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© 2015 Nature America, Inc. All rights reserved.Autism spectrum disorders (ASDs) are characterized by phenotypic and genetic heterogeneity. Our analysis of functional networks perturbed in ASD suggests that both truncating and nontruncating de novo mutatio

A model system for investigating how developmental regulatory networks determine cell fate is spore formation in Bacillus subtilis. The master regulator for sporulation is Spo0A, which is activated by phosphorylation via a phosphorelay that is subject to three positive feedback loops. The ultimate decision to sporulate is, however, stochastic in that only a portion of the population sporulates even under optimal conditions. It was previously assumed that activation of Spo0A and hence entry into sporulation is subject to a bistable switch mediated by one or more feedback loops. Here we reinvestigate the basis for bimodality in sporulation. We show that none of the feedback loops is rate limiting for the synthesis and phosphorylation of Spo0A. Instead, the loops ensure a just-in-time supply of relay components for rising levels of phosphorylated Spo0A, with phosphate flux through the relay being limiting for Spo0A activation and sporulation. In addition, genes under Spo0A control did not exhibit a bimodal pattern of expression as expected for a bistable switch. In contrast, we observed a highly heterogeneous pattern of Spo0A activation that increased in a nonlinear manner with time. We present a computational model for the nonlinear increase and propose that the phosphorelay is a noise generator and that only cells that attain a threshold level of phosphorylated Spo0A sporulate., Statistics

Abstract Background Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes. Results We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases. Conclusion We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities.

Macroecological relationships that describe various statistical associations between species' abundances, their spatial, and temporal variability are among the most general laws in ecology and biology. One of the most commonly observed relationships is a power-law scaling between means and variances of temporal species abundances, known in ecology as Taylor's law. Taylor's law has been observed across many ecosystems, from diverse plant and animal ecosystems to complex microbial communities. While many mathematical models have been proposed to explain the potential origins of Taylor's law, what determines its scaling exponents across species and ecosystems is not understood. Here, we use temporal trajectories of human and baboon gut microbiota to analyze the relationship between functional properties of individual bacterial species and microbial communities with the scaling of species-specific and community-level Taylor's law. The species Taylor law characterizes - for each individual species - the relationship between the species' temporal abundance means and temporal abundance variances across host organisms. On the other hand, community-level Taylor's law characterizes - in each host organism - the scaling across multiple species between their temporal abundance means and temporal abundance variances. For community Taylor's law, we find that the power law scaling is strongly associated with the microbiota abundance of certain nutrient-degrading enzymes in the gut. Notably, our results demonstrate that the availability of enzymes metabolizing starch glycogen significantly increases Taylor's law scaling. We also find that species Taylor's law depends on the individual species' functional properties. Specifically, we observe lower Taylor's law scaling for species with larger metabolic networks, for species that are able to grow on a larger number of carbon sources, and for species with particular metabolic functions, such as glutamine and folate metabolism. Overall, our study reveals that Taylor's law scaling is strongly associated with the functional capabilities of bacterial communities and individual microbial species' biosynthetic properties, which are likely related to their ecological roles in the gut microbiota.

