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Ethylene glycol (EG, 1,2-ethanediol) is a two-carbon dihydroxy alcohol that can be derived from fermentation of plant-derived xylose and arabinose and which can be formed during food fermentations. Here we show that Propionibacterium freudenreichii DSM 20271 is able to convert EG in anaerobic conditions to ethanol and acetate in almost equimolar amounts. The metabolism of EG led to a moderate increase of biomass, indicating its metabolism is energetically favourable. A proteomic analysis revealed EG induced expression of the pdu -cluster, which encodes a functional bacterial microcompartment (BMC) involved in the degradation of 1,2-propanediol, with the presence of BMCs confirmed using transmission electron microscopy. Cross-examination of the proteomes of 1,2-propanediol and EG grown cells revealed PDU BMC-expressing cells have elevated levels of DNA repair proteins and cysteine biosynthesis proteins. Cells grown in 1,2-propanediol and EG also showed enhanced resistance against acid and bile salt-induced stresses compared to lactate-grown cells. Our analysis of whole genome sequences of selected genomes of BMC-encoding microorganisms able to metabolize EG with acetaldehyde as intermediate indicate a potentially broad-distributed role of the pdu operon in metabolism of EG. Based on our results we conclude EG is metabolized to acetate and ethanol with acetaldehyde as intermediate within BMCs in P. freudenreichii ., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alexander Dank reports financial support was provided by Arla Foods amba., (© 2024 The Authors.)

Listeria monocytogenes is a facultative anaerobe which can cause a severe food-borne infection known as listeriosis. L. monocytogenes is capable of utilizing various nutrient sources including rhamnose, a naturally occurring deoxy sugar abundant in foods. L. monocytogenes can degrade rhamnose into lactate, acetate and 1,2-propanediol. Our previous study showed that addition of vitamin B 12 stimulated anaerobic growth of L. monocytogenes on rhamnose due to the activation of bacterial microcompartments for 1,2-propanediol utilization (pdu BMC) with concomitant production of propionate and propanol. Notably, anaerobic 1,2-propanediol metabolism has been linked to virulence of enteric pathogens including Salmonella spp. and L. monocytogenes. In this study we investigated the impact of B 12 and BMC activation on i) aerobic and anerobic growth of L. monocytogenes on rhamnose and ii) the level of virulence. We observed B 12 -induced pdu BMC activation and growth stimulation only in anaerobically grown cells. Comparative Caco-2 virulence assays showed that these pdu BMC-induced cells have significantly higher translocation efficiency compared to non-induced cells (anaerobic growth without B 12 ; aerobic growth with or without B 12 ), while adhesion and invasion capacity is similar for all cells. Comparative proteome analysis showed specific and overlapping responses linked to metabolic shifts, activation of stress defense proteins and virulence factors, with RNA polymerase sigma factor SigL, teichoic acid export ATP-binding protein TagH, DNA repair and protection proteins, RadA and DPS, and glutathione synthase GshAB, previously linked to activation of virulence response in L. monocytogenes, uniquely upregulated in anaerobically rhamnose grown pdu-induced cells. Our results shed light on possible effects of B 12 on L. monocytogenes competitive fitness and virulence activation when utilizing rhamnose in anaerobic conditions encountered during transmission and the human intestine., Competing Interests: Declaration of competing interest The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Fermentation employing Saccharomyces cerevisiae has produced alcoholic beverages and bread for millennia. More recently, S. cerevisiae has been used to manufacture specific metabolites for the food, pharmaceutical, and cosmetic industries. Among the most important of these metabolites are compounds associated with desirable aromas and flavors, including higher alcohols and esters. Although the physiology of yeast has been well-studied, its metabolic modulation leading to aroma production in relevant industrial scenarios such as winemaking is still unclear. Here we ask what are the underlying metabolic mechanisms that explain the conserved and varying behavior of different yeasts regarding aroma formation under enological conditions? We employed dynamic flux balance analysis (dFBA) to answer this key question using the latest genome-scale metabolic model (GEM) of S. cerevisiae. The model revealed several conserved mechanisms among wine yeasts, for example, acetate ester formation is dependent on intracellular metabolic acetyl-CoA/CoA levels, and the formation of ethyl esters facilitates the removal of toxic fatty acids from cells using CoA. Species-specific mechanisms were also found, such as a preference for the shikimate pathway leading to more 2-phenylethanol production in the Opale strain as well as strain behavior varying notably during the carbohydrate accumulation phase and carbohydrate accumulation inducing redox restrictions during a later cell growth phase for strain Uvaferm. In conclusion, our new metabolic model of yeast under enological conditions revealed key metabolic mechanisms in wine yeasts, which will aid future research strategies to optimize their behavior in industrial settings., (© 2023 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) has revolutionized genome editing and has great potential for many applications, such as correcting human genetic disorders. To increase the safety of genome editing applications, CRISPR-Cas may benefit from strict control over Cas enzyme activity. Previously, anti-CRISPR proteins and designed oligonucleotides have been proposed to modulate CRISPR-Cas activity. In this study, we report on the potential of guide-complementary DNA oligonucleotides as controlled inhibitors of Cas9 ribonucleoprotein complexes. First, we show that DNA oligonucleotides inhibit Cas9 activity in human cells, reducing both on- and off-target cleavage. We then used in vitro assays to better understand how inhibition is achieved and under which conditions. Two factors were found to be important for robust inhibition: the length of the complementary region and the presence of a protospacer adjacent motif-loop on the inhibitor. We conclude that DNA oligonucleotides can be used to effectively inhibit Cas9 activity both ex vivo and in vitro .

