Genetic networks

Deciphering sperm functions using biological networks

The global human population is exponentially increasing, which requires the production of quality food through efficient reproduction as well as sustainable production of livestock. Lack of knowledge and technology for assessing semen quality and predicting bull fertility is hindering advances in animal science and food animal production and causing millions of dollars of economic losses annually. The intent of this systemic review is to summarize methods from computational biology for analysis of gene, metabolite, and protein networks to identify potential markers that can be applied to improve livestock reproduction, with a focus on bull fertility. We provide examples of available gene, metabolic, and protein networks and computational biology methods to show how the interactions between genes, proteins, and metabolites together drive the complex process of spermatogenesis and regulate fertility in animals. We demonstrate the use of the National Center for Biotechnology Information (NCBI) and Ensembl for finding gene sequences, and then use them to create and understand gene, protein and metabolite networks for sperm associated factors to elucidate global cellular processes in sperm. This study highlights the value of mapping complex biological pathways among livestock and potential for conducting studies on promoting livestock improvement for global food security.

Proteomics of plant pathogenic fungi

Plant pathogenic fungi cause important yield losses in crops. In order to develop efficient and environmental friendly crop protection strategies, molecular studies of the fungal biological cycle, virulence factors, and interaction with its host are necessary. For that reason, several approaches have been performed using both classical genetic, cell biology, and biochemistry and the modern, holistic, and high-throughput, omic techniques. This work briefly overviews the tools available for studying Plant Pathogenic Fungi and is amply focused on MS-based Proteomics analysis, based on original papers published up to December 2009. At a methodological level, different steps in a proteomic workflow experiment are discussed. Separate sections are devoted to fungal descriptive (intracellular, subcellular, extracellular) and differential expression proteomics and interactomics. From the work published we can conclude that Proteomics, in combination with other techniques, constitutes a powerful tool for providing important information about pathogenicity and virulence factors, thus opening up new possibilities for crop disease diagnosis and crop protection.

Confirmation of diabetes-related quantitative trait loci derived from SM/J and A/J mice by using congenic strains fed a high-carbohydrate or high-fat diet

The interaction between causative genes and diet is known to influence the onset of obesity and diabetes in humans, although it has remained difficult to identify diabetogenic gene(s) because humans are genetically and environmentally heterogeneous. Mouse SMXA recombinant inbred (RI) strains are established from parental inbred strains (SM/J and A/J) and have been shown to be beneficial tools for analyzing polygenic traits. We previously mapped a significant quantitative trait locus (QTL, T2dm1sa) on Chromosome (Chr.) 10 and suggestive QTLs on Chr. 2, 6, and 18 for diabetes-related traits by using SMXA RI strains fed a high-carbohydrate diet. As a first step in identifying the responsible gene among QTLs for glucose tolerance mapped on Chr. 10 and 18, we established new strains of A.SM-T2dm1sa and SM.A-D18Mit19-D18Mit7 congenic mice. Each congenic strain bears the diabetogenic allele of an introgressed chromosomal region on a genetic background strain carrying the non-diabetogenic allele. The diabetogenic effect of T2dm1sa mapped on Chr. 10 was not supported by studies of A.SM-T2dm1sa congenic mice when the mice were fed a high-carbohydrate or high-fat diet. SM.A-D18Mit19-D18Mit7 congenic mice showed impaired glucose tolerance not only when they were fed a high-carbohydrate diet, but also when they were fed a high-fat diet. Thus, it appears that gene(s) affecting diabetes-related traits under either dietary condition may be present on Chr. 18.

Algebraic models of biochemical networks

With the rise of systems biology as an important paradigm in the life sciences and the availability and increasingly good quality of high-throughput molecular data, the role of mathematical models has become central in the understanding of the relationship between structure and function of organisms. This chapter focuses on a particular type of models, so-called algebraic models, which are generalizations of Boolean networks. It provides examples of such models and discusses several available methods to construct such models from high-throughput time course data. One specific such method, Polynome, is discussed in detail.