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Metastases arise from subsets of cancer cells that disseminate from the primary tumour 1,2 . The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize 3,4 . Specific cancer types metastasize to consistent subsets of tissues, suggesting that primary tumour-associated factors influence where cancers can grow. We find primary and metastatic pancreatic tumours have metabolic similarities and that the tumour-initiating capacity and proliferation of both primary-derived and metastasis-derived cells is favoured in the primary site relative to the metastatic site. Moreover, propagating cells as tumours in the lung or the liver does not enhance their relative ability to form large tumours in those sites, change their preference to grow in the primary site, nor stably alter aspects of their metabolism relative to primary tumours. Primary liver and lung cancer cells also exhibit a preference to grow in their primary site relative to metastatic sites. These data suggest cancer tissue of origin influences both primary and metastatic tumour metabolism and may impact where cancer cells can metastasize., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Cholesterol biosynthesis is a highly regulated, oxygen-dependent pathway, vital for cell membrane integrity and growth. In fungi, the dependency on oxygen for sterol production has resulted in a shared transcriptional response, resembling prolyl hydroxylation of Hypoxia Inducible Factors (HIFs) in metazoans. Whether an analogous metazoan pathway exists is unknown. Here, we identify Sterol Regulatory Element Binding Protein 2 (SREBP2), the key transcription factor driving sterol production in mammals, as an oxygen-sensitive regulator of cholesterol synthesis. SREBP2 degradation in hypoxia overrides the normal sterol-sensing response, and is HIF independent. We identify MARCHF6, through its NADPH-mediated activation in hypoxia, as the main ubiquitin ligase controlling SREBP2 stability. Hypoxia-mediated degradation of SREBP2 protects cells from statin-induced cell death by forcing cells to rely on exogenous cholesterol uptake, explaining why many solid organ tumours become auxotrophic for cholesterol. Our findings therefore uncover an oxygen-sensitive pathway for governing cholesterol synthesis through regulated SREBP2-dependent protein degradation., (© 2023. Springer Nature Limited.)

Production of oxidized biomass, which requires regeneration of the cofactor NAD + , can be a proliferation bottleneck that is influenced by environmental conditions. However, a comprehensive quantitative understanding of metabolic processes that may be affected by NAD + deficiency is currently missing. Here, we show that de novo lipid biosynthesis can impose a substantial NAD + consumption cost in proliferating cancer cells. When electron acceptors are limited, environmental lipids become crucial for proliferation because NAD + is required to generate precursors for fatty acid biosynthesis. We find that both oxidative and even net reductive pathways for lipogenic citrate synthesis are gated by reactions that depend on NAD + availability. We also show that access to acetate can relieve lipid auxotrophy by bypassing the NAD + consuming reactions. Gene expression analysis demonstrates that lipid biosynthesis strongly anti-correlates with expression of hypoxia markers across tumor types. Overall, our results define a requirement for oxidative metabolism to support biosynthetic reactions and provide a mechanistic explanation for cancer cell dependence on lipid uptake in electron acceptor-limited conditions, such as hypoxia., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity. Despite recent progress in understanding ASD genetics, the nature of phenotypic heterogeneity across probands remains unclear. Notably, likely gene-disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. Nevertheless, we find that truncating mutations affecting the same exon frequently lead to strikingly similar intellectual phenotypes in unrelated ASD probands. Analogous patterns are observed for two independent proband cohorts and several other important ASD-associated phenotypes. We find that exons biased toward prenatal and postnatal expression preferentially contribute to ASD cases with lower and higher IQ phenotypes, respectively. These results suggest that exons, rather than genes, often represent a unit of effective phenotypic impact for truncating mutations in autism. The observed phenotypic patterns are likely mediated by nonsense-mediated decay (NMD) of splicing isoforms, with autism phenotypes usually triggered by relatively mild (15-30%) decreases in overall gene dosage. We find that each ASD gene with recurrent mutations can be characterized by a parameter, phenotype dosage sensitivity (PDS), which quantifies the relationship between changes in a gene's dosage and changes in a given disease phenotype. We further demonstrate analogous relationships between exon LGDs and gene expression changes in multiple human tissues. Therefore, similar phenotypic patterns may be also observed in other human genetic disorders.

The gut microbiota is now widely recognized as a dynamic ecosystem that plays an important role in health and disease. Although current sequencing technologies make it possible to explore how relative abundances of host-associated bacteria change over time, the biological processes governing microbial dynamics remain poorly understood. Therefore, as in other ecological systems, it is important to identify quantitative relationships describing various aspects of gut microbiota dynamics. In the present study, we use multiple high-resolution time series data obtained from humans and mice to demonstrate that, despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, the observed patterns are highly similar to those previously identified across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations and the performance of publicly traded companies. Specifically, we find power-law relationships describing short- and long-term changes in gut microbiota abundances, species residence and return times, and the correlation between the mean and the temporal variance of species abundances. The observed scaling laws are altered in mice receiving different diets and are affected by context-specific perturbations in humans. We use the macroecological relationships to reveal specific bacterial taxa, the dynamics of which are substantially perturbed by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding the complex dynamics of diverse microbial communities.

Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in constantly fluctuating environments. It is currently not well understood how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, that is perform fold change detection. Using the growth factor-activated PI3K-Akt signaling pathway, we develop here computational and analytical models, and experimentally validate a novel non-transcriptional mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. The surface receptor abundance is regulated by background signal-dependent receptor endocytosis and down-regulation. We show the robustness and specificity of relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of cell memory and fold change detection may be important in diverse signaling cascades and multiple biological contexts., Competing Interests: EL, MN, PD, SL, PS, DV No competing interests declared, (© 2020, Lyashenko et al.)

Functional conservation is known to constrain protein evolution. Nevertheless, the long-term divergence patterns of proteins maintaining the same molecular function and the possible limits of this divergence have not been explored in detail. We investigate these fundamental questions by characterizing the divergence between ancient protein orthologs with conserved molecular function. Our results demonstrate that the decline of sequence and structural similarities between such orthologs significantly slows down after ~1-2 billion years of independent evolution. As a result, the sequence and structural similarities between ancient orthologs have not substantially decreased for the past billion years. The effective divergence limit (>25% sequence identity) is not primarily due to protein sites universally conserved in all linages. Instead, less than four amino acid types are accepted, on average, per site across orthologous protein sequences. Our analysis also reveals different divergence patterns for protein sites with experimentally determined small and large fitness effects of mutations., Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)., Competing Interests: MK, GP, JP, DU, HW, DV No competing interests declared, (© 2019, Konaté et al.)

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal variability remains an important challenge. Here, we present decomposition of variance using replicate sampling (DIVERS), a method based on replicate sampling and spike-in sequencing. The method quantifies the contributions of temporal dynamics, spatial sampling variability, and technical noise to the variances and covariances of absolute bacterial abundances. We applied DIVERS to investigate a high-resolution time series of the human gut microbiome and a spatial survey of a soil bacterial community in Manhattan's Central Park. Our analysis showed that in the gut, technical noise dominated the abundance variability for nearly half of the detected taxa. DIVERS also revealed substantial spatial heterogeneity of gut microbiota, and high temporal covariances of taxa within the Bacteroidetes phylum. In the soil community, spatial variability primarily contributed to abundance fluctuations at short time scales (weeks), while temporal variability dominated at longer time scales (several months).

The avoidance of cytotoxic effects associated with protein misfolding has been proposed as a dominant constraint on the sequence evolution and molecular clock of highly expressed proteins. Recently, Leuenberger et al. developed an elegant experimental approach to measure protein thermal stability at the proteome scale. The collected data allow us to rigorously test the predictions of the misfolding avoidance hypothesis that highly expressed proteins have evolved to be more stable, and that maintaining thermodynamic stability significantly constrains their evolution. Notably, reanalysis of the Leuenberger et al. data across four different organisms reveals no substantial correlation between protein stability and protein abundance. Therefore, the key predictions of the misfolding toxicity and related hypotheses are not supported by available empirical data. The data also suggest that, regardless of protein expression, protein stability does not substantially affect the protein molecular clock across organisms., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Tumor genetics guides patient selection for many new therapies, and cell culture studies have demonstrated that specific mutations can promote metabolic phenotypes. However, whether tissue context defines cancer dependence on specific metabolic pathways is unknown. Kras activation and Trp53 deletion in the pancreas or the lung result in pancreatic ductal adenocarinoma (PDAC) or non-small cell lung carcinoma (NSCLC), respectively, but despite the same initiating events, these tumors use branched-chain amino acids (BCAAs) differently. NSCLC tumors incorporate free BCAAs into tissue protein and use BCAAs as a nitrogen source, whereas PDAC tumors have decreased BCAA uptake. These differences are reflected in expression levels of BCAA catabolic enzymes in both mice and humans. Loss of Bcat1 and Bcat2, the enzymes responsible for BCAA use, impairs NSCLC tumor formation, but these enzymes are not required for PDAC tumor formation, arguing that tissue of origin is an important determinant of how cancers satisfy their metabolic requirements., (Copyright © 2016, American Association for the Advancement of Science.)

