New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. II. Inference of the precisely timed control system regulating glycolysis

Hepatic cholesterol biosynthesis and dioxin-induced dysregulation: A multiscale computational approach

Humans are constantly exposed to lipophilic persistent organic pollutants (POPs) that accumulate in fatty foods. Among the numerous POPs, dioxins, in particular 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), can impact several organ systems. While the hazard is clearly recognized, it is still difficult to develop a comprehensive understanding of the overall health impacts of dioxins. As chemical toxicity testing is steadily adopting new approach methodologies (NAMs), it becomes imperative to develop computational models that can bridge the data gaps between in vitro testing and in vivo outcomes. As an effort to address this challenge, we propose a multiscale computational approach using a "template-and-anchor" (T&A) structure. A template is a high-level umbrella model that permits the integration of information from various, detailed anchor models. In the present study, we use this T&A approach to describe the effect of TCDD on cholesterol dynamics. Specifically, we represent hepatic cholesterol biosynthesis as an anchor model that is perturbed by TCDD, leading to steatosis, along with alterations of plasma cholesterol. In the future, incorporating pertinent information from all anchor models into the template model will allow the characterization of the global effects of dioxin, which can subsequently be translated into overall - and ultimately personalized - human health risk assessment.

Perspective: Dimensions of the scientific method

The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining-inspired, and "allochthonous" knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

Quantitative Proteomic Analysis of the Response of Probiotic Putative Lactococcus lactis NCDO 2118 Strain to Different Oxygen Availability Under Temperature Variation

Lactococcus lactis is a gram positive facultative anaerobe widely used in the dairy industry and human health. L. lactis subsp. lactis NCDO 2118 is a strain that exhibits anti-inflammatory and immunomodulatory properties. In this study, we applied a label-free shotgun proteomic approach to characterize and quantify the NCDO 2118 proteome in response to variations of temperature and oxygen bioavailability, which constitute the environmental conditions found by this bacterium during its passage through the host gastro-intestinal tract and in other industrial processes. From this proteomic analysis, a total of 1,284 non-redundant proteins of NCDO 2118 were characterized, which correspond to approximately 54% of its predicted proteome. Comparative proteomic analysis identified 149 and 136 proteins in anaerobic (30°C and 37°C) and non-aerated (30°C and 37°C) conditions, respectively. Our label-free proteomic analysis quantified a total of 1,239 proteins amongst which 161 proteins were statistically differentially expressed. Main differences were observed in cellular metabolism, stress response, transcription and proteins associated to cell wall. In addition, we identified six strain-specific proteins of NCDO 2118. Altogether, the results obtained in our study will help to improve the understanding about the factors related to both physiology and adaptive processes of L. lactis NCDO 2118 under changing environmental conditions.

The three-legged stool of understanding metabolism: integrating metabolomics with biochemical genetics and computational modeling

Traditional biochemical research has resulted in a good understanding of many aspects of metabolism. However, this reductionist approach is time consuming and requires substantial resources, thus raising the question whether modern metabolomics and genomics should take over and replace the targeted experiments of old. We proffer that such a replacement is neither feasible not desirable and propose instead the tight integration of modern, system-wide omics with traditional experimental bench science and dedicated computational approaches. This integration is an important prerequisite toward the optimal acquisition of knowledge regarding metabolism and physiology in health and disease. The commentary describes advantages and drawbacks of current approaches to assessing metabolism and highlights the challenges to be overcome as we strive to achieve a deeper level of metabolic understanding in the future.

Gas chromatography-time of flight/mass spectrometry-based metabonomics of changes in the urinary metabolic profile in osteoarthritic rats

