Giovanni Bacci

Soil salinity poses a major challenge to the legume‐rhizobia symbiosis development, thereby affecting sustainable agriculture. Selecting NaCl‐tolerant strains and enhancing the native strains' fitness under salt stress are essential steps for the restoration of marginal areas. In this work, 49 Sinorhizobium meliloti strains, the rhizobial species forming symbiotic nitrogen‐fixing associations with alfalfa—including 21 de novo‐sequenced field isolates—were subjected to a thorough in vitro screening for salt tolerance at progressively higher NaCl concentrations. Field isolates showed genome‐based geographical clustering but contrasting salt tolerance abilities. Indeed, genome‐wide association (GWA) analysis on the strains' whole‐genome sequencing data indicated several loci associated with the variability in salt tolerance. Candidate genes were involved in various processes including cell wall organisation, LPS biosynthesis, quorum sensing, and carbohydrate transport and metabolism. The relationship with carbohydrate metabolism was further confirmed by Phenotype Microarray analysis which indicated salt‐tolerant strains having enhanced capacity in carbon source usage. These findings reveal synergistic pathways underlying salt tolerance and suggest candidate traits (e.g., quorum sensing, carbohydrate synthesis and modification) for developing bioinoculants to enhance legume performance in saline soils.

Even if their impact is often underestimated, yeasts and yeast-like fungi represent the most prevalent eukaryotic members of microbial communities on Earth. They play numerous roles in natural ecosystems and in association with their hosts. They are involved in the food industry and pharmaceutical production, but they can also cause diseases in other organisms, making the understanding of their biology mandatory. The ongoing loss of biodiversity due to overexploitation of environmental resources is a growing concern in many countries. Therefore, it becomes crucial to understand the ecology and evolutionary history of these organisms to systematically classify them. To achieve this, it is essential that our knowledge of the mycobiota reaches a level similar to that of the bacterial communities. To overcome the existing challenges in the study of fungal communities, the first step should be the establishment of standardized techniques for the correct identification of species, even from complex matrices, both in wet lab practices and in bioinformatic tools.

Propolis is a resinous material produced by honeybees from different plant sources and used in the hive as a building material and to protect the colony from parasites and pathogens. Despite its antimicrobial properties, recent studies showed that propolis hosts diverse microbial strains, some with great antimicrobial potential. In this study, the first description of the bacterial community of propolis produced by the gentle Africanized honeybee was reported. Propolis was sampled from hives of two different geographic areas of Puerto Rico (PR, USA), and the associated microbiota investigated by both cultivation and metataxonomic approaches. Metabarcoding analysis showed appreciable bacterial diversity in both areas and statistically significant dissimilarity in the taxa composition of the two areas, probably due to the different climatic conditions. Both metabarcoding and cultivation data revealed the presence of taxa already detected in other hive components and compatible with the bee’s foraging environment. Isolated bacteria and propolis extracts showed antimicrobial activity against Gram-positive and Gram-negative bacterial tester strains. These results support the hypothesis that the propolis microbiota could contribute to propolis’ antimicrobial properties.

Advances in Next Generation Sequencing technologies allow us to inspect and unlock the genome to a level of detail that was unimaginable only a few decades ago. Omics-based studies are casting a light on the patterns and determinants of disease conditions in populations, as well as on the influence of microbial communities on human health, just to name a few. Through increasing volumes of sequencing information, for example, it is possible to compare genomic features and analyze the modulation of the transcriptome under different environmental stimuli. Although protocols for NGS preparation are intended to leave little to no space for contamination of any kind, a noticeable fraction of sequencing reads still may not uniquely represent what was intended to be sequenced in the first place. If a natural consequence of a sequencing sample is to assess the presence of features of interest by mapping the obtained reads to a genome of reference, sometimes it is useful to determine the fraction of those that do not map, or that map discordantly, and store this information to a new file for subsequent analyses. Here we propose a new mapper, which we called Squid, that among other accessory functionalities finds and returns sequencing reads that match or do not match to a reference sequence database in any orientation. We encourage the use of Squid prior to any quantification pipeline to assess, for instance, the presence of contaminants, especially in RNA-Seq experiments.

Cystic fibrosis (CF) disease leads to altered lung and gut microbiomes compared to healthy subjects. The magnitude of this dysbiosis is influenced by organ-specific microenvironmental conditions at different stages of the disease. However, how this gut-lung dysbiosis is influenced by Pseudomonas aeruginosa chronic infection is unclear. To test the relationship between CFTR dysfunction and gut-lung microbiome under chronic infection, we established a model of P. aeruginosa infection in wild-type (WT) and gut-corrected CF mice. Using 16S ribosomal RNA gene, we compared lung, stool, and gut microbiota of C57Bl/6 Cftr ^tm1UNCTgN(FABPCFTR) or WT mice at the naïve state or infected with P. aeruginosa. P. aeruginosa infection influences murine health significantly changing body weight both in CF and WT mice. Both stool and gut microbiota revealed significantly higher values of alpha diversity in WT mice than in CF mice, while lung microbiota showed similar values. Infection with P. aeruginosa did not changed the diversity of the stool and gut microbiota, while a drop of diversity of the lung microbiota was observed compared to non-infected mice. However, the taxonomic composition of gut microbiota was shown to be influenced by P. aeruginosa infection in CF mice but not in WT mice. This finding indicates that P. aeruginosa chronic infection has a major impact on microbiota diversity and composition in the lung. In the gut, CFTR genotype and P. aeruginosa infection affected the overall diversity and taxonomic microbiota composition, respectively. Overall, our results suggest a cross-talk between lung and gut microbiota in relation to P. aeruginosa chronic infection and CFTR mutation.