Autism spectrum disorders (ASDs) are characterized by phenotypic and genetic heterogeneity. Our analysis of functional networks perturbed in ASD suggests that both truncating and nontruncating de novo mutations contribute to autism, with a bias against truncating mutations in early embryonic development. We find that functional mutations are preferentially observed in genes likely to be haploinsufficient. Multiple cell types and brain areas are affected, but the impact of ASD mutations appears to be strongest in cortical interneurons, pyramidal neurons and the medium spiny neurons of the striatum, implicating cortical and corticostriatal brain circuits. In females, truncating ASD mutations on average affect genes with 50-100% higher brain expression than in males. Our results also suggest that truncating de novo mutations play a smaller role in the etiology of high-functioning ASD cases. Overall, we find that stronger functional insults usually lead to more severe intellectual, social and behavioral ASD phenotypes.

For many decades comparative analyses of protein sequences and structures have been used to investigate fundamental principles of molecular evolution. In contrast, relatively little is known about the long-term evolution of species' phenotypic and genetic properties. This represents an important gap in our understanding of evolution, as exactly these proprieties play key roles in natural selection and adaptation to diverse environments. Here we perform a comparative analysis of bacterial growth and gene deletion phenotypes using hundreds of genome-scale metabolic models. Overall, bacterial phenotypic evolution can be described by a two-stage process with a rapid initial phenotypic diversification followed by a slow long-term exponential divergence. The observed average divergence trend, with approximately similar fractions of phenotypic properties changing per unit time, continues for billions of years. We experimentally confirm the predicted divergence trend using the phenotypic profiles of 40 diverse bacterial species across more than 60 growth conditions. Our analysis suggests that, at long evolutionary distances, gene essentiality is significantly more conserved than the ability to utilize different nutrients, while synthetic lethality is significantly less conserved. We also find that although a rapid phenotypic evolution is sometimes observed within the same species, a transition from high to low phenotypic similarity occurs primarily at the genus level.

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.

Reprogramming of cellular metabolism is an emerging hallmark of neoplastic transformation. However, it is not known how the expression of metabolic genes in tumors differs from that in normal tissues, or whether different tumor types exhibit similar metabolic changes. Here we compare expression patterns of metabolic genes across 22 diverse types of human tumors. Overall, the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues. Although expression changes of some metabolic pathways (e.g., upregulation of nucleotide biosynthesis and glycolysis) are frequently observed across tumors, expression changes of other pathways (e.g., oxidative phosphorylation) are very heterogeneous. Our analysis also suggests that the expression changes of some metabolic genes (e.g., isocitrate dehydrogenase and fumarate hydratase) may enhance or mimic the effects of recurrent mutations in tumors. On the level of individual biochemical reactions, many hundreds of metabolic isoenzymes show significant and tumor-specific expression changes. These isoenzymes are potential targets for anticancer therapy.

Competing Interests: The authors have declared that no competing interests exist.

Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas ignoring it (e.g., by treating parameters as a simple list of values and variances) yields nonsensical predictions. Computing the Bayes factor from joint distributions yields the odds ratio (∼20-fold) for competing 'direct' and 'indirect' apoptosis models having different numbers of parameters. Our results illustrate how Bayesian approaches to model calibration and discrimination combined with single-cell data represent a generally useful and rigorous approach to discriminate between competing hypotheses in the face of parametric and topological uncertainty.

Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

Annotation of organism-specific metabolic networks is one of the main challenges of systems biology. Importantly, owing to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a global probabilistic approach to annotate genome-scale metabolic networks that integrates sequence homology and context-based correlations under a single principled framework. The developed method for global biochemical reconstruction using sampling (GLOBUS) not only provides annotation probabilities for each functional assignment but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote sequence identity to known enzymes. We apply GLOBUS to genomes of Bacillus subtilis and Staphylococcus aureus and validate the method predictions by experimentally demonstrating the 6-phosphogluconolactonase activity of YkgB and the role of the Sps pathway for rhamnose biosynthesis in B. subtilis.

Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants., (Copyright © 2011 Elsevier Inc. All rights reserved.)

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

Genome sequencing of multiple species makes it possible to understand the main principles behind the evolution of developmental regulatory networks. It is especially interesting to analyze the evolution of well-defined model systems in which conservation patterns can be directly correlated with the functional roles of various network components. Endospore formation (sporulation), extensively studied in Bacillus subtilis, is driven by such a model bacterial network of cellular development and differentiation. In this review, we analyze the evolution of the sporulation network in multiple endospore-forming bacteria. Importantly, the network evolution is not random but primarily follows the hierarchical organization and functional logic of the sporulation process. Specifically, the sporulation sigma factors and the master regulator of sporulation, Spo0A, are conserved in all considered spore-formers. The sequential activation of these global regulators is also strongly conserved. The feed-forward loops, which are likely used to fine-tune waves of gene expression within regulatory modules, show an intermediate level of conservation. These loops are less conserved than the sigma factors but significantly more than the structural sporulation genes, which form the lowest level in the functional and evolutionary hierarchy of the sporulation network. Interestingly, in spore-forming bacteria, gene regulation is more conserved than gene presence for sporulation genes, while the opposite is true for non-sporulation genes. The observed patterns suggest that, by understanding the functional organization of a developmental network in a model organism, it is possible to understand the logic behind the evolution of this network in multiple related species., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme-gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.

Background: The nature of the protein molecular clock, the protein-specific rate of amino acid substitutions, is among the central questions of molecular evolution. Protein expression level is the dominant determinant of the clock rate in a number of organisms. It has been suggested that highly expressed proteins evolve slowly in all species mainly to maintain robustness to translation errors that generate toxic misfolded proteins. Here we investigate this hypothesis experimentally by comparing the growth rate of Escherichia coli expressing wild type and misfolding-prone variants of the LacZ protein., Results: We show that the cost of toxic protein misfolding is small compared to other costs associated with protein synthesis. Complementary computational analyses demonstrate that there is also a relatively weaker, but statistically significant, selection for increasing solubility and polarity in highly expressed E. coli proteins., Conclusions: Although we cannot rule out the possibility that selection against misfolding toxicity significantly affects the protein clock in species other than E. coli, our results suggest that it is unlikely to be the dominant and universal factor determining the clock rate in all organisms. We find that in this bacterium other costs associated with protein synthesis are likely to play an important role. Interestingly, our experiments also suggest significant costs associated with volume effects, such as jamming of the cellular environment with unnecessary proteins.

With the increasing role of computational tools in the analysis of sequenced genomes, there is an urgent need to maintain high accuracy of functional annotations. Misannotations can be easily generated and propagated through databases by functional transfer based on sequence homology. We developed and optimized an automatic policing method to detect biochemical misannotations using context genomic correlations. The method works by finding genes with unusually weak genomic correlations in their assigned network positions. We demonstrate the accuracy of the method using a cross-validated approach. In addition, we show that the method identifies a significant number of potential misannotations in Bacillus subtilis, including metabolic assignments already shown to be incorrect experimentally. The experimental analysis of the mispredicted genes forming the leucine degradation pathway in B. subtilis demonstrates that computational policing tools can generate important biological hypotheses.