The prediction of protein-protein interaction networks in rice blast fungus

Background

Protein-protein interaction (PPI) maps are useful tools for investigating the cellular functions of genes. Thus far, large-scale PPI mapping projects have not been implemented for the rice blast fungus Magnaporthe grisea, which is responsible for the most severe rice disease. Inspired by recent advances in PPI prediction, we constructed a PPI map of this important fungus.

Results

Using a well-recognized interolog approach, we have predicted 11,674 interactions among 3,017 M. grisea proteins. Although the scale of the constructed map covers approximately only one-fourth of the M. grisea's proteome, it is the first PPI map for this crucial organism and will therefore provide new insights into the functional genomics of the rice blast fungus. Focusing on the network topology of proteins encoded by known pathogenicity genes, we have found that pathogenicity proteins tend to interact with higher numbers of proteins. The pathogenicity proteins and their interacting partners in the entire network were then used to construct a subnet called a pathogenicity network. These data may provide further clues for the study of these pathogenicity proteins. Finally, it has been established that secreted proteins in M. grisea interact with fewer proteins. These secreted proteins and their interacting partners were also compiled into a network of secreted proteins, which may be helpful in constructing an interactome between the rice blast fungus and rice.

Conclusion

We predicted the PPIs of M. grisea and compiled them into a database server called MPID. It is hoped that MPID will provide new hints as to the functional genomics of this fungus. MPID is available at .

Steady state approach to model gene regulatory networks—Simulation of microarray experiments

Genetic regulatory networks (GRN) represent complex interactions between genes brought about through proteins that they code for. Quantification of expression levels in GRN either through experiments or theoretical modeling is a challenging task. Recently, microarray experiments have gained importance in evaluating GRN at the genome level. Microarray experiments yield log fold change in mRNA abundance which is helpful in deciphering connectivity in GRN. Current approaches such as data mining, Boolean or Bayesian modeling and combined use of expression and location data are useful in analyzing microarray data. However, these methodologies lack underlying mechanistic details present in GRN. We present here a steady state gene expression simulator (SSGES) which sets up steady state equations and simulates the response for a given network structure of a GRN. SSGES includes mechanistic details such as stoichiometry, protein-DNA and protein-protein interactions, translocation of regulatory proteins and autoregulation. SSGES can be used to simulate the response of a GRN in terms of fractional transcription and protein expression. SSGES can also be used to generate log fold change in mRNA abundance and protein expression implying that it is useful to simulate microarray type experiments. We have demonstrated these capabilities of SSGES by modeling the steady state response of GAL regulatory system in Saccharomyces cerevisiae. We have demonstrated that the predicted data qualitatively matched the microarray data obtained experimentally by Ideker et al. [Ideker, T., Thorsson, V., Ranish, J.A., Christmas, R., Buhler, J., Eng, J.K., Bumgarner, R., Goodlett, D.R., Aebersold, R., Hood, L., 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929-934]. SSGES is available from authors upon request.

Protein interaction networks in plants

Protein-protein interactions are fundamental to virtually every aspect of cellular functions. With the development of high-throughput technologies of both the yeast two-hybrid system and tandem mass spectrometry, genome-wide protein-linkage mapping has become a major objective in post-genomic research. While at least partial "interactome" networks of several model organisms are already available, in the plant field, progress in this respect is slow. However, even with comprehensive protein interaction data still missing, substantial recent advance in the graph-theoretical functional interpretation of complex network architectures might pave the way for novel approaches in plant research. This article reviews current progress and discussions in network biology. Emphasis is put on the question of what can be learned about protein functions and cellular processes by studying the topology of complex protein interaction networks and the evolutionary mechanisms underlying their development. Particularly the intermediate and local levels of network organization--the modules, motifs and cliques--are increasingly recognized as the operational units of biological functions. As demonstrated by some recent results from systematic analyses of plant protein families, protein interaction networks promise to be a valuable tool for a molecular understanding of functional specificities and for identifying novel regulatory components and pathways.

