Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing.

As the availability of data sets increases, meta-analysis leveraging aggregated and interoperable data types is proving valuable. This study leveraged a meta-analysis workflow to identify mutations that could improve robustness to reactive oxygen species (ROS) stresses using an industrially important melatonin production strain as an example. ROS stresses often occur during cultivation and negatively affect strain performance. Cellular response to ROS is also linked to the SOS response and resistance to pH fluctuations, which is important to strain robustness in large-scale biomanufacturing. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations across 59 experiments to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 unique conditions. Mutant oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute fitness in ROS stress. Across these genes, 259 total mutations were inspected in conjunction with transcriptomics from 46 iModulon experiments. Ten mutations were chosen for reintroduction based on mutation clustering and coinciding transcriptional changes as evidence of fitness impact. Strains with mutations reintroduced into oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress and reduced SOS response, all of which are related to ROS. Additionally, new evidence was generated toward understanding the function of ygfZ, an uncharacterized gene. This meta-analysis approach utilized aggregated and interoperable multiomics data sets to identify mutations conferring industrially relevant phenotypes with the least drawbacks, describing an approach for data-driven strain engineering to optimize microbial cell factories.

Pangenome reconstruction of Lactobacillaceae metabolism predicts species-specific metabolic traits.

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.IMPORTANCELactobacillaceae, a bacterial family foundational to a trillion-dollar industry, is increasingly relevant to biosustainability initiatives. Our study, leveraging approximately 2,400 genome sequences, provides a pangenomic analysis of Lactobacillaceae metabolism, creating over 2,400 curated and validated genome-scale models (GEMs). These GEMs successfully predict (i) unique, species-specific metabolic reactions; (ii) niche-enriched reactions that increase organism fitness; (iii) essential media components, offering insights into the global amino acid essentiality of Lactobacillaceae; and (iv) fermentation capabilities across the family, shedding light on the metabolic basis of Lactobacillaceae-based commercial products. This quantitative understanding of Lactobacillaceae metabolic properties and their genomic basis will have profound implications for the food industry and biosustainability, offering new insights and tools for strain selection and manipulation.

Metabolomics comparison of red cells stored in four additive solutions reveals differences in citrate anticoagulant permeability and metabolism.

BACKGROUND AND OBJECTIVES: Metabolomics studies have revealed transition points in metabolic signatures of red cells during storage in SAGM, whose clinical significance is unclear. We set out to investigate whether these transition points occur independent of storage media and define differences in the metabolism of red cells in additive solutions. MATERIALS AND METHODS: Red cell concentrates were stored in SAGM, AS-1, AS-3 or PAGGSM, and sampled fourteen times spanning Day 1-46. Following quality control, the samples were split into extracellular and intracellular aliquots. These were analysed with ultra-high-performance liquid chromatography coupled to mass spectrometry analysis affording quantitative metabolic profiles of both intra- and extracellular red cell metabolites. RESULTS: Differences were observed in glycolysis, purine salvage, glutathione synthesis and citrate metabolism on account of the storage solutions. Donor variability however hindered the accurate characterization of metabolic transition time-points. Intracellular citrate concentrations were increased in red cells stored in AS-3 and PAGGSM media. The metabolism of citrate in red cells in SAGM was subsequently confirmed using 13 C citrate isotope labelling and shown to originate from citrate anticoagulant. CONCLUSION: Metabolic signatures that discriminate between 'fresh' and 'old' stored red cells are dependent upon additive solutions. Specifically, the incorporation and metabolism of citrate in additive solutions with lower chloride ion concentration is altered and impacts glycolysis.

What Makes a Bacterial Species Pathogenic?: Comparative Genomic Analysis of the Genus Leptospira