Motivation: Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level 'underground' enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering., Results: Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development., Availability and Implementation: The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Metabolomics coupled with genome-scale metabolic modeling approaches have been employed recently to quantitatively analyze the physiological states of various organisms, including Saccharomyces cerevisiae. Although yeast physiology in laboratory strains is well-studied, the metabolic states under industrially relevant scenarios such as winemaking are still not sufficiently understood, especially as there is considerable variation in metabolism between commercial strains. To study the potential causes of strain-dependent variation in the production of volatile compounds during enological conditions, random flux sampling and statistical methods were used, along with experimental extracellular metabolite flux data to characterize the differences in predicted intracellular metabolic states between strains., Results: It was observed that four selected commercial wine yeast strains (Elixir, Opale, R2, and Uvaferm) produced variable amounts of key volatile organic compounds (VOCs). Principal component analysis was performed on extracellular metabolite data from the strains at three time points of cell cultivation (24, 58, and 144 h). Separation of the strains was observed at all three time points. Furthermore, Uvaferm at 24 h, for instance, was most associated with propanol and ethyl hexanoate. R2 was found to be associated with ethyl acetate and Opale could be associated with isobutanol while Elixir was most associated with phenylethanol and phenylethyl acetate. Constraint-based modeling (CBM) was employed using the latest genome-scale metabolic model of yeast (Yeast8) and random flux sampling was performed with experimentally derived fluxes at various stages of growth as constraints for the model. The flux sampling simulations allowed us to characterize intracellular metabolic flux states and illustrate the key parts of metabolism that likely determine the observed strain differences. Flux sampling determined that Uvaferm and Elixir are similar while R2 and Opale exhibited the highest degree of differences in the Ehrlich pathway and carbon metabolism, thereby causing strain-specific variation in VOC production. The model predictions also established the top 20 fluxes that relate to phenotypic strain variation (e.g. at 24 h). These fluxes indicated that Opale had a higher median flux for pyruvate decarboxylase reactions compared with the other strains. Conversely, R2 which was lower in all VOCs, had higher median fluxes going toward central metabolism. For Elixir and Uvaferm, the differences in metabolism were most evident in fluxes pertaining to transaminase and hexokinase associated reactions. The applied analysis of metabolic divergence unveiled strain-specific differences in yeast metabolism linked to fusel alcohol and ester production., Conclusions: Overall, this approach proved useful in elucidating key reactions in amino acid, carbon, and glycerophospholipid metabolism which suggest genetic divergence in activity in metabolic subsystems among these wine strains related to the observed differences in VOC formation. The findings in this study could steer more focused research endeavors in developing or selecting optimal aroma-producing yeast stains for winemaking and other types of alcoholic fermentations., (© 2021. The Author(s).)

