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Genes from H37Rv that are deleted in the genome of 18b. (DOCX 19 kb)

Genes from H37Rv that are deleted in the genome of 18b. (DOCX 19 kb)

Genes from 18b that are deleted in the genome of H37Rv. (DOCX 16 kb)

Genes from 18b that are deleted in the genome of H37Rv. (DOCX 16 kb)

Positions of IS6110 in the genome sequences of M. tuberculosis 18b and H37Rv. (DOCX 27 kb)

Genes in the genome of 18b that span two genes in H37Rv. (DOCX 24 kb)

Background: In Mycobacterium tuberculosis and in Mycobacterium smegmatis the furA-katG loci, encoding the FurA regulatory protein and the KatG catalase-peroxidase, are highly conserved. In M. tuberculosis furA-katG constitute a single operon, whereas in M. smegmatis a single mRNA covering both genes could not be found. In both species, specific 5' ends have been identified: the first one, located upstream of the furA gene, corresponds to transcription initiation from the furA promoter; the second one is the katG mRNA 5' end, located in the terminal part of furA.

Genes in the genome of 18b that span two genes in H37Rv. (DOCX 24 kb)

Positions of IS6110 in the genome sequences of M. tuberculosis 18b and H37Rv. (DOCX 27 kb)

The ability to detect and characterize bacteria within a biological sample is crucial for the monitoring of infections and epidemics, as well as for the study of human health and its relationship with commensal microorganisms. To this aim, a commonly used technique is the 16S rRNA gene targeted sequencing. PCR-amplified 16S sequences derived from the sample of interest are usually clustered into the so-called Operational Taxonomic Units (OTUs) based on pairwise similarities. Then, representative OTU sequences are compared with reference (human-made) databases to derive their phylogeny and taxonomic classification. Here, we propose a new reference-free approach to define the phylogenetic distance between bacteria based on protein domains, which are the evolving units of proteins. We extract the protein domain profiles of 3368 bacterial genomes and we use an ecological approach to model their Relative Species Abundance distribution. Based on the model parameters, we then derive a new measurement of phylogenetic distance. Finally, we show that such model-based distance is capable of detecting differences between bacteria in cases in which the 16S rRNA-based method fails, providing a possibly complementary approach , which is particularly promising for the analysis of bacterial populations measured by shotgun sequencing.

In this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.

The aim of this study was to investigate the impact of supplementation of a low protein diet on ceca microbiome and productive performances of broiler chickens. A total of 1,170 one-day-old male chicks (Ross 308) were divided in 2 diet groups and reared in the same conditions up to 42 D. Birds belonging to the control group were fed a basal diet. Birds belonging to the low protein group the basal diet with a reduced level of crude protein (-7%). Cecum contents from randomly selected birds were collected at 14 and 42 D within each diet group, submitted to DNA extraction and then tested by shotgun metagenomic sequencing. Abundances of species belonging to Actinobacteria and Proteobacteria were mainly affected by the diet as well as interaction between diet and time, while species belonging to Firmicutes and Cyanobacteria changed mainly according to the age of the birds. At family level, Lactobacillaceae significantly decreased in the low protein group up to 14 D. However, at the end of the rearing period the same family was significantly higher in the low protein group. The most abundant functional genes, represented by cystine desulfurase, alpha-galactosidase, and serine hydroxymethyltransferase, displayed comparable abundances in both diet groups, although significative differences were identified for less abundant functional genes at both sampling times. Birds fed control and low protein diets showed similar productive performances. However, in the finisher phase, feed conversion rate was significantly better in chickens fed the low protein diet. Overall, this study showed that a reduced intake of crude protein in broilers increases the abundance of Lactobacillaceae in the ceca over time and this seems to be linked to a better feed conversion rate between 36 and 42 D. A reduced intake of crude protein in chicken production can help to improve exploitation of edible resources, while reducing the emission of nitrogen pollutants in the environment.

Background: Interest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity. Results: We analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov's model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov's model also differentiates the animal species and is in good agreement with the first and second order Hill's numbers, that are common evenness indexes simply based on the fraction of individuals per species. Conclusions: We conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov's model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.