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Contains fulltext : 287056.pdf (Publisher’s version ) (Open Access), Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Contains fulltext : 171054.pdf (Publisher’s version ) (Open Access), The ability of bacteria to adapt to diverse environmental conditions is well-known. The process of bacterial adaptation to a niche has been linked to large changes in the genome content, showing that many bacterial genomes reflect the constraints imposed by their habitat. However, some highly versatile bacteria are found in diverse habitats that almost share nothing in common. Lactobacillus plantarum is a lactic acid bacterium that is found in a large variety of habitat. With the aim of unravelling the link between evolution and ecological versatility of L. plantarum, we analysed the genomes of 54 L. plantarum strains isolated from different environments. Comparative genome analysis identified a high level of genomic diversity and plasticity among the strains analysed. Phylogenomic and functional divergence studies coupled with gene-trait matching analyses revealed a mixed distribution of the strains, which was uncoupled from their environmental origin. Our findings revealed the absence of specific genomic signatures marking adaptations of L. plantarum towards the diverse habitats it is associated with. This suggests fundamentally similar trends of genome evolution in L. plantarum, which occur in a manner that is apparently uncoupled from ecological constraint and reflects the nomadic lifestyle of this species.

Contains fulltext : 155072.pdf (publisher's version ) (Open Access), Here, we describe the draft genome sequence and annotation of Lactobacillus plantarum strain Lp90, the first sequenced genome of a L. plantarum strain isolated from wine. This strain has a noticeable ropy phenotype and showed potential probiotic properties. The genome consists of 3,324,076 bp (33 contigs) and contains 3,155 protein coding genes, 34 pseudogenes, and 84 RNA genes.

Enterococcus faecium is an important nosocomial pathogen causing biofilm-mediated infections. Elucidation of E. faecium biofilm pathogenesis is pivotal for the development of new strategies to treat these infections. In several bacteria, extracellular DNA (eDNA) and proteins act as matrix components contributing to biofilm development. In this study, we investigated biofilm formation capacity and the roles of eDNA and secreted proteins for 83 E. faecium strains with different phylogenetic origins that clustered in clade A1 and clade B. Although there was no significant difference in biofilm formation between E. faecium strains from these two clades, the addition of DNase I or proteinase K to biofilms demonstrated that eDNA is essential for biofilm formation in most E. faecium strains, whereas proteolysis impacted primarily biofilms of E. faecium clade A1 strains. Secreted antigen A (SagA) was the most abundant protein in biofilms from E. faecium clade A1 and B strains, although its localization differed between the two groups. sagA was present in all sequenced E. faecium strains, with a consistent difference in the repeat region between the clades, which correlated with the susceptibility of biofilms to proteinase K. This indicates an association between the SagA variable repeat profile and the localization and contribution of SagA in E. faecium biofilms.

Contains fulltext : 136254.pdf (publisher's version ) (Open Access), 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.

Contains fulltext : 118486.pdf (publisher's version ) (Open Access), 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.

Item does not contain fulltext, 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.

Contains fulltext : 109042.pdf (publisher's version ) (Open Access), BACKGROUND: Linking phenotypes to high-throughput molecular biology information generated by ~omics technologies allows revealing cellular mechanisms underlying an organism's phenotype. ~Omics datasets are often very large and noisy with many features (e.g., genes, metabolite abundances). Thus, associating phenotypes to ~omics data requires an approach that is robust to noise and can handle large and diverse data sets. RESULTS: We developed a web-tool PhenoLink (http://bamics2.cmbi.ru.nl/websoftware/phenolink/) that links phenotype to ~omics data sets using well-established as well new techniques. PhenoLink imputes missing values and preprocesses input data (i) to decrease inherent noise in the data and (ii) to counterbalance pitfalls of the Random Forest algorithm, on which feature (e.g., gene) selection is based. Preprocessed data is used in feature (e.g., gene) selection to identify relations to phenotypes. We applied PhenoLink to identify gene-phenotype relations based on the presence/absence of 2847 genes in 42 Lactobacillus plantarum strains and phenotypic measurements of these strains in several experimental conditions, including growth on sugars and nitrogen-dioxide production. Genes were ranked based on their importance (predictive value) to correctly predict the phenotype of a given strain. In addition to known gene to phenotype relations we also found novel relations. CONCLUSIONS: PhenoLink is an easily accessible web-tool to facilitate identifying relations from large and often noisy phenotype and ~omics datasets. Visualization of links to phenotypes offered in PhenoLink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data. PhenoLink can be used to uncover phenotype links to a multitude of ~omics data, e.g., gene presence/absence (determined by e.g.: CGH or next-generation sequencing), gene expression (determined by e.g.: microarrays or RNA-seq), or metabolite abun

Radboud Universiteit Nijmegen, 6 juni 2012, Promotor : Siezen, R.J. Co-promotor : Hijum, S.A.F.T. van, Contains fulltext : 93604.pdf (publisher's version ) (Open Access)

