Experimental Systems Biology Research Articles

Experimental systems biology approaches reveal interaction mechanisms in model multispecies communities.

Interactions between microorganisms in multispecies communities are thought to have substantial consequences for the community. Identifying the molecules and genetic pathways that contribute to such interplay is thus crucial to understand as well as modulate community dynamics. Here I focus on recent studies that utilize experimental systems biology techniques to study these phenomena in simplified model microbial communities. These unbiased biochemical and genomic approaches have identified novel interactions and described the underlying genetic and molecular mechanisms. I discuss the insights provided by these studies, describe innovative strategies used to investigate less tractable organisms and environments, and highlight the utility of integrating these and more targeted methods to comprehensively characterize interactions between species in microbial communities.

Omics-Driven Biotechnology for Industrial Applications.

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on “omics” driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in “omics” technologies, compare the pros and cons of the different “omics” technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of “omics” technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of “omics” technologies to agricultural and food biotechnology, and review the impact of “omics” on current COVID-19 research.

Following Transcriptome to Uncover FOXO Biological Functions.

Two and half million red blood cells (RBC) are generated every second in a healthy adult. The process of RBC production known as erythropoiesis requires a meticulous synchrony between signaling processes and the activity of many transcription factor complexes. FOXO3 is a transcription factor that is responsive to signaling processes and essential for the erythroid proliferation and maturation, RBC formation, and lifespan. Here, we discuss how using an integrated computational and experimental systems biology approach new and unanticipated FOXO3 functions in terminal erythropoiesis were uncovered. These combinatory approaches identified FOXO3 as a key regulator of terminal erythropoiesis. As a result, a new mode of FOXO3 participation in erythroid transcription complex formation has been proposed.

Representing and querying disease networks using graph databases.

BackgroundSystems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.ResultsWe show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.ConclusionsOur study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0102-8) contains supplementary material, which is available to authorized users.

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.

A Systems Approach Identifies Essential FOXO3 Functions at Key Steps of Terminal Erythropoiesis.

Circulating red blood cells (RBCs) are essential for tissue oxygenation and homeostasis. Defective terminal erythropoiesis contributes to decreased generation of RBCs in many disorders. Specifically, ineffective nuclear expulsion (enucleation) during terminal maturation is an obstacle to therapeutic RBC production in vitro. To obtain mechanistic insights into terminal erythropoiesis we focused on FOXO3, a transcription factor implicated in erythroid disorders. Using an integrated computational and experimental systems biology approach, we show that FOXO3 is essential for the correct temporal gene expression during terminal erythropoiesis. We demonstrate that the FOXO3-dependent genetic network has critical physiological functions at key steps of terminal erythropoiesis including enucleation and mitochondrial clearance processes. FOXO3 loss deregulated transcription of genes implicated in cell polarity, nucleosome assembly and DNA packaging-related processes and compromised erythroid enucleation. Using high-resolution confocal microscopy and imaging flow cytometry we show that cell polarization is impaired leading to multilobulated Foxo3 -/- erythroblasts defective in nuclear expulsion. Ectopic FOXO3 expression rescued Foxo3 -/- erythroblast enucleation-related gene transcription, enucleation defects and terminal maturation. Remarkably, FOXO3 ectopic expression increased wild type erythroblast maturation and enucleation suggesting that enhancing FOXO3 activity may improve RBCs production. Altogether these studies uncover FOXO3 as a novel regulator of erythroblast enucleation and terminal maturation suggesting FOXO3 modulation might be therapeutic in disorders with defective erythroid maturation.

CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments.

BackgroundGene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments. However, determining differential GO and pathway enrichment between DNA-binding experiments or using the GO structure to classify experiments has received little attention.ResultsHerein, we present a bioinformatics tool, CompGO, for identifying Differentially Enriched Gene Ontologies, called DiEGOs, and pathways, through the use of a z-score derivation of log odds ratios, and visualizing these differences at GO and pathway level. Through public experimental data focused on the cardiac transcription factor NKX2-5, we illustrate the problems associated with comparing GO enrichments between experiments using a simple overlap approach.ConclusionsWe have developed an R/Bioconductor package, CompGO, which implements a new statistic normally used in epidemiological studies for performing comparative GO analyses and visualizing comparisons from .BED data containing genomic coordinates as well as gene lists as inputs. We justify the statistic through inclusion of experimental data and compare to the commonly used overlap method. CompGO is freely available as a R/Bioconductor package enabling easy integration into existing pipelines and is available at: http://www.bioconductor.org/packages/release/bioc/html/CompGO.html packages/release/bioc/html/CompGO.html

Molecular mechanism matters: Benefits of mechanistic computational models for drug development

Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.

CellPACK: a virtual mesoscope to model and visualize structural systems biology.

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10−7–10−8m) between molecular and cellular biology. cellPACK’s modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive 3D models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is currently available as open source code, with tools for validation of models and with recipes and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators, and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org.

