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Background and aims: Hepatocytes undergo profound metabolic rewiring when primed to proliferate during compensatory regeneration and in hepatocellular carcinoma (HCC). However, the metabolic control of these processes is not fully understood. In order to capture the metabolic signature of proliferating hepatocytes, we applied state-of-the-art systems biology approaches to models of liver regeneration, pharmacologically and genetically activated cell proliferation, and HCC.Approach and results: Integrating metabolomics, lipidomics, and transcriptomics, we link changes in the lipidome of proliferating hepatocytes to altered metabolic pathways including lipogenesis, fatty acid desaturation, and generation of phosphatidylcholine (PC). We confirm this altered lipid signature in human HCC and show a positive correlation of monounsaturated PC with hallmarks of cell proliferation and hepatic carcinogenesis.Conclusions: Overall, we demonstrate that specific lipid metabolic pathways are coherently altered when hepatocytes switch to proliferation. These represent a source of targets for the development of therapeutic strategies and prognostic biomarkers of HCC.

The aim of the current study was to test the hypothesis that maternal lipid metabolism was modulated during normal pregnancy and that these modulations are altered in gestational diabetes mellitus (GDM). We tested this hypothesis using an established mouse model of diet-induced obesity with pregnancy-associated loss of glucose tolerance and a novel lipid analysis tool, Lipid Traffic Analysis, that uses the temporal distribution of lipids to identify differences in the control of lipid metabolism through a time course. Our results suggest that the start of pregnancy is associated with several changes in lipid metabolism, including fewer variables associated with de novo lipogenesis and fewer PUFA-containing lipids in the circulation. Several of the changes in lipid metabolism in healthy pregnancies were less apparent or occurred later in dams who developed GDM. Some changes in maternal lipid metabolism in the obese-GDM group were so late as to only occur as the control dams’ systems began to switch back towards the non-pregnant state. These results demonstrate that lipid metabolism is modulated in healthy pregnancy and the timing of these changes is altered in GDM pregnancies. These findings raise important questions about how lipid metabolism contributes to changes in metabolism during healthy pregnancies. Furthermore, as alterations in the lipidome are present before the loss of glucose tolerance, they could contribute to the development of GDM mechanistically.

Summary Increasing brown adipose tissue (BAT) mass and activation is a therapeutic strategy to treat obesity and complications. Obese and diabetic patients possess low amounts of BAT, so an efficient way to expand their mass is necessary. There is limited knowledge about how human BAT develops, differentiates, and is optimally activated. Accessing human BAT is challenging, given its low volume and anatomical dispersion. These constraints make detailed BAT-related developmental and functional mechanistic studies in humans virtually impossible. We have developed and characterized functionally and molecularly a new chemically defined protocol for the differentiation of human pluripotent stem cells (hPSCs) into brown adipocytes (BAs) that overcomes current limitations. This protocol recapitulates step by step the physiological developmental path of human BAT. The BAs obtained express BA and thermogenic markers, are insulin sensitive, and responsive to β-adrenergic stimuli. This new protocol is scalable, enabling the study of human BAs at early stages of development., Graphical Abstract, Highlights • This is a novel, robust and scalable protocol to differentiate hPSCs into BAs • The protocol recapitulates the different stages of human BAT development • hPSC-derived BAs show transcriptomic and functional features of bona fide human BAs • It represents a useful tool for temporal analysis of human BAs differentiation, Obese patients possess low amounts of BAT, so an efficient way to expand its mass and activation is necessary to treat obesity and complications. There is limited knowledge about human BAT physiology and accessing it is challenging. To overcome this, Carobbio and colleagues developed a new protocol to differentiate human PSCs into functional brown adipocytes, recapitulating human BAT developmental path.

Summary Adipogenin (Adig) is an adipocyte-enriched transmembrane protein. Its expression is induced during adipogenesis in rodent cells, and a recent genome-wide association study associated body mass index (BMI)-adjusted leptin levels with the ADIG locus. In order to begin to understand the biological function of Adig, we studied adipogenesis in Adig-deficient cultured adipocytes and phenotyped Adig null (Adig−/−) mice. Data from Adig-deficient cells suggest that Adig is required for adipogenesis. In vivo, Adig−/− mice are leaner than wild-type mice when fed a high-fat diet and when crossed with Ob/Ob hyperphagic mice. In addition to the impact on fat mass accrual, Adig deficiency also reduces fat-mass-adjusted plasma leptin levels and impairs leptin secretion from adipose explants, suggesting an additional impact on the regulation of leptin secretion., Graphical abstract, Highlights • The absence of Adig impairs adipogenesis in vitro • High-fat diet (HFD)-induced weight gain is ameliorated in Adig−/− mice • Fat-mass-adjusted leptin levels are reduced in Adig−/− mice • Leptin secretion is reduced in Adig−/− adipose explants, Alvarez-Guaita et al. show that Adipogenin (Adig) deficiency impairs adipogenesis in cultured cells. High-fat diet (HFD)-induced weight gain is ameliorated in Adig−/− mice, and fat-mass-adjusted leptin levels are lower. Leptin secretion from Adig−/− adipose explants is also reduced, suggesting that Adig influences leptin secretion.

