A unifying view of 21st century systems biology

Reading the palimpsest of cell interactions: What questions may we ask of the data?

Biological function depends on the composition and structure of the organism, the latter describing the organization of interactions between parts. While cells in multicellular organisms are capable of a remarkable degree of autonomy, most functions do require cell communication: the coordination of functions (growth, differentiation, and apoptosis), the compartmentalization of cellular processes, and the integration of cells into higher levels of structural organization. A wealth of data on putative cell interactions has become available, yet its biological interpretation depends on our expectations about the structure of interaction networks. Here, we attempt to formulate basic questions to ask when interpreting cell interaction data. We build on the understanding that cells fulfill two general functions: the integrity-maintaining and the organismal service function. We derive the expected patterns of cell interactions considering two intertwined aspects: the functional and the evolutionary. Based on these, we propose guidelines for analysis and interpretation of transcriptional cell-interactome data.

Identification of metabolic pathways and key genes associated with atypical parkinsonism using a systems biology approach

Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes.

Missed connections: Exploring features of undergraduate biology students' knowledge networks relating gene regulation, cell-cell communication, and phenotypic expression

Explaining biological phenomena requires understanding how different processes function and describing interactions between components at various levels of organization over time and space in biological systems. This is a desired competency yet is a complicated and often challenging task for undergraduate biology students. Therefore, we need a better understanding of their integrated knowledge regarding important biological concepts. Informed by the theory of knowledge integration and mechanistic reasoning, in this qualitative case study, we elicited and characterized knowledge networks of nine undergraduate biology students. We investigated students' conceptions of and the various ways they connect three fundamental subsystems in biology: 1) gene regulation, 2) cell-cell communication, and 3) phenotypic expression. We found that only half of the conceptual questions regarding the three subsystems were answered correctly by the majority of students. Knowledge networks tended to be linear and unidirectional, with little variation in the types of relationships displayed. Students did not spontaneously express mechanistic connections, mainly described undefined, cellular, and macromolecular levels of organization, and mainly discussed unspecified and intracellular localizations. These results emphasize the need to support students' understanding of fundamental concepts, and promoting knowledge integration in the classroom could assist students' ability to understand biological systems.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.

Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).

Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning

The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.

Virus systems biology: Proteomics profiling of dynamic protein networks during infection

The host cell proteome undergoes a variety of dynamic changes during viral infection, elicited by the virus itself or host cell defense mechanisms. Studying these changes on a global scale by integrating functional and physical interactions within protein networks during infection is an important tool to understand pathology. Indeed, proteomics studies dissecting protein signaling cascades and interaction networks upon infection showed how global information can significantly improve understanding of disease mechanisms of diverse viral infections. Here, we summarize and give examples of different experimental designs, proteomics approaches and bioinformatics analyses that allow profiling proteome changes and host-pathogen interactions to gain a molecular systems view of viral infection.

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.

Drug Research Meets Network Science: Where Are We?

Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.

A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.

Measuring rank robustness in scored protein interaction networks

BACKGROUND

Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds.

RESULTS

We propose three measures-rank continuity, identifiability, and instability-to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges.

CONCLUSION

Due to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.

Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders

Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high‐dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases.

Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.

Bioproduction of the Recombinant Sweet Protein Thaumatin: Current State of the Art and Perspectives

There is currently a worldwide trend to reduce sugar consumption. This trend is mostly met by the use of artificial non-nutritive sweeteners. However, these sweeteners have also been proven to have adverse health effects such as dizziness, headaches, gastrointestinal issues, and mood changes for aspartame. One of the solutions lies in the commercialization of sweet proteins, which are not associated with adverse health effects. Of these proteins, thaumatin is one of the most studied and most promising alternatives for sugars and artificial sweeteners. Since the natural production of these proteins is often too expensive, biochemical production methods are currently under investigation. With these methods, recombinant DNA technology is used for the production of sweet proteins in a host organism. The most promising host known today is the methylotrophic yeast, Pichia pastoris. This yeast has a tightly regulated methanol-induced promotor, allowing a good control over the recombinant protein production. Great efforts have been undertaken for improving the yields and purities of thaumatin productions, but a further optimization is still desired. This review focuses on (i) the motivation for using and producing sweet proteins, (ii) the properties and history of thaumatin, (iii) the production of recombinant sweet proteins, and (iv) future possibilities for process optimization based on a systems biology approach.

