Transcription control reprogramming in genetic backup circuits

Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks

In this survey paper, we will present a number of core algorithmic questions concerning several transitive reduction problems on network that have applications in network synthesis and analysis involving cellular processes. Our starting point will be the so-called minimum equivalent digraph problem, a classic computational problem in combinatorial algorithms. We will subsequently consider a few non-trivial extensions or generalizations of this problem motivated by applications in systems biology. We will then discuss the applications of these algorithmic methodologies in the context of three major biological research questions: synthesizing and simplifying signal transduction networks, analyzing disease networks, and measuring redundancy of biological networks.

Systems biology and the integration of mechanistic explanation and mathematical explanation

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models-which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism's ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.

Genetic robustness and functional evolution of gene duplicates

Gene duplications are a major source of evolutionary innovations. Understanding the functional divergence of duplicates and their role in genetic robustness is an important challenge in biology. Previously, analyses of genetic robustness were primarily focused on duplicates essentiality and epistasis in several laboratory conditions. In this study, we use several quantitative data sets to understand compensatory interactions between Saccharomyces cerevisiae duplicates that are likely to be relevant in natural biological populations. We find that, owing to their high functional load, close duplicates are unlikely to provide substantial backup in the context of large natural populations. Interestingly, as duplicates diverge from each other, their overall functional load is reduced. At intermediate divergence distances the quantitative decrease in fitness due to removal of one duplicate becomes smaller. At these distances, yeast duplicates display more balanced functional loads and their transcriptional control becomes significantly more complex. As yeast duplicates diverge beyond 70% sequence identity, their ability to compensate for each other becomes similar to that of random pairs of singletons.

Reverse PCA, a systematic approach for identifying genes important for the physical interaction between protein pairs

Protein-protein interactions (PPIs) are of central importance for many areas of biological research. Several complementary high-throughput technologies have been developed to study PPIs. The wealth of information that emerged from these technologies led to the first maps of the protein interactomes of several model organisms. Many changes can occur in protein complexes as a result of genetic and biochemical perturbations. In the absence of a suitable assay, such changes are difficult to identify, and thus have been poorly characterized. In this study, we present a novel genetic approach (termed “reverse PCA”) that allows the identification of genes whose products are required for the physical interaction between two given proteins. Our assay starts with a yeast strain in which the interaction between two proteins of interest can be detected by resistance to the drug, methotrexate, in the context of the protein-fragment complementation assay (PCA). Using synthetic genetic array (SGA) technology, we can systematically screen mutant libraries of the yeast Saccharomyces cerevisiae to identify those mutations that disrupt the physical interaction of interest. We were able to successfully validate this novel approach by identifying mutants that dissociate the conserved interaction between Cia2 and Mms19, two proteins involved in Iron-Sulfur protein biogenesis and genome stability. This method will facilitate the study of protein structure-function relationships, and may help in elucidating the mechanisms that regulate PPIs.

Protein–protein interactions (PPI) occur when two or more proteins bind together to form large molecular machines. The importance of PPIs led to the development of multitude technologies to detect them, and to the first maps of the protein interactomes. One important challenge in biology is to understand how protein complexes respond to genetic perturbations; however, in the absence of a suitable assay, such changes have been poorly characterized. Here, we present a novel systematic genetic approach (termed “reverse PCA”), that demonstrates how the yeast protein complementation assay (PCA), coupled with the synthetic genetic array (SGA) technology may be used to study the modulation of protein–protein interactions in-vivo in response to genetic perturbations. Our assay starts with a yeast strain in which the interaction between given proteins can be detected by resistance to the drug, methotrexate. Using the SGA technology, we can systematically identify yeast mutants that reverse this interaction. We were able to successfully validate this approach by identifying mutants that dissociate the conserved interaction between Cia2 and Mms19, two proteins involved in Iron-Sulfur protein biogenesis and genome stability. This method will facilitate the study of protein structure-function relationships, and elucidate the mechanisms that regulate PPIs.

Limited agreement of independent RNAi screens for virus-required host genes owes more to false-negative than false-positive factors

Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, ∼19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be ∼2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis.

Genome-wide RNA interference assays of gene functions offer the potential for systematic, global analysis of biological processes. A pressing challenge is to develop meta-analysis methods that effectively combine information from multiple studies. One puzzle is that implicated gene lists from independent studies of the same process often show relatively low overlap. This disagreement might arise from false-positive factors, such as imperfect gene targeting (off-target effects), or from false negatives if separate studies access different components of large, complex systems. We present new methods to examine the relations between individual genome-wide RNAi studies, using studies of host genes in influenza virus replication as a test case. We find that cross-study agreement is greater than suggested by overlap of reported gene lists. This better agreement is evidenced by the strong relation of independent gene lists in functional pathways and protein interaction networks, and by a statistical model that relates multi-study, gene-level findings to factors driving correct, false-negative, and false-positive gene identification. Our analysis of multiple genome-wide studies predicts that there are many undetected host genes important for influenza virus infection, and that false negatives are the major concerns for genome-wide studies.