1 september 2011, Item does not contain fulltext, With the advent of the -omics era, classical technology platforms, such as hyphenated mass spectrometry, are currently undergoing a transformation toward high-throughput application. These novel platforms yield highly detailed metabolite profiles in large numbers of samples. Such profiles can be used as fingerprints for the accurate identification and classification of samples as well as for the study of effects of experimental conditions on the concentrations of specific metabolites. Challenges for the application of these methods lie in the acquisition of high-quality data, data normalization, and data mining. Here, a high-throughput fingerprinting approach based on analysis of headspace volatiles using ultrafast gas chromatography coupled to time of flight mass spectrometry (ultrafast GC/TOF-MS) was developed and evaluated for classification and screening purposes in food fermentation. GC-MS mass spectra of headspace samples of milk fermented by different mixed cultures of lactic acid bacteria (LAB) were collected and preprocessed in MetAlign, a dedicated software package for the preprocessing and comparison of liquid chromatography (LC)-MS and GC-MS data. The Random Forest algorithm was used to detect mass peaks that discriminated combinations of species or strains used in fermentations. Many of these mass peaks originated from key flavor compounds, indicating that the presence or absence of individual strains or combinations of strains significantly influenced the concentrations of these components. We demonstrate that the approach can be used for purposes like the selection of strains from collections based on flavor characteristics and the screening of (mixed) cultures for the presence or absence of strains. In addition, we show that strain-specific flavor characteristics can be traced back to genetic markers when comparative genome hybridization (CGH) data are available.

1 mei 2010, Contains fulltext : 89780.pdf (publisher's version ) (Open Access) Contains fulltext : 89780_pub.pdf (publisher's version ) (Open Access), Lactococcus lactis is a lactic acid bacterium used in the production of many fermented dairy products. We report the complete genome sequence of L. lactis subsp. lactis KF147, a nondairy strain isolated from mung bean sprouts. The circular chromosome of 2,598,144 bp, the largest among the sequenced lactococcal strains, encodes many properties related to adaptation to the plant environment.

Contains fulltext : 87750.pdf (publisher's version ) (Open Access), BACKGROUND: Lactic acid bacteria (LAB) are a group of gram-positive, lactic acid producing Firmicutes. They have been extensively used in food fermentations, including the production of various dairy products. The proteolytic system of LAB converts proteins to peptides and then to amino acids, which is essential for bacterial growth and also contributes significantly to flavor compounds as end-products. Recent developments in high-throughput genome sequencing and comparative genomics hybridization arrays provide us with opportunities to explore the diversity of the proteolytic system in various LAB strains. RESULTS: We performed a genome-wide comparative genomics analysis of proteolytic system components, including cell-wall bound proteinase, peptide transporters and peptidases, in 22 sequenced LAB strains. The peptidase families PepP/PepQ/PepM, PepD and PepI/PepR/PepL are described as examples of our in silico approach to refine the distinction of subfamilies with different enzymatic activities. Comparison of protein 3D structures of proline peptidases PepI/PepR/PepL and esterase A allowed identification of a conserved core structure, which was then used to improve phylogenetic analysis and functional annotation within this protein superfamily.The diversity of proteolytic system components in 39 Lactococcus lactis strains was explored using pangenome comparative genome hybridization analysis. Variations were observed in the proteinase PrtP and its maturation protein PrtM, in one of the Opp transport systems and in several peptidases between strains from different Lactococcus subspecies or from different origin. CONCLUSIONS: The improved functional annotation of the proteolytic system components provides an excellent framework for future experimental validations of predicted enzymatic activities. The genome sequence data can be coupled to other "omics" data e.g. transcriptomics and metabolomics for prediction of proteolytic and flavor-forming potential of LAB strain

Contains fulltext : 89840.pdf (publisher's version ) (Closed access), A pangenome is the total of genes present in strains of the same species. Pangenome microarrays allow determining the genomic content of bacterial strains more accurately than conventional comparative genome hybridization microarrays. PanCGHweb is the first tool that effectively calls genotype based on pangenome microarray data. AVAILABILITY: PanCGHweb, the web tool is accessible from: http://bamics2.cmbi.ru.nl/websoftware/pancgh/.

Contains fulltext : 81762.pdf (publisher's version ) (Closed access), MOTIVATION: Pangenome arrays contain DNA oligomers targeting several sequenced reference genomes from the same species. In microbiology, these can be employed to investigate the often high genetic variability within a species by comparative genome hybridization (CGH). The biological interpretation of pangenome CGH data depends on the ability to compare strains at a functional level, particularly by comparing the presence or absence of orthologous genes. Due to the high genetic variability, available genotype-calling algorithms can not be applied to pangenome CGH data. RESULTS: We have developed the algorithm PanCGH that incorporates orthology information about genes to predict the presence or absence of orthologous genes in a query organism using CGH arrays that target the genomes of sequenced representatives of a group of microorganisms. PanCGH was tested and applied in the analysis of genetic diversity among 39 Lactococcus lactis strains from three different subspecies (lactis.cremoris, hordniae) and isolated from two different niches (dairy and plant). Clustering of these strains using the presence/absence data of gene orthologs revealed a clear separation between different subspecies and reflected the niche of the strains.