Genetic background and environmental conditions affect the production of sensory impact compounds by Saccharomyces cerevisiae. The relative importance of the strain-specific metabolic capabilities for the production of volatile organic compounds (VOCs) remains unclear. We investigated which amino acids contribute to VOC production and whether amino acid-VOC relations are conserved among yeast strains. Amino acid consumption and production of VOCs during grape juice fermentation was investigated using four commercial wine yeast strains: Elixir, Opale, R2, and Uvaferm. Principal component analysis of the VOC data demonstrated that Uvaferm correlated with ethyl acetate and ethyl hexanoate production, R2 negatively correlated with the acetate esters, and Opale positively correlated with fusel alcohols. Biomass formation was similar for all strains, pointing to metabolic differences in the utilization of nutrients to form VOCs. Partial least-squares linear regression showed that total aroma production is a function of nitrogen utilization ( R 2 = 0.87). We found that glycine, tyrosine, leucine, and lysine utilization were positively correlated with fusel alcohols and acetate esters. Mechanistic modeling of the yeast metabolic network via parsimonious flux balance analysis and flux enrichment analysis revealed enzymes with crucial roles, such as transaminases and decarboxylases. Our work provides insights in VOC production in wine yeasts. IMPORTANCE Saccharomyces cerevisiae is widely used in grape juice fermentation to produce wines. Along with the genetic background, the nitrogen in the environment in which S. cerevisiae grows impacts its regulation of metabolism. Also, commercial S. cerevisiae strains exhibit immense diversity in their formation of aromas, and a desirable aroma bouquet is an essential characteristic for wines. Since nitrogen affects aroma formation in wines, it is essential to know the extent of this connection and how it leads to strain-dependent aroma profiles in wines. We evaluated the differences in the production of key aroma compounds among four commercial wine strains. Moreover, we analyzed the role of nitrogen utilization on the formation of various aroma compounds. This work illustrates the unique aroma-producing differences among industrial yeast strains and suggests more intricate, nitrogen-associated routes influencing those aroma-producing differences.

The foodborne pathogen Listeria monocytogenes can form proteinaceous organelles called bacterial microcompartments (BMCs) that optimize the utilization of substrates, such as 1,2-propanediol, and confer an anaerobic growth advantage. Rhamnose is a deoxyhexose sugar abundant in a range of environments, including the human intestine, and can be degraded in anaerobic conditions into 1,2-propanediol, next to acetate and lactate. Rhamnose-derived 1,2-propanediol was found to link with BMCs in some human pathogens such as Salmonella enterica, but the involvement of BMCs in rhamnose metabolism and potential physiological effects on L. monocytogenes are still unknown. In this study, we first test the effect of rhamnose uptake and utilization on anaerobic growth of L. monocytogenes EGDe without or with added vitamin B 12 , followed by metabolic analysis. We show that vitamin B 12 -dependent activation of pdu stimulates metabolism and anaerobic growth of L. monocytogenes EGDe on rhamnose via 1,2-propanediol degradation into 1-propanol and propionate. Transmission electron microscopy of pdu -induced cells shows that BMCs are formed, and additional proteomics experiments confirm expression of pdu BMC shell proteins and enzymes. Finally, we discuss the physiological effects and energy efficiency of L. monocytogenes pdu BMC-driven anaerobic rhamnose metabolism and the impact on competitive fitness in environments such as the human intestine. IMPORTANCE Listeria monocytogenes is a foodborne pathogen causing severe illness and, as such, it is crucial to understand the molecular mechanisms contributing to its survival strategy and pathogenicity. Rhamnose is a deoxyhexose sugar abundant in a range of environments, including the human intestine, and can be degraded in anaerobic conditions into 1,2-propanediol. In our previous study, the utilization of 1,2-propanediol ( pdu ) in L. monocytogenes was proved to be metabolized in bacterial microcompartments (BMCs), which are self-assembling subcellular proteinaceous structures and analogs of eukaryotic organelles. Here, we show that the vitamin B 12 -dependent activation of pdu stimulates metabolism and anaerobic growth of L. monocytogenes EGDe on rhamnose via BMC-dependent 1,2-propanediol utilization. Combined with metabolic and proteomics analysis, our discussion on the physiological effects and energy efficiency of BMC-driven rhamnose metabolism shed new light to understand the impact on L. monocytogenes competitive fitness in ecosystems such as the human intestine.

Bacterial microcompartments (BMCs) are proteinaceous prokaryotic organelles that enable the utilization of substrates such as 1,2-propanediol and ethanolamine. BMCs are mostly linked to the survival of particular pathogenic bacteria by providing a growth advantage through utilization of 1,2-propanediol and ethanolamine which are abundantly present in the human gut. Although a 1,2-propanediol utilization cluster was found in the probiotic bacterium Propionibacterium freudenreichii , BMC-mediated metabolism of 1,2-propanediol has not been demonstrated experimentally in P. freudenreichii . In this study we show that P. freudenreichii DSM 20271 metabolizes 1,2-propanediol in anaerobic conditions to propionate and 1-propanol. Furthermore, 1,2-propanediol induced the formation of BMCs, which were visualized by transmission electron microscopy and resembled BMCs found in other bacteria. Proteomic analysis of 1,2-propanediol grown cells compared to L-lactate grown cells showed significant upregulation of proteins involved in propanediol-utilization ( pdu -cluster), DNA repair mechanisms and BMC shell proteins while proteins involved in oxidative phosphorylation were down-regulated. 1,2-Propanediol utilizing cells actively produced vitamin B 12 (cobalamin) in similar amounts as cells growing on L-lactate. The ability to metabolize 1,2-propanediol may have implications for human gut colonization and modulation, and can potentially aid in delivering propionate and vitamin B 12 in situ ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dank, Zeng, Boeren, Notebaart, Smid and Abee.)

