BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons

Long-term intake of Lactobacillus helveticus enhances bioavailability of omega-3 fatty acids in the mouse retina

Omega-3 (n-3) polyunsaturated fatty acids (PUFAs), particularly docosahexaenoic acid (DHA), are required for the structure and function of the retina. Several observational studies indicate that consumption of a diet with relatively high levels of n-3 PUFAs, such as those provided by fish oils, has a protective effect against the development of age-related macular degeneration. Given the accumulating evidence showing the role of gut microbiota in regulating retinal physiology and host lipid metabolism, we evaluated the potential of long-term dietary supplementation with the Gram-positive bacterium Lactobacillus helveticus strain VEL12193 to modulate the retinal n-3 PUFA content. A set of complementary approaches was used to study the impact of such a supplementation on the gut microbiota and host lipid/fatty acid (FA) metabolism. L. helveticus-supplementation was associated with a decrease in retinal saturated FAs (SFAs) and monounsaturated FAs (MUFAs) as well as an increase in retinal n-3 and omega-6 (n-6) PUFAs. Interestingly, supplementation with L. helveticus enriched the retina in C22:5n-3 (docosapentaenoic acid, DPA), C22:6n-3 (DHA), C18:2n-6 (linoleic acid, LA) and C20:3n-6 (dihomo gamma-linolenic acid, DGLA). Long-term consumption of L. helveticus also modulated gut microbiota composition and some changes in OTUs abundance correlated with the retinal FA content. This study provides a proof of concept that targeting the gut microbiota could be an effective strategy to modulate the retinal FA content, including that of protective n-3 PUFAs, thus opening paths for the design of novel preventive and/or therapeutical strategies for retinopathies.

Differential responses of soil microbial biomass, diversity and interactions to land use intensity at a territorial scale

Impact of land use intensification on soil microbial communities across a territory remains poorly documented. Yet, it has to be deciphered to validate the results obtained at local and global scales by integrating the variations of environmental conditions and agricultural systems at a territorial scale. We investigated the impact of different land uses (from forest to agricultural systems) and associated soil management practices on soil molecular microbial biomass and diversity across a territory of 3300 km2 in Burgundy (France). Microbial biomass and diversity were determined by quantifying and high-throughput sequencing of soil DNA from 300 soils, respectively. Geostatistics were applied to map the soil macro-ecological patterns and variance partitioning analysis was used to rank the influence of soil physicochemical characteristics, land uses and associated practices on soil microbial communities. Geographical patterns differed between microbial biomass and diversity, emphasizing that distinct environmental drivers shaped these parameters. Soil microbial biomass was mainly driven by the soil organic carbon content and was significantly altered by agricultural land uses, with a loss of about 71 % from natural to agricultural ecosystems. The best predictors of bacterial and fungal richness were soil texture and pH, respectively. Microbial diversity was less sensitive than microbial biomass to land use intensification, and fungal richness appeared more impacted than bacteria. Co-occurrence network analysis of the interactions among microbial communities showed a decline of about 95 % of network complexity with land use intensification, which counterbalanced the weak response of microbial diversity. Grouping of the 147 cropland plots in four clusters according to their agricultural practices confirmed that microbial parameters exhibited different responses to soil management intensification, especially soil tillage and crop protection. Our results altogether allow evaluating the different levels of microbial parameters' vulnerability to land use intensity at a territorial scale.

Effects of biochar on soil microbial communities: A meta-analysis

Changes in soil microbial communities may impact soil fertility and stability because microbial communities are key to soil functioning by supporting soil ecological quality and agricultural production. The effects of soil amendment with biochar on soil microbial communities are widely documented but studies highlighted a high degree of variability in their responses following biochar application. The multiple conditions under which they were conducted (experimental designs, application rates, soil types, biochar properties) make it difficult to identify general trends. This supports the need to better determine the conditions of biochar production and application that promote soil microbial communities. In this context, we performed the first ever meta-analysis of the biochar effects on soil microbial biomass and diversity (prokaryotes and fungi) based on high-throughput sequencing data. The majority of the 181 selected publications were conducted in China and evaluated the short-term impact (<3 months) of biochar. We demonstrated that a large panel of variables corresponding to biochar properties, soil characteristics, farming practices or experimental conditions, can affect the effects of biochar on soil microbial characteristics. Using a variance partitioning approach, we showed that responses of soil microbial biomass and prokaryotic diversity were highly dependent on biochar properties. They were influenced by pyrolysis temperature, biochar pH, application rate and feedstock type, as wood-derived biochars have particular physico-chemical properties (high C:N ratio, low nutrient content, large pores size) compared to non-wood-derived biochars. Fungal community data was more heterogenous and scarcer than prokaryote data (30 publications). Fungal diversity indices were rather dependent on soil properties: they were higher in medium-textured soils, with low pH but high soil organic carbon. Altogether, this meta-analysis illustrates the need for long-term field studies in European agricultural context for documenting responses of soil microbial communities to biochar application under diverse conditions combining biochar types, soil properties and conditions of use.