Fine-mapping gene-by-diet interactions on chromosome 13 in a LG/J x SM/J murine model of obesity

Obesity is one of the most serious threats to human health today. Although there is general agreement that environmental factors such as diet have largely caused the current obesity pandemic, the environmental changes have not affected all individuals equally. To model gene-by-environment interactions in a mouse model system, our group has generated an F(16) advanced intercross line (AIL) from the SM/J and LG/J inbred strains. Half of our sample was fed a low-fat (15% energy from fat) diet while the other half was fed a high-fat (43% energy from fat) diet. The sample was assayed for a variety of obesity- and diabetes-related phenotypes such as growth rate, response to glucose challenge, organ and fat pad weights, and serum lipids and insulin. An examination in the F(16) sample of eight adiposity quantitative trait loci previously identified in an F(2) intercross of SM/J and LG/J mouse strains reveals locus-by-diet interactions for all previously mapped loci. Adip7, located on proximal chromosome 13, demonstrated the most interactions and therefore was selected for fine mapping with microsatellite markers. Three phenotypic traits, liver weight in male animals, serum insulin in male animals, and reproductive fat pad weight, show locus-by-diet interactions in the 127-kb region between markers D13Mit1 and D13Mit302. The phosphofructokinase (PFK) C (Pfkp) and the pitrilysin metalloprotease 1 (Pitrm1) genes are compelling positional candidate genes in this region that show coding sequence differences between the parental strains in functional domains.

The evolution of molecular genetic pathways and networks

There is growing interest in the evolutionary dynamics of molecular genetic pathways and networks, and the extent to which the molecular evolution of a gene depends on its position within a pathway or network, as well as over-all network topology. Investigations on the relationships between network organization, topological architecture and evolutionary dynamics provide intriguing hints as to how networks evolve. Recent studies also suggest that genetic pathway and network structures may influence the action of evolutionary forces, and may play a role in maintaining phenotypic robustness in organisms.

Diet, obesity, and hyperglycemia in LG/J and SM/J mice

OBJECTIVE

To examine the differential response of obesity- and diabetes-related traits to a high- or low-fat diet in LG/J and SM/J mice. We also examined food consumption in these strains.

RESEARCH METHODS AND PROCEDURES

Mice were placed on a high- or low-fat diet after weaning. Animals were weighed once per week and subjected to glucose tolerance tests at 20 weeks. At sacrifice, fat pads and internal organs were removed along with serum samples. For food consumption, LG/J and SM/J mice of each sex were assigned to a high-fat or low-fat diet after reaching maturity. Mice were weighed three times per week, and food consumed was determined by subtraction.

RESULTS

LG/J animals consume more total food, but SM/J animals consume more food per gram of body weight. LG/J mice grow faster to 10 weeks but slower from 10 to 20 weeks, have higher cholesterol and free fatty acid levels, and have lower basal glucose levels and better response to a glucose challenge than SM/J mice. For most traits, SM/J mice respond more strongly to a high-fat diet than LG/J mice, including body weight and growth, basal glucose levels, organ weights, fat distribution, and circulating triglycerides and cholesterol levels.

DISCUSSION

Obesity-related phenotypes, as well as response to increased dietary fat, differ genetically between LG/J and SM/J and can, therefore, be mapped. This study indicates that the cross of SM/J and LG/J mice would be an excellent model system for the study of gene-by-diet interaction in obesity.

Gene networks: how to put the function in genomics

An increasingly popular model of regulation is to represent networks of genes as if they directly affect each other. Although such gene networks are phenomenological because they do not explicitly represent the proteins and metabolites that mediate cell interactions, they are a logical way of describing phenomena observed with transcription profiling, such as those that occur with popular microarray technology. The ability to create gene networks from experimental data and use them to reason about their dynamics and design principles will increase our understanding of cellular function. We propose that gene networks are also a good way to describe function unequivocally, and that they could be used for genome functional annotation. Here, we review some of the concepts and methods associated with gene networks, with emphasis on their construction based on experimental data.

Modeling and simulation of genetic regulatory systems: a literature review

In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.

Dynamical analysis of gene networks requires both mRNA and protein expression information

One of the important goals of biology is to understand the relationship between DNA sequence information and nonlinear cellular responses. This relationship is central to the ability to effectively engineer cellular phenotypes, pathways, and characteristics. Expression arrays for monitoring total gene expression based on mRNA can provide quantitative insight into which gene or genes are on or off; but this information is insufficient to fully predict dynamic biological phenomena. Using nonlinear stability analysis we show that a combination of gene expression information at the message level and at the protein level is required to describe even simple models of gene networks. To help illustrate the need for such information we consider a mechanistic model for circadian rhythmicity which shows agreement with experimental observations when protein and mRNA information are included and we propose a framework for acquiring and analyzing experimental and mathematically derived information about gene networks.