Ethanolamine (EA) is a valuable microbial carbon and nitrogen source derived from cell membranes. EA catabolism is suggested to occur in a cellular metabolic subsystem called a bacterial microcompartment (BMC), and the activation of EA utilization ( eut ) genes is linked to bacterial pathogenesis. Despite reports showing that the activation of eut is regulated by a vitamin B 12 -binding riboswitch and that upregulation of eut genes occurs in mice, it remains unknown whether EA catabolism is BMC dependent in Listeria monocytogenes Here, we provide evidence for BMC-dependent anaerobic EA utilization via metabolic analysis, proteomics, and electron microscopy. First, we show vitamin B 12 -induced activation of the eut operon in L. monocytogenes coupled to the utilization of EA, thereby enabling growth. Next, we demonstrate BMC formation connected with EA catabolism with the production of acetate and ethanol in a molar ratio of 2:1. Flux via the ATP-generating acetate branch causes an apparent redox imbalance due to the reduced regeneration of NAD + in the ethanol branch resulting in a surplus of NADH. We hypothesize that the redox imbalance is compensated by linking eut BMCs to anaerobic flavin-based extracellular electron transfer (EET). Using L. monocytogenes wild-type, BMC mutant, and EET mutant strains, we demonstrate an interaction between BMCs and EET and provide evidence for a role of Fe 3+ as an electron acceptor. Taken together, our results suggest an important role of BMC-dependent EA catabolism in L. monocytogenes growth in anaerobic environments like the human gastrointestinal tract, with a crucial role for the flavin-based EET system in redox balancing. IMPORTANCE Listeria monocytogenes is a foodborne pathogen causing severe illness, and as such, it is crucial to understand the molecular mechanisms contributing to pathogenicity. One carbon source that allows L. monocytogenes to grow in humans is ethanolamine (EA), which is derived from phospholipids present in eukaryotic cell membranes. It is hypothesized that EA utilization occurs in bacterial microcompartments (BMCs), self-assembling subcellular proteinaceous structures and analogs of eukaryotic organelles. Here, we demonstrate that BMC-driven utilization of EA in L. monocytogenes results in increased energy production essential for anaerobic growth. However, exploiting BMCs and the encapsulated metabolic pathways also requires the balancing of oxidative and reductive pathways. We now provide evidence that L. monocytogenes copes with this by linking BMC activity to flavin-based extracellular electron transfer (EET) using iron as an electron acceptor. Our results shed new light on an important molecular mechanism that enables L. monocytogenes to grow using host-derived phospholipid degradation products., (Copyright © 2021 Zeng et al.)

The majority of cellular energy is produced by the mitochondrial oxidative phosphorylation (OXPHOS) system. Failure of the first OXPHOS enzyme complex, NADH:ubiquinone oxidoreductase or complex I (CI), is associated with multiple signs and symptoms presenting at variable ages of onset. There is no approved drug treatment yet to slow or reverse the progression of CI-deficient disorders. Here, we present a comprehensive human metabolic network model of genetically characterized CI-deficient patient-derived fibroblasts. Model calculations predicted that increased cholesterol production, export, and utilization can counterbalance the surplus of reducing equivalents in patient-derived fibroblasts, as these pathways consume considerable amounts of NAD(P)H. We show that fibrates attenuated increased NAD(P)H levels and improved CI-deficient fibroblast growth by stimulating the production of cholesterol via enhancement of its cellular efflux. In CI-deficient (Ndufs4 -/- ) mice, fibrate treatment resulted in prolonged survival and improved motor function, which was accompanied by an increased cholesterol efflux from peritoneal macrophages. Our results shine a new light on the use of compensatory biological pathways in mitochondrial dysfunction, which may lead to novel therapeutic interventions for mitochondrial diseases for which currently no cure exists., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

