The role of breast-feeding in infant immune system: a systems perspective on the intestinal microbiome

BACKGROUND: The human intestinal microbiota changes from being sparsely populated and variable to possessing a mature, adult-like stable microbiome during the first 2 years of life. This assembly process of the microbiota can lead to either negative or positive effects on health, depending on the colonization sequence and diet. An integrative study on the diet, the microbiota, and genomic activity at the transcriptomic level may give an insight into the role of diet in shaping the human/microbiome relationship. This study aims at better understanding the effects of microbial community and feeding mode (breast-fed and formula-fed) on the immune system, by comparing intestinal metagenomic and transcriptomic data from breast-fed and formula-fed babies.RESULTS: We re-analyzed a published metagenomics and host gene expression dataset from a systems biology perspective. Our results show that breast-fed samples co-express genes associated with immunological, metabolic, and biosynthetic activities. The diversity of the microbiota is higher in formula-fed than breast-fed infants, potentially reflecting the weaker dependence of infants on maternal microbiome. We mapped the microbial composition and the expression patterns for host systems and studied their relationship from a systems biology perspective, focusing on the differences.CONCLUSIONS: Our findings revealed that there is co-expression of more genes in breast-fed samples but lower microbial diversity compared to formula-fed. Applying network-based systems biology approach via enrichment of microbial species with host genes revealed the novel key relationships of the microbiota with immune and metabolic activity. This was supported statistically by data and literature

Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins

<p>Abstract</p> <p>Background</p> <p>Nutritional systems biology offers the potential for comprehensive predictions that account for all metabolic changes with the intricate biological organization and the multitudinous interactions between the cellular proteins. Protein-protein interaction (PPI) networks can be used for an integrative description of molecular processes. Although widely adopted in nutritional systems biology, these networks typically encompass a single category of functional interaction (<it>i.e</it>., metabolic, regulatory or signaling) or nutrient. Incorporating multiple nutrients and functional interaction categories under an integrated framework represents an informative approach for gaining system level insight on nutrient metabolism.</p> <p>Results</p> <p>We constructed a multi-level PPI network starting from the interactions of 200 vitamin-related proteins. Its final size was 1,657 proteins, with 2,700 interactions. To characterize the role of the proteins we computed 6 centrality indices and applied model-based clustering. We detected a subgroup of 22 proteins that were highly central and significantly related to vitamin D. Immune system and cancer-related processes were strongly represented among these proteins. Clustering of the centralities revealed a degree of redundancy among the indices; a repeated analysis using subsets of the centralities performed well in identifying the original set of 22 most central proteins.</p> <p>Conclusions</p> <p>Hierarchical and model-based clustering revealed multi-centrality hubs in a vitamin PPI network and redundancies among the centrality indices. Vitamin D-related proteins were strongly represented among network hubs, highlighting the pervasive effects of this nutrient. Our integrated approach to network construction identified promiscuous transcription factors, cytokines and enzymes - primarily related to immune system and cancer processes - representing potential gatekeepers linking vitamin intake to disease.</p

A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases

The involvement of vitamins and other micronutrients in intermediary metabolism was elucidated in the mid 1900's at the level of individual biochemical reactions. Biochemical pathways remain the foundational knowledgebase for understanding how micronutrient adequacy modulates health in all life stages. Current daily recommended intakes were usually established on the basis of the association of a single nutrient to a single, most sensitive adverse effect and thus neglect interdependent and pleiotropic effects of micronutrients on biological systems. Hence, the understanding of the impact of overt or sub-clinical nutrient deficiencies on biological processes remains incomplete. Developing a more complete view of the role of micronutrients and their metabolic products in protein-mediated reactions is of importance. We thus integrated and represented cofactor-protein interaction data from multiple and diverse sources into a multi-layer network representation that links cofactors, cofactor-interacting proteins, biological processes, and diseases. Network representation of this information is a key feature of the present analysis and enables the integration of data from individual biochemical reactions and protein-protein interactions into a systems view, which may guide strategies for targeted nutritional interventions aimed at improving health and preventing diseases

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

peer-reviewedBackground Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways - selenoamino acid metabolism and steroid biosynthesis - illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease.Department of Agriculture and Food, Ireland - Food Institutional Research Measure (project no. 5254); IRCSET postgraduate scholarship scheme (MJM); Science Foundation Ireland Principal Investigator Programme (HMR) Programme

Systems view of adipogenesis via novel omics-driven and tissue-specific activity scoring of network functional modules

