Genome-based Predictions Research Articles

BacDive in 2025: the core database for prokaryotic strain data.

In 2025, the bacterial diversity database BacDive is the leading database for strain-level bacterial and archaeal information. It has been selected as an ELIXIR Core Data Resource as well as a Global Core Biodata Resource. Since its initial release more than ten years ago, BacDive (https://bacdive.dsmz.de) has grown tremendously in content and functionalities, and is a comprehensive resource covering the phenotypic diversity of prokaryotes with data on taxonomy, morphology, physiology, cultivation, and more. The current release (2023.2) contains 2.6 million data points on 97334 strains, reflecting an increase by 52% since the previous publication in 2021. This remarkable growth can largely be attributed to the integration of the world-wide largest collection of Analytical Profile Index (API) test results, which are now fully integrated into the database and searchable. A novel BacDive knowledge graph provides powerful search options through a SPARQL endpoint, including the possibility for federated searches across multiple data sources. The high-quality data provided by BacDive is increasingly being used for the training of artificial intelligence models and resulting genome-based predictions with high confidence are now used to fill content gaps in the database.

Amplicon-guided isolation and cultivation of previously uncultured microbial species from activated sludge.

Biological wastewater treatment relies on complex microbial communities that assimilate nutrients and break down pollutants in the wastewater. Knowledge about the physiology and metabolism of bacteria in wastewater treatment plants (WWTPs) may therefore be used to improve the efficacy and economy of wastewater treatment. Our current knowledge is largely based on 16S rRNA gene amplicon profiling, fluorescence in situ hybridization studies, and predictions based on metagenome-assembled genomes. Bacterial isolates are often required to validate genome-based predictions as they allow researchers to analyze a specific species without interference from other bacteria and with simple bulk measurements. Unfortunately, there are currently very few pure cultures representing the microbes commonly found in WWTPs. To address this, we introduce an isolation strategy that takes advantage of state-of-the-art microbial profiling techniques to uncover suitable growth conditions for key WWTP microbes. We furthermore demonstrate that this information can be used to isolate key organisms representing global WWTPs.

Anammox bacterium ‘Candidatus Kuenenia stuttgartiensis’: a review

Anaerobic ammonium oxidation (Anammox) process is a type of biological nitrogen removal technology which is known to directly convert ammonium and nitrite to nitrogen gas. The freshwater ‘Candidatus Kuenenia stuttgartiensis’ anammox under the phylum of Planctomycetes is used to study the parameters that affect the anammox development and the metabolic pathways alongside the associated enzymes. These observations were made using state-of-the art techniques for detecting anammox bacteria based on their small-subunit ribosomal RNA genes, functional genes and unique reaction pathways. This review systematically summarizes up-to-date studies on the parameters affecting the growth of the anammox bacteria and metabolic networks driving anammox bacterial anabolism and mixotrophy beyond genome-based predictions. The K. stuttgartiensis survives in summer and winter conditions besides in the aerobic zones (dissolved oxygen >2 mg/L), which consequently contribute to better nitrogen removal in the wastewater treatment. Furthermore, the K. stuttgartiensis utilizes the Wood–Ljungdahl pathway to directly assimilate extracellular formation instead of oxidising it completely to CO2 prior reassimilation.

The impact of using different ancestral reference populations in assessing crossbred population admixture and influence on performance.