Leptospirosis, caused by spirochetes of the genus Leptospira, is a globally widespread, neglected and emerging zoonotic disease. While whole genome analysis of individual pathogenic, intermediately pathogenic and saprophytic Leptospira species has been reported, comprehensive cross-species genomic comparison of all known species of infectious and non-infectious Leptospira, with the goal of identifying genes related to pathogenesis and mammalian host adaptation, remains a key gap in the field. Infectious Leptospira, comprised of pathogenic and intermediately pathogenic Leptospira, evolutionarily diverged from non-infectious, saprophytic Leptospira, as demonstrated by the following computational biology analyses: 1) the definitive taxonomy and evolutionary relatedness among all known Leptospira species; 2) genomically-predicted metabolic reconstructions that indicate novel adaptation of infectious Leptospira to mammals, including sialic acid biosynthesis, pathogen-specific porphyrin metabolism and the first-time demonstration of cobalamin (B12) autotrophy as a bacterial virulence factor; 3) CRISPR/Cas systems demonstrated only to be present in pathogenic Leptospira, suggesting a potential mechanism for this clade's refractoriness to gene targeting; 4) finding Leptospira pathogen-specific specialized protein secretion systems; 5) novel virulence-related genes/gene families such as the Virulence Modifying (VM) (PF07598 paralogs) proteins and pathogen-specific adhesins; 6) discovery of novel, pathogen-specific protein modification and secretion mechanisms including unique lipoprotein signal peptide motifs, Sec-independent twin arginine protein secretion motifs, and the absence of certain canonical signal recognition particle proteins from all Leptospira; and 7) and demonstration of infectious Leptospira-specific signal-responsive gene expression, motility and chemotaxis systems. By identifying large scale changes in infectious (pathogenic and intermediately pathogenic) vs. non-infectious Leptospira, this work provides new insights into the evolution of a genus of bacterial pathogens. This work will be a comprehensive roadmap for understanding leptospirosis pathogenesis. More generally, it provides new insights into mechanisms by which bacterial pathogens adapt to mammalian hosts

Spontaneous splenic rupture and multiple lung embolisms due to cytomegalovirus infection: a case report and review of the literature.

A 53-year-old woman presented with a spontaneous splenic rupture. The splenic rupture was considered a complication of a primary cytomegalovirus (CMV) infection as were multiple pulmonary embolisms. CMV infections are common but are most often asymptomatic, and to our knowledge only 15 cases complicated with splenic rupture have been published.

Using the reconstructed genome-scale human metabolic network to study physiology and pathology.

Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology and pharmacology. There are currently more than 20 publications that utilize Recon 1, including studies of cancer, diabetes, host-pathogen interactions, heritable metabolic disorders and off-target drug binding effects. In this mini-review, we focus on the reconstruction of the global human metabolic network and four classes of its application. We show that computational simulations for numerous pathologies have yielded clinically relevant results, many corroborated by existing or newly generated experimental data.

A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks.

We derive a convex optimization problem on a steady-state nonequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the problem solution. This suggests a new variational principle for biochemical networks that can be implemented in a computationally tractable manner. We derive the Lagrange dual of the optimization problem and use strong duality to demonstrate that a biochemical analogue of Tellegen's theorem holds at optimality. Each optimal flux is dependent on a free parameter that we relate to an elementary kinetic parameter when mass action kinetics is assumed.

Multicentre prospective study of fascial closure rate after open abdomen with vacuum and mesh-mediated fascial traction.

BACKGROUND: Damage control surgery and temporary open abdomen (OA) have been adopted widely, in both trauma and non-trauma situations. Several techniques for temporary abdominal closure have been developed. The main objective of this study was to evaluate the fascial closure rate in patients after vacuum-assisted wound closure and mesh-mediated fascial traction (VAWCM) for long-term OA treatment, and to describe complications. METHODS: This prospective study included all patients who received VAWCM treatment between 2006 and 2009 at four hospitals. Patients with anticipated OA treatment for fewer than 5 days and those with non-midline incisions were excluded. RESULTS: Among 151 patients treated with an OA, 111 received VAWCM treatment. Median age was 68 years. Median OA treatment time was 14 days. Main disease aetiologies were vascular (45 patients), visceral surgical disease (57) and trauma (9). The fascial closure rate was 76·6 per cent in intention-to-treat analysis and 89 per cent in per-protocol analysis. Eight patients developed an intestinal fistula, of whom seven had intestinal ischaemia. Intestinal fistula was an independent factor associated with failure of fascial closure (odds ratio (OR) 8·55, 95 per cent confidence interval 1·47 to 49·72; P = 0·017). The in-hospital mortality rate was 29·7 per cent. Age (OR 1·21, 1·02 to 1·43; P = 0·027) and failure of fascial closure (OR 44·50, 1·13 to 1748·52; P = 0·043) were independently associated with in-hospital mortality. CONCLUSION: The VAWCM method provided a high fascial closure rate after long-term treatment of OA. Technique-related complications were few. No patient was left with a large planned ventral hernia.

Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling.