The fitness impact of loss-of-function mutations is generally assumed to reflect the loss of specific molecular functions associated with the perturbed gene. Here, we propose that rewiring of the transcriptome upon deleterious gene inactivation is frequently nonspecific and mimics stereotypic responses to external environmental change. Consequently, transcriptional response to gene deletion could be suboptimal and incur an extra fitness cost. Analysis of the transcriptomes of ∼1,500 single-gene deletion Saccharomyces cerevisiae strains supported this scenario. First, most transcriptomic changes are not specific to the deleted gene but are rather triggered by perturbations in functionally diverse genes. Second, gene deletions that alter the expression of dosage-sensitive genes are especially harmful. Third, by elevating the expression level of downregulated genes, we could experimentally mitigate the fitness defect of gene deletions. Our work shows that rewiring of genomic expression upon gene inactivation shapes the harmful effects of mutations., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Bacterial microcompartments (BMCs) are proteinaceous organelles that optimize specific metabolic pathways referred to as metabolosomes involving transient production of toxic volatile metabolites such as aldehydes. Previous bioinformatics analysis predicted the presence of BMCs in 23 bacterial phyla including foodborne pathogens and a link with gene clusters for the utilization of host-derived substrates such as 1,2-propanediol utilization, i.e., the Pdu cluster. Although, transcriptional regulation of the Pdu cluster and its role in Listeria monocytogenes virulence in animal models have recently been reported, the experimental identification and the physiological role of BMCs in L. monocytogenes is still unexplored. Here, we ask whether BMCs could enable utilization of 1,2-propanediol (Pd) in L. monocytogenes under anaerobic conditions. Using L. monocytogenes EGDe as a model strain, we could demonstrate efficient utilization of Pd with concomitant production of 1-propanol and propionate after 24 h of anaerobic growth, while the utilization was significantly reduced in aerobic conditions. In line with this, expression of genes encoding predicted shell proteins and the signature enzyme propanediol dehydratase is upregulated more than 20-fold in cells anaerobically grown in Pdu-induced versus non-induced control conditions. Additional proteomics analysis confirmed the presence of BMC shell proteins and Pdu enzymes in cells that show active degradation of Pd. Furthermore, using transmission electron microscopy, BMC structures have been detected in these cells linking gene expression, protein composition, and BMCs to activation of the Pdu cluster in anaerobic growth of L. monocytogenes . Studies in defined minimal medium with Pd as an energy source showed a significant increase in cell numbers, indicating that Pdu and the predicted generation of ATP in the conversion of propionyl-phosphate to the end product propionate can support anaerobic growth of L. monocytogenes . Our findings may suggest a role for BMC-dependent utilization of Pd in L. monocytogenes growth, transmission, and interaction with the human host., (Copyright © 2019 Zeng, Smid, Boeren, Notebaart and Abee.)

The advent of CRISPR-based technologies has revolutionized genetics over the past decade, and genome editing is now widely implemented for diverse medical and agricultural applications, such as correcting genetic disorders and improving crop and livestock breeding. CRISPR-based technologies are also of great potential to alter the genetic content of food bacteria in order to control the composition and activity of microbial populations across the food supply chain, from the farm to consumer products. Advancing the food supply chain is of great societal importance as it involves optimizing fermentation processes to enhance taste and sensory properties of food products, as well as improving food quality and safety by controlling spoilage bacteria and pathogens. Here, we discuss the various CRISPR technologies that can alter bacterial functionalities and modulate the composition of microbial communities in foods. We illustrate how these applications can be harnessed along the food supply chain to manipulate microbiomes that encompass spoilage and pathogenic bacteria as well as desirable starter cultures and health-promoting probiotics., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in Escherichia coli K-12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non-native substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high-efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity., (© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)

Here we propose to use bacterial cells as factories that generate EVs harboring both the RNP complex (or any other CRISPR-mediated tool) and a specific ligand molecule. The ligand would allow specific binding of EVs to cells with the matching receptors, adding a level of specificity to the delivery, hence stimulating precise genome editing., (© 2018 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.)

Objective: Saccharomyces cerevisiae is used worldwide for the production of ale-type beers. This yeast is responsible for the production of the characteristic fruity aroma compounds. Esters constitute an important group of aroma active secondary metabolites produced by S. cerevisiae. Previous work suggests that esterase activity, which results in ester degradation, may be the key factor determining the abundance of fruity aroma compounds. Here, we test this hypothesis by deletion of two S. cerevisiae esterases, IAH1 and TIP1, using CRISPR-Cas9 genome editing and by studying the effect of these deletions on esterase activity and extracellular ester pools., Results: Saccharomyces cerevisiae mutants were constructed lacking esterase IAH1 and/or TIP1 using CRISPR-Cas9 genome editing. Esterase activity using 5-(6)-carboxyfluorescein diacetate (cFDA) as substrate was found to be significantly lower for ΔIAH1 and ΔIAH1ΔTIP1 mutants compared to wild type (WT) activity (P < 0.05 and P < 0.001, respectively). As expected, we observed an increase in relative abundance of acetate and ethyl esters and an increase in ethyl esters in ΔIAH1 and ΔTIP1, respectively. Interestingly, the double gene disruption mutant ΔIAH1ΔTIP1 showed an aroma profile comparable to WT levels, suggesting the existence and activation of a complex regulatory mechanism to compensate multiple genomic alterations in aroma metabolism.