Urban land uses shape soil microbial abundance and diversity

Soil microbial biodiversity provides many useful services in cities. However, the ecology of microbial communities in urban soils remains poorly documented, and studies are required to better predict the impact of urban land use. We characterized microbial communities (archea/bacteria and fungi) in urban soils in Dijon (Burgundy, France). Three main land uses were considered - public leisure, traffic, and urban agriculture - sub-categorized in sub-land uses according to urban indexes and management practices. Microbial biomass and diversity were determined by quantifying and high-throughput sequencing of soil DNA. Variation partitioning analysis was used to rank soil physicochemical characteristics and land uses according to their relative contribution to the variation of soil microbial communities. Urban soils in Dijon harbored high levels of microbial biomass and diversity that varied according to land uses. Microbial biomass was 1.8 times higher in public leisure and traffic sites than in urban agriculture sites. Fungal richness increased by 25 % in urban agriculture soils, and bacterial richness was lower (by 20 %) in public leisure soils. Partitioning models explained 25.7 %, 46.2 % and 75.6 % of the variance of fungal richness, bacterial richness and microbial biomass, respectively. The organic carbon content and the C/N ratio were the best predictors of microbial biomass, whereas soil bacterial diversity was mainly explained by soil texture and land use. Neither metal trace elements nor polycyclic aromatic hydrocarbons contents explained variations of microbial communities, probably due to their very low concentration in the soils. The microbial composition results highlighted that leisure sites represented a stabilized habitat favoring specialized microbial groups and microbial plant symbionts, as opposed to urban agriculture sites that stimulated opportunistic populations able to face the impact of agricultural practices. Altogether, our results provide evidence that there is scope for urban planners to drive soil microbial diversity through sustainable urban land use and associated management practices.

Translational informatics for human microbiota: data resources, models and applications

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

Dynamic of bacterial and archaeal diversity in a tropical soil over 6 years of repeated organic and inorganic fertilization

The soil microbial community plays important roles in nutrient cycling, plant pathogen suppression, decomposition of residues and degradation of pollutants; as such, it is often regarded as a good indicator of soil quality. Repeated applications of mixed organic and inorganic materials in agriculture improve the soil microbial quality and in turn crop productivity. The soil microbial quality following several years of repeated fertilizer inputs has received considerable attention, but the dynamic of this community over time has never been assessed. We used high-throughput sequencing targeting 16S ribosomal RNA genes to investigate the evolution of the bacterial and archaeal community throughout 6 years of repeated organic and inorganic fertilizer applications. Soils were sampled from a field experiment in La Mare (Reunion Island, France), where different mixed organic-inorganic fertilizer inputs characterized by more or less stable organic matter were applied regularly for 6 years. Soil samples were taken each year, more than 6 months after the latest fertilizer application. The soil molecular biomass significantly increased in some organically fertilized plots (by 35–45% on average), 3–5 years after the first fertilizers application. The significant variations in soil molecular microbial biomass were explained by the fertilization practices (cumulated organic carbon inputs) and sometimes by the soil parameters (sand and soil carbon contents). The structure of the bacterial and archaeal community was more influenced by time than by the fertilization type. However, repeated fertilizer applications over time tended to modify the abundance of the bacterial phyla Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. To conclude, the present study highlights that the soil bacterial and archaeal community is lastingly modified after 6 years of repeated fertilizer inputs. These changes depend on the nature of the organic input and on the fertilization practice (frequency and applied quantity).

