CiteULike: balicea's genome-interactome

Discerning static and causal interactions in genome-wide reverse engineering problems.

M Zampieri — 2008-05-15T02:58:28-00:00

Bioinformatics (Oxford, England) (8 May 2008)

MOTIVATION: In the past years devising methods for discovering gene regulatory mechanisms at a genome-wide level has become a fundamental topic in the field of systems biology. The aim is to infer gene-gene interactions in an increasingly sophisticated and reliable way through the continuous improvement of reverse engineering algorithms exploiting microarray data. RESULTS: This work is inspired by the several studies suggesting that co-expression is mostly related to "static" stable binding relationships, like belonging to the same protein complex, rather than other types of interactions more of a "causal" and transient nature (e.g. transcription factor-binding site interactions). The aim of this work is to verify if direct or conditional network inference algorithms (e.g. Pearson correlation for the former, partial Pearson correlation for the latter) are indeed useful in discerning static from causal dependencies in artificial and real gene networks (derived from E.coli and S.cerevisiae). CONTACT: altafini@sissa.it.

Cell and environment interactions in tumor microregions: the multicell spheroid model.

RM Sutherland — 2008-07-28T00:12:25-00:00

Science (New York, N.Y.), Vol. 240, No. 4849. (8 April 1988), pp. 177-184.

Abnormal vascularization of malignant tumors is associated with the development of microregions of heterogeneous cells and environments. Experimental models such as multicell spheroids and a variety of new techniques are being used to determine the characteristics of these microregions and to study the interactions of the cells and microenvironments. The special cellular microecology of tumors influences responsiveness to therapeutic agents and has implications for future directions in cancer research.

Gene by environment interactions.

RC Culverhouse — 2008-07-22T00:54:52-00:00

Genetic epidemiology, Vol. 31 Suppl 1 (2007)

This paper summarizes the contributions of group 8 to the Genetic Analysis Workshop 15. Group 8 focused on ways to address the possibility that genetic and environmental effects on phenotype may not be independent, but instead may interact in ways that could play important roles in determining phenotype. Among the eight contributors to this group, all three data sets (expression data, rheumatoid arthritis data, and simulated data) were analyzed. Contributions to this section fell into the two broad categories of refining the data (e.g. stratifying or weighting based on a covariate value) and explicitly modeling the interactions. The contributions also illustrate that there are at least two possible goals for such studies. One goal is simply to identify factors contributing to phenotype in the presence of interactions that might mask the signal to univariate methods. A related but distinct goal is to characterize an interaction (e.g. to determine if the interaction is significant).

The use of logic relationships to model colon cancer gene expression networks with mRNA microarray data.

X Ruan — 2008-07-17T13:35:49-00:00

Journal of biomedical informatics, Vol. 41, No. 4. (August 2008), pp. 530-543.

The ultimate goal of genomics research is to describe the network of molecules and interactions that govern all biological functions and disease processes in cells. Nonlinear interactions among genes in terms of their logic relationships play a key role for deciphering the networks of molecules that underlie cellular function. We present a method based on a graph coloring scheme and information theory to identify the gene expression network with lower and higher order logic interactions of genes. The analysis of oncogenes and suppressor genes from a colon cancer mRNA microarray dataset identifies a gene expression network with directionality and weights that reflects intracellular communication pathways. The success of the proposed method in mining hidden, complicated gene interactions and reliably interpreting experimental results suggests that the proposed method is a useful tool for understanding cancer systems. Extension of this method holds the potential to be fruitful for understanding other complex, nonsymmetric systems.

Conserved and Differential Effects of Dietary Energy Intake on the Hippocampal Transcriptomes of Females and Males

Bronwen Martin — 2008-07-01T02:05:40-00:00

PLoS ONE, Vol. 3, No. 6. (11 June 2008), e2398.