A systematic approach for the genetic dissection of protein complexes in living cells

Cells contain many important protein complexes involved in performing and regulating structural, metabolic, and signaling functions. One major challenge in cell biology is to elucidate the organization and mechanisms of robustness of these complexes in vivo. We developed a systematic approach to study structural dependencies within complexes in living cells by deleting subunits and measuring pairwise interactions among other components. We used our methodology to perturb two conserved eukaryotic complexes: the retromer and the nuclear pore complex. Our results identify subunits that are critical for the assembly of these complexes, reveal their structural architecture, and uncover mechanisms by which protein interactions are modulated. Our results also show that paralogous proteins play a key role in the robustness of protein complexes and shape their assembly landscape. Our approach paves the way for studying the response of protein interactomes to mutations and enhances our understanding of genotype-phenotype maps.

eQTL Epistasis - Challenges and Computational Approaches

The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods.

Linking the signaling cascades and dynamic regulatory networks controlling stress responses

Accurate models of the cross-talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time-series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and regulatory components of our networks are linked via a set of common transcription factors that serve as targets in the signaling network and as regulators of the transcriptional response network. Detailed case studies of stress responses in budding yeast demonstrate the predictive power of our method. Our method correctly identifies the core signaling proteins and transcription factors of the response programs. It further predicts the involvement of additional transcription factors and other proteins not previously implicated in the response pathways. We experimentally verify several of these predictions for the osmotic stress response network. Our approach requires little condition-specific data: only a partial set of upstream initiators and time-series gene expression data, which are readily available for many conditions and species. Consequently, our method is widely applicable and can be used to derive accurate, dynamic response models in several species.

Feature Identification of Compensatory Gene Pairs without Sequence Homology in Yeast

Genetic robustness refers to a compensatory mechanism for buffering deleterious mutations or environmental variations. Gene duplication has been shown to provide such functional backups. However, the overall contribution of duplication-based buffering for genetic robustness is rather small. In this study, we investigated whether transcriptional compensation also exists among genes that share similar functions without sequence homology. A set of nonhomologous synthetic-lethal gene pairs was assessed by using a coexpression network, protein-protein interactions, and other types of genetic interactions in yeast. Our results are notably different from those of previous studies on buffering paralogs. The low expression similarity and the conditional coexpression alone do not play roles in identifying the functionally compensatory genes. Additional properties such as synthetic-lethal interaction, the ratio of shared common interacting partners, and the degree of coregulation were, at least in part, necessary to extract functional compensatory genes. Our network-based approach is applicable to select several well-documented cases of compensatory gene pairs and a set of new pairs. The results suggest that transcriptional reprogramming plays a limited role in functional compensation among nonhomologous genes. Our study aids in understanding the mechanism and features of functional compensation more in detail.

Inferring Genetic Interactions via a Data-Driven Second Order Model

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R³) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.

GENECFE-ANFIS: A NEURO-FUZZY INFERENCE SYSTEM TO INFER GENE-GENE INTERACTIONS BASED ON RECOGNITION OF MICROARRAY GENE EXPRESSION PATTERNS

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

Negative feedback confers mutational robustness in yeast transcription factor regulation

Organismal fitness depends on the ability of gene networks to function robustly in the face of environmental and genetic perturbations. Understanding the mechanisms of this stability is one of the key aims of modern systems biology. Dissecting the basis of robustness to mutation has proven a particular challenge, with most experimental models relying on artificial DNA sequence variants engineered in the laboratory. In this work, we hypothesized that negative regulatory feedback could stabilize gene expression against the disruptions that arise from natural genetic variation. We screened yeast transcription factors for feedback and used the results to establish ROX1 (Repressor of hypOXia) as a model system for the study of feedback in circuit behaviors and its impact across genetically heterogeneous populations. Mutagenesis experiments revealed the mechanism of Rox1 as a direct transcriptional repressor at its own gene, enabling a regulatory program of rapid induction during environmental change that reached a plateau of moderate steady-state expression. Additionally, in a given environmental condition, Rox1 levels varied widely across genetically distinct strains; the ROX1 feedback loop regulated this variation, in that the range of expression levels across genetic backgrounds showed greater spread in ROX1 feedback mutants than among strains with the ROX1 feedback loop intact. Our findings indicate that the ROX1 feedback circuit is tuned to respond to perturbations arising from natural genetic variation in addition to its role in induction behavior. We suggest that regulatory feedback may be an important element of the network architectures that confer mutational robustness across biology.

