PhenoLink--a web-tool for linking phenotype to ~omics data for bacteria: application to gene-trait matching for Lactobacillus plantarum strains

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

An α type gluco-oligosaccharide from brown algae Laminaria japonica stimulated the growth of lactic acid bacteria encoding specific ABC transport system components

Glucan is the most widely distributed glycan. Many probiotics such as lactic acid bacteria (LAB) encoded corresponding hydrolytic enzymes, which could use these glucans as energy substances. Brown alga is rich in glucan and has high edible and medicinal value, but research on its regulation to probiotics is not detailed enough. In this study, we determined a novel neutral α type gluco-oligosaccharide from the brown alga Laminaria japonica with a degree of polymerization (DP) of 2-8 and a structure that mainly consists of α-(1→4)-linked glycosidic bonds called Laminaria japonica gluco-oligosaccharide (LJGO). Fermentation in vitro and gene-phenotype correlation analyses revealed that LJGO selectively stimulated the growth of the LAB strain encoding a specific ATP-binding cassette (ABC) transport system in a GH13 gene cluster, with apparent differences among 14 tested species. Comparative genomics further revealed that this transport system is species-specific, implying a potential contribution to species evolution. Transcriptomic analysis based on LAB strains cultured on LJGO and 1H-NMR findings of LJGO residues after strain utilization showed that the GH13 gene cluster contains functional LAB genes involved in LJGO utilization. Further verification by gene knockout studies is needed to expand our findings.

Gene-Phenotype Associations Involving Human-Residential Bifidobacteria (HRB) Reveal Significant Species- and Strain-Specificity in Carbohydrate Catabolism

Bifidobacteria are among the first colonizers of the human gastrointestinal tract. Different bacterial species use different mechanisms for utilization of various carbon sources in order to establish themselves in the complex microbial ecosystem of the gut. However, these mechanisms still need to be explored. Here, a large gene–phenotype correlation analysis was carried out to explore the metabolic and genetic diversity of bifidobacterial carbohydrate utilization abilities. In this study, we used 21 different carbohydrates to determine the growth phenotypes, the distribution of glycoside hydrolases (GHs), and gene clusters related to the utilization of multiple carbon sources in six human-residential Bifidobacterium species. Five carbohydrates significantly stimulated growth of almost all strains, while the remaining sugars exhibited species- and strain-specificity. Correspondingly, different Bifidobacterium species also had specific GHs involved in fermentation of plant or host glycans. Moreover, we analyzed several carbohydrate utilization gene clusters, such as 2-fucosyllactose (2′FL), sialic acid (SA), and fructooligosaccharide (FOS). In summary, by using 217 bifidobacterial strains and a wide range of growth substrates, our research revealed inter- and intra-species differences in bifidobacterial in terms of carbohydrate utilization. The findings of this study are useful for the process of developing prebiotics for optimum growth of probiotics, especially Bifidobacterium species.

Phenotypic and genetic characterization of differential galacto-oligosaccharide utilization in Lactobacillus plantarum

Several Lactobacillus plantarum strains are marketed as probiotics for their potential health benefits. Prebiotics, e.g., galacto-oligosaccharides (GOS), have the potential to selectively stimulate the growth of L. plantarum probiotic strains based on their phenotypic diversity in carbohydrate utilization, and thereby enhance their health promoting effects in the host in a strain-specific manner. Previously, we have shown that GOS variably promotes the strain-specific growth of L. plantarum. In this study we investigated this variation by molecular analysis of GOS utilization by L. plantarum. HPAEC-PAD analysis revealed two distinct GOS utilization phenotypes in L. plantarum. Linking these phenotypes to the strain-specific genotypes led to the identification of a lac operon encoding a β-galactosidase (lacA), a permease (lacS), and a divergently oriented regulator (lacR), that are predicted to be involved in the utilization of higher degree of polymerization (DP) constituents present in GOS (specifically DP of 3-4). Mutation of lacA and lacS in L. plantarum NC8 resulted in reduced growth on GOS, and HPAEC analysis confirmed the role of these genes in the import and utilization of higher-DP GOS constituents. Overall, the results enable the design of highly-selective synbiotic combinations of L. plantarum strain-specific probiotics and specific GOS-prebiotic fractions.