Cellular metabolism can influence host immune responses to Mycobacterium tuberculosis. Using a systems biology approach, differential expression of 292 metabolic genes involved in glycolysis, glutathione, pyrimidine, and inositol phosphate pathways was evident at the site of a human tuberculin skin test challenge in patients with active tuberculosis infection. For 28 metabolic genes, we identified single nucleotide polymorphisms that were trans-acting for in vitro cytokine responses to M. tuberculosis stimulation, including glutathione and pyrimidine metabolism genes that alter production of Th1 and Th17 cytokines. Our findings identify novel therapeutic targets in host metabolism that may shape protective immunity to tuberculosis.

Background: Immunopathology contributes to the high mortality of tuberculous meningitis, but the biological pathways involved are mostly unknown. We aimed to compare cerebrospinal fluid (CSF) and serum metabolomes of patients with tuberculous meningitis with that of controls without tuberculous meningitis, and assess the link between metabolite concentrations and mortality., Methods: In this observational cohort study at the Hasan Sadikin Hospital (Bandung, Indonesia) we measured 425 metabolites using liquid chromatography-mass spectrometry in CSF and serum from 33 HIV-negative Indonesian patients with confirmed or probable tuberculous meningitis and 22 control participants with complete clinical data between March 12, 2009, and Oct 27, 2013. Associations of metabolite concentrations with survival were validated in a second cohort of 101 patients from the same centre. Genome-wide single nucleotide polymorphism typing was used to identify tryptophan quantitative trait loci, which were used for survival analysis in a third cohort of 285 patients., Findings: Concentrations of 250 (70%) of 351 metabolites detected in CSF were higher in patients with tuberculous meningitis than in controls, especially in those who died during follow-up. Only five (1%) of the 390 metobolites detected in serum differed between patients with tuberculous meningitis and controls. CSF tryptophan concentrations showed a pattern different from most other CSF metabolites; concentrations were lower in patients who survived compared with patients who died (9-times) and to controls (31-times). The association of low CSF tryptophan with patient survival was confirmed in the validation cohort (hazard ratio 0·73; 95% CI 0·64-0·83; p<0·0001; per each halving). 11 genetic loci predictive for CSF tryptophan concentrations in tuberculous meningitis were identified (p<0·00001). These quantitative trait loci predicted survival in a third cohort of 285 HIV-negative patients in a prognostic index including age and sex, also after correction for possible confounders (p=0·0083)., Interpretation: Cerebral tryptophan metabolism, which is known to affect Mycobacterium tuberculosis growth and CNS inflammation, is important for the outcome of tuberculous meningitis. CSF tryptophan concentrations in tuberculous meningitis are under strong genetic influence, probably contributing to the variable outcomes of tuberculous meningitis. Interventions targeting tryptophan metabolism could improve outcomes of tuberculous meningitis., Funding: Royal Dutch Academy of Arts and Sciences; Netherlands Foundation for Scientific Research; Radboud University; National Academy of Sciences; Ministry of Research, Technology, and Higher Education, Indonesia; European Research Council; and PEER-Health., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A key challenge in molecular systems biology is understanding how new pathways arise during evolution and how to exploit them for biotechnological applications. New pathways in metabolic networks often evolve by recruiting weak promiscuous activities of pre-existing enzymes. Here we describe recent systems biology advances to map such 'underground' activities and to predict and analyze their contribution to new metabolic functions. Underground activities are prevalent in cellular metabolism and can form novel pathways that either enable evolutionary adaptation to new environments or provide bypass to genetic lesions. We also illustrate the potential of integrating computational models of underground metabolism and experimental approaches to study the evolution of novel metabolic phenotypes and advance the field of biotechnology., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Monocytes are innate immune cells that play a pivotal role in antifungal immunity, but little is known regarding the cellular metabolic events that regulate their function during infection. Using complementary transcriptomic and immunological studies in human primary monocytes, we show that activation of monocytes by Candida albicans yeast and hyphae was accompanied by metabolic rewiring induced through C-type lectin-signaling pathways. We describe that the innate immune responses against Candida yeast are energy-demanding processes that lead to the mobilization of intracellular metabolite pools and require induction of glucose metabolism, oxidative phosphorylation and glutaminolysis, while responses to hyphae primarily rely on glycolysis. Experimental models of systemic candidiasis models validated a central role for glucose metabolism in anti-Candida immunity, as the impairment of glycolysis led to increased susceptibility in mice. Collectively, these data highlight the importance of understanding the complex network of metabolic responses triggered during infections, and unveil new potential targets for therapeutic approaches against fungal diseases.

