pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis.

Summary: pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements. Motivation: High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions. Results: We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli., and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA. Availability and implementation: Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/. Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models.

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

A computational framework for integration of lipidomics data into metabolic pathways.

Lipids are important compounds for human physiology and as renewable resources for fuels and chemicals. In lipid research, there is a big gap between the currently available pathway-level representations of lipids and lipid structure databases in which the number of compounds is expanding rapidly with high-throughput mass spectrometry methods. In this work, we introduce a computational approach to bridge this gap by making associations between metabolic pathways and the lipid structures discovered increasingly thorough lipidomics studies. Our approach, called NICELips (Network Integrated Computational Explorer for Lipidomics), is based on the formulation of generalized enzymatic reaction rules for lipid metabolism, and it employs the generalized rules to postulate novel pathways of lipid metabolism. It further integrates all discovered lipids in biological networks of enzymatic reactions that consist their biosynthesis and biodegradation pathways. We illustrate the utility of our approach through a case study of bis(monoacylglycero)phosphate (BMP), a biologically important glycerophospholipid with immature synthesis and catabolic route(s). Using NICELips, we were able to propose various synthesis and degradation pathways for this compound and several other lipids with unknown metabolism like BMP, and in addition several alternative novel biosynthesis and biodegradation pathways for lipids with known metabolism. NICELips has potential applications in designing therapeutic interventions for lipid-associated disorders and in the metabolic engineering of model organisms for improving the biobased production of lipid-derived fuels and chemicals.

Burnout Prevalence and Associated Factors Among Palliative and Hospice Care Professionals During the COVID-19 Endemic: An Exploratory Survey.

BACKGROUND: Burnout is a significant issue for palliative and hospice professionals, exacerbated by the impact of Coronavirus Disease 2019 (COVID-19) on healthcare professionals. It is crucial to update our understanding of prevalence data, identify associated factors, and evaluate support resources during the COVID-19 pandemic. METHODS: We aimed to explore the prevalence of burnout among palliative and hospice care workers, 2 years into the COVID-19 pandemic by using the Maslach's Burnout Inventory; anxiety, using General Anxiety Disorder-7 (GAD-7), workload, risk perception of COVID-19, confidence in protective measures (personal, workplace, and government), and usage and perceived helpfulness of support resources. Univariate logistic regression analysis was conducted to analyse burnout against these factors. RESULTS: Of the 115 respondents encompassing doctors, nurses and social workers (76.5% female; average age 40.9), 48.7% experienced burnout. Burnout correlated with increased anxiety, higher COVID-19 risk perception, heavier workload, and reduced confidence in protective measures. Peer support, COVID information, and psychological programs were rated as the most effective for coping. CONCLUSION: The study indicates considerable levels of burnout among palliative and hospice care workers, linked to workload, anxiety, and perceived risk. Traditional mental health interventions had limited efficacy; respondents favoured peer support and organisational changes. The findings stress the need for a holistic approach, including diverse resources, workload management, and regular mental health assessments.

Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols.

Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

A Federated Database for Obesity Research: An IMI-SOPHIA Study.

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Colonic architectural change on colonoscopy in patients taking psychotropic medications.

BACKGROUND: Patients on psychotropic medications have been clinically observed to have higher rates of abnormal colonic architecture resulting in difficult colonoscopies. This study aims to determine if a correlation between use of psychotropic medications and colonic architectural change seen on colonoscopy exists. METHODS: A retrospective case-control study was undertaken with 252 adults selected from the hospital endoscopy database between January 2006 and July 2008. Cases were selected if they had 'capacious', 'megacolon', 'redundant' and/or 'featureless' colonic architecture reported in their first completed colonoscopy (n = 63). Demographic information and medication records were collected for both cases and controls. Logistic regression analysis was performed for each of the medication groups. RESULTS: Medication groups associated with increased incidence for colonic architectural changes observed during colonoscopy include: antipsychotic medications [odds ratio (OR) 7.79, confidence interval (CI) 2.59-23.41], benzhexol (OR 23.50, CI 2.83-195.08) and iron tablets (OR 2.97, CI 1.39-6.33). Antidepressants, laxatives, benzodiazepines, gastroprotective medications and antihypertensive medications were not found to have any significant effect on changes to colonic architecture. CONCLUSIONS: Use of antipsychotic medications is associated with changes to colonic architecture. This could predispose such a patient to difficult colonoscopy and therefore increase colonoscopy-associated risks. Medication history should be elicited prior to colonoscopy.

When the Psychiatrist Morally Disapproves.