By analyzing, in parallel, large literature-derived and high-throughput experimental datasets we investigate genes harboring human inherited disease mutations in the context of molecular interaction networks. Our results demonstrate that network properties influence the likelihood and phenotypic consequences of disease mutations. Genes with intermediate connectivities have the highest probability of harboring germ-line disease mutations, suggesting that disease genes tend to occupy an intermediate niche in terms of their physiological and cellular importance. Our analysis of tissue expression profiles supports this view. We show that disease mutations are less likely to occur in essential genes compared with all human genes. Disease genes display significant functional clustering in the analyzed molecular network. For about one-third of known disorders with two or more associated genes we find physical clusters of genes with the same phenotype. These clusters are likely to represent disorder-specific functional modules and suggest a framework for identifying yet-undiscovered disease genes.

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., Competing Interests: The authors have declared that no competing interests exist.

The computational reconstruction and analysis of cellular models of microbial metabolism is one of the great success stories of systems biology. The extent and quality of metabolic network reconstructions is, however, limited by the current state of biochemical knowledge. Can experimental high-throughput data be used to improve and expand network reconstructions to include unexplored areas of metabolism? Recent advances in experimental technology and analytical methods bring this aim an important step closer to realization. Data integration will play a particularly important part in exploiting the new experimental opportunities.

Although NAD(+)-dependent succinate semialdehyde dehydrogenase activity was first described in Escherichia coli more than 25 years ago, the responsible gene has remained elusive so far. As an experimental proof of concept for a gap-filling algorithm for metabolic networks developed earlier, we demonstrate here that the E. coli gene yneI is responsible for this activity. Our biochemical results demonstrate that the yneI-encoded succinate semialdehyde dehydrogenase can use either NAD(+) or NADP(+) to oxidize succinate semialdehyde to succinate. The gene is induced by succinate semialdehyde, and expression data indicate that yneI plays a unique physiological role in the general nitrogen metabolism of E. coli. In particular, we demonstrate using mutant growth experiments that the yneI gene has an important, but not essential, role during growth on arginine and probably has an essential function during growth on putrescine as the nitrogen source. The NADP(+)-dependent succinate semialdehyde dehydrogenase activity encoded by the functional homolog gabD appears to be important for nitrogen metabolism under N limitation conditions. The yneI-encoded activity, in contrast, functions primarily as a valve to prevent toxic accumulation of succinate semialdehyde. Analysis of available genome sequences demonstrated that orthologs of both yneI and gabD are broadly distributed across phylogenetic space.

A significant fraction (30-40%) of known metabolic activities is currently orphan. Although orphan activities have been biochemically characterized, we do not know a single gene responsible for these reactions in any organism. The problem of orphan activities represents one of the major challenges of modern biochemistry. We analyze the distribution of orphans across biochemical space, through years of enzymatic characterization, and by biological organisms. We find that orphan metabolic activities have been accumulating for many decades. They are widely distributed across enzymatic functional space and metabolic network neighborhoods. Although orphans are relatively more abundant in less studied species, over half of orphan reactions have been experimentally characterized in more than one organism. Shrinking the space of orphan activities will likely require a close collaboration between computational and experimental laboratories.

Multiple solvent crystal structures (MSCS) of porcine pancreatic elastase were used to map the binding surface the enzyme. Crystal structures of elastase in neat acetonitrile, 95% acetone, 55% dimethylformamide, 80% 5-hexene-1,2-diol, 80% isopropanol, 80% ethanol and 40% trifluoroethanol showed that the organic solvent molecules clustered in the active site, were found mostly unclustered in crystal contacts and in general did not bind elsewhere on the surface of elastase. Mixtures of 40% benzene or 40% cyclohexane in 50% isopropanol and 10% water showed no bound benzene or cyclohexane molecules, but did reveal bound isopropanol. The clusters of organic solvent probe molecules coincide with pockets occupied by known inhibitors. MSCS also reveal the areas of plasticity within the elastase binding site and allow for the visualization of a nearly complete first hydration shell. The pattern of organic solvent clusters determined by MSCS for elastase is consistent with patterns for hot spots in protein-ligand interactions determined from database analysis in general. The MSCS method allows probing of hot spots, plasticity and hydration simultaneously, providing a powerful complementary strategy to guide computational methods currently in development for binding site determination, ligand docking and design.