Increasing brown adipose tissue (BAT) mass and activation has been proposed as a potential therapeutic strategy to treat obesity and associated cardiometabolic complications. Given that obese and diabetic patients possess low amounts of BAT, an efficient way to expand their BAT mass would be necessary if BAT is to be useful. Currently, there is limited knowledge about how human BAT develops, differentiates, and is optimally activated. Moreover, to have access to human BAT is challenging, given its low volume and being anatomically dispersed. These constrain makes detailed mechanistic studies related to BAT development and function in humans virtually impossible. To overcome these limitations, we have developed a human-relevant new protocol for the differentiation of human pluripotent stem cells (hPSCs) into brown adipocytes (BAs). Unique to this protocol is that it is chemically-defined to recapitulate a physiological step-by-step developmental path of BAT that captures transient paraxial mesoderm and BAT progenitor states, on its way to reaching the adipocyte stage finally. These hPSC-derived BAs express brown adipocyte and thermogenic markers, are insulin sensitive, and respond to β-adrenergic stimuli. This new protocol is a scalable tool to study human BAs during development.

While the consequences of poor maternal diet on the offspring’s cardio-metabolic health have been studied in detail, the role of the father’s diet on the health of his offspring is poorly understood. We used a known mouse model to establish the impact of an isocaloric paternal low-protein high-carbohydrate diet on the offspring’s lipid metabolism. Detailed lipid profiles were acquired from F1 neonate (3 weeks), F1 adult (16 weeks) and F2 neonate male and female offspring, in serum, liver, brain, heart and abdominal adipose tissues by Mass Spectrometry and Nuclear Magnetic Resonance. Using a purpose-built computational tool for analysing lipid metabolism as a network, we characterised the number, type and abundance of lipid variables in and between tissues (Lipid Traffic Analysis), finding a variety of alterations associated with paternal diet. These elucidate a mechanism for the defective physiological behaviour of systems at risk of cardio-metabolic disease.

Summary Splenic red pulp macrophages (RPMs) contribute to erythrocyte homeostasis and are required for iron recycling. Heme induces the expression of SPIC transcription factor in monocyte-derived macrophages and promotes their differentiation into RPM precursors, pre-RPMs. However, the requirements for differentiation into mature RPMs remain unknown. Here, we have demonstrated that interleukin (IL)-33 associated with erythrocytes and co-cooperated with heme to promote the generation of mature RPMs through activation of the MyD88 adaptor protein and ERK1/2 kinases downstream of the IL-33 receptor, IL1RL1. IL-33- and IL1RL1-deficient mice showed defective iron recycling and increased splenic iron deposition. Gene expression and chromatin accessibility studies revealed a role for GATA transcription factors downstream of IL-33 signaling during the development of pre-RPMs that retained full potential to differentiate into RPMs. Thus, IL-33 instructs the development of RPMs as a response to physiological erythrocyte damage with important implications to iron recycling and iron homeostasis., Graphical Abstract, Highlights • IL-33 signaling promotes development of monocyte-derived red pulp macrophages (RPMs) • Il33−/− and Il1rl1−/− mice have decreased RPMs and splenic erythrophagocytosis • ERK activation is required for RPM development and is potentiated by hemin and IL-33 • GATA2 instructs RPM development and is aberrant in Il1rl1−/− RPM precursors, Splenic red pulp macrophages (RPMs) are critical for iron recycling. Here, Lu et al. implicate a role for IL-33 signaling in the regulation of GATA2, which controls the transition of monocytes to produce pre-RPMs that are competent to terminally differentiate into mature RPMs.

