The reactive species interactome: evolutionary emergence, biological significance, and opportunities for redox metabolomics and personalized medicine

SIGNIFICANCE: Oxidative stress is thought to account for aberrant redox homeostasis and contribute to aging and disease. However, more often than not administration of antioxidants is ineffective, suggesting our current understanding of the underlying regulatory processes is incomplete. Recent Advances. Similar to reactive oxygen and nitrogen species (ROS, RNS), reactive sulfur species (RSS) are now emerging as important signaling molecules, targeting regulatory cysteine redox switches in proteins, affecting gene regulation, ion transport, intermediary metabolism and mitochondrial function. To rationalize the complexity of chemical interactions of reactive species with themselves and their targets and help define their role in systemic metabolic control, we here introduce a novel integrative concept coined the reactive species interactome (RSI). The RSI is a primeval multi-level redox-regulatory system whose architecture, together with the physicochemical characteristics of its constituents, allows efficient sensing and rapid adaptation to environmental changes and various other stresses to enhance fitness and resilience at the local and whole-organism level. CRITICAL ISSUES: To better characterise the RSI-related processes that determine fluxes through specific pathways and enable integration, it is necessary to disentangle the chemical biology and activity of reactive species (including precursors and reaction products), their targets, communication systems and effects on cellular, organ and whole-organism bioenergetics using systems-level/network analyses. FUTURE DIRECTIONS: Understanding the mechanisms through which the RSI operates will enable a better appreciation of the possibilities to modulate the entire biological system; moreover, unveiling molecular signatures that characterize specific environmental challenges or other stresses will provide new prevention/intervention opportunities for personalized medicine

Metabolomic fingerprinting of renal disease progression in Bardet-Biedl syndrome reveals mitochondrial dysfunction in kidney tubular cells.

Chronic kidney disease (CKD) is a major clinical sign of patients with Bardet-Biedl syndrome (BBS), especially in those carrying BBS10 mutations. Twenty-nine patients with BBS and 30 controls underwent a serum-targeted metabolomic analysis. In vitro studies were conducted in two kidney-derived epithelial cell lines, where Bbs10 was stably deleted (IMCD3-Bbs10-/-cells) and over-expressed. The CKD status affected plasmatic metabolite fingerprinting in both patients with BBS and controls. Specific phosphatidylcholine and acylcarnitines discriminated eGFR decline only in patients with BBS. IMCD3-Bbs10-/ cells displayed intracellular lipidaccumulation, reduced mitochondrial potential membrane and citrate synthase staining. Mass-Spectrometry-based analysis revealed that human BBS10 interacted with six mitochondrial proteins, in vitro. In conclusion, renal dysfunction correlated with abnormal phosphatidylcholine and acylcarnitines plasma levels in patients with BBS; in vitro, Bbs10 depletion caused mitochondrial defects while human BBS10 interacted with several mitochondria-related proteins, suggesting an unexplored role of this protein

Identifying aging-related genes in mouse hippocampus using gateway nodes

BACKGROUND: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes. RESULTS: By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus. CONCLUSIONS: This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network

Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks

Background: Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. Results: In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator. Availability: Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/

Mining for novel candidate clock genes in the circadian regulatory network

Background Most physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein expression. Here, we aim to identify novel additions to the clock network using a meta- analysis of public chromatin immunoprecipitation sequencing (ChIP-seq), proteomics and protein-protein interaction data starting from a published list of 1000 genes with robust transcriptional rhythms and circadian phenotypes of knockdowns. Results We identified 20 candidate genes including nine known clock genes that received significantly high scores and were also robust to the relative weights assigned to different data types. Our scoring was consistent with the original ranking of the 1000 genes, but also provided novel complementary insights. Candidate genes were enriched for genes expressed in a circadian manner in multiple tissues with regulation driven mainly by transcription factors BMAL1 and REV-ERB α,β. Moreover, peak transcription of candidate genes was remarkably consistent across tissues. While peaks of the 1000 genes were distributed uniformly throughout the day, candidate gene peaks were strongly concentrated around dusk. Finally, we showed that binding of specific transcription factors to a gene promoter was predictive of peak transcription at a certain time of day and discuss combinatorial phase regulation. Conclusions Combining complementary publicly- available data targeting different levels of regulation within the circadian network, we filtered the original list and found 11 novel robust candidate clock genes. Using the criteria of circadian proteomic expression, circadian expression in multiple tissues and independent gene knockdown data, we propose six genes (Por, Mtss1, Dgat2, Pim3, Ppp1r3b, Upp2) involved in metabolism and cancer for further experimental investigation. The availability of public high-throughput databases makes such meta-analysis a promising approach to test consistency between sources and tap their entire potential

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

Background: Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network. Methodology/Principal Findings: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. Conclusions/Significance: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from mul-tiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes

A classification of tasks for the systematic study of immune response using functional genomics data

A full understanding of the immune system and its responses to infection by different pathogens is important for the development of anti-parasitic vaccines. A growing number of large-scale experimental techniques, such as microarrays, are being used to gain a better understanding of the immune system. To analyse the data generated by these experiments, methods such as clustering are widely used. However, individual applications of these methods tend to analyse the experimental data without taking publicly available biological and immunological knowledge into account systematically and in an unbiased manner. To make best use of the experimental investment, to benefit from existing evidence, and to support the findings in the experimental data, available biological information should be included in the analysis in a systematic manner. In this review we present a classification of tasks that shows how experimental data produced by studies of the immune system can be placed in a broader biological context. Taking into account available evidence, the classification can be used to identify different ways of analysing the experimental data systematically. We have used the classification to identify alternative ways of analysing microarray data, and illustrate its application using studies of immune responses in mice to infection with the intestinal nematode parasites Trichuris muris and Heligmosomoides polygyrus