Crossbreeding is a process in which animals from different breeds are mated together. The animals produced will exhibit a combination of both additive and non-additive genetic improvement from parental breeds that increase heterozygosity and negate inbreeding depression. However, crossbreeding may also break up the unique and often beneficial gene combinations in parental breeds, possibly reducing performance potential as the benefits of heterosis depends on the type of crossbreeding systems used and heritability of the traits. This effect of crossbreeding, especially on the genome architecture, is still poorly understood with respect to 3-breed crossbreeding systems. Thus, this study examined variation in genomic ancestry estimations relative to pedigree-based estimations and correlated breed composition to key production and health traits. Two rotational crossbred populations, referenced as ProCROSS and Grazecross were assessed and totaled 607 crossbred cattle. ProCROSS is a product of rotational crossbreeding of Viking Red (VKR), Holstein (HOL), and Montbeliarde (MON). In contrast, Grazecross consists of Viking Red (VKR), Normande (NOR), and Jersey (JER). Both breeding programs were aimed at capitalizing on the positive effect of heterosis. The VKR is a marketing term for Swedish Red, Danish Red, and Finnish Ayrshire breed which complicated breed determination. Therefore, genomic breed composition estimates were compared using two different representations of VKR, one of which was based on parents used in the crossing system and a second based on genotypes from the ancestral breeds that comprise VKR. Variation of breed composition estimates were assessed between pedigree and genome-based predictions. Lastly, Genomic estimations were correlated with production and health traits by comparing extreme performance groups to identify the relationship between breed ancestry and performance. With the exception of the JER breed composition in Grazecross, all other estimates of the purebred contribution to the ProCROSS and Grazecross showed a significant difference in their genomic breed estimation when using the VKR ancestral versus the VKR parental reference populations for admixture analysis. These observations were expected given the different relationship of each VKR representation to the crossbred cattle. Further analysis showed that regardless of which VKR reference population was used, the degree of MON and HOL breed composition plays a significant role in milk and fat production in ProCROSS, while the degree of VKR and NOR ancestry were related to improved health performance in Grazecross. In all, identifying the most appropriate and informative animals to use as reference animals in admixture analysis is an important factor when interpreting results of relationship and population structure, but some degree of uncertainty exists when assessing the relationship of breed composition to phenotypic performance.

Lme4GS: An R-Package for Genomic Selection.

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

Closely Related Vibrio alginolyticus Strains Encode an Identical Repertoire of Caudovirales-Like Regions and Filamentous Phages.

Many filamentous vibriophages encode virulence genes that lead to the emergence of pathogenic bacteria. Most genomes of filamentous vibriophages characterized up until today were isolated from human pathogens. Despite genome-based predictions that environmental Vibrios also contain filamentous phages that contribute to bacterial virulence, empirical evidence is scarce. This study aimed to characterize the bacteriophages of a marine pathogen, Vibrio alginolyticus (Kiel-alginolyticus ecotype) and to determine their role in bacterial virulence. To do so, we sequenced the phage-containing supernatant of eight different V. alginolyticus strains, characterized the phages therein and performed infection experiments on juvenile pipefish to assess their contribution to bacterial virulence. We were able to identify two actively replicating filamentous phages. Unique to this study was that all eight bacteria of the Kiel-alginolyticus ecotype have identical bacteriophages, supporting our previously established theory of a clonal expansion of the Kiel-alginolyticus ecotype. We further found that in one of the two filamentous phages, two phage-morphogenesis proteins (Zot and Ace) share high sequence similarity with putative toxins encoded on the Vibrio cholerae phage CTXΦ. The coverage of this filamentous phage correlated positively with virulence (measured in controlled infection experiments on the eukaryotic host), suggesting that this phage contributes to bacterial virulence.

Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping

Anaerobic ammonium-oxidizing (anammox) bacteria mediate a key step in the biogeochemical nitrogen cycle and have been applied worldwide for the energy-efficient removal of nitrogen from wastewater. However, outside their core energy metabolism, little is known about the metabolic networks driving anammox bacterial anabolism and use of different carbon and energy substrates beyond genome-based predictions. Here, we experimentally resolved the central carbon metabolism of the anammox bacterium Candidatus ‘Kuenenia stuttgartiensis’ using time-series 13C and 2H isotope tracing, metabolomics, and isotopically nonstationary metabolic flux analysis. Our findings confirm predicted metabolic pathways used for CO2 fixation, central metabolism, and amino acid biosynthesis in K. stuttgartiensis, and reveal several instances where genomic predictions are not supported by in vivo metabolic fluxes. This includes the use of the oxidative branch of an incomplete tricarboxylic acid cycle for alpha-ketoglutarate biosynthesis, despite the genome not having an annotated citrate synthase. We also demonstrate that K. stuttgartiensis is able to directly assimilate extracellular formate via the Wood–Ljungdahl pathway instead of oxidizing it completely to CO2 followed by reassimilation. In contrast, our data suggest that K. stuttgartiensis is not capable of using acetate as a carbon or energy source in situ and that acetate oxidation occurred via the metabolic activity of a low-abundance microorganism in the bioreactor’s side population. Together, these findings provide a foundation for understanding the carbon metabolism of anammox bacteria at a systems-level and will inform future studies aimed at elucidating factors governing their function and niche differentiation in natural and engineered ecosystems.

Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans

The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications.

MAGI: A Method for Metabolite Annotation and Gene Integration.

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi .

Efficient genetic value prediction using incomplete omics data.

Covering a subset of individuals with a quantitative predictor, while imputing records for all others using pedigree or genomic data, could improve the precision of predictions while controlling for costs. Predicting genetic values with high accuracy is pivotal for effective candidate selection in animal and plant breeding. Novel 'omics'-based predictors have been shown to improve upon established genome-based predictions of important complex traits but require laborious and expensive assays. As a consequence, there are various datasets with full genetic marker coverage of all studied individuals but incomplete coverage with other 'omics' data. In animal breeding, single-step prediction was introduced to efficiently combine pedigree information, collected on a large number of animals, with genomic information, collected on a smaller subset of animals, for breeding value estimation without bias. Using two maize datasets of inbred lines and hybrids, we show that the single-step framework facilitates imputing transcriptomic data, boosting forecasts when their predictive ability exceeds that of pedigree or genomic data. Our results suggest that covering only a subset of inbred lines with 'omics' predictors and imputing all others using pedigree or genomic data could enable breeders to improve trait predictions while keeping costs under control. Employing 'omics' predictors could particularly improve candidate selection in hybrid breeding because the success of forecasts is a strongly convex function of predictive ability.

Insights into the genome and proteome of Sphingomonas paucimobilis strain 20006FA involved in the regulation of polycyclic aromatic hydrocarbon degradation.

In order to study the mechanisms regulating the phenanthrene degradation pathway and the intermediate-metabolite accumulation in strain S. paucimobilis 20006FA, we sequenced the genome and compared the genome-based predictions to experimental proteomic analyses. Physiological studies indicated that the degradation involved the salicylate and protocatechuate pathways, reaching 56.3% after 15 days. Furthermore, the strain degraded other polycyclic aromatic hydrocarbons (PAH) such as anthracene (13.1%), dibenzothiophene (76.3%), and fluoranthene. The intermediate metabolite 1-hydroxy-2-naphthoic acid (HNA) accumulated during phenanthrene catabolism and inhibited both bacterial growth and phenanthrene degradation, but exogenous-HNA addition did not affect further degradation. Genomic analysis predicted 126 putative genes encoding enzymes for all the steps of phenanthrene degradation, which loci could also participate in the metabolism of other PAH. Proteomic analysis identified enzymes involved in 19 of the 23 steps needed for the transformation of phenanthrene to trichloroacetic-acid intermediates that were upregulated in phenanthrene cultures relative to the levels in glucose cultures. Moreover, the protein-induction pattern was temporal, varying between 24 and 96h during phenanthrene degradation, with most catabolic proteins being overexpressed at 96 h-e. g., the biphenyl dioxygenase and a multispecies (2Fe-2S)-binding protein. These results provided the first clues about regulation of expression of phenanthrene degradative enzymes in strain 20006FA and enabled an elucidation of the metabolic pathway utilized by the bacterium. To our knowledge the present work represents the first investigation of genomic, proteomic, and physiological studies of a PAH-degrading Sphingomonas strain.

Tethering Complexes in the Arabidopsis Endomembrane System.