Function Analysis of Human Protein Interactions Based on a Novel Minimal Loop Algorithm

Background:

Various properties of Protein-Protein Interaction (PPI) network have been widely exploited to discover the topological organizing principle and the crucial function motifs involving specific biological pathway or disease process. The current motifs of PPI network are either detected by the topology-based coarse grain algorithms, i.e. community discovering, or depended on the limited-accessible protein annotation data derived precise algorithms. However, the identified network motifs are hardly compatible with the well-defined biological functions according to those two types of methods.

Method:

In this paper, we proposed a minimal protein loop finding method to explore the elementary structural motifs of human PPI network. Initially, an improved article exchange model was designed to search all the independent shortest protein loops of PPI network. Furthermore, Gene Ontology (GO) based function clustering analysis was implemented to identify the biological functions of the shortest protein loops. Additionally, the disease process associated shortest protein loops were considered as the potential drug targets. </P><P> Result: Our proposed method presents the lowest computational complexity and the highest functional consistency, compared to the three other methods. The functional enrichment and clustering analysis for the identified minimal protein loops revealed the high correlation between the protein loops and the corresponding biological functions, particularly, statistical analysis presenting the protein loops with the length less than 4 is closely connected with some disease process, suggesting the potential drug target.

Conclusion:

Our minimal protein loop method provides a novel manner to precisely define the functional motif of PPI network, which extends the current knowledge about the cooperating mechanisms and topological properties of protein modules composed of the short loops.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Blood-Based Biomarker Screening with Agnostic Biological Definitions for an Accurate Diagnosis Within the Dimensional Spectrum of Neurodegenerative Diseases

The discovery, development, and validation of novel candidate biomarkers in Alzheimer's disease (AD) and other neurodegenerative diseases (NDs) are increasingly gaining momentum. As a result, evolving diagnostic research criteria of NDs are beginning to integrate biofluid and neuroimaging indicators of pathophysiological mechanisms. More than 10% of people aged over 65 suffer from NDs. There is an urgent need for a refined two-stage diagnostic model to first initiate an early, sensitive, and noninvasive process in primary care settings. Individuals that meet detection criteria will then be channeled to more specific, costly (positron-emission tomography), and invasive (cerebrospinal fluid) assessment methods for confirmatory biological characterization and diagnosis.A reliable and sensitive blood test for AD and other NDs is not yet established; however, it would provide the golden screening gate for an efficient primary care management. A limitation to the development of a large-scale blood-screening biomarker-based test is the traditional application of clinically descriptive criteria for the categorization of single late-stage ND constructs. These are genetically and biologically heterogeneous, reflected in multiple pathophysiological mechanisms and subsequent pathologies throughout a dimensional continuum. Evidence suggests that a shared, "open-source" integrated multilevel categorization of NDs that clusters individuals based on descriptive clinical phenotypes and pathophysiological biomarker signatures will provide the next incremental step toward an improved diagnostic process of NDs. This intermediate objective toward unbiased biomarker-guided early detection of individuals at risk for NDs is currently carried out by the international pilot Alzheimer Precision Medicine Initiative Cohort Program (APMI-CP).

sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

Background

sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles.

Results

The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected.

Conclusions

sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1731-8) contains supplementary material, which is available to authorized users.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Leucine-rich repeat kinase 2 and Parkinson's disease

Leucine-rich repeat kinase 2 (LRRK2) is a large multidomain protein that is expressed in many tissues and participates in numerous biological pathways. Mutations in LRRK2 are recognized as genetic risk factors for familial Parkinson's disease (PD) and may also represent causal factors in the more common sporadic form of PD. The structure of LRRK2 comprises a combination of GTPase, kinase, and scaffolding domains. This functional diversity, combined with a potentially central role in genetic and idiopathic PD motivates significant effort to further credential LRRK2 as a therapeutic target. Here, we review the current understanding for LRRK2 function in normal physiology and PD, with emphasis on insight gained from proteomic approaches.

