Top-down Systems Biology Approach Research Articles

Transcriptomic determinants of the response of ST-111 Pseudomonas aeruginosa AG1 to ciprofloxacin identified by a top-down systems biology approach

Pseudomonas aeruginosa is an opportunistic pathogen that thrives in diverse environments and causes a variety of human infections. Pseudomonas aeruginosa AG1 (PaeAG1) is a high-risk sequence type 111 (ST-111) strain isolated from a Costa Rican hospital in 2010. PaeAG1 has both blaVIM-2 and blaIMP-18 genes encoding for metallo-β-lactamases, and it is resistant to β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. Ciprofloxacin (CIP) is an antibiotic commonly used to treat P. aeruginosa infections, and it is known to produce DNA damage, triggering a complex molecular response. In order to evaluate the effects of a sub-inhibitory CIP concentration on PaeAG1, growth curves using increasing CIP concentrations were compared. We then measured gene expression using RNA-Seq at three time points (0, 2.5 and 5 h) after CIP exposure to identify the transcriptomic determinants of the response (i.e. hub genes, gene clusters and enriched pathways). Changes in expression were determined using differential expression analysis and network analysis using a top–down systems biology approach. A hybrid model using database-based and co-expression analysis approaches was implemented to predict gene–gene interactions. We observed a reduction of the growth curve rate as the sub-inhibitory CIP concentrations were increased. In the transcriptomic analysis, we detected that over time CIP treatment resulted in the differential expression of 518 genes, showing a complex impact at the molecular level. The transcriptomic determinants were 14 hub genes, multiple gene clusters at different levels (associated to hub genes or as co-expression modules) and 15 enriched pathways. Down-regulation of genes implicated in several metabolism pathways, virulence elements and ribosomal activity was observed. In contrast, amino acid catabolism, RpoS factor, proteases, and phenazines genes were up-regulated. Remarkably, > 80 resident-phage genes were up-regulated after CIP treatment, which was validated at phenomic level using a phage plaque assay. Thus, reduction of the growth curve rate and increasing phage induction was evidenced as the CIP concentrations were increased. In summary, transcriptomic and network analyses, as well as the growth curves and phage plaque assays provide evidence that PaeAG1 presents a complex, concentration-dependent response to sub-inhibitory CIP exposure, showing pleiotropic effects at the systems level. Manipulation of these determinants, such as phage genes, could be used to gain more insights about the regulation of responses in PaeAG1 as well as the identification of possible therapeutic targets. To our knowledge, this is the first report of the transcriptomic analysis of CIP response in a ST-111 high-risk P. aeruginosa strain, in particular using a top-down systems biology approach.

Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections.

In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.

Systems biology of host-microbe metabolomics.

The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301For further resources related to this article, please visit the WIREs website.Conflict of interest: The authors have declared no conflicts of interest for this article.

Systems-wide analysis of acclimation responses to long-term heat stress and recovery in the photosynthetic model organism Chlamydomonas reinhardtii.

We applied a top-down systems biology approach to understand how Chlamydomonas reinhardtii acclimates to long-term heat stress (HS) and recovers from it. For this, we shifted cells from 25 to 42°C for 24 h and back to 25°C for ≥8 h and monitored abundances of 1856 proteins/protein groups, 99 polar and 185 lipophilic metabolites, and cytological and photosynthesis parameters. Our data indicate that acclimation of Chlamydomonas to long-term HS consists of a temporally ordered, orchestrated implementation of response elements at various system levels. These comprise (1) cell cycle arrest; (2) catabolism of larger molecules to generate compounds with roles in stress protection; (3) accumulation of molecular chaperones to restore protein homeostasis together with compatible solutes; (4) redirection of photosynthetic energy and reducing power from the Calvin cycle to the de novo synthesis of saturated fatty acids to replace polyunsaturated ones in membrane lipids, which are deposited in lipid bodies; and (5) when sinks for photosynthetic energy and reducing power are depleted, resumption of Calvin cycle activity associated with increased photorespiration, accumulation of reactive oxygen species scavengers, and throttling of linear electron flow by antenna uncoupling. During recovery from HS, cells appear to focus on processes allowing rapid resumption of growth rather than restoring pre-HS conditions.

Metabolomics in the fight against malaria.

Metabolomics uses high-resolution mass spectrometry to provide a chemical fingerprint of thousands of metabolites present in cells, tissues or body fluids. Such metabolic phenotyping has been successfully used to study various biologic processes and disease states. High-resolution metabolomics can shed new light on the intricacies of host-parasite interactions in each stage of the Plasmodium life cycle and the downstream ramifications on the host’s metabolism, pathogenesis and disease. Such data can become integrated with other large datasets generated using top-down systems biology approaches and be utilised by computational biologists to develop and enhance models of malaria pathogenesis relevant for identifying new drug targets or intervention strategies. Here, we focus on the promise of metabolomics to complement systems biology approaches in the quest for novel interventions in the fight against malaria. We introduce the Malaria Host-Pathogen Interaction Center (MaHPIC), a new systems biology research coalition. A primary goal of the MaHPIC is to generate systems biology datasets relating to human and non-human primate (NHP) malaria parasites and their hosts making these openly available from an online relational database. Metabolomic data from NHP infections and clinical malaria infections from around the world will comprise a unique global resource.