Genetic redundancies and their evolutionary maintenance

Genetic redundancy refers to the common phenomenon that deleting or mutating a gene from a genome has minimal or no impact on the phenotype or fitness of the organism because of functional compensation conferred by one or more other genes. Here I summarize studies of functional redundancies between duplicate genes and those among metabolic reactions that respectively represent genetic redundancies at the individual gene level and at the systems level. I discuss the prevalence of genetic redundancies in a genome, evolutionary origins of these redundancies, and mechanisms responsible for their stable maintenance. I show that genetic redundancies are highly abundant. While some of them may be evolutionarily transient, many are stable. The majority of the stable redundancies are likely to have been selectively kept, not because of their potential benefits in regard to future deleterious mutations, but because of their actual benefits at present or in the recent past. The rest are probably preserved by selection on nonredundant pleiotropic functions. The studies summarized here illustrate the utility of systems analysis for understanding evolutionary phenomena and the importance of evolutionary thinking in uncovering the functions and origins of systemic properties.

Predicting mutation outcome from early stochastic variation in genetic interaction partners

Many mutations, including those that cause disease, only have a detrimental effect in a subset of individuals. The reasons for this are usually unknown, but may include additional genetic variation and environmental risk factors. However, phenotypic discordance remains even in the absence of genetic variation, for example between monozygotic twins, and incomplete penetrance of mutations is frequent in isogenic model organisms in homogeneous environments. Here we propose a model for incomplete penetrance based on genetic interaction networks. Using Caenorhabditis elegans as a model system, we identify two compensation mechanisms that vary among individuals and influence mutation outcome. First, feedback induction of an ancestral gene duplicate differs across individuals, with high expression masking the effects of a mutation. This supports the hypothesis that redundancy is maintained in genomes to buffer stochastic developmental failure. Second, during normal embryonic development we find that there is substantial variation in the induction of molecular chaperones such as Hsp90 (DAF-21). Chaperones act as promiscuous buffers of genetic variation, and embryos with stronger induction of Hsp90 are less likely to be affected by an inherited mutation. Simultaneously quantifying the variation in these two independent responses allows the phenotypic outcome of a mutation to be more accurately predicted in individuals. Our model and methodology provide a framework for dissecting the causes of incomplete penetrance. Further, the results establish that inter-individual variation in both specific and more general buffering systems combine to determine the outcome inherited mutations in each individual.

Computationally efficient measure of topological redundancy of biological and social networks

It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.

Coexpression of linked gene pairs persists long after their separation

In many eukaryotes, physically linked gene pairs tend to be coexpressed. However, it is still controversial to what extent this neighbor coexpression is maintained by selection and to what extent it is nonselective, purely mechanistic "leaky expression." Here, we analyze expression patterns of gene pairs that have lost their linkage in the evolution of Saccharomyces cerevisiae since its last common ancestor with Kluyveromyces waltii or that were never linked in the S. cerevisiae lineage but became neighbors in a related yeast. We demonstrate that coexpression of many linked genes is retained long after their separation and is thus likely to be functionally important. In addition, unlinked gene pairs that recently became neighbors in other yeast species tend to be coexpressed in S. cerevisiae. This suggests that natural selection often favors chromosomal rearrangements in which coexpressed genes become neighbors. Contrary to previous suggestions, selectively favorable coexpression appears not to be restricted to bidirectional promoters.

Molecular mechanisms of epistasis within and between genes

'Disease-causing' mutations do not cause disease in all individuals. One possible important reason for this is that the outcome of a mutation can depend upon other genetic variants in a genome. These epistatic interactions between mutations occur both within and between molecules, and studies in model organisms show that they are extremely prevalent. However, epistatic interactions are still poorly understood at the molecular level, and consequently difficult to predict de novo. Here I provide an overview of our current understanding of the molecular mechanisms that can cause epistasis, and areas where more research is needed. A more complete understanding of epistasis will be vital for making accurate predictions about the phenotypes of individuals.

Intracellular and intercellular processes determine robustness of the circadian clock

Circadian clocks are present in most organisms and provide an adaptive mechanism to coordinate physiology and behavior with predictable changes in the environment. Genetic, biochemical, and cellular experiments have identified more than a dozen component genes and a signal transduction pathway that support cell-autonomous, circadian clock function. One of the hallmarks of biological clocks is their ability to reset to relevant stimuli while ignoring most others. We review recent results showing intracellular and intercellular mechanisms that convey this robust timekeeping to a variety of circadian cell types.

The effects of network neighbours on protein evolution

Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network effects explaining only between 2% and 7% of the variation. The strongest known predictor of the rate of protein evolution remains expression level. We confirmed the previous observation that similar expression levels of neighbours indeed explain their similar evolution rates in protein-protein networks, and showed that the same is true for metabolic networks. In co-expression and synthetic lethal genetic interaction networks, however, neighbouring genes still show somewhat similar evolutionary rates even after simultaneously controlling for expression level, gene essentiality and gene length. Thus, similar expression levels and related functions (as inferred from co-expression and synthetic lethal interactions) seem to explain correlated evolutionary rates of network neighbours across all currently available types of biological networks.