The level of dietary energy intake influences metabolism, reproductive function, the development of age-related diseases, and even cognitive behavior. Because males and females typically play different roles in the acquisition and allocation of energy resources, we reasoned that dietary energy intake might differentially affect the brains of males and females at the molecular level. To test this hypothesis, we performed a gene array analysis of the hippocampus in male and female rats that had been maintained for 6 months on either ad libitum (control), 20% caloric restriction (CR), 40% CR, intermittent fasting (IF) or high fat/high glucose (HFG) diets. These diets resulted in expected changes in body weight, and circulating levels of glucose, insulin and leptin. However, the CR diets significantly increased the size of the hippocampus of females, but not males. Multiple genes were regulated coherently in response to energy restriction diets in females, but not in males. Functional physiological pathway analyses showed that the 20% CR diet down-regulated genes involved in glycolysis and mitochondrial ATP production in males, whereas these metabolic pathways were up-regulated in females. The 40% CR diet up-regulated genes involved in glycolysis, protein deacetylation, PGC-1α and mTor pathways in both sexes. IF down-regulated many genes in males including those involved in protein degradation and apoptosis, but up-regulated many genes in females including those involved in cellular energy metabolism, cell cycle regulation and protein deacetylation. Genes involved in energy metabolism, oxidative stress responses and cell death were affected by the HFG diet in both males and females. The gender-specific molecular genetic responses of hippocampal cells to variations in dietary energy intake identified in this study may mediate differential behavioral responses of males and females to differences in energy availability.

A matter of timing: microRNA-controlled temporal identities in worms and flies

Manfred Frasch — 2008-06-29T19:01:14-00:00

Genes Dev., Vol. 22, No. 12. (15 June 2008), pp. 1572-1576.

The first microRNAs were identified in Caenorhabditis elegans based on their functions in the temporal regulation of stage-specific cell fate decisions. Until now, it was not known whether the so-called heterochronic genes that encode miRNAs are also involved in controlling developmental transitions in other organisms. New findings by Sokol et al. (this issue of Genes & Development, pp. 1591-1596) demonstrate that the Drosophila counterpart of a heterochronic miRNA gene from C. elegans, let-7, does indeed play a role in promoting stage-specific developmental events in neuromuscular tissues during the transition from larval to adult stages, thus pointing to a more widespread utilization of miRNAs in temporal regulation of animal development. 10.1101/gad.1690608

An in Vivo Map of the Yeast Protein Interactome

Kirill Tarassov — 2008-06-13T17:39:23-00:00

Science, Vol. 320, No. 5882. (13 June 2008), pp. 1465-1470.

Protein interactions regulate the systems-level behavior of cells; thus, deciphering the structure and dynamics of protein interaction networks in their cellular context is a central goal in biology. We have performed a genome-wide in vivo screen for protein-protein interactions in Saccharomyces cerevisiae by means of a protein-fragment complementation assay (PCA). We identified 2770 interactions among 1124 endogenously expressed proteins. Comparison with previous studies confirmed known interactions, but most were not known, revealing a previously unexplored subspace of the yeast protein interactome. The PCA detected structural and topological relationships between proteins, providing an 8-nanometer-resolution map of dynamically interacting complexes in vivo and extended networks that provide insights into fundamental cellular processes, including cell polarization and autophagy, pathways that are evolutionarily conserved and central to both development and human health. 10.1126/science.1153878

Predicting disease genes using protein-protein interactions.

M Oti — 2007-08-22T12:18:45-00:00

J Med Genet, Vol. 43, No. 8. (August 2006), pp. 691-698.

BACKGROUND: The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. OBJECTIVE: To investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases. METHODS: 72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. RESULTS: Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. CONCLUSIONS: Exploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.

Protein networks in disease

Trey Ideker — 2008-04-01T18:31:26-00:00

Genome Res., Vol. 18, No. 4. (1 April 2008), pp. 644-652.

During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage. 10.1101/gr.071852.107

Genes and causation.