From Top-Down to Bottom-Up: Computational Modeling Approaches for Cellular Redoxin Networks

Thioredoxin, glutaredoxin, and peroxiredoxin systems play critical roles in a large number of redox-sensitive cellular processes. These systems are linked to each other by coupled redox cycles and common reaction intermediates into a larger network. Given the scale and connectivity of this network, computational approaches are required to analyze its dynamics and organization. Theoretical advances, as well as new redox proteomic methods, have led to the development of both top-down and bottom-up systems biology approaches to analyze the these systems and the network as a whole. Top-down approaches have been based on modifications to the Nernst equation or on graph theoretical approaches, while bottom-up approaches have been based on kinetic or stoichiometric modeling techniques. This review will consider the rationale behind these approaches and focus on their advantages and limitations. Further, the review will discuss modeling standards to ensure model accuracy and availability. Top-down and bottom-up approaches have distinct strengths and limitations in describing cellular redoxin networks. The availability of methods to overcome these limitations, together with the adoption of common modeling standards, is expected to increase the pace of model-led discovery within the redox biology field.

PL2-1. Bottom-up and Top-Down Systems Biology Approaches to Understanding Immunity

PL2-1. Bottom-up and Top-Down Systems Biology Approaches to Understanding Immunity

Gut Microbiota-targeted, Whole-Body Systems Biology for Understanding Traditional Chinese Medicine

Abstract Traditional Chinese medicine (TCM) is a medical system with its own theoretical framework focusing on functions at the whole-body level, and it has accumulated abundant clinical experience. However, TCM approaches are empirical, and the manifestations that it reads in human bodies can hardly be communicated in molecular language with modern medicine. Whole-body systems biology considers the human body as a “superorganism,” which consists of cells of both human and symbiotic microorganisms. Taking advantage of “-omic” technologies, biological information in urine, fecal, and blood samples can be profiled as emergent functions of the complex human system for understanding its dynamics in disease and health. This top-down systems biology approach emphasizes the functions of gut microbiota in human health. It is a holistic approach similar to TCM, but it assesses human health with molecular-marker-based methods; therefore, it can serve as a bridge between TCM and western medical science. This article summarizes the concepts, methodologies, clinical applications, and advantages of “functional metagenomics,” a whole-body systems-biology platform, as a new research strategy for modernization of TCM.

The multi-systemic nature of diabetes mellitus: Genotype or phenotype?

Background:This article discusses factors which materially influence the diagnosis, prevention and treatment of diabetes mellitus but which may be overlooked by the prevailing biomedical paradigm. That cognition can be mathematically linked to the function of the autonomic nervous system and physiological systems casts new light upon the mechanisms responsible for homeostasis and origins of disease. In particular, it highlights the limitations of the reductionist biomedical approach which considers mainly the biochemistry of single pathologies rather than considering the neural mechanisms which regulate the function of physiological systems, and inherent visceral organs; and which are subsequently manifest as biochemistries of varying degrees of complexity and severity. As a consequence, histopathological tests are fraught with inherent limitations and many categories of drugs are significantly ineffective.Aims:Such limitations may be explained if disease (in particular diabetes mellitus) has multiple origins, is multi-systemic in nature and, depending upon the characteristics of each pathology, is influenced by genotype and/or phenotype.Results:This article highlights the influence of factors which are not yet considered re. the aetiology of diabetes mellitus e.g. the influence of light and sensory input upon the stability of the autonomic nervous system; the influence of raised plasma viscosity upon rates of reaction; the influence of viruses and/or of modified live viruses given in vaccinations; systemic instability, in particular the adverse influence of drinks and lack of exercise upon the body's prevailing pH and its subsequent influence upon levels of magnesium and other essential trace elements.Conclusions:This application of the top-down systems biology approach may provide a plausible and inclusive explanation for the nature and occurrence of diabetes mellitus.

Global Metabolic Responses of NMRI Mice to an Experimental Plasmodium berghei Infection

We present a metabolism-driven top-down systems biology approach to characterize metabolic changes in the mouse resulting from an infection with Plasmodium berghei, using high-resolution (1)H NMR spectroscopy and multivariate data analysis techniques. Twelve female NMRI mice were infected intravenously with approximately 20 million P. berghei-parasitized erythrocytes. Urine and plasma samples were collected 4-6 h before infection, and at days 1, 2, 3, and 4 postinfection. Multivariate analysis of spectral data showed differentiation between samples collected before and after infection, with growing metabolic distinction as the time postinfection progressed. Our analysis of plasma from P. berghei-infected mice showed marked increases in lactate and pyruvate levels, and decreased glucose, creatine, and glycerophosphoryl choline compared with preinfection, indicating glycolytic upregulation, and increased energy demand due to P. berghei infection. The dominant changes in the urinary metabolite profiles included increased levels of pipecolic acid, phenylacetylglycine, and dimethylamine, and decreased concentrations of taurine and trimethylamine- N-oxide, which may, among other factors, indicate a disturbance of the gut microbial community caused by the parasite. Although several of the observed metabolic changes are also associated with other parasitic infections, the combination of metabolic changes and, in particular, the occurrence of pipecolic acid in mouse urine postinfection are unique to a P. berghei infection. Hence, metabolic profiling may provide a sensitive diagnostic tool of Plasmodium infection and the control of malaria more generally.