Differential response to dietary fat in large (LG/J) and small (SM/J) inbred mouse strains

The "large" (LG/J) and "small" (SM/J) inbred mouse strains differ for a wide variety of traits related to body size and obesity. Ninety-three LG/J and SM/J mice were divided into two treatment categories and fed a moderately high-fat diet (21% kcal fat) or a low-fat diet (12% kcal fat) from weaning to necropsy. Strain differences in obesity-related traits and differential response to dietary fat increases were analyzed using ANOVA. LG/J animals grow faster from 3 to 10 wk, have longer tails, and have heavier body weight, liver weight, and fat pad weight than SM/J animals. SM/J animals grow faster after 10 wk of age and have higher fasting glucose levels than LG/J animals. SM/J mice were more responsive to increased dietary fat than LG/J mice for growth after 10 wk, necropsy weight, liver weight, fat pad weights, and fasting glucose levels (in males). The growth from 3 to 10 wk had a much greater response in the LG/J strain, whereas tail length had no response. This pattern of dietary response is similar to that expected under the "thrifty" phenotype hypothesis. Genes affecting strain differences and the differential response of the strains to dietary fat can be successfully mapped in the intercross of the LG/J and SM/J strains. This intercross provides an excellent multigenic model for the genetic basis of complex traits and diseases related to body size and obesity.

Complexity of developmental control: analysis of embryonic cell lineage specification in Caenorhabditis elegans using pes-1 as an early marker

In the early Caenorhabditis elegans embryo five somatic founder cells are born during the first cleavages. The first of these founder cells, named AB, gives rise to 389 of the 558 nuclei present in the hatching larva. Very few genes directly involved in the specification of the AB lineage have been identified so far. Here we describe a screen of a large collection of maternal-effect embryonic lethal mutations for their effect on the early expression of a pes-1::lacZ fusion gene. This fusion gene is expressed in a characteristic pattern in 14 of the 32 AB descendants present shortly after the initiation of gastrulation. Of the 37 mutations in 36 genes suspected to be required specifically during development, 12 alter the expression of the pes-1::lacZ marker construct. The gene expression pattern alterations are of four types: reduction of expression, variable expression, ectopic expression in addition to the normal pattern, and reduction of the normal pattern together with ectopic expression. We estimate that approximately 100 maternal functions are required to establish the pes-1 expression pattern in the early embryo.

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the ULTRASAN package is used to present results from numerical analysis and the outcome of simulations.

'Proteomic contigs' of Mycobacterium tuberculosis and Mycobacterium bovis (BCG) using novel immobilised pH gradients

Tuberculosis remains a major health problem throughout the world and the failure of the existing bacille Calmette-Guérin (BCG) vaccine in recent trials has prompted a search for potential replacements. Recent advances in molecular and cell biology have cast doubts on the ability of genetic analysis alone to predict polygenic human diseases and other complex phenotypes and have therefore redirected our attention to proteome studies to complement information obtained from DNA sequencing initiatives. Novel acidic (pH 2.3-5) and basic (pH 6-11) IPG gel gradients were employed in conjunction with commercially available pH 4-7 gradients to significantly increase (fourfold) the number of protein spots previously resolved on two-dimensional (2-D) gels of Mycobacterium species. A total of 772 and 638 protein spots were observed for M. bovis BCG and M. tuberculosis H37Rv, respectively, the latter corresponding to only the pH regions 4-7 and 6-11. Of interest was the bimodal distribution observed for proteins separated from M. bovis BCG across both M(r) and pH ranges. Some differences in protein expression were observed between these two organisms, contrary to what may have been expected considering the high degree of conservation in gene order and sequence similarity between homologous genes. Further work will be directed towards a more detailed analysis of these differences, so as to allow more accurate diagnosis between vaccination and active tuberculosis. The latter is of major importance to epidemiological studies and for patient management.