Targeting of endomembrane transport containers is of the utmost importance for proper land plant growth and development. Given the immobility of plant cells, localized membrane vesicle secretion and recycling are amongst the main processes guiding proper cell, tissue and whole plant morphogenesis. Cell wall biogenesis and modification are dependent on vectorial membrane traffic, not only during normal development, but also in stress responses and in plant defense against pathogens and/or symbiosis. It is surprising how little we know about these processes in plants, from small GTPase regulation to the tethering complexes that act as their effectors. Tethering factors are single proteins or protein complexes mediating first contact between the target membrane and arriving membrane vesicles. In this review we focus on the tethering complexes of the best-studied plant model—Arabidopsis thaliana. Genome-based predictions indicate the presence of all major tethering complexes in plants that are known from a hypothetical last eukaryotic common ancestor (LECA). The evolutionary multiplication of paralogs of plant tethering complex subunits has produced the massively expanded EXO70 family, indicating a subfunctionalization of the terminal exocytosis machinery in land plants. Interpretation of loss of function (LOF) mutant phenotypes has to consider that related, yet clearly functionally-specific complexes often share some common core subunits. It is therefore impossible to conclude with clarity which version of the complex is responsible for the phenotypic deviations observed. Experimental interest in the analysis of plant tethering complexes is growing and we hope to contribute with this review by attracting even more attention to this fascinating field of plant cell biology.

Proteomic profiling of a robust Wolbachia infection in an Aedes albopictus mosquito cell line

Wolbachia pipientis, a widespread vertically transmitted intracellular bacterium, provides a tool for insect control through manipulation of host-microbe interactions. We report proteomic characterization of wStr, a Wolbachia strain associated with a strong cytoplasmic incompatibility phenotype in its native host, Laodelphax striatellus. In the Aedes albopictus C/wStr1 mosquito cell line, wStr maintains a robust, persistent infection. MS/MS analyses of gel bands revealed a protein 'footprint' dominated by Wolbachia-encoded chaperones, stress response and cell membrane proteins, including the surface antigen WspA, a peptidoglycan-associated lipoprotein and a 73 kDa outer membrane protein. Functional classifications and estimated abundance levels of 790 identified proteins suggested that expression, stabilization and secretion of proteins predominate over bacterial genome replication and cell division. High relative abundances of cysteine desulphurase, serine/glycine hydroxymethyl transferase, and components of the α-ketoglutarate dehydrogenase complex in conjunction with above average abundances of glutamate dehydrogenase and proline utilization protein A support Wolbachia genome-based predictions for amino acid metabolism as a primary energy source. wStr expresses 15 Vir proteins of a Type IV secretion system and its transcriptional regulator. Proteomic characterization of a robust insect-associated Wolbachia strain provides baseline information that will inform further development of in vitro protocols for Wolbachia manipulation.

Combining Genomic and Genealogical Information in a Reproducing Kernel Hilbert Spaces Regression Model for Genome-Enabled Predictions in Dairy Cattle

Genome-enhanced genotypic evaluations are becoming popular in several livestock species. For this purpose, the combination of the pedigree-based relationship matrix with a genomic similarities matrix between individuals is a common approach. However, the weight placed on each matrix has been so far established with ad hoc procedures, without formal estimation thereof. In addition, when using marker- and pedigree-based relationship matrices together, the resulting combined relationship matrix needs to be adjusted to the same scale in reference to the base population. This study proposes a semi-parametric Bayesian method for combining marker- and pedigree-based information on genome-enabled predictions. A kernel matrix from a reproducing kernel Hilbert spaces regression model was used to combine genomic and genealogical information in a semi-parametric scenario, avoiding inversion and adjustment complications. In addition, the weights on marker- versus pedigree-based information were inferred from a Bayesian model with Markov chain Monte Carlo. The proposed method was assessed involving a large number of SNPs and a large reference population. Five phenotypes, including production and type traits of dairy cattle were evaluated. The reliability of the genome-based predictions was assessed using the correlation, regression coefficient and mean squared error between the predicted and observed values. The results indicated that when a larger weight was given to the pedigree-based relationship matrix the correlation coefficient was lower than in situations where more weight was given to genomic information. Importantly, the posterior means of the inferred weight were near the maximum of 1. The behavior of the regression coefficient and the mean squared error was similar to the performance of the correlation, that is, more weight to the genomic information provided a regression coefficient closer to one and a smaller mean squared error. Our results also indicated a greater accuracy of genomic predictions when using a large reference population.