Synbiotic Matchmaking in Lactobacillus plantarum: Substrate Screening and Gene-Trait Matching To Characterize Strain-Specific Carbohydrate Utilization

Synbiotics are food supplements that combine probiotics and prebiotics to synergistically elicit a health effect in humans. Lactobacillus plantarum exhibits remarkable genetic and phenotypic diversity, in particular in strain-specific carbohydrate utilization capacities, and several strains are marketed as probiotics. We have screened 77 L. plantarum strains for their abilities to utilize specific prebiotic fibers, revealing variable and strain-specific growth efficiencies on isomalto- and galactooligosaccharides. We identified a single strain within the screening panel that was able to effectively utilize inulin and fructooligosaccharides (FOS), which did not support efficient growth of the rest of the strains. In the panel we tested, we did not find strains that could utilize arabinoxylooligosaccharides or sulfated fucoidan. The strain-specific growth phenotype on isomaltooligosaccharides was further analyzed using high-performance anion-exchange chromatography, which revealed distinct substrate utilization phenotypes within the strain panel. The strain-specific phenotypes could be linked to the strains' genotypes by identifying gene clusters coding for carbohydrate membrane transport systems that are predicted to be involved in the utilization of isomaltose and other (unidentified) oligosaccharides in the isomaltooligosaccharide substrate.IMPORTANCE Synbiotics combine prebiotics and probiotics to synergistically enhance the health benefits associated with these ingredients. Lactobacillus plantarum is encountered as a natural inhabitant of the gastrointestinal tract, and specific strains are marketed as probiotics based on their strain-specific health-promoting activities. Strain-specific stimulation of growth through prebiotic substrates could enhance the persistence and/or activity of L. plantarum in situ Our study establishes a high-throughput screening model for prebiotic substrate utilization by individual strains of bacteria, which can be readily employed for synbiotic matchmaking approaches that aim to enhance the intestinal delivery of probiotics through strain-specific, selective growth stimulation.

A Genomic Toolkit for the Mechanistic Dissection of Intractable Human Gut Bacteria

Despite the remarkable microbial diversity found within humans, our ability to link genes to phenotypes is based upon a handful of model microorganisms. We report a comparative genomics platform for Eggerthella lenta and other Coriobacteriia, a neglected taxon broadly relevant to human health and disease. We uncover extensive genetic and metabolic diversity and validate a tool for mapping phenotypes to genes and sequence variants. We also present a tool for the quantification of strains from metagenomic sequencing data, enabling the identification of genes that predict bacterial fitness. Competitive growth is reproducible under laboratory conditions and attributable to intrinsic growth rates and resource utilization. Unique signatures of in vivo competition in gnotobiotic mice include an adhesin enriched in poor colonizers. Together, these computational and experimental resources represent a strong foundation for the continued mechanistic dissection of the Coriobacteriia and a template that can be applied to study other genetically intractable taxa.

Novel sequencing technologies to support industrial biotechnology

Industrial biotechnology develops and applies microorganisms for the production of bioproducts and enzymes with applications ranging from food and feed ingredients and processing to bio-based chemicals, biofuels and pharmaceutical products. Next generation DNA sequencing technologies play an increasingly important role in improving and accelerating microbial strain development for existing and novel bio-products via screening, gene and pathway discovery, metabolic engineering and additional optimization and understanding of large-scale manufacturing. In this mini-review, we describe novel DNA sequencing and analysis technologies with a focus on applications to industrial strain development, enzyme discovery and microbial community analysis.

Comparative Genome Analysis of Lactococcus lactis Indicates Niche Adaptation and Resolves Genotype/Phenotype Disparity

Lactococcus lactis is one of the most important micro-organisms in the dairy industry for the fermentation of cheese and buttermilk. Besides the conversion of lactose to lactate it is responsible for product properties such as flavor and texture, which are determined by volatile metabolites, proteolytic activity and exopolysaccharide production. While the species Lactococcus lactis consists of the two subspecies lactis and cremoris their taxonomic position is confused by a group of strains that, despite of a cremoris genotype, display a lactis phenotype. Here we compared and analyzed the (draft) genomes of 43 L. lactis strains, of which 19 are of dairy and 24 are of non-dairy origin. Machine-learning algorithms facilitated the identification of orthologous groups of protein sequences (OGs) that are predictors for either the taxonomic position or the source of isolation. This allowed the unambiguous categorization of the genotype/phenotype disparity of ssp. lactis and ssp. cremoris strains. A detailed analysis of phenotypic properties including plasmid-encoded genes indicates evolutionary changes during niche adaptations. The results are consistent with the hypothesis that dairy isolates evolved from plant isolates. The analysis further suggests that genomes of cremoris phenotype strains are so eroded that they are restricted to a dairy environment. Overall the genome comparison of a diverse set of strains allowed the identification of niche and subspecies specific genes. This explains evolutionary relationships and will aid the identification and selection of industrial starter cultures.

Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica

Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses.

Cell Surface Properties of Lactococcus lactis Reveal Milk Protein Binding Specifically Evolved in Dairy Isolates

Surface properties of bacteria are determined by the molecular composition of the cell wall and they are important for interactions of cells with their environment. Well-known examples of bacterial interactions with surfaces are biofilm formation and the fermentation of solid materials like food and feed. Lactococcus lactis is broadly used for the fermentation of cheese and buttermilk and it is primarily isolated from either plant material or the dairy environment. In this study, we characterized surface hydrophobicity, charge, emulsification properties, and the attachment to milk proteins of 55 L. lactis strains in stationary and exponential growth phases. The attachment to milk protein was assessed through a newly developed flow cytometry-based protocol. Besides finding a high degree of biodiversity, phenotype-genotype matching allowed the identification of candidate genes involved in the modification of the cell surface. Overexpression and gene deletion analysis allowed to verify the predictions for three identified proteins that altered surface hydrophobicity and attachment of milk proteins. The data also showed that lactococci isolated from a dairy environment bind higher amounts of milk proteins when compared to plant isolates. It remains to be determined whether the alteration of surface properties also has potential to alter starter culture functionalities.

Intervention in prediction measure: a new approach to assessing variable importance for random forests

Background

Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction performance are not unique or there is not even a clear definition, as in the case of multivariate response random forests.

Methods

A new alternative importance measure, called Intervention in Prediction Measure, is investigated. It depends on the structure of the trees, without depending on performance measures. It is compared with other well-known variable importance measures in different contexts, such as a classification problem with variables of different types, another classification problem with correlated predictor variables, and problems with multivariate responses and predictors of different types.

Results

Several simulation studies are carried out, showing the new measure to be very competitive. In addition, it is applied in two well-known bioinformatics applications previously used in other papers. Improvements in performance are also provided for these applications by the use of this new measure.

Conclusions

This new measure is expressed as a percentage, which makes it attractive in terms of interpretability. It can be used with new observations. It can be defined globally, for each class (in a classification problem) and case-wise. It can easily be computed for any kind of response, including multivariate responses. Furthermore, it can be used with any algorithm employed to grow each individual tree. It can be used in place of (or in addition to) other variable importance measures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1650-8) contains supplementary material, which is available to authorized users.

Carbohydrate metabolism in Oenococcus oeni: a genomic insight

Background

Oenococcus oeni is the bacterial species that drives malolactic fermentation in most wines. Several studies have described a high intraspecific diversity regarding carbohydrate degradation abilities but the link between the phenotypes and the genes and metabolic pathways has been poorly described.

Results

A collection of 41 strains whose genomic sequences were available and representative of the species genomic diversity was analyzed for growth on 18 carbohydrates relevant in wine. The most frequently used substrates (more than 75% of the strains) were glucose, trehalose, ribose, cellobiose, mannose and melibiose. Fructose and L-arabinose were used by about half the strains studied, sucrose, maltose, xylose, galactose and raffinose were used by less than 25% of the strains and lactose, L-sorbose, L-rhamnose, sorbitol and mannitol were not used by any of the studied strains. To identify genes and pathways associated with carbohydrate catabolic abilities, gene-trait matching and a careful analysis of gene mutations and putative complementation phenomena were performed.

Conclusions

For most consumed sugars, we were able to propose putatively associated metabolic pathways. Most associated genes belong to the core genome. O. oeni appears as a highly specialized species, ideally suited to fermented fruit juice and more specifically to wine for a subgroup of strains.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3338-2) contains supplementary material, which is available to authorized users.

The landscape of microbial phenotypic traits and associated genes

Bacteria and Archaea display a variety of phenotypic traits and can adapt to diverse ecological niches. However, systematic annotation of prokaryotic phenotypes is lacking. We have therefore developed ProTraits, a resource containing ∼545 000 novel phenotype inferences, spanning 424 traits assigned to 3046 bacterial and archaeal species. These annotations were assigned by a computational pipeline that associates microbes with phenotypes by text-mining the scientific literature and the broader World Wide Web, while also being able to define novel concepts from unstructured text. Moreover, the ProTraits pipeline assigns phenotypes by drawing extensively on comparative genomics, capturing patterns in gene repertoires, codon usage biases, proteome composition and co-occurrence in metagenomes. Notably, we find that gene synteny is highly predictive of many phenotypes, and highlight examples of gene neighborhoods associated with spore-forming ability. A global analysis of trait interrelatedness outlined clusters in the microbial phenotype network, suggesting common genetic underpinnings. Our extended set of phenotype annotations allows detection of 57 088 high confidence gene-trait links, which recover many known associations involving sporulation, flagella, catalase activity, aerobicity, photosynthesis and other traits. Over 99% of the commonly occurring gene families are involved in genetic interactions conditional on at least one phenotype, suggesting that epistasis has a major role in shaping microbial gene content.