Geobacter sulfurreducens is a well-studied representative of the Geobacteraceae, which play a critical role in organic matter oxidation coupled to Fe(III) reduction, bioremediation of groundwater contaminated with organics or metals, and electricity production from waste organic matter. In order to investigate G. sulfurreducens central metabolism and electron transport, a metabolic model which integrated genome-based predictions with available genetic and physiological data was developed via the constraint-based modeling approach. Evaluation of the rates of proton production and consumption in the extracellular and cytoplasmic compartments revealed that energy conservation with extracellular electron acceptors, such as Fe(III), was limited relative to that associated with intracellular acceptors. This limitation was attributed to lack of cytoplasmic proton consumption during reduction of extracellular electron acceptors. Model-based analysis of the metabolic cost of producing an extracellular electron shuttle to promote electron transfer to insoluble Fe(III) oxides demonstrated why Geobacter species, which do not produce shuttles, have an energetic advantage over shuttle-producing Fe(III) reducers in subsurface environments. In silico analysis also revealed that the metabolic network of G. sulfurreducens could synthesize amino acids more efficiently than that of Escherichia coli due to the presence of a pyruvate-ferredoxin oxidoreductase, which catalyzes synthesis of pyruvate from acetate and carbon dioxide in a single step. In silico phenotypic analysis of deletion mutants demonstrated the capability of the model to explore the flexibility of G. sulfurreducens central metabolism and correctly predict mutant phenotypes. These results demonstrate that iterative modeling coupled with experimentation can accelerate the understanding of the physiology of poorly studied but environmentally relevant organisms and may help optimize their practical applications.

Psychological distress and coping in women married to men with a diagnosis of solvent-induced chronic toxic encephalopathy.

BACKGROUND: Solvent-induced chronic toxic encephalopathy (TE) is a slowly developing brain disorder associated with both a direct effect on the nervous system and as indirect experienced psychological distress. It can presumably also imply negative influence on the subject's social surroundings. METHODS: Seventeen women married to men diagnosed with TE (WTE) and 51 referent women of the same age married to healthy husbands were examined. Symptoms, social network and coping style were measured by questionnaires. RESULTS: The WTE reported slightly more psychological distress and fewer social contacts than did the referents. The WTE did not report affected stress management. Retired women in the WTE group accounted for most of the deviances from the referents. CONCLUSIONS: The conclusion is that becoming a WTE does not necessarily imply more psychological distress, social isolation or poorer stress management capability if they continue with their work and social activities.

Properties of metabolic networks: structure versus function.

Biological data from high-throughput technologies describing the network components (genes, proteins, metabolites) and their associated interactions have driven the reconstruction and study of structural (topological) properties of large-scale biological networks. In this article, we address the relation of the functional and structural properties by using extensively experimentally validated genome-scale metabolic network models to compute observable functional states of a microorganism and compare the "structure versus function" attributes of metabolic networks. It is observed that, functionally speaking, the essentiality of reactions in a node is not correlated with node connectivity as structural analyses of other biological networks have suggested. These findings are illustrated with the analysis of the genome-scale biochemical networks of three species with distinct modes of metabolism. These results also suggest fundamental differences among different biological networks arising out of their representation and functional constraints.

Quantitative analysis of Escherichia coli metabolic phenotypes within the context of phenotypic phase planes.

In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior and propose experimentally testable hypotheses. However, a thorough assessment of the metabolic phenotype requires well-designed experimentation and reproducible experimental techniques. A method for the quantitative analysis of E. coli metabolism in vivo within the framework of in silico phenotypic phase plane analysis is presented. Using this approach, we have quantitatively studied E. coli metabolism in various environmental conditions and nutritional media. Our experimental methodology, in combination with steady-state metabolic models, can be used to study biological properties and evaluate the metabolic capabilities of microbes.

In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism.

The completion of the human genome project and the construction of single nucleotide polymorphism (SNP) maps have lead to significant efforts to find SNPs that can be linked to pathophysiology. In silico models of complete biochemical reaction networks relate a cell's individual reactions to the function of the entire network. Sequence variations can in turn be related to kinetic properties of individual enzymes, thus allowing an in silico model-driven assessment of the effects of defined SNPs on overall cellular functions. This process is applied to defined SNPs in two key enzymes of human red blood cell metabolism: glucose-6-phosphate dehydrogenase and pyruvate kinase. The results demonstrate the utility of in silico models in providing insight into differences between red cell function in patients with chronic and nonchronic anemia. In silico models of complex cellular processes are thus likely to aid in defining and understanding key SNPs in human pathophysiology.