Predicting host traits from metagenomes presents new challenges that can be difficult to overcome for researchers without a strong background in bioinformatics and/or statistics. Profiling bacterial communities using shotgun metagenomics often leads to the generation of a large amount of data that cannot be used directly for training a model. In this chapter we provide a detailed description of how to build a working machine learning model based on taxonomic and functional features of bacterial communities inhabiting the lungs of cystic fibrosis patients. Models are built in the R environment by using different freely available machine learning algorithms.

Brachyuran crabs originated in the oceans and evolved specific morphological and physiological adaptations to live in freshwater, intertidal and even terrestrial habitats but the role of a selection mechanism involving symbiotic microorganisms long these colonization processes are not known. In this work we investigated the associated microbiota of three populations of a terrestrial brachyuran crab, Chiromantes haematocheir , to find evidence of a conserved crab-specific microbiome unrelated to the population of origin and dissimilar from environmental microbial assemblages. Bacterial 16S rRNA gene and fungal ITS sequences were obtained from selected crab organs and environmental matrices to profile microbial communities. In spite of the presence of truly marine larval stages and no gregarious behaviour, favouring microbiota exchanges, we found common, organ-specific microbiota, associated to the gut and the gills of the crabs (with more than 15% of the genera detected specifically enriched only in one organ). Our results suggest an early establishment of a new common, stable microbiota in the transition from water to land.

Rhizobia are ecologically important, facultative plant-symbiotic microbes. In nature, there is a large variability in the association of rhizobial strains and host plants of the same species. Here, we evaluated whether plant and rhizobial genotypes influence the initial transcriptional response of rhizobium following perception of a host plant. RNA sequencing of the model rhizobium Sinorhizobium meliloti exposed to root exudates or luteolin (an inducer of nod genes, involved in the early steps of symbiotic interaction) was performed on a combination of three S. meliloti strains and three alfalfa varieties as host plants. The response to root exudates involved hundreds of changes in the rhizobium transcriptome. Of the differentially expressed genes, 35% were influenced by the strain genotype, 16% were influenced by the plant genotype, and 29% were influenced by strain-by-host plant genotype interactions. We also examined the response of a hybrid S. meliloti strain in which the symbiotic megaplasmid (∼20% of the genome) was mobilized between two of the above-mentioned strains. Dozens of genes were upregulated in the hybrid strain, indicative of nonadditive variation in the transcriptome. In conclusion, this study demonstrated that transcriptional responses of rhizobia upon perception of legumes are influenced by the genotypes of both symbiotic partners and their interaction, suggesting a wide spectrum of genetic determinants involved in the phenotypic variation of plant-rhizobium symbiosis. IMPORTANCE A sustainable way for meeting the need of an increased global food demand should be based on a holobiont perspective, viewing crop plants as intimately associated with their microbiome, which helps improve plant nutrition, tolerance to pests, and adverse climate conditions. However, the genetic repertoire needed for efficient association with plants by the microbial symbionts is still poorly understood. The rhizobia are an exemplary model of facultative plant symbiotic microbes. Here, we evaluated whether genotype-by-genotype interactions could be identified in the initial transcriptional response of rhizobium perception of a host plant. We performed an RNA sequencing study to analyze the transcriptomes of different rhizobial strains elicited by root exudates of three alfalfa varieties as a proxy of an early step of the symbiotic interaction. The results indicated strain- and plant variety-dependent variability in the observed transcriptional changes, providing fundamentally novel insights into the genetic basis of rhizobium-plant interactions. Our results provide genetic insights and perspective to aid in the exploitation of natural rhizobium variation for improvement of legume growth in agricultural ecosystems.

This study aimed to characterise the gut microbiome composition of European hares (Lepus europaeus) and its potential changes after a short-term diet modification. The high sensitivity of European hare to habitat changes makes this species a good model to analyse possible alterations in gut microbiome after the introduction of additional nourishment into the diet. In total, 20 pairs were chosen for the experiments; 10 pairs formed the control group and were fed with standard fodder. The other 10 pairs represented the experimental group, whose diet was integrated with apples and carrots. The DNA from fresh faecal pellets collected after 4 days from the start of the experiment was extracted and the V3-V4 hypervariable regions were amplified and sequenced using the Illumina MiSeq^® platform. The obtained amplicon sequence variants were classified into 735 bacterial genera belonging to 285 families and 36 phyla. The control and the experimental groups appeared to have a homogenous dispersion for the two taxonomic levels analysed with the most abundant phyla represented by Bacteroidetes and Firmicutes. No difference between control and experimental samples was detected, suggesting that the short-term variation in food availability did not alter the hares’ gut microbiome. Further research is needed to estimate significant time threshold.