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Metabolic coherence (MC) is a network-based approach to dimensionality reduction that can be used, for example, to interpret the joint expression of genes linked to human metabolism. Computationally, the derivation of ‘transcriptomic’ MC involves mapping of an individual gene expression profile onto a gene-centric network derived beforehand from a metabolic network (currently Recon2), followed by the determination of the connectivity of a particular, profile-specific subnetwork. The biological significance of MC has been exemplified previously in the context of human inflammatory bowel disease, among others, but the genetic architecture of this quantitative cellular trait is still unclear. Therefore, we performed a genome-wide association study (GWAS) of MC in the 1000 Genomes/ GEUVADIS data (n = 457) and identified a solitary genome-wide significant association with single nucleotide polymorphisms (SNPs) in the intronic region of the cadherin 18 (CDH18) gene on chromosome 5 (lead SNP: rs11744487, p = 1.2 × 10− 8). Cadherin 18 is a transmembrane protein involved in human neural development and cell-to-cell signaling. Notably, genetic variation at the CDH18 locus has been associated with metabolic syndrome-related traits before. Replication of our genome-wide significant GWAS result was successful in another population study from the Netherlands (BIOS, n = 2661; lead SNP), but failed in two additional studies (KORA, Germany, n = 711; GENOA, USA, n = 411). Besides sample size issues, we surmise that these discrepant findings may be attributable to technical differences. While 1000 Genomes/GEUVADIS and BIOS gene expression profiles were generated by RNA sequencing, the KORA and GENOA data were microarray-based. In addition to providing first evidence for a link between regional genetic variation and a metabolism-related characteristic of human transcriptomes, our findings highlight the benefit of adopting a systems biology-oriented approach to molecular data analysis. Electronic supplementary material The online version of this article (10.1007/s00439-019-01994-x) contains supplementary material, which is available to authorized users.

This paper uses well-established tools of statistical mechanics to provide a model of gene expression profiles for clinical cohorts in medical research. The model reveals under which conditions the true set of disease-associated genes can be recovered.

Hexokinases catalyze the first step of glycolysis by phosphorylating glucose. In the liver, normal hepatocytes express the low-affinity hexokinase 4, also known as glucokinase (GCK), which is adapting hepatocyte function to glycaemia. Conversely, hepatocellular carcinoma (HCC) cells express the high-affinity hexokinase 2 (HK2) to sustain tumor proliferation even at low glucose concentrations. A switch from GCK to HK2 isoenzymes is occurring during the transition from primary hepatocytes to HCC. HK2 induction is a recurrent event in the carcinogenic process and relates to the highly glycolytic phenotype of HCC but the consequence of GCK loss is unknown. To explore functional consequences of the GCK-to-HK2 isoenzyme switch, HK2 was knocked-out in the HCC cell line Huh7 and replaced by GCK, to generate the Huh7-GCK+/HK2- cell line. We then compared the transcriptomic, metabolomic and immunological profiles of both cell lines. The analysis of transcriptomic data from human HCC tumors shows that GCK and HK2 expression levels are inversely correlated and associated with patient survival. The replacement of HK2 by GCK rewired central carbon metabolism and restored some essential metabolic functions of normal hepatocytes such as lipogenesis, VLDL secretion and glycogen storage. It also reactivated innate immune responses and sensitivity to NK lysis. Our results suggest that the GCK-to-HK2 isoenzyme switch is playing a key role in dedifferentiation and immune escape of HCC cells.

International audience; Given a pattern on a network, i.e. a subset of nodes, can we assess, whether they are randomly distributed on the network or have been generated in a systematic fashion following the network architecture? This question is at the core of network-based data analyses across a range of disciplines-from incidents of infection in social networks to sets of differentially expressed genes in biological networks. Here we introduce generic 'pattern generators' based on an Eden growth model. We assess the capacity of different pattern measures like connectivity, edge density or various average distances, to infer the parameters of the generator from the observed patterns. Some measures perform consistently better than others in inferring the underlying pattern generator, while the best performing measures depend on the global topology of the underlying network. Moreover, we show that pattern generator inference remains possible in case of limited visibility of the patterns.

Within this paper we explore the idea of a critical value representing the proportion of majority members within a group that affects dramatic changes in influence targets’ conformity. We consider the threshold q-voter model when the responses of the Willis-Nail model, a well-established two-dimensional model of social response, are used as a foundation. Specifically, we study a generalized threshold q-voter model when all basic types of social response described by Willis-Nail model are considered, i.e. conformity, anticonformity, independence, and uniformity/congruence. These responses occur in our model with complementary probabilities. We introduce independently two thresholds: one needed for conformity, as well as a second one for anticonformity. In the case of conformity, at least r individuals among q neighbors have to share the same opinion in order to persuade a voter to follow majority’s opinion, whereas in the case of anticonformity, at least w individuals among q neighbors have to share the same opinion in order to influence voters to take an opinion that goes against that of their own reference group. We solve the model on a complete graph and show that the threshold for conformity significantly influences the results. For example, there is a critical threshold for conformity above which the system behaves as in the case of unanimity, i.e. displays continuous and discontinuous phase transitions. On the other hand, the threshold for anticonformity is almost irrelevant. We discuss our results from the perspective of theories of social psychology, as well as the philosophy of agent-based modeling.

The aim of this paper is to examine how different types of social influence, introduced on the microscopic (individual) level, manifest on the macroscopic level, i.e. in the society. The inspiration for this task came mainly from two sources—social psychology that recognize two different types of nonconformity (anticonformity and independence) and the observation related to the agent-based modeling that was verbalized in 2002 by Macy and Willer that there was a little effort to provide analysis of how results differ depending on the model designs. To achieve the goal, we propose a generalized model of opinion dynamics, that as a special cases reduces to the linear voter model, Sznajd model, q-voter model and the majority rule. We use the model to examine the differences, that appear at the macroscopic level, under the influence of two types of nonconformity, introduced on the microscopic level. We answer the question if the observed differences are universal or model dependent.

In this paper we study nonlinear $q$-voter model with stochastic driving on a complete graph. We investigate two types of stochasticity that, using the language of social sciences, can be interpreted as different kinds of nonconformity. From a social point of view, it is very important to distinguish between two types nonconformity, so called anti-conformity and independence. A majority of works suggests that these social differences may be completely irrelevant in terms of microscopic modeling that uses tools of statistical physics and that both types of nonconformity play the role of so called 'social temperature'. In this paper we clarify the concept of 'social temperature' and show that different type of 'noise' may lead to qualitatively different emergent properties. In particularly, we show that in the model with anti-conformity the critical value of noise increases with parameter $q$, whereas in the model with independence the critical value of noise decreases with the $q$. Moreover, in the model with anti-conformity the phase transition is continuous for any value of $q$, whereas in the model with independence the transition is continuous for $q \le 5$ and discontinuous for $q>5$.

In this paper we investigate the model of opinion dynamics with anticonformity on a complete graph. We show that below some threshold value of anticonformal behavior spontaneous reorientations occur between two stable states. Dealing with a complete graph allows us also for analytical treatment. We show that opinion dynamics can be understood as a movement of a public opinion in a symmetric bistable effective potential. We focus also on the spontaneous transitions between stable states and show that a typical waiting time can be observed., Comment: 19 pages, 9 figures