A Primer on Infectious Disease Bacterial Genomics

The number of large-scale genomics projects is increasing due to the availability of affordable high-throughput sequencing (HTS) technologies. The use of HTS for bacterial infectious disease research is attractive because one whole-genome sequencing (WGS) run can replace multiple assays for bacterial typing, molecular epidemiology investigations, and more in-depth pathogenomic studies. The computational resources and bioinformatics expertise required to accommodate and analyze the large amounts of data pose new challenges for researchers embarking on genomics projects for the first time. Here, we present a comprehensive overview of a bacterial genomics projects from beginning to end, with a particular focus on the planning and computational requirements for HTS data, and provide a general understanding of the analytical concepts to develop a workflow that will meet the objectives and goals of HTS projects.

Stress Physiology of Lactic Acid Bacteria

Lactic acid bacteria (LAB) are important starter, commensal, or pathogenic microorganisms. The stress physiology of LAB has been studied in depth for over 2 decades, fueled mostly by the technological implications of LAB robustness in the food industry. Survival of probiotic LAB in the host and the potential relatedness of LAB virulence to their stress resilience have intensified interest in the field. Thus, a wealth of information concerning stress responses exists today for strains as diverse as starter (e.g., Lactococcus lactis), probiotic (e.g., several Lactobacillus spp.), and pathogenic (e.g., Enterococcus and Streptococcus spp.) LAB. Here we present the state of the art for LAB stress behavior. We describe the multitude of stresses that LAB are confronted with, and we present the experimental context used to study the stress responses of LAB, focusing on adaptation, habituation, and cross-protection as well as on self-induced multistress resistance in stationary phase, biofilms, and dormancy. We also consider stress responses at the population and single-cell levels. Subsequently, we concentrate on the stress defense mechanisms that have been reported to date, grouping them according to their direct participation in preserving cell energy, defending macromolecules, and protecting the cell envelope. Stress-induced responses of probiotic LAB and commensal/pathogenic LAB are highlighted separately due to the complexity of the peculiar multistress conditions to which these bacteria are subjected in their hosts. Induction of prophages under environmental stresses is then discussed. Finally, we present systems-based strategies to characterize the "stressome" of LAB and to engineer new food-related and probiotic LAB with improved stress tolerance.

Individual strains of Lactobacillus paracasei differentially inhibit human basophil and mouse mast cell activation

INTRODUCTION

The microbiota controls a variety of biological functions, including immunity, and alterations of the microbiota in early life are associated with a higher risk of developing allergies later in life. Several probiotic bacteria, and particularly lactic acid bacteria, were described to reduce both the induction of allergic responses and allergic manifestations. Although specific probiotic strains were used in these studies, their protective effects on allergic responses also might be common for all lactobacilli.

METHODS

To determine whether allergic effector cells inhibition is a common feature of lactobacilli or whether it varies among lactobacilli strains, we compared the ability of 40 strains of the same Lactobacillus paracasei species to inhibit IgE-dependent mouse mast cell and human basophil activation.

RESULTS

We uncovered a marked heterogeneity in the inhibitory properties of the 40 Lactobacillus strains tested. These segregated into three to four clusters depending on the intensity of inhibition. Some strains inhibited both mouse mast cell and human basophil activation, others strains inhibited only one cell type and another group induced no inhibition of activation for either cell type.

CONCLUSIONS

Individual Lactobacillus strains of the same species differentially inhibit IgE-dependent activation of mouse mast cells and human basophils, two cell types that are critical in the onset of allergic manifestations. Although we failed to identify specific bacterial genes associated with inhibition by gene-trait matching analysis, our findings demonstrate the complexity of the interactions between the microbiota and the host. These results suggest that some L. paracasei strains might be more beneficial in allergies than others strains and provide the bases for a rational screening of lactic acid bacteria strains as next-generation probiotics in the field of allergy.