Functional overlap and regulatory links shape genetic interactions between signaling pathways

To understand relationships between phosphorylation-based signaling pathways, we analyzed 150 deletion mutants of protein kinases and phosphatases in S. cerevisiae using DNA microarrays. Downstream changes in gene expression were treated as a phenotypic readout. Double mutants with synthetic genetic interactions were included to investigate genetic buffering relationships such as redundancy. Three types of genetic buffering relationships are identified: mixed epistasis, complete redundancy, and quantitative redundancy. In mixed epistasis, the most common buffering relationship, different gene sets respond in different epistatic ways. Mixed epistasis arises from pairs of regulators that have only partial overlap in function and that are coupled by additional regulatory links such as repression of one by the other. Such regulatory modules confer the ability to control different combinations of processes depending on condition or context. These properties likely contribute to the evolutionary maintenance of paralogs and indicate a way in which signaling pathways connect for multiprocess control.

Design and connection of robust genetic circuits

Phenotypic robustness is a highly sought after goal for synthetic biology. There are many well-studied examples of robust systems in biology, and for the advancement of synthetic biology, particularly in performance-critical applications, fundamental understanding of how robustness is both achieved and maintained is very important. A synthetic circuit may fail to behave as expected for a multitude of reasons, and since many of these failures are difficult to predict a priori, a better understanding of a circuit's behavior as well as its possible failures are needed. In this chapter, we outline work that has been done in developing design principles for robust synthetic circuits, as well as sharing our experiences designing and constructing gene circuits.

The cellular robustness by genetic redundancy in budding yeast

The frequent dispensability of duplicated genes in budding yeast is heralded as a hallmark of genetic robustness contributed by genetic redundancy. However, theoretical predictions suggest such backup by redundancy is evolutionarily unstable, and the extent of genetic robustness contributed from redundancy remains controversial. It is anticipated that, to achieve mutual buffering, the duplicated paralogs must at least share some functional overlap. However, counter-intuitively, several recent studies reported little functional redundancy between these buffering duplicates. The large yeast genetic interactions released recently allowed us to address these issues on a genome-wide scale. We herein characterized the synthetic genetic interactions for ∼500 pairs of yeast duplicated genes originated from either whole-genome duplication (WGD) or small-scale duplication (SSD) events. We established that functional redundancy between duplicates is a pre-requisite and thus is highly predictive of their backup capacity. This observation was particularly pronounced with the use of a newly introduced metric in scoring functional overlap between paralogs on the basis of gene ontology annotations. Even though mutual buffering was observed to be prevalent among duplicated genes, we showed that the observed backup capacity is largely an evolutionarily transient state. The loss of backup capacity generally follows a neutral mode, with the buffering strength decreasing in proportion to divergence time, and the vast majority of the paralogs have already lost their backup capacity. These observations validated previous theoretic predictions about instability of genetic redundancy. However, departing from the general neutral mode, intriguingly, our analysis revealed the presence of natural selection in stabilizing functional overlap between SSD pairs. These selected pairs, both WGD and SSD, tend to have decelerated functional evolution, have higher propensities of co-clustering into the same protein complexes, and share common interacting partners. Our study revealed the general principles for the long-term retention of genetic redundancy.

Evidences for increased expression variation of duplicate genes in budding yeast: from cis- to trans-regulation effects

Duplicate genes tend to have a more variable expression program than singleton genes, which was thought to be an important way for the organism to respond and adapt to fluctuating environment. However, the underlying molecular mechanisms driving such expression variation remain largely unexplored. In this work, we first rigorously confirmed that duplicate genes indeed have higher gene expression variation than singleton genes in several aspects, i.e. responses to environmental perturbation, between-strain divergence, and expression noise. To investigate the underlying mechanism, we further analyzed a previously published expression dataset of yeast segregants produced from genetic crosses. We dissected the observed expression divergence between segregant strains into cis- and trans-variabilities, and demonstrated that trans-regulation effect can explain larger fraction of the expression variation than cis-regulation effect. This is true for both duplicate genes and singleton genes. In contrast, we found, between a pair of sister paralogs, cis-variability explains more of the expression divergence between the paralogs than trans-variability. We next investigated the presence of cis- and trans-features that are associated with elevated expression variations. For cis-acting regulation, duplicate genes have higher genetic diversity in their promoters and coding regions than singleton genes. For trans-acting regulation, duplicate and singleton genes are differentially regulated by chromatin regulators and transcription factors, and duplicate genes are more severely affected by the deletion of histone tails. These results showed that both cis-and trans-factors have great effect in causing the increased expression variation of duplicate genes, and explained the previously observed differences in transcription regulation between duplicate genes and singleton genes.

Need-based up-regulation of protein levels in response to deletion of their duplicate genes

Duplicated genes compensate for loss of one of the paralogs by up-regulating the remaining paralog only under growth conditions in which paralog activity is required for survival.

