Shift in chicken intestinal gene association networks after infection with Salmonella

A primary infection of Salmonella enteritidis causes a spatial-temporal dependent change in the gene expression patterns in the intestine of chickens (Gallus gallus). This is the result of a dynamic intestinal response to adapt to the altered environment and to optimize its ‘health’ and functionality under the new circumstances. By inferring gene association networks (GANs), the complexities of and changes in biological networks can be uncovered. Within such GANs highly interacting (hub) genes can be identified, which are supposed to be high-level regulators connected to multiple processes. By exploring the intestinal expression of genes differing between control and Salmonella infected chicken in a time-dependent manner differences in GANs were found. In control chickens more developmental processes were observed, whereas in infected chickens relatively more processes were associated to ‘defense/pathogen response’. Moreover the conserved protein domains of the identified hub genes in controls were nuclear-associated, whereas hub genes in infected chickens were involved in ‘cellular communication’. The shift in topology and functionality of the intestinal GANs in control and Salmonella infected animals and the identification of GAN-specific hubs is a first step to understand the complexity of biological networks and processes regulating intestinal health and functionality under normal and disturbed conditions.     

Not my brother's keeper: A thought experiment for Hamilton's rule

The now popular ‘selfish gene’ view defines evolutionary fitness at the gene level - in terms of the number of gene copies residing in future generations (or propelled from previous generations). Yet, most current biology textbooks still apply the concept of fitness to the individual, where it is defined more traditionally as the number of descendants residing in future generations. The existing literature remains ambiguous regarding whether one of these concepts is more meaningful than the other, or whether they both represent legitimate, functional definitions of fitness. In support of the latter view, I present a composite perspective that recognizes the gene as evolutionarily ‘selfish’, but also the individual as a ‘selfish vehicle’ for resident genes. Hamilton's rule explains, based on genetic relatedness, why natural selection has favoured behaviours that compel individuals (as ‘donors’ of help) to act for the good of copies of their genes residing in close kin (‘recipients’). I propose however, that natural selection should particularly favour helping behaviours directed at those recipient kin who have the highest relative probability of being the vehicle for a remarkably adaptive newly mutant gene, weighted by the proportion of genes shared with the donor. According to this ‘adaptive-genetic-novelty-rescue’ (AGNR) hypothesis, these favoured vehicles for shared gene copies are more likely to involve direct descendants (e.g. offspring) than other close kin from one's collateral lineage (e.g. siblings), even when the donor (e.g. a father) shares fewer genes with an offspring (e.g. a son) than with a sibling (e.g. a brother).     

Peptidergic neurohormonal control systems in invertebrates

The concerted activity of many neuropeptides has been implicated in the neurohormonal control of specific behaviors and various physiological functions in some invertebrate model systems. What are the functional consequences of this neuropeptide multiplicity? The distinct actions of closely related neuropeptides have been detected in molluscs and insects; however, recent work provides examples of systems in which some of the multiple isoforms may be functionally redundant. Groups of functionally distinct neuropeptides encoded by the same gene can be expressed in different neurons by alternative gene splicing or cell-specific post-translational processing; therefore, as shown recently, they can be targeted for release as ‘cocktails’ to act on specific sets of muscles or neurons. One prominent role of neuropeptides is to modulate the activity of rhythm-generating circuits, as exemplified by recent research on mollusc neural networks, the crab stromatogastric ganglion, and fly circadian pacemakers.     

"Guilt by association" is the exception rather than the rule in gene networks

Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.     

Arabidopsis paves the way: genomic and network analyses in crops

Arabidopsis genomic and network analyses have facilitated crop research towards the understanding of many biological processes of fundamental importance for agriculture. Genes that were identified through genomic analyses in Arabidopsis have been used to manipulate crop traits such as pathogen resistance, yield, water-use efficiency, and drought tolerance, with the effects being tested in field conditions. The integration of diverse Arabidopsis genome-wide datasets in probabilistic functional networks has been demonstrated as a feasible strategy to associate novel genes with traits of interest, and novel genomic methods continue to be developed. The combination of genome-wide location studies, using ChIP-Seq, with gene expression profiling data is affording a genome-wide view of regulatory networks previously delineated through genetic and molecular analyses, leading to the identification of novel components and of new connections within these networks.     