A mobile genetic element profoundly increases heat resistance of bacterial spores

Bacterial endospores are among the most resilient forms of life on earth and are intrinsically resistant to extreme environments and antimicrobial treatments. Their resilience is explained by unique cellular structures formed by a complex developmental process often initiated in response to nutrient deprivation. Although the macromolecular structures of spores from different bacterial species are similar, their resistance to environmental insults differs widely. It is not known which of the factors attributed to spore resistance confer very high-level heat resistance. Here, we provide conclusive evidence that in Bacillus subtilis, this is due to the presence of a mobile genetic element (Tn1546-like) carrying five predicted operons, one of which contains genes that encode homologs of SpoVAC, SpoVAD and SpoVAEb and four other genes encoding proteins with unknown functions. This operon, named spoVA^2mob, confers high-level heat resistance to spores. Deletion of spoVA^2mob in a B. subtilis strain carrying Tn1546 renders heat-sensitive spores while transfer of spoVA^2mob into B. subtilis 168 yields highly heat-resistant spores. On the basis of the genetic conservation of different spoVA operons among spore-forming species of Bacillaceae, we propose an evolutionary scenario for the emergence of extremely heat-resistant spores in B. subtilis, B. licheniformis and B. amyloliquefaciens. This discovery opens up avenues for improved detection and control of spore-forming bacteria able to produce highly heat-resistant spores.

Comparative Genomic Analyses of the Moraxella catarrhalis Serosensitive and Seroresistant Lineages Demonstrate Their Independent Evolution

The bacterial speciesMoraxella catarrhalishas been hypothesized as being composed of two distinct lineages (referred to as the seroresistant [SR] and serosensitive [SS]) with separate evolutionary histories based on several molecular typing methods, whereas 16S ribotyping has suggested an additional split within the SS lineage. Previously, we characterized whole-genome sequences of 12 SR-lineage isolates, which revealed a relatively small supragenome when compared with other opportunistic nasopharyngeal pathogens, suggestive of a relatively short evolutionary history. Here, we performed whole-genome sequencing on 18 strains from both ribotypes of the SS lineage, an additional SR strain, as well as four previously identified highly divergent strains based on multilocus sequence typing analyses. All 35 strains were subjected to a battery of comparative genomic analyses which clearly show that there are three lineages-the SR, SS, and the divergent. The SR and SS lineages are closely related, but distinct from each other based on three different methods of comparison: Allelic differences observed among core genes; possession of lineage-specific sets of core and distributed genes; and by an alignment of concatenated core sequences irrespective of gene annotation. All these methods show that the SS lineage has much longer interstrain branches than the SR lineage indicating that this lineage has likely been evolving either longer or faster than the SR lineage. There is evidence of extensive horizontal gene transfer (HGT) within both of these lineages, and to a lesser degree between them. In particular, we identified very high rates of HGT between these two lineages for ß-lactamase genes. The four divergent strains aresui generis, being much more distantly related to both the SR and SS groups than these other two groups are to each other. Based on average nucleotide identities, gene content, GC content, and genome size, this group could be considered as a separate taxonomic group. The SR and SS lineages, although distinct, clearly form a single species based on multiple criteria including a large common core genome, average nucleotide identity values, GC content, and genome size. Although neither of these lineages arose from within the other based on phylogenetic analyses, the question of how and when these lineages split and then subsequently reunited in the human nasopharynx is explored.

Microbial bioinformatics for food safety and production

In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput 'omics' technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety.

Correlation of Lactobacillus rhamnosus Genotypes and Carbohydrate Utilization Signatures Determined by Phenotype Profiling

Lactobacillus rhamnosus is a bacterial species commonly colonizing the gastrointestinal (GI) tract of humans and also frequently used in food products. While some strains have been studied extensively, physiological variability among isolates of the species found in healthy humans or their diet is largely unexplored. The aim of this study was to characterize the diversity of carbohydrate utilization capabilities of human isolates and food-derived strains of L. rhamnosus in relation to their niche of isolation and genotype. We investigated the genotypic and phenotypic diversity of 25 out of 65 L. rhamnosus strains from various niches, mainly human feces and fermented dairy products. Genetic fingerprinting of the strains by amplified fragment length polymorphism (AFLP) identified 11 distinct subgroups at 70% similarity and suggested niche enrichment within particular genetic clades. High-resolution carbohydrate utilization profiling (OmniLog) identified 14 carbon sources that could be used by all of the strains tested for growth, while the utilization of 58 carbon sources differed significantly between strains, enabling the stratification of L. rhamnosus strains into three metabolic clusters that partially correlate with the genotypic clades but appear uncorrelated with the strain's origin of isolation. Draft genome sequences of 8 strains were generated and employed in a gene-trait matching (GTM) analysis together with the publicly available genomes of L. rhamnosus GG (ATCC 53103) and HN001 for several carbohydrates that were distinct for the different metabolic clusters: l-rhamnose, cellobiose, l-sorbose, and α-methyl-d-glucoside. From the analysis, candidate genes were identified that correlate with l-sorbose and α-methyl-d-glucoside utilization, and the proposed function of these genes could be confirmed by heterologous expression in a strain lacking the genes. This study expands our insight into the phenotypic and genotypic diversity of the species L. rhamnosus and explores the relationships between specific carbohydrate utilization capacities and genotype and/or niche adaptation of this species.