Denis Noble — 2008-06-19T15:26:18-00:00

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences (17 June 2008)

Relating genotypes to phenotypes is problematic not only owing to the extreme complexity of the interactions between genes, proteins and high-level physiological functions but also because the paradigms for genetic causality in biological systems are seriously confused. This paper examines some of the misconceptions, starting with the changing definitions of a gene, from the cause of phenotype characters to the stretches of DNA. I then assess whether the 'digital' nature of DNA sequences guarantees primacy in causation compared to non-DNA inheritance, whether it is meaningful or useful to refer to genetic programs, and the role of high-level (downward) causation. The metaphors that served us well during the molecular biological phase of recent decades have limited or even misleading impacts in the multilevel world of systems biology. New paradigms are needed if we are to succeed in unravelling multifactorial genetic causation at higher levels of physiological function and so to explain the phenomena that genetics was originally about. Because it can solve the 'genetic differential effect problem', modelling of biological function has an essential role to play in unravelling genetic causation.

Mapping and quantifying mammalian transcriptomes by RNA-Seq

2008-06-02T03:58:47-00:00

Nature Methods (2008)

The Coming of Age for Piwi Proteins

Anita Seto — 2008-06-16T02:56:12-00:00

Molecular Cell, Vol. 26, No. 5. (8 June 2007), pp. 603-609.

Piwi proteins, a subfamily of Argonaute (Ago) proteins, have recently been shown to bind endogenous small RNAs. However, differences between Ago proteins (which bind microRNAs and small interfering RNAs) and Piwi proteins and Piwi-interacting RNAs (piRNAs) suggest novel functions for Piwi proteins. Here, we highlight the recent progress in understanding Piwi function and the implications for germline and stem cell development.

A temporal map of transcription factor activity: mef2 directly regulates target genes at all stages of muscle development.

T Sandmann — 2008-03-24T13:52:18-00:00

Dev Cell, Vol. 10, No. 6. (June 2006), pp. 797-807.

Dissecting components of key transcriptional networks is essential for understanding complex developmental processes and phenotypes. Genetic studies have highlighted the role of members of the Mef2 family of transcription factors as essential regulators in myogenesis from flies to man. To understand how these transcription factors control diverse processes in muscle development, we have combined chromatin immunoprecipitation analysis with gene expression profiling to obtain a temporal map of Mef2 activity during Drosophila embryonic development. This global approach revealed three temporal patterns of Mef2 enhancer binding, providing a glimpse of dynamic enhancer use within the context of a developing embryo. Our results provide mechanistic insight into the regulation of Mef2's activity at the level of DNA binding and suggest cooperativity with the bHLH protein Twist. The number and diversity of new direct target genes indicates a much broader role for Mef2, at all stages of myogenesis, than previously anticipated.

piRNAs--the ancient hunters of genome invaders.

JV Hartig — 2007-07-21T01:25:08-00:00

Genes Dev, Vol. 21, No. 14. (15 July 2007), pp. 1707-1713.

In addition to miRNAs and siRNAs, a third small RNA silencing system has been uncovered that prevents the spreading of selfish genetic elements. Production of the Piwi-associated RNAs (piRNAs), which mediate the silencing activity in this pathway, is initiated at a few master control regions within the genome. The nature of the primary piRNA-generating transcript is still unknown, but RNA interference (RNAi)-like cleavage events are likely defining the 5'-ends of mature piRNAs. We summarize the recent literature on piRNA biogenesis and function with an emphasis on work in Drosophila, where genetics and biochemistry have met very successfully.

A synthetic gene network for tuning protein degradation in Saccharomyces cerevisiae

Chris Grilly — 2007-08-01T06:23:36-00:00

Mol Syst Biol, Vol. 3 (31 July 2007)

Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments

Fatima Al-Shahrour — 2008-06-01T06:01:43-00:00

Nucl. Acids Res. (31 May 2008), gkn318.