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the "exhaustive" extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen) were used. Raw data were pre-processed with MarkerLynx^TM software (Waters Corporation, Manchester, UK). Here, two parameters were varied: the intensity threshold (50-100 counts) and the mass tolerance (0.005-0.01 Da). After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden) for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc.) and data transformation (log and power) methods were explored. The results showed that the pre-processing parameters (or algorithms) influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables). Thus, as informed by the results, to maximize the value of untargeted metabolomic data, understanding of the data structures and exploration of different algorithms and methods (at different steps of the data analysis pipeline) might be the best trade-off, currently, and possibly an epistemological imperative.

Targeting protein-protein interfaces using macrocyclic peptides

Protein-protein interactions (PPIs) are critical in numerous biological processes including signaling transduction, function regulations, and disease development. To regulate PPIs has been thought to be challenging due to their highly dynamic and expansive interfacial areas. Nonetheless, successful examples have been reported of targeting PPIs using small molecules, peptides, and proteins. Peptides, especially macrocyclic peptides have proven to be a particularly useful tool to inhibit PPIs for their exquisite potency, stability and selectivity. Herein we review the recent developments of this area of research, focusing on the macrocyclic peptides isolated from natural products, identified from library screening, and rationally designed based on structures, as PPI regulators.

Metabolomic Analysis in Brain Research: Opportunities and Challenges

Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results.

Probiotic (Enterococcus faecium) induced responses of the hepatic proteome improves metabolic efficiency of broiler chickens (Gallus gallus)

BACKGROUND

The liver plays important roles in nutrient metabolism, detoxification and immunity. Enterococcus faecium (E. faecium) is a probiotic that has been shown to have positive effects on broiler production. However, its molecular effects on liver metabolism have not been characterized. This study aims to further identify the biological roles of E. faecium by characterizing the hepatic proteomic changes of broilers (Gallus gallus) fed E. faecium using two-dimensional fluorescence difference gel electrophoresis (2-D DIGE) and mass spectrometry (MS).

RESULTS

Thirty-three proteins (50 protein spots) involved in nutrient metabolism, immunity and the antioxidant system were shown to be differentially expressed in the liver of broilers fed E. faecium than from birds not fed the probiotic. The biological processes of sulphur amino acids, vitamin and cellular hormone metabolism, sulphur compound biosynthesis and protein tetramerization were enhanced in the liver of broilers fed E. faecium. However, proteins involved in calcium ion flux, cell redox homeostasis and platelet activation related to hepatic immune responses were down-regulated in broilers fed E. faecium. These results indicate that the supplementation of poultry feed with E. faecium may alter the partitioning of nutrients and promote optimal nutrient utilization.

CONCLUSIONS

This study assists in unraveling the molecular effects of the dietary probiotic, E. faecium, in the liver of broiler chickens. It shows that the probiotic improves the metabolism of nutrients and decreases inflammatory responses. Our findings extend previous knowledge of the mechanism of dietary probiotic action and provide new findings for research and future probiotic development.

Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks

Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.

Neuroblastoma tyrosine kinase signaling networks involve FYN and LYN in endosomes and lipid rafts

Protein phosphorylation plays a central role in creating a highly dynamic network of interacting proteins that reads and responds to signals from growth factors in the cellular microenvironment. Cells of the neural crest employ multiple signaling mechanisms to control migration and differentiation during development. It is known that defects in these mechanisms cause neuroblastoma, but how multiple signaling pathways interact to govern cell behavior is unknown. In a phosphoproteomic study of neuroblastoma cell lines and cell fractions, including endosomes and detergent-resistant membranes, 1622 phosphorylated proteins were detected, including more than half of the receptor tyrosine kinases in the human genome. Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data. Clusters of proteins in these networks are indicative of functional signaling pathways. The analysis indicates that receptor tyrosine kinases are functionally compartmentalized into distinct collaborative groups distinguished by activation and intracellular localization of SRC-family kinases, especially FYN and LYN. Changes in intracellular localization of activated FYN and LYN were observed in response to stimulation of the receptor tyrosine kinases, ALK and KIT. The results suggest a mechanism to distinguish signaling responses to activation of different receptors, or combinations of receptors, that govern the behavior of the neural crest, which gives rise to neuroblastoma.