From pangenome to panphenome and back

The ability to relate genomic differences in bacterial species to their variability in expressed phenotypes is one of the most challenging tasks in today's biology. Such task is of paramount importance towards the understanding of biotechnologically relevant pathways and possibly for their manipulation. Fundamental prerequisites are the genome-wide reconstruction of metabolic pathways and a comprehensive measurement of cellular phenotypes. Cellular pathways can be reliably reconstructed using the KEGG database, while the OmniLog™ Phenotype Microarray (PM) technology may be used to measure nearly 2,000 growth conditions over time. However, few computational tools that can directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are available. In this chapter the use of the DuctApe software suite is presented, which allows the joint analysis of bacterial genomic and phenomic data, highlighting those pathways and reactions most probably associated with phenotypic variability. A case study on four Sinorhizobium meliloti strains is presented; more example datasets are available online.

Microbial taxonomy in the post-genomic era: rebuilding from scratch?

Microbial taxonomy should provide adequate descriptions of bacterial, archaeal, and eukaryotic microbial diversity in ecological, clinical, and industrial environments. Its cornerstone, the prokaryote species has been re-evaluated twice. It is time to revisit polyphasic taxonomy, its principles, and its practice, including its underlying pragmatic species concept. Ultimately, we will be able to realize an old dream of our predecessor taxonomists and build a genomic-based microbial taxonomy, using standardized and automated curation of high-quality complete genome sequences as the new gold standard.

Comparative genomics of 274 Vibrio cholerae genomes reveals mobile functions structuring three niche dimensions

Background

Vibrio cholerae is a globally dispersed pathogen that has evolved with humans for centuries, but also includes non-pathogenic environmental strains. Here, we identify the genomic variability underlying this remarkable persistence across the three major niche dimensions space, time, and habitat.

Results

Taking an innovative approach of genome-wide association applicable to microbial genomes (GWAS-M), we classify 274 complete V. cholerae genomes by niche, including 39 newly sequenced for this study with the Ion Torrent DNA-sequencing platform. Niche metadata were collected for each strain and analyzed together with comprehensive annotations of genetic and genomic attributes, including point mutations (single-nucleotide polymorphisms, SNPs), protein families, functions and prophages.

Conclusions

Our analysis revealed that genomic variations, in particular mobile functions including phages, prophages, transposable elements, and plasmids underlie the metadata structuring in each of the three niche dimensions. This underscores the role of phages and mobile elements as the most rapidly evolving elements in bacterial genomes, creating local endemicity (space), leading to temporal divergence (time), and allowing the invasion of new habitats. Together, we take a data-driven approach for comparative functional genomics that exploits high-volume genome sequencing and annotation, in conjunction with novel statistical and machine learning analyses to identify connections between genotype and phenotype on a genome-wide scale.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-654) contains supplementary material, which is available to authorized users.

Trait-based approaches for understanding microbial biodiversity and ecosystem functioning

In ecology, biodiversity-ecosystem functioning (BEF) research has seen a shift in perspective from taxonomy to function in the last two decades, with successful application of trait-based approaches. This shift offers opportunities for a deeper mechanistic understanding of the role of biodiversity in maintaining multiple ecosystem processes and services. In this paper, we highlight studies that have focused on BEF of microbial communities with an emphasis on integrating trait-based approaches to microbial ecology. In doing so, we explore some of the inherent challenges and opportunities of understanding BEF using microbial systems. For example, microbial biologists characterize communities using gene phylogenies that are often unable to resolve functional traits. Additionally, experimental designs of existing microbial BEF studies are often inadequate to unravel BEF relationships. We argue that combining eco-physiological studies with contemporary molecular tools in a trait-based framework can reinforce our ability to link microbial diversity to ecosystem processes. We conclude that such trait-based approaches are a promising framework to increase the understanding of microbial BEF relationships and thus generating systematic principles in microbial ecology and more generally ecology.

DuctApe: a suite for the analysis and correlation of genomic and OmniLog™ Phenotype Microarray data

Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLog™ Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are still missing. Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented. The source code and tutorials are available at http://combogenomics.github.io/DuctApe/.