The aim of the present study was to explore changes in the urinary metabolic spectrum in rats with knee osteoarthritis, using gas chromatography-time of flight/mass spectrometry (GC-TOF/MS) to determine the metabonomic disease pathogenesis. Sprague-Dawley rats were randomly divided into the control and model groups (n=8/group), and 20 µl of 4% papain and 0.03 M L-cysteine was injected into the right knee on days 1, 3 and 7 to establish the knee osteoarthritis model. Following 14 days, urine was collected over 12 h and cartilage ultrastructural damage was assessed by hematoxylin-eosin staining. GC-TOF/MS, combined with principal component analysis, partial least squares discriminant modeling and orthogonal partial least squares discriminant modeling, was used to analyze the changes in the metabolic spectrum trajectory and to identify potential biomarkers and their related metabolic pathways. Compared with the control group, the synovial cell lining of the knee joint exhibited proliferation, inflammatory cell infiltration and collagen fiber hyperplasia in the knee osteoarthritis group. A total of 23 potential biomarkers were identified, including alanine, α-ketoglutarate, asparagine, maltose and glutamine. Furthermore, metabolomic pathogenesis of osteoarthritis may be related to disorders of amino acid metabolism, energy metabolism, fatty acid metabolism, vitamin B₆ metabolism and nucleic acid metabolism.

Systems biology of lactic acid bacteria: For food and thought

Lactic acid bacteria (LAB) ferment plants, fish, meats and milk and turn them into tasty food products with increased shelf life; other LAB help digesting food and create a healthy environment in the intestine. The economic and societal importance of these relatively simple and small bacteria is immense. In this review we hope to show that their adaptations to nutrient-rich environments provides fascinating and often puzzling behaviours that give rise to many fundamental evolutionary biological questions in need of a systems biology approach. We will provide examples of such questions, compare the (metabolic) behaviour of LAB to that of other model organisms, and provide the latest insights, if available.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

High lactic acid and fructose production via Mn2+-mediated conversion of inulin by Lactobacillus paracasei

Lactobacillus paracasei DSM 23505 is able to produce high amounts of lactic acid (LA) by simultaneous saccharification and fermentation (SSF) of inulin. Aiming to obtain the highest possible amounts of LA and fructose, the present study is devoted to evaluate the impact of bivalent metal ions on the process of inulin conversion. It was shown that Mn2+ strongly increases the activity of the purified key enzyme β-fructosidase. In vivo, batch fermentation kinetics revealed that the high Mn2+ concentrations accelerated inulin hydrolysis by raise of the inulinase activity, and increased sugars conversion to LA through enhancement of the whole glycolytic flux. The highest LA concentration and yield were reached by addition of 15 mM Mn2+-151 g/L (corresponding to 40% increase) and 0.83 g/g, respectively. However, the relative quantification by real-time reverse transcription assay showed that the presence of Mn2+ decreases the expression levels of fosE gene encoding β-fructosidase. Contrariwise, the full exclusion of metal ions resulted in fosE gene expression enhancement, blocked fructose transport, and hindered fructose conversion thus leading to huge fructose accumulation. During fed-batch with optimized medium and fermentation parameters, the fructose content reached 35.9% (w/v), achieving yield of 467 g fructose from 675 g inulin containing chicory flour powder (0.69 g/g). LA received in course of the batch fermentation and fructose gained by the fed-batch are the highest amounts ever obtained from inulin, thus disclosing the key role of Mn2+ as a powerful tool to guide inulin conversion to targeted bio-chemicals.

Nonparametric dynamic modeling

Challenging as it typically is, the estimation of parameter values seems to be an unavoidable step in the design and implementation of any dynamic model. Here, we demonstrate that it is possible to set up, diagnose, and simulate dynamic models without the need to estimate parameter values, if the situation is favorable. Specifically, it is possible to establish nonparametric models for nonlinear compartment models, including metabolic pathway models, if sufficiently many high-quality time series data are available that describe the biological phenomenon under investigation in an appropriate and representative manner. The proposed nonparametric strategy is a variant of the method of Dynamic Flux Estimation (DFE), which permits the estimation of numerical flux profiles from metabolic time series data. However, instead of attempting to formulate these numerical profiles as explicit functions and to optimize their parameter values, as it is done in DFE, the metabolite and flux profiles are used here directly as a scaffold for a library from which values are interpolated and retrieved for the simulation of the differential equations describing the model. Beyond simulations, the proposed methods render it possible to determine steady states from non-steady state data, perform sensitivity analyses, and estimate the Jacobian of the system at a steady state.

Identification of Metabolic Pathway Systems

The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE) was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.

New insights into the complex regulation of the glycolytic pathway in Lactococcus lactis. I. Construction and diagnosis of a comprehensive dynamic model

This article and the companion paper use computational systems modeling to decipher the complex coordination of regulatory signals controlling the glycolytic pathway in the dairy bacterium Lactococcus lactis.