Many duplicate genes maintain functional overlap despite divergence over long evolutionary time scales. Deleting one member of a paralogous pair often has no phenotypic effect, unless its paralog is also deleted. It has been suggested that this functional compensation might be mediated by active up-regulation of expression of a gene in response to deletion of its paralog. However, it is not clear how prevalent such paralog responsiveness is, nor whether it is hardwired or dependent on feedback from environmental conditions. Here, we address these questions at the genomic scale using high-throughput flow cytometry of single-cell protein levels in differentially labeled cocultures of wild-type and paralog-knockout Saccharomyces cerevisiae strains. We find that only a modest fraction of proteins (22 out of 202) show significant up-regulation to deletion of their duplicate genes. However, these paralog-responsive proteins match almost exclusively duplicate pairs whose overlapping function is required for growth. Moreover, media conditions that add or remove requirements for the function of a duplicate gene pair specifically eliminate or create paralog responsiveness. Together, our results suggest that paralog responsiveness in yeast is need-based: it appears only in conditions in which the gene function is required. Physiologically, such need-based responsiveness could provide an adaptive mechanism for compensation of genetic, environmental, or stochastic perturbations in protein abundance.

Despite sequence divergence over long evolutionary times, many genes that have undergone duplication can still compensate for the loss of their duplicates. This compensation depends, not only on functional overlap between the paralogous genes, but also on overlap in their expression patterns. It has been proposed that compensation might therefore involve active up-regulation of a gene in response to deletion of its paralog. To test for such paralog responsiveness in the yeast Saccharomyces cerevisiae, we systematically measured changes in single-cell protein levels for approximately 200 duplicate genes in the presence or absence of their paralogs. Only a small fraction (∼11%) of proteins increased in level in response to deletion of their paralog, but this set matched almost exclusively the subset of paralogs whose overlapping function is required for viability. Further, when we examined yeast grown in different media, we found that genes had either gained or lost paralog responsiveness exactly according to their importance for growth in the tested conditions. Responsiveness, therefore, is need-based: it appears only in conditions in which the function of one or both paralogs is required. We propose that such need-based responsiveness of duplicate genes could play an important adaptive role, not just in the artificial event of paralog deletion, but also in the maintenance of functions that are compromised by natural genetic, environmental, or stochastic perturbations.

The evolution of skeletal muscle performance: gene duplication and divergence of human sarcomeric alpha-actinins

In humans, there are two skeletal muscle alpha-actinins, encoded by ACTN2 and ACTN3, and the ACTN3 genotype is associated with human athletic performance. Remarkably, approximately 1 billion people worldwide are deficient in alpha-actinin-3 due to the common ACTN3 R577X polymorphism. The alpha-actinins are an ancient family of actin-binding proteins with structural, signalling and metabolic functions. The skeletal muscle alpha-actinins diverged approximately 250-300 million years ago, and ACTN3 has since developed restricted expression in fast muscle fibres. Despite ACTN2 and ACTN3 retaining considerable sequence similarity, it is likely that following duplication there was a divergence in function explaining why alpha-actinin-2 cannot completely compensate for the absence of alpha-actinin-3. This paper focuses on the role of skeletal muscle alpha-actinins, and how possible changes in functions between these duplicates fit in the context of gene duplication paradigms.

Revisiting the contribution of cis-elements to expression divergence between duplicated genes: the role of chromatin structure

Although divergence in expression is thought to be a hallmark of functional dispersal between paralogs postduplication, there is currently a limited understanding of the mechanisms underlying the necessary transcriptional alterations as recent studies have suggested that only a very small proportion of expression variation could be explained by transcriptional variation between paralogs. To further this understanding, we examined comprehensively curated regulatory interactions and genomewide nucleosome occupancy in budding yeast to specifically determine the contribution of cis-elements to expression divergence between extant duplicates. We found that divergence in activation by transcription factors plays a more important role in expression divergence of paralogs than previously appreciated; further, analysis of promoter chromatin structure demonstrated that differential nucleosome organization is coupled with divergent expression of paralogs. By incorporating information of cis-elements encoding transcriptional regulation and chromatin structure, we improved the fraction of expression variation that was previously shown to be explained based on known cis-transcriptional effects by approximately 3-fold. Taken together, our analysis highlights the importance of chromatin divergence involved in expression evolution between paralogs.

Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

Robustness of transcriptional regulatory program influences gene expression variability

Background

Most genes are not affected when any transcription factor (TF) is knocked out, indicating that they have robust transcriptional regulatory program. Yet the mechanism underlying robust transcriptional regulatory program is less clear.

Results

Here, we studied the cause and effect of robust transcriptional regulatory program. We found that cooperative TFs in the robust transcriptional regulatory program regulate their common target genes in an activity-redundant fashion, and they are able to compensate for each other's loss. As a result, their target genes are insensitive to their single perturbation. We next revealed that the degree of robustness of transcriptional regulatory program influences gene expression variability. Genes with fragile (unrobust) transcriptional regulatory program under normal growth condition could be readily reprogrammed to significantly modulate gene expression upon changing conditions. They also have high evolutionary rates of gene expression. We further showed that the fragile transcriptional regulatory program is a major source of expression variability.