Diversity in robustness of Lactococcus lactis strains during heat stress, oxidative stress, and spray drying stress

In this study we tested 39 Lactococcus lactis strains isolated from diverse habitats for their robustness under heat and oxidative stress, demonstrating high diversity in survival (up to 4 log units). Strains with an L. lactis subsp. lactis phenotype generally displayed more-robust phenotypes than strains with an L. lactis subsp. cremoris phenotype, whereas the habitat from which the strains had been isolated did not appear to influence stress survival. Comparison of the stress survival phenotypes with already available comparative genomic data sets revealed that the absence or presence of specific genes, including genes encoding a GntR family transcriptional regulator, a manganese ABC transporter permease, a cellobiose phosphotransferase system (PTS) component, the FtsY protein, and hypothetical proteins, was associated with heat or oxidative stress survival. Finally, 14 selected strains also displayed diversity in survival after spray drying, ranging from 20% survival for the most robust strains, which appears acceptable for industrial application, to 0.1% survival for the least-tolerant strains. The high and low levels of survival upon spray drying correlated clearly with the combined robustness under heat and oxidative stress. These results demonstrate the relevance of screening culture collections for robustness under heat and oxidative stress on top of the typical screening for acidifying and flavor-forming properties.

Lactobacillus paracasei comparative genomics: towards species pan-genome definition and exploitation of diversity

Lactobacillus paracasei is a member of the normal human and animal gut microbiota and is used extensively in the food industry in starter cultures for dairy products or as probiotics. With the development of low-cost, high-throughput sequencing techniques it has become feasible to sequence many different strains of one species and to determine its "pan-genome". We have sequenced the genomes of 34 different L. paracasei strains, and performed a comparative genomics analysis. We analysed genome synteny and content, focussing on the pan-genome, core genome and variable genome. Each genome was shown to contain around 2800-3100 protein-coding genes, and comparative analysis identified over 4200 ortholog groups that comprise the pan-genome of this species, of which about 1800 ortholog groups make up the conserved core. Several factors previously associated with host-microbe interactions such as pili, cell-envelope proteinase, hydrolases p40 and p75 or the capacity to produce short branched-chain fatty acids (bkd operon) are part of the L. paracasei core genome present in all analysed strains. The variome consists mainly of hypothetical proteins, phages, plasmids, transposon/conjugative elements, and known functions such as sugar metabolism, cell-surface proteins, transporters, CRISPR-associated proteins, and EPS biosynthesis proteins. An enormous variety and variability of sugar utilization gene cassettes were identified, with each strain harbouring between 25-53 cassettes, reflecting the high adaptability of L. paracasei to different niches. A phylogenomic tree was constructed based on total genome contents, and together with an analysis of horizontal gene transfer events we conclude that evolution of these L. paracasei strains is complex and not always related to niche adaptation. The results of this genome content comparison was used, together with high-throughput growth experiments on various carbohydrates, to perform gene-trait matching analysis, in order to link the distribution pattern of a specific phenotype to the presence/absence of specific sets of genes.

Genotypic and phenotypic analysis of dairy Lactococcus lactis biodiversity in milk: volatile organic compounds as discriminating markers

The diversity of nine dairy strains of Lactococcus lactis subsp. lactis in fermented milk was investigated by both genotypic and phenotypic analyses. Pulsed-field gel electrophoresis and multilocus sequence typing were used to establish an integrated genotypic classification. This classification was coherent with discrimination of the L. lactis subsp. lactis bv. diacetylactis lineage and reflected clonal complex phylogeny and the uniqueness of the genomes of these strains. To assess phenotypic diversity, 82 variables were selected as important dairy features; they included physiological descriptors and the production of metabolites and volatile organic compounds (VOCs). Principal-component analysis (PCA) demonstrated the phenotypic uniqueness of each of these genetically closely related strains, allowing strain discrimination. A method of variable selection was developed to reduce the time-consuming experimentation. We therefore identified 20 variables, all associated with VOCs, as phenotypic markers allowing discrimination between strain groups. These markers are representative of the three metabolic pathways involved in flavor: lipolysis, proteolysis, and glycolysis. Despite great phenotypic diversity, the strains could be divided into four robust phenotypic clusters based on their metabolic orientations. Inclusion of genotypic diversity in addition to phenotypic characters in the classification led to five clusters rather than four being defined. However, genotypic characters make a smaller contribution than phenotypic variables (no genetic distances selected among the most contributory variables). This work proposes an original method for the phenotypic differentiation of closely related strains in milk and may be the first step toward a predictive classification for the manufacture of starters.