Induction of trained immunity (innate immune memory) is mediated by activation of immune and metabolic pathways that result in epigenetic rewiring of cellular functional programs. Through network-level integration of transcriptomics and metabolomics data, we identify glycolysis, glutaminolysis, and the cholesterol synthesis pathway as indispensable for the induction of trained immunity by β-glucan in monocytes. Accumulation of fumarate, due to glutamine replenishment of the TCA cycle, integrates immune and metabolic circuits to induce monocyte epigenetic reprogramming by inhibiting KDM5 histone demethylases. Furthermore, fumarate itself induced an epigenetic program similar to β-glucan-induced trained immunity. In line with this, inhibition of glutaminolysis and cholesterol synthesis in mice reduced the induction of trained immunity by β-glucan. Identification of the metabolic pathways leading to induction of trained immunity contributes to our understanding of innate immune memory and opens new therapeutic avenues., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Differences in susceptibility to immune-mediated diseases are determined by variability in immune responses. In three studies within the Human Functional Genomics Project, we assessed the effect of environmental and non-genetic host factors of the genetic make-up of the host and of the intestinal microbiome on the cytokine responses in humans. We analyzed the association of these factors with circulating mediators and with six cytokines after stimulation with 19 bacterial, fungal, viral, and non-microbial metabolic stimuli in 534 healthy subjects. In this first study, we show a strong impact of non-genetic host factors (e.g., age and gender) on cytokine production and circulating mediators. Additionally, annual seasonality is found to be an important environmental factor influencing cytokine production. Alpha-1-antitrypsin concentrations partially mediate the seasonality of cytokine responses, whereas the effect of vitamin D levels is limited. The complete dataset has been made publicly available as a comprehensive resource for future studies. PAPERCLIP., (Copyright © 2016 Elsevier Inc. All rights reserved.)

A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.

Motivation: Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental "omics" data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more "flexible" metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions., Results: Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information., Availability: MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.

Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.

Mitochondrial ATP production is mediated by the oxidative phosphorylation (OXPHOS) system, which consists of four multi-subunit complexes (CI-CIV) and the FoF1-ATP synthase (CV). Mitochondrial disorders including Leigh Syndrome often involve CI dysfunction, the pathophysiological consequences of which still remain incompletely understood. Here we combined experimental and computational strategies to gain mechanistic insight into the energy metabolism of isolated skeletal muscle mitochondria from 5-week-old wild-type (WT) and CI-deficient NDUFS4-/- (KO) mice. Enzyme activity measurements in KO mitochondria revealed a reduction of 79% in maximal CI activity (Vmax), which was paralleled by 45-72% increase in Vmax of CII, CIII, CIV and citrate synthase. Mathematical modeling of mitochondrial metabolism predicted that these Vmax changes do not affect the maximal rates of pyruvate (PYR) oxidation and ATP production in KO mitochondria. This prediction was empirically confirmed by flux measurements. In silico analysis further predicted that CI deficiency altered the concentration of intermediate metabolites, modestly increased mitochondrial NADH/NAD+ ratio and stimulated the lower half of the TCA cycle, including CII. Several of the predicted changes were previously observed in experimental models of CI-deficiency. Interestingly, model predictions further suggested that CI deficiency only has major metabolic consequences when its activity decreases below 90% of normal levels, compatible with a biochemical threshold effect. Taken together, our results suggest that mouse skeletal muscle mitochondria possess a substantial CI overcapacity, which minimizes the effects of CI dysfunction on mitochondrial metabolism in this otherwise early fatal mouse model., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets.

The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.

Is evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic.

One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy.

Background: The relationships between proteins are often asymmetric: one protein (A) depends for its function on another protein (B), but the second protein does not depend on the first. In metabolic networks there are multiple pathways that converge into one central pathway. The enzymes in the converging pathways depend on the enzymes in the central pathway, but the enzymes in the latter do not depend on any specific enzyme in the converging pathways. Asymmetric relations are analogous to the "if->then" logical relation where A implies B, but B does not imply A (A->B)., Results: We show that the majority of relationships between enzymes in metabolic flux models of metabolism in Escherichia coli and Saccharomyces cerevisiae are asymmetric. We show furthermore that these asymmetric relationships are reflected in the expression of the genes encoding those enzymes, the effect of gene knockouts and the evolution of genomes. From the asymmetric relative dependency, one would expect that the gene that is relatively independent (B) can occur without the other dependent gene (A), but not the reverse. Indeed, when only one gene of an A->B pair is expressed, is essential, is present in a genome after an evolutionary gain or loss, it tends to be the independent gene (B). This bias is strongest for genes encoding proteins whose asymmetric relationship is evolutionarily conserved., Conclusions: The asymmetric relations between proteins that arise from the system properties of metabolic networks affect gene expression, the relative effect of gene knockouts and genome evolution in a predictable manner.

