Francesco Asnicar

Compact graphical representation of phylogenetic data and metadata with GraPhlAn

The increased availability of genomic and metagenomic data poses challenges at multiple analysis levels, including visualization of very large-scale microbial and microbial community data paired with rich metadata. We developed GraPhlAn (Graphical Phylogenetic Analysis), a computational tool that produces high-quality, compact visualizations of microbial genomes and metagenomes. This includes phylogenies spanning up to thousands of taxa, annotated with metadata ranging from microbial community abundances to microbial physiology or host and environmental phenotypes. GraPhlAn has been developed as an open-source command-driven tool in order to be easily integrated into complex, publication-quality bioinformatics pipelines. It can be executed either locally or through an online Galaxy web application. We present several examples including taxonomic and phylogenetic visualization of microbial communities, metabolic functions, and biomarker discovery that illustrate GraPhlAn’s potential for modern microbial and community genomics.

Strain-level microbial epidemiology and population genomics from shotgun metagenomics

Identifying microbial strains and characterizing their functional potential is essential for pathogen discovery, epidemiology and population genomics. We present pangenome-based phylogenomic analysis (PanPhlAn; http://segatalab.cibio.unitn.it/tools/panphlan), a tool that uses metagenomic data to achieve strain-level microbial profiling resolution. PanPhlAn recognized outbreak strains, produced the largest strain-level population genomic study of human-associated bacteria and, in combination with metatranscriptomics, profiled the transcriptional activity of strains in complex communities.

Studying Vertical Microbiome Transmission from Mothers to Infants by Strain-Level Metagenomic Profiling.

Early infant exposure is important in the acquisition and ultimate development of a healthy infant microbiome. There is increasing support for the idea that the maternal microbial reservoir is a key route of microbial transmission, and yet much is inferred from the observation of shared species in mother and infant. The presence of common species, per se, does not necessarily equate to vertical transmission, as species exhibit considerable strain heterogeneity. It is therefore imperative to assess whether shared microbes belong to the same genetic variant (i.e., strain) to support the hypothesis of vertical transmission. Here we demonstrate the potential of shotgun metagenomics and strain-level profiling to identify vertical transmission events. Combining these data with metatranscriptomics, we show that it is possible not only to identify and track the fate of microbes in the early infant microbiome but also to investigate the actively transcribing members of the community. These approaches will ultimately provide important insights into the acquisition, development, and community dynamics of the infant microbiome. ABSTRACT The gut microbiome becomes shaped in the first days of life and continues to increase its diversity during the first months. Links between the configuration of the infant gut microbiome and infant health are being shown, but a comprehensive strain-level assessment of microbes vertically transmitted from mother to infant is still missing. We collected fecal and breast milk samples from multiple mother-infant pairs during the first year of life and applied shotgun metagenomic sequencing followed by computational strain-level profiling. We observed that several specific strains, including those of Bifidobacterium bifidum, Coprococcus comes, and Ruminococcus bromii, were present in samples from the same mother-infant pair, while being clearly distinct from those carried by other pairs, which is indicative of vertical transmission. We further applied metatranscriptomics to study the in vivo gene expression of vertically transmitted microbes and found that transmitted strains of Bacteroides and Bifidobacterium species were transcriptionally active in the guts of both adult and infant. By combining longitudinal microbiome sampling and newly developed computational tools for strain-level microbiome analysis, we demonstrated that it is possible to track the vertical transmission of microbial strains from mother to infants and to characterize their transcriptional activity. Our work provides the foundation for larger-scale surveys to identify the routes of vertical microbial transmission and its influence on postinfancy microbiome development. IMPORTANCE Early infant exposure is important in the acquisition and ultimate development of a healthy infant microbiome. There is increasing support for the idea that the maternal microbial reservoir is a key route of microbial transmission, and yet much is inferred from the observation of shared species in mother and infant. The presence of common species, per se, does not necessarily equate to vertical transmission, as species exhibit considerable strain heterogeneity. It is therefore imperative to assess whether shared microbes belong to the same genetic variant (i.e., strain) to support the hypothesis of vertical transmission. Here we demonstrate the potential of shotgun metagenomics and strain-level profiling to identify vertical transmission events. Combining these data with metatranscriptomics, we show that it is possible not only to identify and track the fate of microbes in the early infant microbiome but also to investigate the actively transcribing members of the community. These approaches will ultimately provide important insights into the acquisition, development, and community dynamics of the infant microbiome.

Unexplored diversity and strain-level structure of the skin microbiome associated with psoriasis

Psoriasis is an immune-mediated inflammatory skin disease that has been associated with cutaneous microbial dysbiosis by culture-dependent investigations and rRNA community profiling. We applied, for the first time, high-resolution shotgun metagenomics to characterise the microbiome of psoriatic and unaffected skin from 28 individuals. We demonstrate psoriatic ear sites have a decreased diversity and psoriasis is associated with an increase in Staphylococcus, but overall the microbiomes of psoriatic and unaffected sites display few discriminative features at the species level. Finer strain-level analysis reveals strain heterogeneity colonisation and functional variability providing the intriguing hypothesis of psoriatic niche-specific strain adaptation or selection. Furthermore, we accessed the poorly characterised, but abundant, clades with limited sequence information in public databases, including uncharacterised Malassezia spp. These results highlight the skins hidden diversity and suggests strain-level variations could be key determinants of the psoriatic microbiome. This illustrates the need for high-resolution analyses, particularly when identifying therapeutic targets. This work provides a baseline for microbiome studies in relation to the pathogenesis of psoriasis.Psoriasis: investigating microbial diversityAnalysing microbial populations on the skin provides an insight into the diversity of microbes associated with psoriasis. Nicola Segata and colleagues at the University of Trento, Italy, used genetic analysis to compare the microbial populations on regions of skin affected and unaffected by psoriasis. Staphylococcus bacteria were more prevalent in psoriasis, but there was little clearly defined difference in microbial species on psoriasis-affected and unaffected skin. There was, however, decreased microbial diversity on psoriatic ear sites. Deeper strain-level computational analysis suggested that psoriasis could offer niche locations for colonisation by specific strains of staphylococci and propionibacteria. The results highlight the diversity of microbial populations on the skin, and the need for larger cohorts to build on the baseline data now established. Further studies might help identify targets for treating skin bacteria associated with psoriasis.