We present a new version of Babelomics, a complete suite of web tools for the functional profiling of genome scale experiments, with new and improved methods as well as more types of functional definitions. Babelomics includes different flavours of conventional functional enrichment methods as well as more advanced gene set analysis methods that makes it a unique tool among the similar resources available. In addition to the well-known functional definitions (GO, KEGG), Babelomics includes new ones such as Biocarta pathways or text mining-derived functional terms. Regulatory modules implemented include transcriptional control (Transfac, CisRed) and other levels of regulation such as miRNA-mediated interference. Moreover, Babelomics allows for sub-selection of terms in order to test more focused hypothesis. Also gene annotation correspondence tables can be imported, which allows testing with user-defined functional modules. Finally, a tool for the de novo' functional annotation of sequences has been included in the system. This allows using yet unannotated organisms in the program. Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. Babelomics is available at http://www.babelomics.org 10.1093/nar/gkn318

Estimating the size of the human interactome

Michael Stumpf — 2008-05-13T07:34:25-00:00

Proceedings of the National Academy of Sciences (12 May 2008), 0708078105.

After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be approx650,000. We find that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and approx3 times bigger than in Caenorhabditis elegans. 10.1073/pnas.0708078105

Mutation of miRNA target sequences during human evolution.

Paul P Gardner — 2008-05-28T16:59:35-00:00

Trends in genetics : TIG (8 May 2008)

It has long-been hypothesized that changes in non-protein-coding genes and the regulatory sequences controlling expression could undergo positive selection. Here we identify 402 putative microRNA (miRNA) target sequences that have been mutated specifically in the human lineage and show that genes containing such deletions are more highly expressed than their mouse orthologs. Our findings indicate that some miRNA target mutations are fixed by positive selection and might have been involved in the evolution of human-specific traits.

'Deep phenotyping': characterizing populations in the era of genomics and systems biology.

RP Tracy — 2008-05-06T15:56:26-00:00

Current opinion in lipidology, Vol. 19, No. 2. (April 2008), pp. 151-157.

PURPOSE OF REVIEW: Large-scale genomic studies establish genotype-phenotype associations, but they use phenotypes that represent current views of disease. There is an opportunity to enhance our understanding of genotype-phenotype associations by extending phenotypes into much greater detail ('deep phenotyping'). RECENT FINDINGS: We should engage in deep phenotyping for the following reasons. First, the current emphasis on clinical outcomes, although necessary for the advancement of clinical medicine, is not sufficient. Second, analytical and biological variance embedded in traditional phenotypes dilutes statistical power and strength of association. Finally, even relatively precise phenotypes may vary in terms of underlying pathophysiology across an individual's life history. Deep phenotyping focuses on the biological relevance of pathways and metabolic flux, increasing the 'granularity' of phenotypes. SUMMARY: Focus on medical phenotypes is critical, but long-term interests require additional studies that illuminate underlying biology. Deep phenotyping is less likely to yield dramatic changes in current medical practice but it offers an opportunity to gain scientific insight in an incremental manner and to make progress in redefining clinical outcomes with greater precision. It is expensive, and debate is needed to determine when and how it should be applied.

Everything you wanted to know about small RNA but were afraid to ask

SD Boyd — 2008-05-01T20:09:20-00:00

Laboratory Investigation, Vol. doi: 10.1038/labinvest.2008.32 (2008)

Multiplex amplification of large sets of human exons

Gregory Porreca — 2007-11-01T18:40:45-00:00

Nature Methods, Vol. 4, No. 11. (14 October 2007), pp. 931-936.

Psoriasis is associated with increased -defensin genomic copy number.

2008-01-25T19:35:32-00:00

Nature, Vol. 40, No. 1. (2008), pp. 23-25.

The Human Connectome: A Structural Description of the Human Brain

Olaf Sporns — 2007-02-12T12:21:57-00:00

PLoS Computational Biology, Vol. 1, No. 4. (1 September 2005), e42.

ABSTRACTThe connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks

David Juan — 2008-01-17T07:50:29-00:00

Proceedings of the National Academy of Sciences (16 January 2008), 0709671105.

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology. 10.1073/pnas.0709671105