Prediction of clinical mastitis outcomes within and between environments using whole-genome markers

The objective of this study was to evaluate genome-enabled predictions of daughter yield deviations for clinical mastitis in Norwegian Red cows within and between environments according to mastitis pathogen status. Genome-based predictions of daughter yield deviations for clinical mastitis for 1,126 bulls within and between 5 environments were performed using Bayesian ridge regression. The environments were defined as herd-5-yr classes with the following prevalence of bacteriological milk samples found positive for contagious mastitis pathogens: <50% (L50), ≥50% (H50), ≤25% (L75), >25% and <75% (M75), and ≥75% (H75). In addition, predictions based on all data across environment groups (the full data set, FD) were calculated to provide a benchmark for comparison. Predictive ability was evaluated using a 10-fold cross validation. A bootstrap procedure was used to obtain 95% confidence intervals for the cross-validation distribution of predictive ability for each data set. Predictive ability ranged from 0.04 for L75 to 0.19 for FD. Similar predictions within and between environments showed no evidence of genotype by environment interaction. The 95% confidence interval for all 5 environmental data sets included zero and ranged from 0.02 to 0.35 for FD. The bootstrap distribution showed large variation within each data set and small variation between data sets. Although we found no evidence of genotype by environment interaction, rank correlations of the single nucleotide polymorphism effects between different environments ranged from 0.15 (L75 – H75) to 0.92 (M75 – FD), indicating that single nucleotide polymorphisms may have a differential contribution to predictive ability in environments with distinct pathogen loads.

A Computational Approach for MicroRNA Identification in Plants: Combining Genome-Based Predictions with RNA-Seq Data

MicroRNAs are endogenous molecules that act by silencing targeted messenger RNAs, and which have an important regulatory role in many physiological processes in both plants and animals. Here, we propose a pipeline that makes use of CRAVELA, a single-genome microRNA finding tool originally developed for microRNA discovery in animals, and an NGS data analysis algorithm that provides a novel scoring function to evaluate the expression profile of candidates, taking advantage of the expected relative abundance of RNA fragments originating from the mature sequence, compared to other portions of the microRNA precursor. This approach was tested in Eucalyptus spp. for which, despite their economic importance, no microRNAs have been documented. The outcome of our approach was a short list of candidates, including both conserved and non-conserved sequences. Experimental validation showed amplification in 6 out of 8 candidates chosen from the best-scoring non-conserved sequences.

Synbreed: a framework for the analysis of genomic prediction data using R

We present a novel R package named synbreed to derive genome-based predictions from high-throughput genotyping and large-scale phenotyping data. The package contains a comprehensive collection of functions required to fit and cross-validate genomic prediction models. All functions are embedded within the framework of a single, unified data object. Thereby a versatile genomic prediction analysis pipeline covering data processing, visualization and analysis is established within one software package. The implementation is flexible with respect to a wide range of data formats and models. The package fills an existing gap in the availability of user-friendly software for next-generation genetics research and education. synbreed is open-source and available through CRAN http://cran.r-project.org/web/packages/synbreed. The latest development version is available from R-Forge. The package synbreed is released with a vignette, a manual and three large-scale example datasets (from package synbreedData). chris.schoen@wzw.tum.de Supplementary data are available at Bioinformatics online.