Ribosome profiling (Ribo-seq profiling) is the most advanced tool to study the translational control of gene expression. Unfortunately, the resolution of this cutting edge technique is severely limited by a low signal to noise ratio. To tackle this issue, we introduce here a newly designed statistical method for the identification of reproducible Ribo-seq profiles. In the case of E. coli, the analysis of 2238 Ribo-seq profiles across 9 independent datasets revealed that only 11 profiles are significantly reproducible. A subsequent data quality check led us to identify one outgroup dataset. By ruling it out, the number of highly reproducible profiles could be raised to 49. Despite its surprisingly small size, this set represents a reliable workbench to both assess the quality of the data and study the factors that influence the translation process.

Enteroendocrine cells (EECs) produce hormones such as glucagon-like peptide 1 and peptide YY that regulate food absorption, insulin secretion, and appetite. Based on the success of glucagon-like peptide 1–based therapies for type 2 diabetes and obesity, EECs are themselves the focus of drug discovery programs to enhance gut hormone secretion. The aim of this study was to identify the transcriptome and peptidome of human EECs and to provide a cross-species comparison between humans and mice. By RNA sequencing of human EECs purified by flow cytometry after cell fixation and staining, we present a first transcriptomic analysis of human EEC populations and demonstrate a strong correlation with murine counterparts. RNA sequencing was deep enough to enable identification of low-abundance transcripts such as G-protein–coupled receptors and ion channels, revealing expression in human EECs of G-protein–coupled receptors previously found to play roles in postprandial nutrient detection. With liquid chromatography–tandem mass spectrometry, we profiled the gradients of peptide hormones along the human and mouse gut, including their sequences and posttranslational modifications. The transcriptomic and peptidomic profiles of human and mouse EECs and cross-species comparison will be valuable tools for drug discovery programs and for understanding human metabolism and the endocrine impacts of bariatric surgery.

The behavior of biochemical systems such as metabolic and signaling pathways may depend on either the location of the reactants or on the time needed for a reaction to occur. In this paper we propose a formalism for specifying and verifying properties of biochemical systems that combines, coherently, temporal and spatial modalities. To this aim, we consider a fragment of intuitionistic linear logic with subexponentials (SELL). The subexponential signature allows us to capture the spatial relations among the different components of the system and the timed constraints. We illustrate our approach by specifying some well-known biological systems and verifying properties of them. Moreover, we show that our framework is general enough to give a logic-based semantics to P systems. We show that the proposed logical characterizations have a strong level of adequacy. Hence, derivations in SELL follow exactly the behavior of the modeled system.

Premature ribosome drop-off is one of the major errors in translation of mRNA by ribosomes. However, repeated analyses of Ribo-seq data failed to quantify its strength in E. coli. Relying on a novel highly sensitive data analysis method we show that a significant rate of ribosome drop-off is measurable and can be quantified also when cells are cultured under non-stressing conditions. Moreover, we find that the drop-off rate is highly variable, depending on multiple factors. In particular, under environmental stress such as amino acid starvation or ethanol intoxication, the drop-off rate markedly increases.

The link-calculus has been recently proposed as a process calculus for representing interactions that are open (i.e., that the number of processes may vary), and multiparty (i.e., that may involve more than two processes). Here, we apply the link-calculus for expressing, possibly hierarchical and non dyadic, biological interactions. In particular, we provide a natural encoding of Cardelli's Brane calculus, a compartment-based calculus, introduced to model the behaviour of nested membranes. Notably, the link-calculus is flat, but we can model membranes just as special processes taking part in the biological reaction. Moreover, we give evidence that the link-calculus allows one to directly model biological phenomena at the more appropriate level of abstraction.

Summary Ambiguity regarding the role of glucose-dependent insulinotropic polypeptide (GIP) in obesity arises from conflicting reports asserting that both GIP receptor (GIPR) agonism and antagonism are effective strategies for inhibiting weight gain. To enable identification and manipulation of Gipr-expressing (Gipr) cells, we created Gipr-Cre knockin mice. As GIPR-agonists have recently been reported to suppress food intake, we aimed to identify central mediators of this effect. Gipr cells were identified in the arcuate, dorsomedial, and paraventricular nuclei of the hypothalamus, as confirmed by RNAscope in mouse and human. Single-cell RNA-seq identified clusters of hypothalamic Gipr cells exhibiting transcriptomic signatures for vascular, glial, and neuronal cells, the latter expressing somatostatin but little pro-opiomelanocortin or agouti-related peptide. Activation of Gq-DREADDs in hypothalamic Gipr cells suppressed food intake in vivo, which was not obviously additive with concomitant GLP1R activation. These data identify hypothalamic GIPR as a target for the regulation of energy balance., Graphical Abstract, Highlights • Gipr is expressed in the hypothalamus, as demonstrated using a new Gipr-Cre mouse model • Gipr cells included somatostatin-positive neurons, glia, and vascular cells • Gipr overlapped partially with Glp1r in human and mouse hypothalamus by RNAscope • Activation of Gipr neurons using local AAV-delivered Gq-DREADDs reduced food intake, Adriaenssens et al. identified cells expressing receptors for the gut hormone GIP in human and mouse hypothalamus using a new Gipr-Cre mouse model and RNAscope. Local hypothalamic delivery of AAVs expressing Cre-dependent Gq-DREADDs revealed that activation of Gipr-positive neurons in mice reduced food consumption.