Potential of Meta-Omics to Provide Modern Microbial Indicators for Monitoring Soil Quality and Securing Food Production

Soils are fundamental resources for agricultural production and play an essential role in food security. They represent the keystone of the food value chain because they harbor a large fraction of biodiversity—the backbone of the regulation of ecosystem services and “soil health” maintenance. In the face of the numerous causes of soil degradation such as unsustainable soil management practices, pollution, waste disposal, or the increasing number of extreme weather events, it has become clear that (i) preserving the soil biodiversity is key to food security, and (ii) biodiversity-based solutions for environmental monitoring have to be developed. Within the soil biodiversity reservoir, microbial diversity including Archaea, Bacteria, Fungi and protists is essential for ecosystem functioning and resilience. Microbial communities are also sensitive to various environmental drivers and to management practices; as a result, they are ideal candidates for monitoring soil quality assessment. The emergence of meta-omics approaches based on recent advances in high-throughput sequencing and bioinformatics has remarkably improved our ability to characterize microbial diversity and its potential functions. This revolution has substantially filled the knowledge gap about soil microbial diversity regulation and ecology, but also provided new and robust indicators of agricultural soil quality. We reviewed how meta-omics approaches replaced traditional methods and allowed developing modern microbial indicators of the soil biological quality. Each meta-omics approach is described in its general principles, methodologies, specificities, strengths and drawbacks, and illustrated with concrete applications for soil monitoring. The development of metabarcoding approaches in the last 20 years has led to a collection of microbial indicators that are now operational and available for the farming sector. Our review shows that despite the recent huge advances, some meta-omics approaches (e.g., metatranscriptomics or meta-proteomics) still need developments to be operational for environmental bio-monitoring. As regards prospects, we outline the importance of building up repositories of soil quality indicators. These are essential for objective and robust diagnosis, to help actors and stakeholders improve soil management, with a view to or to contribute to combining the food and environmental quality of next-generation farming systems in the context of the agroecological transition.

Microbial transfers from permanent grassland ecosystems to milk in dairy farms in the Comté cheese area

The specificity of dairy Protected Designation of Origin (PDO) products is related to their “terroir” of production. This relationship needs better understanding for efficient and sustainable productions preserving the agroecological equilibrium of agroecosystems, especially grasslands. Specificity of PDO Comté cheese was related to the diversity of natural raw milk bacterial communities, but their sources need to be determined. It is hypothesized that raw milk indigenous microbial communities may originate from permanent grazed grasslands by the intermediate of dairy cows according to the sequence soil–phyllosphere–teat–milk. This hypothesis was evaluated on a 44 dairy farms network across PDO Comté cheese area by characterizing prokaryotic and fungal communities of these compartments by metabarcoding analysis (16S rRNA gene: V3–V4 region, 18S rRNA gene: V7–V8 region). Strong and significant links were highlighted between the four compartments through a network analysis (0.34 < r < 0.58), and were modulated by soil pH, plant diversity and elevation; but also by farming practices: organic fertilization levels, cattle intensity and cow-teat care. This causal relationship suggests that microbial diversity of agroecosystems is a key player in relating a PDO product to its “terroir”; this under the dependency of farming practices. Altogether, this makes the “terroir” even more local and needs to be considered for production sustainability.

Soil microbial communities in the face of changing farming practices: A case study in an agricultural landscape in France

According to biogeography studies, the abundance and richness of soil microorganisms vary across multiple spatial scales according to soil properties and farming practices. However, soil microorganisms also exhibit poorly understood temporal variations. This study aimed at better understanding how soil microbial communities respond to changes in farming practices at a landscape scale over time. A regular grid of 269 sites was set up across a 1,200 ha farming landscape, and soil samples were characterized for their molecular microbial biomass and bacterial richness at two dates (2011 and 2016). A mapping approach highlighted that spatial microbial patterns were stable over time, while abundance and richness levels were modified. The drivers of these changes were investigated though a PLS-PM (partial least square path-modeling) approach. Soil properties were stable over time, but farming practices changed. Molecular microbial biomass was mainly driven by soil resources, whereas bacterial richness depended on both farming practices and ecological parameters. Previous-crop and management effects and a temporal dependence of the microbial community on the historical farming management were also highlighted.

Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.