Conclusion

We showed that activity-redundant TFs guarantee the robustness of transcriptional regulatory programs, and the fragility of transcriptional regulatory program plays a major role in gene expression variability. These findings reveal the mechanisms underlying robust transcription and expression variability.

An overview of recent developments in genomics and associated statistical methods

The landscape of genomics has changed drastically in the last two decades. Increasingly inexpensive sequencing has shifted the primary focus from the acquisition of biological sequences to the study of biological function. Assays have been developed to study many intricacies of biological systems, and publicly available databases have given rise to integrative analyses that combine information from many sources to draw complex conclusions. Such research was the focus of the recent workshop at the Isaac Newton Institute, 'High dimensional statistics in biology'. Many computational methods from modern genomics and related disciplines were presented and discussed. Using, as much as possible, the material from these talks, we give an overview of modern genomics: from the essential assays that make data-generation possible, to the statistical methods that yield meaningful inference. We point to current analytical challenges, where novel methods, or novel applications of extant methods, are presently needed.

From elements to modules: regulatory evolution in Ascomycota fungi

Regulatory divergence is likely a major driving force in evolution. Comparative transcriptomics provides a new glimpse into the evolution of gene regulation. Ascomycota fungi are uniquely suited among eukaryotes for studies of regulatory evolution, because of broad phylogenetic scope, many sequenced genomes, and facility of genomic analysis. Here we review the substantial divergence in gene expression in Ascomycota and how this is reconciled with the modular organization of transcriptional networks. We show that flexibility and redundancy in both cis-regulation and trans-regulation can lead to changes from altered expression of single genes to wholesale rewiring of regulatory modules. Redundancy thus emerges as a major driving force facilitating expression divergence while preserving the coherent functional organization of a transcriptional response.

Robustness: mechanisms and consequences

Biological systems are robust to perturbation by mutations and environmental fluctuations. New data are shedding light on the biochemical and network-level mechanisms responsible for robustness. Robustness to mutation might have evolved as an adaptation to reduce the effect of mutations, as a congruent byproduct of adaptive robustness to environmental variation, or as an intrinsic property of biological systems selected for their primary functions. Whatever its mechanism or origin, robustness to mutation results in the accumulation of phenotypically cryptic genetic variation. Partial robustness can lead to pre-adaptation, and thereby might contribute to evolvability. The identification and characterization of phenotypic capacitors - which act as switches of the degree of robustness - are critical to understanding the mechanisms and consequences of robustness.

Backup in gene regulatory networks explains differences between binding and knockout results

The complementarity of gene expression and protein–DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional.

Network features of the mammalian circadian clock

The mammalian circadian clock is a cell-autonomous system that drives oscillations in behavior and physiology in anticipation of daily environmental change. To assess the robustness of a human molecular clock, we systematically depleted known clock components and observed that circadian oscillations are maintained over a wide range of disruptions. We developed a novel strategy termed Gene Dosage Network Analysis (GDNA) in which small interfering RNA (siRNA)-induced dose-dependent changes in gene expression were used to build gene association networks consistent with known biochemical constraints. The use of multiple doses powered the analysis to uncover several novel network features of the circadian clock, including proportional responses and signal propagation through interacting genetic modules. We also observed several examples where a gene is up-regulated following knockdown of its paralog, suggesting the clock network utilizes active compensatory mechanisms rather than simple redundancy to confer robustness and maintain function. We propose that these network features act in concert as a genetic buffering system to maintain clock function in the face of genetic and environmental perturbation.

Buffering by gene duplicates: an analysis of molecular correlates and evolutionary conservation

Background

One mechanism to account for robustness against gene knockouts or knockdowns is through buffering by gene duplicates, but the extent and general correlates of this process in organisms is still a matter of debate. To reveal general trends of this process, we provide a comprehensive comparison of gene essentiality, duplication and buffering by duplicates across seven bacteria (Mycoplasma genitalium, Bacillus subtilis, Helicobacter pylori, Haemophilus influenzae, Mycobacterium tuberculosis, Pseudomonas aeruginosa, Escherichia coli), and four eukaryotes (Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Mus musculus (mouse)).

Results

In nine of the eleven organisms, duplicates significantly increase chances of survival upon gene deletion (P-value ≤ 0.05), but only by up to 13%. Given that duplicates make up to 80% of eukaryotic genomes, the small contribution is surprising and points to dominant roles of other buffering processes, such as alternative metabolic pathways. The buffering capacity of duplicates appears to be independent of the degree of gene essentiality and tends to be higher for genes with high expression levels. For example, buffering capacity increases to 23% amongst highly expressed genes in E. coli. Sequence similarity and the number of duplicates per gene are weak predictors of the duplicate's buffering capacity. In a case study we show that buffering gene duplicates in yeast and worm are somewhat more similar in their functions than non-buffering duplicates and have increased transcriptional and translational activity.