There has been considerable recent interest in deciphering the adaptive properties underlying the structure and function of metabolic networks. Various features of metabolic networks such as the global topology, distribution of fluxes, and mutational robustness, have been proposed to have adaptive significance and hence reflect design principles. However, whether evolutionary processes alternative to direct selection on the trait under investigation also play a role is often ignored and the selection pressures maintaining a given metabolic trait often remain speculative. Some systems-level traits might simply arise as by-products of selection on other traits or even through random genetic drift. Here, we ask which systems-level aspects of metabolism are likely to have adaptive utility and which could be better explained as by-products of other evolutionary forces. We conclude that the global topological characteristics of metabolic networks and their mutational robustness are unlikely to be directly shaped by natural selection. Conversely, models of optimal design revealed that various aspects of individual pathways and the behavior of the whole network show signs of adaptations, even though the exact selective forces often remain elusive. Comparative and experimental approaches, which so far have been relatively rarely employed, could help to distinguish between alternative adaptive scenarios.

To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

The coxsackievirus B3 3A protein forms homodimers and plays important roles in both viral RNA (vRNA) replication and the viral inhibition of intracellular protein transport. The molecular determinants that are required for each of these functions are yet poorly understood. Based on the NMR structure of the closely related poliovirus 3A protein, a molecular model of the coxsackievirus B3 3A protein was constructed. Using this structural model, specific mutants were designed to study the structure-function relationship of 3A. The mutants were tested for their capacity to dimerize, support vRNA replication, and block protein transport. A hydrophobic interaction between the monomers and an intermolecular salt bridge were identified as major determinants required for dimerization. We show that dimerization is important for both efficient vRNA replication and inhibition of protein transport. In addition, determinants were identified that were not required for dimerization but that were essential for either one of the biological functions of 3A. The combination of the in silico and in vivo results obtained in this study provides important insights in both the structural and functional aspects of 3A.

Background: The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks., Results: We have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 - 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes., Conclusion: We show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic networks will become available in the near future, the usefulness of our method in network prediction is likely to increase.

A key complication in comparative genomics for reliable gene function prediction is the existence of duplicated genes. To study the effect of gene duplication on function prediction, we analyze orthologs between pairs of genomes where in one genome the orthologous gene has duplicated after the speciation of the two genomes (i.e. inparalogs). For these duplicated genes we investigate whether the gene that is most similar on the sequence level is also the gene that has retained the ancestral gene-neighborhood. Although the majority of investigated cases show a consistent pattern between sequence similarity and gene-neighborhood conservation, a substantial fraction, 29-38%, is inconsistent. The observation of inconsistency is not the result of a chance outcome owing to a lack of divergence time between inparalogs, but rather it seems to be the result of a chance outcome caused by very similar rates of sequence evolution of both inparalogs relative to their ortholog. If one-to-one orthologous relationships are required, it is advisable to combine contextual information (i.e. gene-neighborhood in prokaryotes and co-expression in eukaryotes) with protein sequence information to predict the most probable functional equivalent ortholog in the presence of inparalogs.

The ability of the 3A protein of coxsackievirus B (CVB) to inhibit protein secretion was investigated for this study. Here we show that the ectopic expression of CVB 3A blocked the transport of both the glycoprotein of vesicular stomatitis virus, a membrane-bound secretory marker, and the alpha-1 protease inhibitor, a luminal secretory protein, at a step between the endoplasmic reticulum (ER) and the Golgi complex. CVB 3A contains a conserved proline-rich region in its N terminus. The importance of this proline-rich region was investigated by introducing Pro-to-Ala substitutions. The mutation of Pro19 completely abolished the ability of 3A to inhibit ER-to-Golgi transport. The mutation of Pro14, Pro17, or Pro20 also impaired this ability, but to a lesser extent. The mutation of Pro18 had no effect. We also investigated the possible importance of this proline-rich region for the function of 3A in viral RNA replication. To this end, we introduced the Pro-to-Ala mutations into an infectious cDNA clone of CVB3. The transfection of cells with in vitro-transcribed RNAs of these clones gave rise to mutant viruses that replicated with wild-type characteristics. We concluded that the proline-rich region in CVB 3A is required for its ability to inhibit ER-to-Golgi transport, but not for its function in viral RNA replication. The functional relevance of the proline-rich region is discussed in light of the proposed structural model of 3A.