Simultaneous extraction of proteins and metabolites from cells in culture

Proper sample preparation is an integral part of all omics approaches, and can drastically impact the results of a wide number of analyses. As metabolomics and proteomics research approaches often yield complementary information, it is desirable to have a sample preparation procedure which can yield information for both types of analyses from the same cell population. This protocol explains a method for the separation and isolation of metabolites and proteins from the same biological sample, in order for downstream use in metabolomics and proteomics analyses simultaneously. In this way, two different levels of biological regulation can be studied in a single sample, minimizing the variance that would result from multiple experiments. This protocol can be used with both adherent and suspension cell cultures, and the extraction of metabolites from cellular medium is also detailed, so that cellular uptake and secretion of metabolites can be quantified.Advantages of this technique includes:1.Inexpensive and quick to perform; this method does not require any kits.2.Can be used on any cells in culture, including cell lines and primary cells extracted from living organisms.3.A wide variety of different analysis techniques can be used, adding additional value to metabolomics data analyzed from a sample; this is of high value in experimental systems biology.

Vaccine Adjuvant Informatics: From Data Integration and Analysis to Rational Vaccine Adjuvant Design

GENERAL COMMENTARY article Front. Immunol., 03 February 2014Sec. Vaccines and Molecular Therapeutics https://doi.org/10.3389/fimmu.2014.00032

Small Molecule-Mediated Directed Differentiation of Human Embryonic Stem Cells Toward Ventricular Cardiomyocytes

The generation of human ventricular cardiomyocytes from human embryonic stem cells and/or induced pluripotent stem cells could fulfill the demand for therapeutic applications and in vitro pharmacological research; however, the production of a homogeneous population of ventricular cardiomyocytes remains a major limitation. By combining small molecules and growth factors, we developed a fully chemically defined, directed differentiation system to generate ventricular-like cardiomyocytes (VCMs) from human embryonic stem cells and induced pluripotent stem cells with high efficiency and reproducibility. Molecular characterization revealed that the differentiation recapitulated the developmental steps of cardiovascular fate specification. Electrophysiological analyses further illustrated the generation of a highly enriched population of VCMs. These chemically induced VCMs exhibited the expected cardiac electrophysiological and calcium handling properties as well as the appropriate chronotropic responses to cardioactive compounds. In addition, using an integrated computational and experimental systems biology approach, we demonstrated that the modulation of the canonical Wnt pathway by the small molecule IWR-1 plays a key role in cardiomyocyte subtype specification. In summary, we developed a reproducible and efficient experimental platform that facilitates a chemical genetics-based interrogation of signaling pathways during cardiogenesis that bypasses the limitations of genetic approaches and provides a valuable source of ventricular cardiomyocytes for pharmacological screenings as well as cell replacement therapies.

Population-expression models of immune response

The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable.

Using graph models to find transcription factor modules: the hitting set problem and an exact algorithm

Systematically perturbing a cellular system and monitoring the effects of the perturbations on gene expression provide a powerful approach to study signal transduction in gene expression systems. A critical step of revealing a signal transduction pathway regulating gene expression is to identify transcription factors transmitting signals in the system. In this paper, we address the task of identifying modules of cooperative transcription factors based on results derived from systems-biology experiments at two levels: First, a graph algorithm is developed to identify a minimum set of co-operative TFs that covers the differentially expressed genes under each systematic perturbation. Second, using a clique-finding approach, modules of TFs that tend to consistently cooperate together under various perturbations are further identified. Our results indicate that this approach is capable of identifying many known TF modules based on the individual experiment; thus we provide a novel graph-based method of identifying context-specific and highly reused TF-modules.

POMO - Plotting Omics analysis results for Multiple Organisms

BackgroundSystems biology experiments studying different topics and organisms produce thousands of data values across different types of genomic data. Further, data mining analyses are yielding ranked and heterogeneous results and association networks distributed over the entire genome. The visualization of these results is often difficult and standalone web tools allowing for custom inputs and dynamic filtering are limited.ResultsWe have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views. The circular graph represents the chromosome lengths as perimeter segments, as a reference outer ring, such as cytoband for human. The inner arcs between nodes represent the uploaded network. Further, multiple annotation rings, for example depiction of gene copy number changes, can be uploaded as text files and represented as bar, histogram or heatmap rings. POMO has built-in references for human, mouse, nematode, fly, yeast, zebrafish, rice, tomato, Arabidopsis, and Escherichia coli. In addition, POMO provides custom options that allow integrated plotting of unsupported strains or closely related species associations, such as human and mouse orthologs or two yeast wild types, studied together within a single analysis. The web application also supports interactive label and weight filtering. Every iterative filtered result in POMO can be exported as image file and text file for sharing or direct future input.ConclusionsThe POMO web application is a unique tool for omics data analysis, which can be used to visualize and filter the genome-wide networks in the context of chromosomal locations as well as multiple network layouts. With the several illustration and filtering options the tool supports the analysis and visualization of any heterogeneous omics data analysis association results for many organisms. POMO is freely available and does not require any installation or registration.

A systems approach identifies HIPK2 as a key regulator of kidney fibrosis

Kidney fibrosis is a common process that leads to the progression of kidney diseases. We used an integrated computational/experimental systems biology approach to identify upstream protein kinases that regulate gene expression changes in kidneys of HIV-1 transgenic mice (Tg26), which have both tubulo-interstitial fibrosis and glomerulosclerosis. We identified the homeo-domain interacting protein kinase 2 (HIPK2) as a key regulator of kidney fibrosis. HIPK2 was upregulated in kidneys of Tg26 and patients with various kidney diseases. HIV infection increased the protein level of HIPK2 by promoting oxidative stress, which inhibited SIAH1-mediated proteasomal degradation of HIPK2. HIPK2 induced apoptosis and expression of epithelial-mesenchymal trans-differentiation markers in kidney epithelial cells by activating p53, TGF-β/Smad3, and Wnt/Notch pathways. Knockout of HIPK2 improved renal function and attenuated proteinuria and kidney fibrosis in Tg26 as well as in other animal models of kidney fibrosis. We conclude that HIPK2 is a potential target for anti-fibrosis therapy.

Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action

Systems approaches have long been used in pharmacology to understand drug action at the organ and organismal levels. The application of computational and experimental systems biology approaches to pharmacology allows us to expand the definition of systems pharmacology to include network analyses at multiple scales of biological organization and to explain both therapeutic and adverse effects of drugs. Systems pharmacology analyses rely on experimental "omics" technologies that are capable of measuring changes in large numbers of variables, often at a genome-wide level, to build networks for analyzing drug action. A major use of omics technologies is to relate the genomic status of an individual to the therapeutic efficacy of a drug of interest. Combining pathway and network analyses, pharmacokinetic and pharmacodynamic models, and a knowledge of polymorphisms in the genome will enable the development of predictive models of therapeutic efficacy. Network analyses based on publicly available databases such as the U.S. Food and Drug Administration's Adverse Event Reporting System allow us to develop an initial understanding of the context within which molecular-level drug-target interactions can lead to distal effectors in a process that results in adverse phenotypes at the organ and organismal levels. The current state of systems pharmacology allows us to formulate a set of questions that could drive future research in the field. The long-term goal of such research is to develop polypharmacology for complex diseases and predict therapeutic efficacy and adverse event risk for individuals prior to commencement of therapy.

Fluorescence Fluctuation Spectroscopy Applied to Cell-Free Expression, Chromophore Maturation and Protein-DNA Interaction

Cell-free expression systems are increasingly being employed as platforms for biophysical, biochemical, and systems biology experiments. We demonstrate that combining fluorescence fluctuation spectroscopy (FFS) with cell-free expression provides quick and quantitative measurements of chromophore maturation, protein-protein interactions and protein-DNA interactions. We perform the first study of chromophore maturation as a function of temperature, and demonstrate pronounced temperature dependence of the maturation kinetics for EGFP, EYFP, and mCherry. The Eyring equation successfully reproduces the temperature-dependence of the maturation rate for each of the proteins. Our results for EGFP, EYFP and mCherry provide an explanation for the differences in the reported maturation times studied by de novo protein synthesis. A droplet sample protocol was developed to ensure sufficient oxygenation for chromophore maturation studies, while preventing evaporation of the sample. We further demonstrate the feasibility of protein titrations with the droplet protocol and characterize oligomerization of the nuclear transport factor 2 (NTF2) over a wide concentration range by brightness analysis. We employ the droplet setup to study APOBEC3G, a DNA cytosine deaminase enzyme with innate immune activity against retroviruses, notably HIV-1. Brightness analysis reports the homo-oligomerization of APOBEC3G, which is both concentration and temperature dependent. Using two-color FFS, we simultaneously measure APOBEC3G oligomerization and the interaction between APOBEC3G and single-stranded DNA (ssDNA) in order to characterize the molecular interactions of this important enzyme. This work was supported by grants from the National Institutes of Health (GM64589, GM091743).

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy

ObjectiveKey to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental...

An Omics Approach to Understand the Plant Abiotic Stress

Abiotic stress can lead to changes in development, productivity, and severe stress and may even threaten survival of plants. Several environmental stresses cause drastic changes in the growth, physiology, and metabolism of plants leading to the increased accumulation of secondary metabolites. As medicinal plants are important sources of drugs, steps are taken to understand the effect of stress on the physiology, biochemistry, genomic, proteomic, and metabolic levels. The molecular responses of plants to abiotic stress are often considered as a complex process. They are mainly based on the modulation of transcriptional activity of stress-related genes. Many genes have been induced under stress conditions. The products of stress-inducible genes protecting against these stresses includes the enzymes responsible for the synthesis of various osmoprotectants. Genetic engineering of tolerance to abiotic stresses help in molecular understanding of pathways induced in response to one or more of the abiotic stresses. Systems biology and virtual experiments allow visualizing and understanding how plants work to overcome abiotic stress. This review discusses the omic approach to understand the plant response to abiotic stress with special emphasis on medicinal plant.