The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy.

Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

Regulation of gene expression in flux balance models of metabolism.

Genome-scale metabolic networks can now be reconstructed based on annotated genomic data augmented with biochemical and physiological information about the organism. Mathematical analysis can be performed to assess the capabilities of these reconstructed networks. The constraints-based framework, with flux balance analysis (FBA), has been used successfully to predict time course of growth and by-product secretion, effects of mutation and knock-outs, and gene expression profiles. However, FBA leads to incorrect predictions in situations where regulatory effects are a dominant influence on the behavior of the organism. Thus, there is a need to include regulatory events within FBA to broaden its scope and predictive capabilities. Here we represent transcriptional regulatory events as time-dependent constraints on the capabilities of a reconstructed metabolic network to further constrain the space of possible network functions. Using a simplified metabolic/regulatory network, growth is simulated under various conditions to illustrate systemic effects such as catabolite repression, the aerobic/anaerobic diauxic shift and amino acid biosynthesis pathway repression. The incorporation of transcriptional regulatory events in FBA enables us to interpret, analyse and predict the effects of transcriptional regulation on cellular metabolism at the systemic level.

Elective splenectomy in the elderly--perioperative and long-term course.

BACKGROUND: The hazards of elective splenectomy in the elderly have not been thoroughly investigated. The aim was to assess such a well-defined cohort with respect to perioperative and long-term outcome. METHODS: Fifty-two consecutively splenectomised patients during the period 1971-1995, aged 65 years or older, were followed until death (44 cases) or the end of 1999 (8 cases). RESULTS: No intraoperative deaths occurred, while three patients (5.8%) died postoperatively in the 1970s. Twenty-four patients suffered from thirty-four postoperative complications, dominated by infections and haematomas. No differences were seen comparing patients with and without complications related to the American Society of Anesthesiologists' classes, total transfusion rate, steroid medication, preoperative risk diseases, "giant spleens" or the time period during which the operations were performed. In 69% of the patients, the splenectomy was beneficial. During the long-term follow-up, 25 patients suffered from 59 infectious and thromboembolic episodes and 1 surgical complication. The dominating causes of death were the primary disease (29%), myocardial infarction (20%), sepsis (12%) and cerebrovascular lesions (12%), i.e. not directly related to late effects of the operation. CONCLUSION: High-risk patients older than 65 years with haematological disorders can safely undergo splenectomy with a low mortality rate and a reasonable rate of morbidity. The long-term course demonstrates a fair response rate, minimal surgically related complications, but thromboembolic and infectious events, and the majority of deaths unrelated to late effects of the splenectomy.

Dynamic simulation of the human red blood cell metabolic network.

We have developed a Mathematica application package to perform dynamic simulations of the red blood cell (RBC) metabolic network. The package relies on, and integrates, many years of mathematical modeling and biochemical work on red blood cell metabolism. The extensive data regarding the red blood cell metabolic network and the previous kinetic analysis of all the individual components makes the human RBC an ideal 'model' system for mathematical metabolic models. The Mathematica package can be used to understand the dynamics and regulatory characteristics of the red blood cell.

Selected contribution: role of spleen emptying in prolonging apneas in humans.

This study addressed the interaction between short-term adaptation to apneas with face immersion and erythrocyte release from the spleen. Twenty healthy volunteers, including ten splenectomized subjects, participated. After prone rest, they performed five maximal-duration apneas with face immersion in 10 degrees C water, with 2-min intervals. Cardiorespiratory parameters and venous blood samples were collected. In subjects with spleens, hematocrit and hemoglobin concentration increased by 6.4% and 3.3%, respectively, over the serial apneas and returned to baseline 10 min after the series. A delay of the physiological breaking point of apnea, by 30.5% (17 s), was seen only in this group. These parameters did not change in the splenectomized group. Plasma protein concentration, preapneic alveolar PCO2, inspired lung volume, and diving bradycardia remained unchanged throughout the series in both groups. Serial apneas thus triggered the hematological changes that have been previously observed after long apneic diving shifts; they were rapidly reversed and did not occur in splenectomized subjects. This suggests that splenic contraction occurs in humans as a part of the diving response and may prolong repeated apneas.

Metabolic modeling of microbial strains in silico.

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.