Bioprocess model building using artificial neural networks

Artificial neural networks are made upon of highly interconnected layers of simple neuron-like nodes. The neurons act as non-linear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of learning and characterising non-linear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique. One area where such a technique could be useful is biotechnological systems. Here, for example, the use of a process model within an estimation scheme has long been considered an effective means of overcoming inherent on-line measurement problems. However, the development of an accurate process model is extremely time consuming and often results in a model of limited applicability. Artificial neural networks could therefore prove to be a useful model building tool when striving to improve bioprocess operability. Two large scale industrial fermentation systems have been considered as test cases; a fed-batch penicillin fermentation and a continuous mycelial fermentation. Both systems serve to demonstrate the utility, flexibility and potential of the artificial neural network approach to process modelling.     

Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks

MOTIVATION: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.     

Interpretation of Genomic Variants Using a Unified Biological Network Approach

The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called ‘loss-of-function tolerant’, is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified ‘Multinet’ correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.     

Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes

The rate of conservation of a gene in evolution is believed to be correlated with its biological importance. Recent studies have devised various conservation measures for genes and have shown that they are correlated with several biological characteristics of functional importance. Specifically, the state-of-the-art propensity for gene loss (PGL) measure was shown to be strongly correlated with gene essentiality and its number of protein–protein interactions (PPIs). The observed correlation between conservation and functional importance varies however between conservation measures, underscoring the need for accurate and general measures for the rate of gene conservation. Here we develop a novel maximum-likelihood approach to computing the rate in which a gene is lost in evolution, motivated by the same principles as those underlying PGL. However, in difference to PGL which considers only the most parsimonious ancestral states of the internal nodes of the phylogenetic tree relating the species, our approach weighs in a probabilistic manner all possible ancestral states, and includes the branch length information as part of the probabilistic model. In application to data of 16 eukaryotic genomes, our approach shows higher correlations with experimental data than PGL, including data on gene lethality, level of connectivity in a PPI network and coherence within functionally related genes.     

Annotating gene function by combining expression data with a modular gene network

MOTIVATION: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks. RESULTS: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.     

On the long-run sensitivity of probabilistic Boolean networks

Boolean networks and, more generally, probabilistic Boolean networks, as one class of gene regulatory networks, model biological processes with the network dynamics determined by the logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies. This paper studies this less investigated issue of network sensitivity in the long run. As the underlying model of probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks and call it long-run sensitivity. The steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations is the key measure for intervention. This newly defined long-run sensitivity can provide insight on both network inference and intervention. We show the results for probabilistic Boolean networks generated from random Boolean networks and the results from two real biological networks illustrate preliminary applications of sensitivity in intervention for practical problems.     

Metabolic networks are NP-hard to reconstruct

High-throughput data from various omics and sequencing techniques have rendered the automated metabolic network reconstruction a highly relevant problem. Our approach reflects the inherent probabilistic nature of the steps involved in metabolic network reconstruction. Here, the goal is to arrive at networks which combine probabilistic information with the possibility to obtain a small number of disconnected network constituents by reduction of a given preliminary probabilistic metabolic network. We define automated metabolic network reconstruction as an optimization problem on four-partite graph (nodes representing genes, enzymes, reactions, and metabolites) which integrates: (1) probabilistic information obtained from the existing process for metabolic reconstruction from a given genome, (2) connectedness of the raw metabolic network, and (3) clustering of components in the reconstructed metabolic network. The practical implications of our theoretical analysis refer to the quality of reconstructed metabolic networks and shed light on the problem of finding more efficient and effective methods for automated reconstruction. Our main contributions include: a completeness result for the defined problem, polynomial-time approximation algorithm, and an optimal polynomial-time algorithm for trees. Moreover, we exemplify our approach by the reconstruction of the sucrose biosynthesis pathway in Chlamydomonas reinhardtii.     

Insights into the genomic features and evolutionary impact of the genes configuring duplicated pseudogenes in human

Pseudogenes, regarded as ‘genomic fossils’, are DNA sequences resembling functional genes in perspective of sequence homology but completely non-functional. In this study, we explored the unique characteristic features of human genes, configuring classical duplicated pseudogenes. We found that progenitors of duplicated pseudogenes are characterized by a high expressivity, and ability to encode hub-proteins in association with a high evolutionary rate. Such unusual features are endorsed by longer protein length, elevated CpG content, and a high recombination rate. The non-functionalization of their duplicated copies can be attributed to the overabundance of gene paralog number in concert with functional redundancy.     