Neuroblastoma is a childhood cancer for which therapeutic progress has been slow. We analyzed a large number phosphorylated proteins in neuroblastoma cells to discern patterns that indicate functional signal transduction pathways. To analyze the data, we developed novel techniques that resolve data structure and visualize that structure as networks that represent both protein interactions and statistical relationships. We also fractionated neuroblastoma cells to examine the location of signaling proteins in different membrane fractions and organelles. The analysis revealed that signaling pathways are functionally and physically compartmentalized into distinct collaborative groups distinguished by phosphorylation patterns and intracellular localization. We found that two related proteins (FYN and LYN) act like central hubs in the tyrosine kinase signaling network that change intracellular localization and activity in response to activation of different receptors.

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.

Integrating proteomics profiling data sets: a network perspective

Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell's intricate wiring network, which can ultimately lead to decipher the disease phenotype. Here, we describe what biological pathways are, how they function in normal and abnormal cellular systems, limitations faced by databases for integrating data, and highlight how network models are emerging as a powerful integrative framework to understand and interpret the roles of proteins and peptides in diseases.

The paradigm shift to an “open” model in drug development

The rising cost of healthcare, the rising cost for drug development, the patent cliff for Big pharma, shorter patent protection, decrease reimbursement, and the recession have made it more difficult for the pharmaceutical and biotechnology industry to develop drugs. Due to the unsustainable amount of time and money in developing a drug that will have a significant return on investment (ROI) it has become hard to sustain a robust pipeline. The industry is transforming its business model to meet these challenges. In essence a paradigm shift is occurring; the old “closed” model is giving way to a new “open” business model.

•

Drug development—then and now

•

How systems biology is accelerating drug development and approval.

•

The new business model: pharma/biotech is going “open”

•

FDA is going “open”—what does this mean for drug approval?

Plasma metabolomic profiling suggests early indications for predisposition to latent insulin resistance in children conceived by ICSI

BACKGROUND

There have been increasing indications about an epigenetically-based elevated predisposition of assisted reproductive technology (ART) offspring to insulin resistance, which can lead to an unfavorable cardio-metabolic profile in adult life. However, the relevant long-term systematic molecular studies are limited, especially for the IntraCytoplasmic Sperm Injection (ICSI) method, introduced in 1992. In this study, we carefully defined a group of 42 prepubertal ICSI and 42 naturally conceived (NC) children. We assessed differences in their metabolic profile based on biochemical measurements, while, for a subgroup, plasma metabolomic analysis was also performed, investigating any relevant insulin resistance indices.

METHODS & RESULTS

Auxological and biochemical parameters of 42 6.8±2.1 yrs old ICSI-conceived and 42 age-matched controls were measured. Significant differences between the groups were determined using univariate and multivariate statistics, indicating low urea and low-grade inflammation markers (YKL-40, hsCRP) and high triiodothyronine (T3) in ICSI-children compared to controls. Moreover, plasma metabolomic analysis carried out for a subgroup of 10 ICSI- and 10 NC girls using Gas Chromatography-Mass Spectrometry (GC-MS) indicated clear differences between the two groups, characterized by 36 metabolites linked to obesity, insulin resistance and metabolic syndrome. Notably, the distinction between the two girl subgroups was accentuated when both their biochemical and metabolomic measurements were employed.

CONCLUSIONS

The present study contributes a large auxological and biochemical dataset of a well-defined group of pre-pubertal ICSI-conceived subjects to the research of the ART effect to the offspring's health. Moreover, it is the first time that the relevant usefulness of metabolomics was investigated. The acquired results are consistent with early insulin resistance in ICSI-offspring, paving the way for further systematic investigations. These data support that metabolomics may unravel metabolic differences before they become clinically or biochemically evident, underlining its utility in the ART research.