The investigation of the complex processes involved in cellular differentiation must be based on unbiased, high throughput data processing methods to identify relevant biological pathways. A number of bioinformatics tools are available that can generate lists of pathways ranked by statistical significance (i.e. by p-value), while ideally it would be desirable to functionally score the pathways relative to each other or to other interacting parts of the system or process. We describe a new computational method (Network Activity Score Finder - NASFinder) to identify tissue-specific, omicsdetermined sub-networks and the connections with their upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. Adipogenesis of human SBGS pre-adipocyte cells in vitro was monitored with a transcriptomic data set comprising six time points (0, 6, 48, 96, 192, 384 hours). To elucidate the mechanisms of adipogenesis, NASFinder was used to perform time-point analysis by comparing each time point against the control (0 h) and time-lapse analysis by comparing each time point with the previous one. NASFinder identified the coordinated activity of seemingly unrelated processes between each comparison, providing the first systems view of adipogenesis in culture. NASFinder has been implemented into a web-based, freely available resource associated with novel, easy to read visualization of omics data sets and network modules

Role of CD44 in clear cell renal cell carcinoma invasiveness after antiangiogenic treatment

Treballs Finals de Grau de Farmàcia, Facultat de Farmàcia, Universitat de Barcelona, 2017. Tutor/a: Joan Carles Rodríguez Rubio.[eng] During last century, big effort to understand the biochemical basis of cancer was carried out. One of the principal branches of these cancer investigations used drugs to prevent the formation of new blood vessels –process called angiogenesis– responsible for the nutrients supply of the tumour. These drugs are generally called antiangiogenics. It was discovered that some types of tumour have or develop resistance to these drugs when treatment was long enough. For that reason, mechanisms of resistance, aggressiveness, invasion and/or metastasis after the treatment are nowadays relevant to study. Recently, a protein that could be involved in the increased invasiveness of tumour cells after the antiangiogenic treatment appeared. This project collects some evidence that indicates that this protein, called CD44, might play a role in the increased invasion after antiangiogenic treatment in mouse models of renal carcinoma.[cat] Durant l’últim segle, s’ha fet un gran esforç per aprofundir en la basant bioquímica de la investigació contra el càncer. Una de les branques principals d’aquesta investigació utilitza fàrmacs que prevenen la formació de nous vasos sanguinis –procés anomenat angiogènesis- encarregats de nodrir el tumor. Aquests fàrmacs es diuen generalment antiangiogènics. S’ha descobert que alguns tipus de tumor tenen o desenvolupen resistència a aquests fàrmacs quan el tractament és prou llarg. Per aquesta raó, actualment s’està investigant profundament quins són els mecanismes pels quals apareix aquesta resistència, així com també perquè els tumors es tornen més agressius, invasius i/o metastàtics després del tractament. Recentment s’ha descobert una proteïna que podria estar involucrada en l’augment de la invasivitat de les cèl·lules tumorals després del tractament antiangiogènic. Aquest treball recull algunes de les evidències que apunten cap al paper de la proteïna CD44 en l’increment de la invasió tumoral post-tractament amb fàrmacs antiangiogènics en models ratolins de càncer renal

Transcriptomic Coordination in the Human Metabolic Network Reveals Links between n-3 Fat Intake, Adipose Tissue Gene Expression and Metabolic Health

Understanding the molecular link between diet and health is a key goal in nutritional systems biology. As an alternative to pathway analysis, we have developed a joint multivariate and network-based approach to analysis of a dataset of habitual dietary records, adipose tissue transcriptomics and comprehensive plasma marker profiles from human volunteers with the Metabolic Syndrome. With this approach we identified prominent co-expressed sub-networks in the global metabolic network, which showed correlated expression with habitual n-3 PUFA intake and urinary levels of the oxidative stress marker 8-iso-PGF2α. These sub-networks illustrated inherent cross-talk between distinct metabolic pathways, such as between triglyceride metabolism and production of lipid signalling molecules. In a parallel promoter analysis, we identified several adipogenic transcription factors as potential transcriptional regulators associated with habitual n-3 PUFA intake. Our results illustrate advantages of network-based analysis, and generate novel hypotheses on the transcriptomic link between habitual n-3 PUFA intake, adipose tissue function and oxidative stress

Data Analytical Methods for the Application of Systems Biology in Nutrition

This chapter reviews the prevailing methods and achievements in bioinformatics and systems biology that have driven research in systems nutrition (SN). Gene set analysis (GSA) has arguably become the most widely adopted method for high-throughput gene expression (i.e. transcriptomic) analysis. The common goal of any GSA algorithm is to identify coordinated changes in sets of functionally cohesive genes. Multivariate analysis incorporates a range of approaches for exploring the structure within and between multivariate datasets. A common underlying theme across many multivariate techniques is dimensionality reduction that is, identifying informative low-dimensional representations of high-dimensional datasets. Data analysis and influence/association network inference are mainly based on the correlation of observed quantities and on the localisation into subsystems (tissue, organ, subcellular compartment) of the relevant events. The chapter mentions some of the most used databases, connecting them with the main methods