Conclusion

In sum, the extent of gene essentiality and buffering by duplicates is not conserved across organisms and does not correlate with the organisms' apparent complexity. This heterogeneity goes beyond what would be expected from differences in experimental approaches alone. Buffering by duplicates contributes to robustness in several organisms, but to a small extent – and the relatively large amount of buffering by duplicates observed in yeast and worm may be largely specific to these organisms. Thus, the only common factor of buffering by duplicates between different organisms may be the by-product of duplicate retention due to demands of high dosage.

Preferential regulation of duplicated genes by microRNAs in mammals

Analysis of duplicate genes and predicted microRNA targets in human and mouse shows that microRNAs are important in how the regulatory patterns of mammalian paralogs have evolved.

Background

Although recent advances have been made in identifying and analyzing instances of microRNA-mediated gene regulation, it remains unclear by what mechanisms attenuation of transcript expression through microRNAs becomes an integral part of post-transcriptional modification, and it is even less clear to what extent this process occurs for mammalian gene duplicates (paralogs). Specifically, while mammalian paralogs are known to overcome their initial complete functional redundancy through variation in regulation and expression, the potential involvement of microRNAs in this process has not been investigated.

Results

We comprehensively investigated the impact of microRNA-mediated post-transcriptional regulation on duplicated genes in human and mouse. Using predicted targets derived from several analysis methods, we report the following observations: microRNA targets are significantly enriched for duplicate genes, implying their roles in the differential regulation of paralogs; on average, duplicate microRNA target genes have longer 3' untranslated regions than singleton targets, and are regulated by more microRNA species, suggesting a more sophisticated mode of regulation; ancient duplicates were more likely to be regulated by microRNAs and, on average, have greater expression divergence than recent duplicates; and ancient duplicate genes share fewer ancestral microRNA regulators, and recent duplicate genes share more common regulating microRNAs.

Conclusion

Collectively, these results demonstrate that microRNAs comprise an important element in evolving the regulatory patterns of mammalian paralogs. We further present an evolutionary model in which microRNAs not only adjust imbalanced dosage effects created by gene duplication, but also help maintain long-term buffering of the phenotypic consequences of gene deletion or ablation.

Enzyme isoforms may increase phenotypic robustness

Enzyme isoforms are found in many cellular reactions, and can differ in the kind of reaction they catalyze, in their substrate affinity, or in their reaction rates. The evolutionary significance of enzyme isoforms is only partially understood. We used mathematical modeling to investigate the hypothesis that isoforms may be favored by selection because they can increase the phenotypic robustness of the system. We modify a model for circadian clock gene expression in Drosophila to incorporate the presence of isoforms in the phosphorylation pathway of the period gene. We consider the case in which different isoforms catalyze the same reaction but have different affinities for the substrate. Stability is increased if there is dynamic control of the expression of isoforms relative to each other. Thus, we show that controlling isoform proportion can be a powerful mechanism for reducing the effects of variations in the values of system parameters, increasing system robustness.

The determinants of gene order conservation in yeasts

BACKGROUND

Why do some groups of physically linked genes stay linked over long evolutionary periods? Although several factors are associated with the formation of gene clusters in eukaryotic genomes, the particular contribution of each feature to clustering maintenance remains unclear.

RESULTS

We quantify the strength of the proposed factors in a yeast lineage. First we identify the magnitude of each variable to determine linkage conservation by using several comparator species at different distances to Saccharomyces cerevisiae. For adjacent gene pairs, in line with null simulations, intergenic distance acts as the strongest covariate. Which of the other covariates appear important depends on the comparator, although high co-expression is related to synteny conservation commonly, especially in the more distant comparisons, these being expected to reveal strong but relatively rare selection. We also analyze those pairs that are immediate neighbors through all the lineages considered. Current intergene distance is again the best predictor, followed by the local density of essential genes and co-regulation, with co-expression and recombination rate being the weakest predictors. The genome duplication seen in yeast leaves some mark on linkage conservation, as adjacent pairs resolved as single copy in all post-whole genome duplication species are more often found as adjacent in pre-duplication species.

CONCLUSION

Current intergene distance is consistently the strongest predictor of synteny conservation as expected under a simple null model. Other variables are of lesser importance and their relevance depends both on the species comparison in question and the fate of the duplicates following genome duplication.