Opportunity for selection in human health

Natural selection defined by differential survival and reproduction of individuals in populations is influenced by genetic, developmental, and environmental factors operating at every age and stage in human life history: generation of gametes, conception, birth, maturation, reproduction, senescence, and death. Biological systems are built upon a hierarchical organization nesting subcellular organelles, cells, tissues, and organs within individuals, individuals within families, and families within populations, and the latter among other populations. Natural selection often acts simultaneously at more than one level of biological organization and on specific traits, which we define as multilevel selection. Under this model, the individual is a fundamental unit of biological organization and also of selection, imbedded in a larger evolutionary context, just as it is a unit of medical intervention imbedded in larger biological, cultural, and environmental contexts. Here, we view human health and life span as necessary consequences of natural selection, operating at all levels and phases of biological hierarchy in human life history as well as in sociological and environmental milieu. An understanding of the spectrum of opportunities for natural selection will help us develop novel approaches to improving healthy life span through specific and global interventions that simultaneously focus on multiple levels of biological organization. Indeed, many opportunities exist to apply multilevel selection models employed in evolutionary biology and biodemography to improving human health at all hierarchical levels. Multilevel selection perspective provides a rational theoretical foundation for a synthesis of medicine and evolution that could lead to discovering effective predictive, preventive, palliative, potentially curative, and individualized approaches in medicine and in global health programs.

Network-based approaches for anticancer therapy (Review)

Cancer is a complex disease resulting from alterations of multiple signaling networks. Cancer networks have been identified as scale-free networks and may contain a functionally important key player called a hub that is linked to a large number of interactors. Since a hub can serve as a biological marker in a given network, targeting the hub could be an effective strategy for enhancing the efficacy of cancer treatment. Chemotherapies and radiotherapies are generally used to treat tumors not amenable to resection, and target single or multiple molecules associated with hubs. However, these therapies may unexpectedly induce the resistance of cancer cells to drugs and radiation. Cancer cells can overcome therapy-induced damage via the activation of back-up signaling pathways and flexible modulation of affected networks. These activities are considered to be the main reasons for chemoresistance and radioresistance, and subsequent failure of cancer therapies. Much effort is required to identify the key molecules that control the modulation of signaling networks in response to drugs and radiation. Network-based therapy that affects network flexibility, including rewired network structures and hub molecules in these networks, could minimize the occurrence of side-effects and be a promising strategy for enhancing the therapeutic efficacy of cancer treatments. This review is intended to offer an overview of current research efforts including ones focused on cancer-associated complex networks, their modulation in response to cancer therapy, and further strategies targeting networks that may improve cancer treatment efficacy.

Network-based approaches in drug discovery and early development

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target-drug combination with high potential for yielding clinical success within the efficacy-toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification.

Proteome changes in the intestinal mucosa of broiler (Gallus gallus) activated by probiotic Enterococcus faecium

Probiotics are supplemented to animal diet to support a well-balanced gut microbiota, finally contributing to improved health. The molecular mechanism of probiotics in animal intestine improvement is yet unclear. We investigated the production parameters, gut morphology and microbiota, and mucosal proteome of Arbor Acres broilers (Gallus gallus) supplemented with Enterococcus faecium by performing denaturing gradient gel electrophoresis, quantitative real-time PCR, two-dimensional fluorescence difference gel electrophoresis, and mass spectrometry. E. faecium supplementation promoted the development of immune organs and gut microvilli and enlarged the gut microbial diversity and population. However, it had no effects on daily weight gain and feed intake, and slightly enhanced feed conversion ratio. A total of 42 intestinal mucosal proteins were found to be differentially abundant. Four of them are related to intestinal structure and may extend the absorptive surface area. Of 17 differential proteins related to immune and antioxidant systems, only six are abundant in the broilers fed E. faecium, indicating that these chickens employ less nutrients and energy to deal with immune and antioxidant stresses. These findings have important implications for understanding the probiotic mechanisms of E. faecium on broiler intestine.