Investigation of the Staphylococcus aureus GraSR Regulon Reveals Novel Links to Virulence, Stress Response and Cell Wall Signal Transduction Pathways

The GraS/GraR two-component system has been shown to control cationic antimicrobial peptide (CAMP) resistance in the major human pathogen Staphylococcus aureus. We demonstrated that graX, also involved in CAMP resistance and cotranscribed with graRS, encodes a regulatory cofactor of the GraSR signaling pathway, effectively constituting a three-component system. We identified a highly conserved ten base pair palindromic sequence (5′ ACAAA TTTGT 3′) located upstream from GraR-regulated genes (mprF and the dlt and vraFG operons), which we show to be essential for transcriptional regulation by GraR and induction in response to CAMPs, suggesting it is the likely GraR binding site. Genome-based predictions and transcriptome analysis revealed several novel GraR target genes. We also found that the GraSR TCS is required for growth of S. aureus at high temperatures and resistance to oxidative stress. The GraSR system has previously been shown to play a role in S. aureus pathogenesis and we have uncovered previously unsuspected links with the AgrCA peptide quorum-sensing system controlling virulence gene expression. We also show that the GraSR TCS controls stress reponse and cell wall metabolism signal transduction pathways, sharing an extensive overlap with the WalKR regulon. This is the first report showing a role for the GraSR TCS in high temperature and oxidative stress survival and linking this system to stress response, cell wall and pathogenesis control pathways.

The Bifidobacterium dentium Bd1 Genome Sequence Reflects Its Genetic Adaptation to the Human Oral Cavity

Bifidobacteria, one of the relatively dominant components of the human intestinal microbiota, are considered one of the key groups of beneficial intestinal bacteria (probiotic bacteria). However, in addition to health-promoting taxa, the genus Bifidobacterium also includes Bifidobacterium dentium, an opportunistic cariogenic pathogen. The genetic basis for the ability of B. dentium to survive in the oral cavity and contribute to caries development is not understood. The genome of B. dentium Bd1, a strain isolated from dental caries, was sequenced to completion to uncover a single circular 2,636,368 base pair chromosome with 2,143 predicted open reading frames. Annotation of the genome sequence revealed multiple ways in which B. dentium has adapted to the oral environment through specialized nutrient acquisition, defences against antimicrobials, and gene products that increase fitness and competitiveness within the oral niche. B. dentium Bd1 was shown to metabolize a wide variety of carbohydrates, consistent with genome-based predictions, while colonization and persistence factors implicated in tissue adhesion, acid tolerance, and the metabolism of human saliva-derived compounds were also identified. Global transcriptome analysis demonstrated that many of the genes encoding these predicted traits are highly expressed under relevant physiological conditions. This is the first report to identify, through various genomic approaches, specific genetic adaptations of a Bifidobacterium taxon, Bifidobacterium dentium Bd1, to a lifestyle as a cariogenic microorganism in the oral cavity. In silico analysis and comparative genomic hybridization experiments clearly reveal a high level of genome conservation among various B. dentium strains. The data indicate that the genome of this opportunistic cariogen has evolved through a very limited number of horizontal gene acquisition events, highlighting the narrow boundaries that separate commensals from opportunistic pathogens.

Identification of Streptomyces lividans proteins secreted by the twin-arginine translocation pathway following growth with different carbon sources

Genome-based signal peptide predictions classified Streptomyces coelicolor as the microorganism that secretes the most proteins through the twin-arginine translocation (Tat)-dependent secretion pathway. Availability of a DeltatatC mutant of the closely related strain Streptomyces lividans impaired Tat-dependent protein secretion and enabled identification of many extracellular proteins that are secreted via the Tat pathway. Proteomic techniques were applied to analyze proteins from the supernatants of log-phase cultures. Since the bacterial secretome depends mainly on the carbon sources available during growth, xylose, glucose, chitin, and soil extracts were used. A total of 63 proteins were identified, among which 7 were predicted by the TATscan program, and 20 were not predicted but contained a potential Tat signal motif. Thirteen proteins having no signal sequence could be co-transported by Tat-dependent proteins because the genes that encode these proteins are in close proximity in the genome. Finally, the presence of 23 proteins lacking signal peptides was difficult to explain. More secreted proteins could be identified as Tat substrates in varying carbon sources.