Plasma Biomarkers and Identification of Resilient Metabolic Disruptions in Patients With Venous Thromboembolism Using a Metabolic Systems Approach.

OBJECTIVE: Deep vein thrombosis and pulmonary embolism referred as venous thromboembolism (VTE) are a common cause of morbidity and mortality. Plasma from healthy controls or individuals who have experienced a VTE were analyzed using metabolomics to characterize biomarkers and metabolic systems of patients with VTE. Approach and Results: Polar metabolite and lipidomic profiles from plasma collected 3 months after an incident VTE were obtained using liquid chromatography mass spectrometry. Fasting-state plasma samples from 42 patients with VTE and 42 healthy controls were measured. Plasma metabolomic profiling identified 512 metabolites forming 62 biological clusters. Multivariate analysis revealed a panel of 21 metabolites altogether capable of predicting VTE status with an area under the curve of 0.92 (P=0.00174, selectivity=0.857, sensitivity=0.971). Multiblock systems analysis revealed 25 of the 62 functional biological groups as significantly affected in the VTE group (P<0.05 to control). Complementary correlation network analysis of the dysregulated functions highlighted a subset of the lipidome composed mainly of n-3 long-chain polyunsaturated fatty acids within the predominant triglycerides as a potential regulator of the post-VTE event biological response, possibly controlling oxidative and inflammatory defence systems, and metabolic disorder associated dysregulations. Of interest was microbiota metabolites including trimethylamine N-oxide that remained associated to post incident VTE patients, highlighting a possible involvement of gut microbiota on VTE risk and relapse. CONCLUSIONS: These findings show promise for the elucidation of underlying mechanisms and the design of a diagnostic test to assess the likely efficacy of clinical care in patients with VTE.

The (r)evolution of gene regulatory networks controlling Arabidopsis plant reproduction: a two-decade history.

Successful plant reproduction relies on the perfect orchestration of singular processes that culminate in the product of reproduction: the seed. The floral transition, floral organ development, and fertilization are well-studied processes and the genetic regulation of the various steps is being increasingly unveiled. Initially, based predominantly on genetic studies, the regulatory pathways were considered to be linear, but recent genome-wide analyses, using high-throughput technologies, have begun to reveal a different scenario. Complex gene regulatory networks underlie these processes, including transcription factors, microRNAs, movable factors, hormones, and chromatin-modifying proteins. Here we review recent progress in understanding the networks that control the major steps in plant reproduction, showing how new advances in experimental and computational technologies have been instrumental. As these recent discoveries were obtained using the model species Arabidopsis thaliana, we will restrict this review to regulatory networks in this important model species. However, more fragmentary information obtained from other species reveals that both the developmental processes and the underlying regulatory networks are largely conserved, making this review also of interest to those studying other plant species.

A quantitative and dynamic model of the Arabidopsis flowering time gene regulatory network.

Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1) mutation has a larger impact on APETALA1 (AP1), which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY) which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1) by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.

Interactome-wide prediction of protein-protein binding sites reveals effects of protein sequence variation in Arabidopsis thaliana.

The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.