Uncovering oral Neisseria tropism and persistence using metagenomic sequencing

Microbial epidemiology and population genomics have previously been carried out near-exclusively for organisms grown in vitro. Metagenomics helps to overcome this limitation, but it is still challenging to achieve strain-level characterization of microorganisms from culture-independent data with sufficient resolution for epidemiological modelling. Here, we have developed multiple complementary approaches that can be combined to profile and track individual microbial strains. To specifically profile highly recombinant neisseriae from oral metagenomes, we integrated four metagenomic analysis techniques: single nucleotide polymorphisms in the clades core genome, DNA uptake sequence signatures, metagenomic multilocus sequence typing and strain-specific marker genes. We applied these tools to 520 oral metagenomes from the Human Microbiome Project, finding evidence of site tropism and temporal intra-subject strain retention. Although the opportunistic pathogen Neisseria meningitidis is enriched for colonization in the throat, N. flavescens and N. subflava populate the tongue dorsum, and N. sicca, N. mucosa and N. elongata the gingival plaque. The buccal mucosa appeared as an intermediate ecological niche between the plaque and the tongue. The resulting approaches to metagenomic strain profiling are generalizable and can be extended to other organisms and microbiomes across environments.

Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome

Summary The acquisition and development of the infant microbiome are key to establishing a healthy host-microbiome symbiosis. The maternal microbial reservoir is thought to play a crucial role in this process. However, the source and transmission routes of the infant pioneering microbes are poorly understood. To address this, we longitudinally sampled the microbiome of 25 mother-infant pairs across multiple body sites from birth up to 4 months postpartum. Strain-level metagenomic profiling showed a rapid influx of microbes at birth followed by strong selection during the first few days of life. Maternal skin and vaginal strains colonize only transiently, and the infant continues to acquire microbes from distinct maternal sources after birth. Maternal gut strains proved more persistent in the infant gut and ecologically better adapted than those acquired from other sources. Together, these data describe the mother-to-infant microbiome transmission routes that are integral in the development of the infant microbiome.

NES2RA: Network expansion by stratified variable subsetting and ranking aggregation

Gene network expansion is a task of the foremost importance in computational biology. Gene network expansion aims at finding new genes to expand a given known gene network. To this end, we developed gene@home, a BOINC-based project that finds candidate genes that expand known local gene networks using NESRA. In this paper, we present NES2RA, a novel approach that extends and improves NESRA by modeling, using a probability vector, the confidence of the presence of the genes belonging to the local gene network. NES2RA adopts intensive variable-subsetting strategies, enabled by the computational power provided by gene@home volunteers. In particular, we use the skeleton procedure of the PC-algorithm to discover candidate causal relationships within each subset of variables. Finally, we use state-of-the-art aggregators to combine the results into a single ranked candidate genes list. The resulting ranking guides the discovery of unknown relations between genes and a priori known local gene networks. Our experimental results show that NES2RA outperforms the PC-algorithm and its order-independent PC-stable version, ARACNE, and our previous approach, NESRA. In this paper we extensively discuss the computational aspects of the NES2RA approach and we also present and validate expansions performed on the model plant Arabidopsis thaliana and the model bacteria Escherichia coli.Gene network expansion is a task of the foremost importance in computational biology. Gene network expansion aims at finding new genes to expand a given known gene network. To this end, we develope...

Draft genome sequence of the planktic cyanobacterium Tychonema bourrellyi, isolated from Alpine lentic freshwater

ABSTRACT We describe here the draft genome sequence of the cyanobacterium Tychonema bourrellyi, assembled from a metagenome of a nonaxenic culture. The strain (FEM_GT703) was isolated from a freshwater sample taken from Lake Garda, Italy. The draft genome sequence represents the first assembled T. bourrellyi strain.

Discovering Candidates for Gene Network Expansion by Distributed Volunteer Computing

Our group has recently developed gene@home, a BOINC project that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The gene@home project adopts intensive variable-subsetting strategies enabled by the computational power provided by the volunteers who have joined the project by means of the BOINC client. Our project exploits the PC algorithm (Spirtes and Glymour, 1991) in an iterative way, for discovering putative causal relationships within each subset of variables. This paper presents our infrastructure, called TN-Grid, that is hosting the gene@home project. Gene@home implements a novel method for Network Expansion by Subsetting and Ranking Aggregation (NESRA), producing a list of genes that are candidates for the gene network expansion task. NESRA is an algorithm that has: 1) a ranking procedure that systematically subsets the variables, the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found, 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists, 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. In our experimental results, we show that NESRA outperforms both the PC algorithm and its order-independent version called PC*. Evaluations and experiments are done by means of the gene@home project on a real gene regulatory network of the model plant Arabidopsis thaliana.

Draft Genome Sequences of Novel Pseudomonas, Flavobacterium, and Sediminibacterium Species Strains from a Freshwater Ecosystem

ABSTRACT Freshwater ecosystems represent 0.01% of the water on Earth, but they support 6% of global biodiversity that is still mostly uncharacterized. Here, we describe the genome sequences of three strains belonging to novel species in the Pseudomonas, Flavobacterium, and Sediminibacterium genera recovered from a water sample of Lake Garda, Italy.