Explaining microbial phenotypes on a genomic scale: GWAS for microbes

There is an increasing availability of complete or draft genome sequences for microbial organisms. These data form a potentially valuable resource for genotype-phenotype association and gene function prediction, provided that phenotypes are consistently annotated for all the sequenced strains. In this review, we address the requirements for successful gene-trait matching. We outline a basic protocol for microbial functional genomics, including genome assembly, annotation of genotypes (including single nucleotide polymorphisms, orthologous groups and prophages), data pre-processing, genotype-phenotype association, visualization and interpretation of results. The methodologies for association described herein can be applied to other data types, opening up possibilities to analyze transcriptome-phenotype associations, and correlate microbial population structure or activity, as measured by metagenomics, to environmental parameters.

Genotype-phenotype matching analysis of 38 Lactococcus lactis strains using random forest methods

Background

Lactococcus lactis is used in dairy food fermentation and for the efficient production of industrially relevant enzymes. The genome content and different phenotypes have been determined for multiple L. lactis strains in order to understand intra-species genotype and phenotype diversity and annotate gene functions. In this study, we identified relations between gene presence and a collection of 207 phenotypes across 38 L. lactis strains of dairy and plant origin. Gene occurrence and phenotype data were used in an iterative gene selection procedure, based on the Random Forest algorithm, to identify genotype-phenotype relations.

Results

A total of 1388 gene-phenotype relations were found, of which some confirmed known gene-phenotype relations, such as the importance of arabinose utilization genes only for strains of plant origin. We also identified a gene cluster related to growth on melibiose, a plant disaccharide; this cluster is present only in melibiose-positive strains and can be used as a genetic marker in trait improvement. Additionally, several novel gene-phenotype relations were uncovered, for instance, genes related to arsenite resistance or arginine metabolism.

Conclusions

Our results indicate that genotype-phenotype matching by integrating large data sets provides the possibility to identify gene-phenotype relations, possibly improve gene function annotation and identified relations can be used for screening bacterial culture collections for desired phenotypes. In addition to all gene-phenotype relations, we also provide coherent phenotype data for 38 Lactococcus strains assessed in 207 different phenotyping experiments, which to our knowledge is the largest to date for the Lactococcus lactis species.

A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children

BACKGROUND

Tuberculosis (TB) continues to cause a high toll of disease and death among children worldwide. The diagnosis of childhood TB is challenged by the paucibacillary nature of the disease and the difficulties in obtaining specimens. Whereas scientific and clinical research efforts to develop novel diagnostic tools have focused on TB in adults, childhood TB has been relatively neglected. Blood transcriptional profiling has improved our understanding of disease pathogenesis of adult TB and may offer future leads for diagnosis and treatment. No studies applying gene expression profiling of children with TB have been published so far.

RESULTS

We identified a 116-gene signature set that showed an average prediction error of 11% for TB vs. latent TB infection (LTBI) and for TB vs. LTBI vs. healthy controls (HC) in our dataset. A minimal gene set of only 9 genes showed the same prediction error of 11% for TB vs. LTBI in our dataset. Furthermore, this minimal set showed a significant discriminatory value for TB vs. LTBI for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. In order to identify a robust representative gene set that would perform well in populations of different genetic backgrounds, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to classify 78% of the TB cases correctly with no LTBI subjects wrongly classified as TB (100% specificity).

CONCLUSIONS

Our data justify the further exploration of our signature set as biomarkers for potential childhood TB diagnosis. We show that, as the identification of different biomarkers in ethnically distinct cohorts is apparent, it is important to cross-validate newly identified markers in all available cohorts.

Perspectives on the contribution of lactic acid bacteria to cheese flavor development

It has been known since the 1960s that lactic acid bacteria are essential for the development of cheese flavor. In the ensuing 50 years significant research has been directed at understanding the microbiology, genetics and biochemistry of this process. This review briefly covers the current status of cheese flavor development and then provides our vision for approaches which will enhance our understanding of this process. The long-term goal of this area of research is to enable technology (i.e. cultures and enzymes) that results in consistent rapid development of cheese variety-specific characteristic flavors.

Towards metagenome-scale models for industrial applications--the case of Lactic Acid Bacteria

We review the uses and limitations of modelling approaches that are in use in the field of Lactic Acid Bacteria (LAB). We describe recent developments in model construction and computational methods, starting from application of such models to monocultures. However, since most applications in food biotechnology involve complex nutrient environments and mixed cultures, we extend the scope to discuss developments in modelling such complex systems. With metagenomics and meta-functional genomics data becoming available, the developments in genome-scale community models are discussed. We conclude that exploratory tools are available and useful, but truly predictive mechanistic models will remain a major challenge in the field.

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

In the Life Sciences 'omics' data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.