The extensive and condition-dependent nature of epistasis among whole-genome duplicates in yeast

Since complete redundancy between extant duplicates (paralogs) is evolutionarily unfavorable, some degree of functional congruency is eventually lost. However, in budding yeast, experimental evidence collected for duplicated metabolic enzymes and in global physical interaction surveys had suggested widespread functional overlap between paralogs. While maintained functional overlap is thought to confer robustness against genetic mutation and facilitate environmental adaptability, it has yet to be determined what properties define paralogs that can compensate for the phenotypic consequence of deleting a sister gene, how extensive this epistasis is, and how adaptable it is toward alternate environmental states. To this end, we have performed a comprehensive experimental analysis of epistasis as indicated by aggravating genetic interactions between paralogs resulting from an ancient whole-genome duplication (WGD) event occurring in the budding yeast Saccharomyces cerevisiae, and thus were able to compare properties of large numbers of epistatic and non-epistatic paralogs with identical evolutionary times since divergence. We found that more than one-third (140) of the 399 examinable WGD paralog pairs were epistatic under standard laboratory conditions and that additional cases of epistasis became obvious only under media conditions designed to induce cellular stress. Despite a significant increase in within-species sequence co-conservation, analysis of protein interactions revealed that paralogs epistatic under standard laboratory conditions were not more functionally overlapping than those non-epistatic. As experimental conditions had an impact on the functional categorization of paralogs deemed epistatic and only a fraction of potential stress conditions have been interrogated here, we hypothesize that many epistatic relationships remain unresolved.

Exposing the fitness contribution of duplicated genes

Duplicate genes from the whole-genome duplication (WGD) in yeast are often dispensable--removing one copy has little or no phenotypic consequence. It is unknown, however, whether such dispensability reflects insignificance of the ancestral function or compensation from paralogs. Here, using precise competition-based measurements of the fitness cost of single and double deletions, we estimate the exposed fitness contribution of WGD duplicate genes in metabolism and bound the importance of their ancestral pre-duplication function. We find that the functional overlap between paralogs sufficiently explains the apparent dispensability of individual WGD genes. Furthermore, the lower bound on the fitness value of the ancestral function, which is estimated by the degree of synergistic epistasis, is at least as large as the average fitness cost of deleting single non-WGD genes. These results suggest that most metabolic functions encoded by WGD genes are important today and were also important at the time of duplication.

Role of duplicate genes in robustness against deleterious human mutations

It is now widely recognized that robustness is an inherent property of biological systems [1],[2],[3]. The contribution of close sequence homologs to genetic robustness against null mutations has been previously demonstrated in simple organisms [4],[5]. In this paper we investigate in detail the contribution of gene duplicates to back-up against deleterious human mutations. Our analysis demonstrates that the functional compensation by close homologs may play an important role in human genetic disease. Genes with a 90% sequence identity homolog are about 3 times less likely to harbor known disease mutations compared to genes with remote homologs. Moreover, close duplicates affect the phenotypic consequences of deleterious mutations by making a decrease in life expectancy significantly less likely. We also demonstrate that similarity of expression profiles across tissues significantly increases the likelihood of functional compensation by homologs.

Genetic robustness is the ability of an organism to buffer deleterious genetic mutations. It has been previously demonstrated that the functional compensation by duplicates plays an important role in protection against gene deletions in model organisms. Close duplicates often share similar functions, and loss of one paralog may be buffered by others. In the present work we specifically investigate the contribution of gene duplicates to backup against deleterious human mutations. We find that genes with close homologs are significantly less likely to harbor known disease mutations compared to genes with remote homologs. In addition, close duplicates affect the phenotypic consequences of deleterious mutations by making a decrease in life expectancy less likely. Similarity of expression profiles across tissues increases the likelihood of functional compensation by homologs. Taken together, our analysis demonstrates that functional compensation by close duplicates plays an important role in human genetic disease.

A pattern recognition approach to infer time-lagged genetic interactions

MOTIVATION

For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass.

RESULTS

PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted.

AVAILABILITY

Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm.

Functional characterization of variations on regulatory motifs

Transcription factors (TFs) regulate gene expression through specific interactions with short promoter elements. The same regulatory protein may recognize a variety of related sequences. Moreover, once they are detected it is hard to predict whether highly similar sequence motifs will be recognized by the same TF and regulate similar gene expression patterns, or serve as binding sites for distinct regulatory factors. We developed computational measures to assess the functional implications of variations on regulatory motifs and to compare the functions of related sites. We have developed computational means for estimating the functional outcome of substituting a single position within a binding site and applied them to a collection of putative regulatory motifs. We predict the effects of nucleotide variations within motifs on gene expression patterns. In cases where such predictions could be compared to suitable published experimental evidence, we found very good agreement. We further accumulated statistics from multiple substitutions across various binding sites in an attempt to deduce general properties that characterize nucleotide substitutions that are more likely to alter expression. We found that substitutions involving Adenine are more likely to retain the expression pattern and that substitutions involving Guanine are more likely to alter expression compared to the rest of the substitutions. Our results should facilitate the prediction of the expression outcomes of binding site variations. One typical important implication is expected to be the ability to predict the phenotypic effect of variation in regulatory motifs in promoters.