500 mg Trp/L. To gain more insight into propionate metabolism and its advantages, the genome-scale metabolic model of B. subtilis (iBSU 1147) and computational analysis were used to calculate flux distribution and evaluate the metabolic capabilities to produce IAA using propionate. The metabolic fluxes demonstrate that propionate uptake favors the production of precursors needed for the synthesis of the hormone, and the sensitivity analysis shows that the control of a specific growth rate has a positive impact on the production of IAA.