BIOLOGICAL SIGNIFICANCE

Probiotic supplementation to animal diet is closely related with improved health. The objective of this study is to determine the molecular mechanisms of probiotic E. faecium achieving its biological mission in the gut of Arbor Acres broilers (G. gallus). E. faecium supplementation did not improve daily weight gain and feed intake; however, it had effects on immune organ and gut microvillus development, and gut microbial diversity and population. Quantitative proteomic analysis of the intestinal mucosa of broilers treated with E. faecium identified 42 intestinal mucosal proteins related to substance metabolism, immune and antioxidant systems, and cell structure. This study identified the E. faecium derived probiotic mechanism on the proteome level.

Systems biological approach to investigate the lack of familial link between Down's Syndrome & Neural Tube Disorders

Systems Biology involves the study of the interactions of biological systems and ultimately their functions. Down's syndrome (DS) is one of the most common genetic disorders which are caused by complete, or occasionally partial, triplication of chromosome 21, characterized by cognitive and language dysfunction coupled with sensory and neuromotor deficits. Neural Tube Disorders (NTDs) are a group of congenital malformations of the central nervous system and neighboring structures related to defective neural tube closure during the first trimester of pregnancy usually occurring between days 18-29 of gestation. Several studies in the past have provided considerable evidence that abnormal folate and methyl metabolism are associated with onset of DS & NTDs. There is a possible common etiological pathway for both NTDs and Down's syndrome. But, various research studies over the years have indicated very little evidence for familial link between the two disorders. Our research aimed at the gene expression profiling of microarray datasets pertaining to the two disorders to identify genes whose expression levels are significantly altered in these conditions. The genes which were 1.5 fold unregulated and having a p-value <0.05 were filtered out and gene interaction network were constructed for both NTDs and DS. The top ranked dense clique for both the disorders were recognized and over representation analysis was carried out for each of the constituent genes. The comprehensive manual analysis of these genes yields a hypothetical understanding of the lack of familial link between DS and NTDs. There were no genes involved with folic acid present in the dense cliques. Only - CBL, EGFR genes were commonly present, which makes the allelic variants of these genes - good candidates for future studies regarding the familial link between DS and NTDs.

ABBREVIATIONS

NTD - Neural Tube Disorders, DS - Down's Syndrome, MTHFR - Methylenetetrahydrofolate reductase, MTRR- 5 - methyltetrahydrofolate-homocysteine methyltransferase reductase.

Interfacing cellular networks of S. cerevisiae and E. coli: connecting dynamic and genetic information

Background

In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored.

Results

We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes.

Conclusions

Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes.

Towards systems biology of mycotoxin regulation

Systems biology is a scientific approach that integrates many scientific disciplines to develop a comprehensive understanding of biological phenomena, thus allowing the prediction and accurate simulation of complex biological behaviors. It may be presumptuous to write about toxin regulation at the level of systems biology, but the last decade of research is leading us closer than ever to this approach. Past research has delineated multiple levels of regulation in the pathways leading to the biosynthesis of secondary metabolites, including mycotoxins. At the top of this hierarchy, the global or master transcriptional regulators perceive various environmental cues such as climatic conditions, the availability of nutrients, and the developmental stages of the organism. Information accumulated from various inputs is integrated through a complex web of signalling networks to generate the eventual outcome. This review will focus on adapting techniques such as chemical and other genetic tools available in the model system Saccharomyces cerevisiae, to disentangle the various biological networks involved in the biosynthesis of mycotoxins in the Fusarium spp.