We report on a technique for modelling biological systems based on the ntcc calculus, a model of concurrency where systems are specified by means of constraints (i.e., formulae in logic). We show that the ability of ntcc to express partial information, concurrency, non-determinism and timed behaviour makes it well-suited model and simulate biochemical reactions networks. Based on this technique, we introduce BioWayS (BIOchemical pathWAY Simulator), a software tool for the quantitative modelling and analysis of biological systems. We show the applicability of BioWayS in the context of two well-studied biological systems: the glycogen breakdown pathway and the life cycle of the human immunodeficiency virus.

Generalised Boolean Networks are a well known qualitative model used to analyse the evolution of genetic networks as well as generic biological pathways. Despite the qualitative abstraction due to the few threshold concentration values considered for each biological element in the model, the complexity of the execution of a Generalised Boolean model could be non trivial. In this paper, we propose a tailored process algebra, called Sim-πn, reminiscent of the π-calculus to model GBNs. We further apply the Control Flow Analysis methodology to the resulting computational model for making static (and therefore less computationally expensive) predictions on the dynamical evolution of the investigated networks. The scope is twofold: helping in the setting up of the model, for checking its completeness, and checking the evolution of the model, in terms of the possibility to reach particular threshold values of the biological elements in the model, when varying the initial conditions.

We introduce a Control Flow Analysis for Brane Calculi. This verification technique allows properties regarding the behaviour of biological systems to be checked. This is an approximate technique that focusses on the static specification of a system, rather than on its dynamics, striving for effectiveness. Examples illustrate the approach.

Background Biochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving. Results Our technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes. Conclusions We show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian models.

In cultures of unicellular algae, features of single cells, such as cellular volume and starch content, are thought to be the result of carefully balanced growth and division processes. Single-cell analyses of synchronized photoautotrophic cultures of the unicellular alga Chlamydomonas reinhardtii reveal, however, that the cellular volume and starch content are only weakly correlated. Likewise, other cell parameters, e.g., the chlorophyll content per cell, are only weakly correlated with cell size. We derive the cell size distributions at the beginning of each synchronization cycle considering growth, timing of cell division and daughter cell release, and the uneven division of cell volume. Furthermore, we investigate the link between cell volume growth and starch accumulation. This work presents evidence that, under the experimental conditions of light-dark synchronized cultures, the weak correlation between both cell features is a result of a cumulative process rather than due to asymmetric partition of biomolecules during cell division. This cumulative process necessarily limits cellular similarities within a synchronized cell population.

Graphical abstractDisplay Omitted HighlightsIn living cells gene expression is thoroughly regulated.Computational models help in understanding the mechanisms underlying gene regulation.Hybrid approaches balance model accuracy and computational efficiency.We identify the key sources of stochastic noise in a simple gene expression pathway.We propose a wise method for building hybrid models without ignoring key parameters. In this paper, we explore the impact of different forms of model abstraction and the role of discreteness on the dynamical behaviour of a simple model of gene regulation where a transcriptional repressor negatively regulates its own expression. We first investigate the relation between a minimal set of parameters and the system dynamics in a purely discrete stochastic framework, with the twofold purpose of providing an intuitive explanation of the different behavioural patterns exhibited and of identifying the main sources of noise. Then, we explore the effect of combining hybrid approaches and quasi-steady state approximations on model behaviour (and simulation time), to understand to what extent dynamics and quantitative features such as noise intensity can be preserved.