The understanding of concepts like moral distress and countertransference in mental health settings has advanced over time. While organizational constraints and the clinician's moral values are conventionally thought to play a part in evoking such responses, certain behavioral transgressions might be universally deemed as morally unacceptable. The authors present case scenarios that took place during forensic assessments and routine clinical care. Clinical interactions evoked a diverse range of negative emotional reactions, including anger, disgust, and frustration. The clinicians struggled with moral distress and negative countertransference, which resulted in difficulty mobilizing empathy. Such responses could affect a clinician's ability to best work with the individual and could even affect the clinician's well-being adversely. The authors put forth several suggestions on how to manage one's own negative emotional reactions in similar settings.

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks.

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment.

Manipulating redox and ATP balancing for improved production of succinate in E. coli.

Redox and energy balance plays a key role in determining microbial fitness. Efforts to redirect bacterial metabolism often involve overexpression and deletion of genes surrounding key central metabolites, such as pyruvate and acetyl-coA. In the case of metabolic engineering of Escherichia coli for succinate production, efforts have mainly focused on the manipulation of key pyruvate metabolizing enzymes. E. coli AFP111 strain lacking ldhA, pflB and ptsG encoded activities accumulates acetate and ethanol as well as shows poor anaerobic growth on rich and minimal media. To address these issues, we first deleted genes (adhE, ackA-pta) involved in byproduct formation downstream of acetyl-CoA followed by the deletion of iclR and pdhR to activate the glyoxylate pathway. Based on data from these studies, we hypothesized that the succinate productivity was limited by the insufficient ATP generation. Genome-scale thermodynamics-based flux balance analysis indicated that overexpression of ATP-forming PEPCK from Actinobacillus succinogenes in an ldhA, pflB and ptsG triple mutant strain could result in an increase in biomass and succinate flux. Testing of this prediction confirmed that PEPCK overexpression resulted in a 60% increase in biomass and succinate formation in the ldhA, pflB, ptsG mutant strain.

Somatic and non-affective symptoms of old age depression: ethnic differences among Chinese, Indians and Malays.

BACKGROUND: Somatic and other non-affective symptomatology characterizes late life depression and contributes to its under-diagnosis, especially in some ethnic groups. OBJECTIVES: We examined variations in non-affective presentation and its health and functional significance across different ethnic groups of Chinese, Malays and Indians. METHOD: We analyzed data from the National Mental Health Survey for Elderly, a population-based cross-sectional study of older adults aged 60 and above (N = 1092). RESULTS: Compared to the depressed Chinese as the reference group, depressed Malays were more likely to endorse symptoms of appetite decrease (OR = 5.19), sleep disturbances (OR = 2.93), disabling pain (OR = 3.12), psychomotor slowing (OR = 2.73) and anergia (OR = 3.70), while concurrently reporting poorer general health status and greater role limitations resulting from their mental and emotional problems (OR from 2.13 to 3.31). These differences were not influenced by anxiety, dementia or physical comorbidity. CONCLUSION: We revealed striking differences in the somatic and non-affective symptomatology of geriatric depression among different Asian ethnic groups. Non-affective symptoms in depression have large health and functional significance and important implications for the diagnosis and management of depression among elderly in primary care.

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.

A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models.

BACKGROUND: Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies. RESULTS: We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose-xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain. CONCLUSIONS: We present a design-build-test cycle composed of modeling efforts and experiments with a glucose-xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated "modeling and experiments" systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

Constraining the flux space using thermodynamics and integration of metabolomics data.

Flux balance analysis of stoichiometric metabolic models has become one of the most common methods for estimating intracellular fluxes. However most of these networks are underdetermined and can have multiple alternate optimal flux distributions. Thermodynamic constraints can reduce the solution space significantly and at the same time provide a platform for the integration of metabolomics data. Here we go through the procedure to incorporate thermodynamic constraints and perform thermodynamic analysis of metabolic networks.

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints.

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.

DREAMS of metabolism.

Metabolic networks have been studied for several decades, and sophisticated computational frameworks are needed to augment experimental approaches to harness these complex networks. BNICE (Biochemical Network Integrated Computational Explorer), a computational approach for the discovery of novel biochemical pathways that is based on biochemical transformations, overcomes many of the current limitations. BNICE and similar frameworks can be used in several different areas: (i) 'Design' of novel pathways for metabolic engineering; (ii) 'Retrosynthesis' of metabolic compounds; (iii) 'Evolution' analysis between metabolic pathways of different organisms; (iv) 'Analysis' of metabolic pathways; (v) 'Mining' of omics data; and (vi) 'Selection' of targets for enzyme engineering. Here, we discuss the issues and challenges in building such frameworks as well as the gamut of applications in biotechnology, metabolic engineering and synthetic biology.