An analysis of the class of gene regulatory functions implied by a biochemical model

Understanding the integrated behavior of genetic regulatory networks, in which genes regulate one another's activities via RNA and protein products, is emerging as a dominant problem in systems biology. One widely studied class of models of such networks includes genes whose expression values assume Boolean values (i.e., on or off). Design decisions in the development of Boolean network models of gene regulatory systems include the topology of the network (including the distribution of input- and output-connectivity) and the class of Boolean functions used by each gene (e.g., canalizing functions, post functions, etc.). For example, evidence from simulations suggests that biologically realistic dynamics can be produced by scale-free network topologies with canalizing Boolean functions. This work seeks further insights into the design of Boolean network models through the construction and analysis of a class of models that include more concrete biochemical mechanisms than the usual abstract model, including genes and gene products, dimerization, cis-binding sites, promoters and repressors. In this model, it is assumed that the system consists of N genes, with each gene producing one protein product. Proteins may form complexes such as dimers, trimers, etc. The model also includes cis-binding sites to which proteins may bind to form activators or repressors. Binding affinities are based on structural complementarity between proteins and binding sites, with molecular binding sites modeled by bit-strings. Biochemically plausible gene expression rules are used to derive a Boolean regulatory function for each gene in the system. The result is a network model in which both topological features and Boolean functions arise as emergent properties of the interactions of components at the biochemical level. A highly biased set of Boolean functions is observed in simulations of networks of various sizes, suggesting a new characterization of the subset of Boolean functions that are likely to appear in gene regulatory networks.     

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.     

Genetics and iron in the systems biology of Parkinson’s disease and some related disorders

The systems biology approach to complex diseases recognises that a potentially large number of biochemical network elements may be involved in disease progression, especially where positive feedback loops can be identified. Most of these network elements will be encoded by genes, for which different alleles may affect the network(s) differentially. A primary requirement is therefore to determine the relevant gene-network relationships. A corollary of this is that identification of the network should thereby allow one to ‘explain’ or account for any genetic associations.     

Preferential Duplication of Intermodular Hub Genes: An Evolutionary Signature in Eukaryotes Genome Networks

Whole genome protein-protein association networks are not random and their topological properties stem from genome evolution mechanisms. In fact, more connected, but less clustered proteins are related to genes that, in general, present more paralogs as compared to other genes, indicating frequent previous gene duplication episodes. On the other hand, genes related to conserved biological functions present few or no paralogs and yield proteins that are highly connected and clustered. These general network characteristics must have an evolutionary explanation. Considering data from STRING database, we present here experimental evidence that, more than not being scale free, protein degree distributions of organisms present an increased probability for high degree nodes. Furthermore, based on this experimental evidence, we propose a simulation model for genome evolution, where genes in a network are either acquired de novo using a preferential attachment rule, or duplicated with a probability that linearly grows with gene degree and decreases with its clustering coefficient. For the first time a model yields results that simultaneously describe different topological distributions. Also, this model correctly predicts that, to produce protein-protein association networks with number of links and number of nodes in the observed range for Eukaryotes, it is necessary 90% of gene duplication and 10% of de novo gene acquisition. This scenario implies a universal mechanism for genome evolution.     

Contribution of rare and common variants determine complex diseases—Hirschsprung disease as a model

Finding genes for complex diseases has been the goal of many genetic studies. Most of these studies have been successful by searching for genes and mutations in rare familial cases, by screening candidate genes and by performing genome wide association studies. However, only a small fraction of the total genetic risk for these complex genetic diseases can be explained by the identified mutations and associated genetic loci. In this review we focus on Hirschsprung disease (HSCR) as an example of a complex genetic disorder. We describe the genes identified in this congenital malformation and postulate that both common ‘low penetrant’ variants in combination with rare or private ‘high penetrant’ variants determine the risk on HSCR, and likely, on other complex diseases. We also discuss how new technological advances can be used to gain further insights in the genetic background of complex diseases. Finally, we outline a few steps to develop functional assays in order to determine the involvement of these variants in disease development.