Biochemical systems such as metabolic and signaling pathways tend to be arranged in a physical space: the product of one reaction must be in the right place to become the reactant for the subsequent reaction in the pathway. Moreover, in some cases, the behavior of the systems can depend on both, the location of the reactants as well as on the time needed for the reaction to occur. We address the problem of specifying and verifying properties of biochemical systems that exhibit both temporal and spatial modalities at the same time. For that, we use as specification language a fragment of intuitionistic linear logic with subexponentials (SELL). The subexponential signature allows us to capture the spatial relations among the different components of the system and the timed constraints for reactions to occur. We show that our framework is general enough to give a declarative semantics to P-Systems and we show that such logical characterization has a strong level of adequacy. Hence, derivations in SELL follow exactly the behavior of the modeled system.

International audience; The biological data regarding the signalling pathways often consider single pathways or a small number of them. We propose a methodology for composing this kind of data in a coherent framework, in order to be able to investigate a bigger number of signalling pathways. We specify a biological system by means of a set of stoichiometric-like equations resembling the essential features of molecular interactions. We represent these equations by a timed concurrent constraint (ntcc) language, which can deal with partial information and the time for a reaction to occur. We describe a freely available prototypical implementation of our framework.

We formally characterize a set of causality-based properties of metabolic networks. This set of properties aims at making precise several notions on the production of metabolites, which are familiar in the biologists’ terminology. From a theoretical point of view, biochemical reactions are abstractly represented as causal implications and the produced metabolites as causal consequences of the implication representing the corresponding reaction. The fact that a reactant is produced is represented by means of the chain of reactions that have made it exist. Such representation abstracts away from quantities, stoichiometric and thermodynamic parameters and constitutes the basis for the characterization of our properties. Moreover, we propose an effective method for verifying our properties based on an abstract model of system dynamics. This consists of a new abstract semantics for the system seen as a concurrent network and expressed using the Chemical Ground Form [6] calculus. We illustrate an application of this framework to a portion of a real metabolic pathway.

This paper reports on our experience in modelling whole cells with process calculi. We followed a holistic approach, aiming at investigating the behaviour of biological objects at the system level, in particular of a hypothetical and a of real prokaryote. These cells, namely VICE and Escherichia coli, have been specified through the π-calculus, endowed with a stochastic semantics. We describe a couple of variants of the π-calculus and briefly survey three interpreters of it, with increasing efficiency.We show how the usage of tools based on process calculi greatly helped us in designing the virtual cell VICE, and in comparing it with other prposals. The main properties of the in silico experiments on VICE and on Escherichia coli are then discussed and shown in agreement with those of real prokaryoptes acting in vivo/vitro.

Comparative genomics usually involves managing the functional aspects of genomes, by simply comparing gene-by-gene functions. Following this approach, Mushegian and Koonin proposed a hypothetical minimal genome, Minimal Gene Set (MGS), aiming for a possible oldest ancestor genome. They obtained MGS by comparing the genomes of two simple bacteria and eliminating duplicated or functionally identical genes. The authors raised the fundamental question of whether a hypothetical organism possessing MGS is able to live or not. We attacked this viability problem specifying in silico the metabolic pathways of the MGS-based prokaryote. We then performed a dynamic simulation of cellular metabolic activities in order to check whether the MGS-prokaryote reaches some equilibrium state and produces the necessary biomass. We assumed these two conditions to be necessary for a living organism. Our simulations clearly show that the MGS does not express an organism that is able to live. We then iteratively proceeded with functional replacements in order to obtain a genome composition that gives rise to equilibrium. We ruled out 76 of the original 254 genes in the MGS, because they resulted in duplication from a functional point of view. We also added seven genes not present in the MGS. These genes encode for enzymes involved in critical nodes of the metabolic network. These modifications led to a genome composed of 187 elements expressing a virtually living organism, Virtual Cell (ViCe), that exhibits homeostatic capabilities and produces biomass. Moreover, the steady-state distribution of the concentrations of virtual metabolites that resulted was similar to that experimentally measured in bacteria. We conclude then that ViCe is able to “live in silico.”, Author Summary The origins of life represent a fascinating problem that has been addressed using different approaches and a wide variety of technologies. A theoretical approach consists of inferring a possible oldest ancestor genome from a well-defined comparison of current ones. A crucial problem concerns the validation of the proposed genome. The direct solution of synthesizing such a genome in a laboratory is often extremely difficult, due to the great complexity of a biological cell. In this paper, we present an approach for evaluating the chances a hypothetical organism has to be really viable, relying on computer simulations. Our method is based on a certain formal language, through which we specify a whole metabolic network, and we study its dynamics, in particular for verifying if a living organism has some fundamental properties, e.g., homeostasis. This approach is not equivalent to a wet-lab one, but it allows for early pruning of most of the inconsistently designed hypothetical organisms, thus saving biologists time and resources.