Predicting the next eye pathogen: analysis of a novel adenovirus

For DNA viruses, genetic recombination, addition, and deletion represent important evolutionary mechanisms. Since these genetic alterations can lead to new, possibly severe pathogens, we applied a systems biology approach to study the pathogenicity of a novel human adenovirus with a naturally occurring deletion of the canonical penton base Arg-Gly-Asp (RGD) loop, thought to be critical to cellular entry by adenoviruses. Bioinformatic analysis revealed a new highly recombinant species D human adenovirus (HAdV-D60). A synthesis of in silico and laboratory approaches revealed a potential ocular tropism for the new virus. In vivo, inflammation induced by the virus was dramatically greater than that by adenovirus type 37, a major eye pathogen, possibly due to a novel alternate ligand, Tyr-Gly-Asp (YGD), on the penton base protein. The combination of bioinformatics and laboratory simulation may have important applications in the prediction of tissue tropism for newly discovered and emerging viruses.

IMPORTANCE

The ongoing dance between a virus and its host distinctly shapes how the virus evolves. While human adenoviruses typically cause mild infections, recent reports have described newly characterized adenoviruses that cause severe, sometimes fatal human infections. Here, we report a systems biology approach to show how evolution has affected the disease potential of a recently identified novel human adenovirus. A comprehensive understanding of viral evolution and pathogenicity is essential to our capacity to foretell the potential impact on human disease for new and emerging viruses.

Theoretical aspects of Systems Biology

The natural world consists of hierarchical levels of complexity that range from subatomic particles and molecules to ecosystems and beyond. This implies that, in order to explain the features and behavior of a whole system, a theory might be required that would operate at the corresponding hierarchical level, i.e. where self-organization processes take place. In the past, biological research has focused on questions that could be answered by a reductionist program of genetics. The organism (and its development) was considered an epiphenomenona of its genes. However, a profound rethinking of the biological paradigm is now underway and it is likely that such a process will lead to a conceptual revolution emerging from the ashes of reductionism. This revolution implies the search for general principles on which a cogent theory of biology might rely. Because much of the logic of living systems is located at higher levels, it is imperative to focus on them. Indeed, both evolution and physiology work on these levels. Thus, by no means Systems Biology could be considered a 'simple' 'gradual' extension of Molecular Biology.

Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential

In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

BACKGROUND

Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

METHODS

On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

RESULTS

Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

CONCLUSIONS

The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.

Architecture and conservation of the bacterial DNA replication machinery, an underexploited drug target

New antibiotics with novel modes of action are required to combat the growing threat posed by multi-drug resistant bacteria. Over the last decade, genome sequencing and other high-throughput techniques have provided tremendous insight into the molecular processes underlying cellular functions in a wide range of bacterial species. We can now use these data to assess the degree of conservation of certain aspects of bacterial physiology, to help choose the best cellular targets for development of new broad-spectrum antibacterials.

DNA replication is a conserved and essential process, and the large number of proteins that interact to replicate DNA in bacteria are distinct from those in eukaryotes and archaea; yet none of the antibiotics in current clinical use acts directly on the replication machinery. Bacterial DNA synthesis thus appears to be an underexploited drug target. However, before this system can be targeted for drug design, it is important to understand which parts are conserved and which are not, as this will have implications for the spectrum of activity of any new inhibitors against bacterial species, as well as the potential for development of drug resistance. In this review we assess similarities and differences in replication components and mechanisms across the bacteria, highlight current progress towards the discovery of novel replication inhibitors, and suggest those aspects of the replication machinery that have the greatest potential as drug targets.

Heme and haemoglobin direct macrophage Mhem phenotype and counter foam cell formation in areas of intraplaque haemorrhage

PURPOSE OF REVIEW

Several studies have recently shown that haemoglobin drives a novel macrophage subset that is protected from foam cell formation.

RECENT FINDINGS

In a previously overlooked area, two centres have independently shown that heme and haemoglobin drive an atheroprotective macrophage subset. We compare and contrast the approaches and findings of the laboratories and discuss some of the underlying biology and implications, concentrating on the aspects of lipidological relevance.

SUMMARY

Treatments based on direct heme-mimetics or other agonists of this pathway have enormous potential for linked antioxidant protection via heme oxygenase 1 and reduced foam cell formation via liver X receptor, a potent combination for treating atherosclerosis.

Animal models and conserved processes

BACKGROUND

The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation?

METHODS

We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes.

RESULTS

Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes.

CONCLUSION

We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible.