Systems Biology Methodology Research Articles

Exploring the Anemia Ecology: A New Approach to an Old Problem

Our ability to identify anemia and all its permutations demands an approach that integrates the key elements of a complex “ecology,” which intertwines biology and mechanistic aspects of nutrients with both the health status and underlying factors—physical, economic, social, behavioral, demographic, and environmental. The complexity of anemia demands an ecologic approach that appreciates systems biology, translates sensitive and specific assessment methodologies and interventions, and ultimately improves clinical and public health outcomes. This series of technical papers on anemia by the U.S. Agency for International Development (USAID) Advancing Nutrition Anemia Task Force (ATF) is a first step in translating our ecologic approach to anemia with a view toward balancing research with its translation to effective programs, interventions, and policy. This introductory overview describes the components of our ecologic approach—linking the biology of anemia with its assessment and using the learning from that confluence to devise context-specific interventions. This introductory review briefly discusses the topics that underlie the biology and primary etiologies of anemia and presents a framework for public health assessment of anemia, leading to appropriate public health interventions. The other 3 manuscripts in the supplement provide the details of the arguments laid out in the introduction.

Meeting the Challenges of Myocarditis: New Opportunities for Prevention, Detection, and Intervention-A Report from the 2021 National Heart, Lung, and Blood Institute Workshop.

The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop of international experts to discuss new research opportunities for the prevention, detection, and intervention of myocarditis in May 2021. These experts reviewed the current state of science and identified key gaps and opportunities in basic, diagnostic, translational, and therapeutic frontiers to guide future research in myocarditis. In addition to addressing community-acquired myocarditis, the workshop also focused on emerging causes of myocarditis including immune checkpoint inhibitors and SARS-CoV-2 related myocardial injuries and considered the use of systems biology and artificial intelligence methodologies to define workflows to identify novel mechanisms of disease and new therapeutic targets. A new priority is the investigation of the relationship between social determinants of health (SDoH), including race and economic status, and inflammatory response and outcomes in myocarditis. The result is a proposal for the reclassification of myocarditis that integrates the latest knowledge of immunological pathogenesis to refine estimates of prognosis and target pathway-specific treatments.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture.

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Screening of salt stress in the overexpressed type of Arabidopsis thaliana (L.) Heynh. for the identification of significant hub genes using a systems biology approach

Worldwide, it is known that abiotic and biotic stresses can affect the production of crops by a declining trend. To control the situation, SnRK2 (a subfamily 2 of SNF1-related protein kinase) overexpression levels can induce salt tolerance. This study used a dataset for 2 types of Arabidopsis thaliana including the wild and PtSnRK2.7 overexpressed in mock and salt conditions to compare and identify the salt stress-responsive genes. A computational systems biology approach was employed to identify the differentially expressed genes and determine their mechanisms in terms of molecular functionalities, cellular components, KEGG enrichment pathways and plant ontology analyses. The results indicate that the 15 genes identified for PtSnRK2.7 overexpressed type in mock against salt conditions were upregulated (AT1G19180 and AT2G23150 were downregulated) and related to various environmental stresses. Furthermore, 8 out of 15 identified genes were downregulated for the wild type exposed to salt stress and the rest were upregulated. And, the only upregulated gene found differentially expressed between wild and overexpressed types in salt stress conditions was AT4G15110. In contrast, the other two AT1G15010 and AT4G19430 were downregulated and involved in transient stress and inactivation of chloroplast, respectively. Taken together, it has been shown that A. thaliana PtSnRK2.7 overexpressed type can resist salt stress. Finally, more experimental studies and computational systems biology methodologies are needed to reveal and confirm the responsive gene for salt stress in A. thaliana.

In Silico Investigation of the Biological Implications of Complex DNA Damage with Emphasis in Cancer Radiotherapy through a Systems Biology Approach.

Different types of DNA lesions forming in close vicinity, create clusters of damaged sites termed as “clustered/complex DNA damage” and they are considered to be a major challenge for DNA repair mechanisms resulting in significant repair delays and induction of genomic instability. Upon detection of DNA damage, the corresponding DNA damage response and repair (DDR/R) mechanisms are activated. The inability of cells to process clustered DNA lesions efficiently has a great impact on the normal function and survival of cells. If complex lesions are left unrepaired or misrepaired, they can lead to mutations and if persistent, they may lead to apoptotic cell death. In this in silico study, and through rigorous data mining, we have identified human genes that are activated upon complex DNA damage induction like in the case of ionizing radiation (IR) and beyond the standard DNA repair pathways, and are also involved in cancer pathways, by employing stringent bioinformatics and systems biology methodologies. Given that IR can cause repair resistant lesions within a short DNA segment (a few nm), thereby augmenting the hazardous and toxic effects of radiation, we also investigated the possible implication of the most biologically important of those genes in comorbid non-neoplastic diseases through network integration, as well as their potential for predicting survival in cancer patients.

Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling.

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

Systems Cytogenomics: Are We Ready Yet?

With the introduction of systems theory to genetics, numerous opportunities for genomic research have been identified. Consequences of DNA sequence variations are systematically evaluated using the network- or pathway-based analysis, a technological basis of systems biology or, more precisely, systems genomics. Despite comprehensive descriptions of advantages offered by systems genomic approaches, pathway-based analysis is uncommon in cytogenetic (cytogenomic) studies, i.e. genome analysis at the chromosomal level. Here, we would like to express our opinion that current cytogenomics benefits from the application of systems biology methodology. Accordingly, systems cytogenomics appears to be a biomedical area requiring more attention than it actually receives.

No population left behind: Improving paediatric drug safety using informatics and systems biology.

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age‐dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data‐driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Handling the Cellular Complex Systems in Alzheimer's Disease Through a Graph Mining Approach.

In the last two decades, the medical sciences have changed their approach to pathogenesis as well as to the diagnosis and treatment of complex human diseases. The main reason for this change is the explosive development of biomedical technology and research, which produces a huge amount of information and data which are generated at an increasing rate. Toward this direction is the pathway analysis, a thriving research area of systems biology tools and methodologies which aim to unravel the inherent complexity of high-throughput biological data produced by the advent of omics technologies. Through this graph mining approach, we can deal with the complexity of the cellular systems of various diseases such as Alzheimer's disease. In this work, we developed a subpathway analysis method for single-cell RNA-seq experiments which isolates differentially expressed subpathways indicating potentially perturbed biological processes. The differential expression status of each gene is negotiated among well-established RNA-seq differential expression analysis tools in order to minimize false discoveries. Also, we demonstrate the efficacy of our method on a single-cell RNA-seq dataset for temporal tracking of microglia activation in neurodegeneration. Results suggest that our approach succeeds in isolating several perturbed biological processes known to be associated with neurodegeneration.

DIPG-40. TARGETING MASTER REGULATOR DEPENDENCIES IN DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG)

Diffuse intrinsic pontine glioma (DIPG) remains a fatal disease with no effective drugs to date. Mutation-based precision oncology approaches are limited by lack of targetable mutations and genetic heterogeneity. We leveraged systems biology methodologies to discover common targetable disease drivers—master regulator proteins (MRs)—in DIPG to expand treatment options. Using the metaVIPER algorithm, we interrogated an integrated low grade glioma and GBM gene regulatory network with 31 DIPG-gene expression signatures to identify tumor-specific MRs by differential expression of their transcriptional targets. Unsupervised clustering identified MR signatures of upregulated activity in RRM2/TOP2A in 13 patients, CD3D in 5 patients, and MMP7, TACSTD2, RAC2 and SLC15A1/SLC34A2 in individual patients, all of which can be targeted. Notably, intratumoral administration of etoposide by convection enhanced delivery was effective in murine proneural gliomas in which TOP2 was identified as a MR while RRM2—targetable by drugs such as cladribine—has been shown to be a positive regulator of glioma progression whose knock-down inhibits tumor growth. We also prioritized drugs by their ability to reverse MR-activity signatures using a large drug-perturbation database. Patients clustered by predicted drug sensitivities with distinct groups of tumors predicted to respond to proteasome inhibitors, Thiotepa or Volasertib all of which have early evidence in treating gliomas. We will refine this analysis in a multi-institutional study of >100 patient gene expression profiles to define MR signatures driving known biological/molecular disease subtypes, use DIPG cell lines recapitulating common MR architectures to optimize therapy prioritization, and validate our findings in vivo.

Advice from a systems-biology model of the corona epidemics

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.

Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.

Traditional Chinese medicine (TCM) is a systematic medical method that has existed for more than 3,000 years. Unlike Western medicine, the disease diagnosis in TCM is carried out by inspection, auscultation, olfaction, interrogation, and palpation. The patient is then treated according to the disease and corresponding TCM syndrome. However, the development of Chinese medicine is stagnated, partially because it can be influenced by subjective factors, such as the experience and knowledge of TCM practitioners, and there is a lack of relevant biological research on TCM syndromes. Yin-deficiency-heat (YDH) syndrome in TCM is characterized by a series of pathological changes caused by the insufficiency of Yin-fluid, inability to moisturize, and the failure to suppress Yang. In recent years, systems biology research on TCM syndromes has gradually become the focus of TCM research, including syndrome differentiation and functional research using systems biology methodologies such as proteomics, transcriptomics, and metabolomics. This journal aims to publish a series of issues on the systems biology research of TCM syndromes that can provide biological indicators for the syndrome differentiation of YDH syndrome and can provide perspectives on the biological research of YDH syndrome.

Genetic Influences on Behavioral Outcomes After Childhood TBI: A Novel Systems Biology-Informed Approach.

Objectives: To test whether genetic associations with behavioral outcomes after early childhood traumatic brain injury (TBI) are enriched for biologic pathways underpinning neurocognitive and behavioral networks.Design: Cross-sectional evaluation of the association of genetic factors with early (~ 6 months) and long-term (~ 7 years) post-TBI behavioral outcomes. We combined systems biology and genetic association testing methodologies to identify biologic pathways associated with neurocognitive and behavior outcomes after TBI. We then evaluated whether genes/single nucleotide polymorphism (SNPs) associated with these biologic pathways were more likely to demonstrate a relationship (i.e., enrichment) with short and long-term behavioral outcomes after early childhood TBI compared to genes/SNPs not associated with these biologic pathways.Setting: Outpatient research setting.Participants:140 children, ages 3–6:11 years at time of injury, admitted for a TBI or orthopedic injury (OI).Interventions: Not Applicable.Main Outcome Measures: Child behavior checklist total problems T score.Results: Systems biology methodology identified neuronal systems and neurotransmitter signaling (Glutamate receptor, dopamine, serotonin, and calcium signaling), inflammatory response, cell death, immune systems, and brain development as important biologic pathways to neurocognitive and behavioral outcomes after TBI. At 6 months post injury, the group (TBI versus OI) by polymorphism interaction was significant when the aggregate signal from the highest ranked 40% of case gene associations was compared to the control set of genes. At ~ 7 years post injury, the selected polymorphisms had a significant main effect after controlling for injury type when the aggregate signal from the highest ranked 10% of the case genes were compared to the control set of genesConclusions: Findings demonstrate the promise of applying a genomics approach, informed by systems biology, to understanding behavioral recovery after pediatric TBI. A mixture of biologic pathways and processes are associated with behavioral recovery, specifically genes associated with cell death, inflammatory response, neurotransmitter signaling, and brain development. These results provide insights into the complex biology of TBI recovery.

Evolving systems biology approaches to understanding non-coding RNAs in pulmonary hypertension.

Our appreciation of the roles of non-coding RNAs, in particular microRNAs, in the manifestation of pulmonary hypertension (PH) has advanced considerably over the past decade. Comprised of small nucleotide sequences, microRNAs have demonstrated critical and broad regulatory roles in the pathogenesis of PH via the direct binding to messenger RNA transcripts for degradation or inhibition of translation, thereby exerting a profound influence on cellular activity. Yet, as inherently pleiotropic molecules, microRNAs have been difficult to study using traditional, reductionist approaches alone. With the advent of high-throughput -omics technologies and more advanced computational modelling, the study of microRNAs and their multi-faceted and complex functions in human disease serves as a fertile platform for the application of systems biology methodologies in combination with traditional experimental techniques. Here, we offer our viewpoint of past successes of systems biology in elucidating the otherwise hidden actions of microRNAs in PH, as well as areas for future development to integrate these strategies into the discovery of RNA pathobiology in this disease. We contend that such successful applications of systems biology in elucidating the functional architecture of microRNA regulation will further reveal the molecular mechanisms of disease, while simultaneously revealing potential diagnostic and therapeutic strategies in disease amelioration.

The current status and perspective of metabonomics in breast cancer biomarker research

Metabonomics is a developing branch of the systems biology methodology. It mainly concentrates on the metabolic variations of biological systems in various physiological and pathological states to discover the essence of abnormal metabolism. For the last decade, metabonomics has been rapidly and widely applied in cancer research with its unique research perspective and technological advantages, which have shown a broad prospect of clinical application. Currently, breast cancer is the second most common type of cancer found overall and is prevalent in women worldwide. Metabonomics had been performed to screen potential biomarkers of breast cancer that include an early diagnosis, a therapeutic evaluation, prognostic monitoring, and individualized management, which are crucial in improving the survival rate of breast cancer patients. This review mainly presents an introduction to the concept and research techniques of metabonomics and its current status and prospect of applications in breast cancer.

Applying Systems Biology Methodology To Identify Genetic Factors Possibly Associated with Recovery after Traumatic Brain Injury.

Traumatic brain injury (TBI) is one of the leading causes of morbidity and mortality worldwide. It is linked with a number of medical, neurological, cognitive, and behavioral sequelae. The influence of genetic factors on the biology and related recovery after TBI is poorly understood. Studies that seek to elucidate the impact of genetic influences on neurorecovery after TBI will lead to better individualization of prognosis and inform development of novel treatments, which are considerably lacking. Current genetic studies related to TBI have focused on specific candidate genes. The objectives of this study were to use a system biology-based approach to identify biologic processes over-represented with genetic variants previously implicated in clinical outcomes after TBI and identify unique genes potentially related to recovery after TBI. After performing a systematic review to identify genes in the literature associated with clinical outcomes, we used the genes identified to perform a systems biology-based integrative computational analysis to ascertain the interactions between molecular components and to develop models for regulation and function of genes involved in TBI recovery. The analysis identified over-representation of genetic variants primarily in two biologic processes: response to injury (cell proliferation, cell death, inflammatory response, and cellular metabolism) and neurocognitive and behavioral reserve (brain development, cognition, and behavior). Overall, this study demonstrates the use of a systems biology-based approach to identify unique/novel genes or sets of genes important to the recovery process. Findings from this systems biology-based approach provide additional insight into the potential impact of genetic variants on the underlying complex biological processes important to TBI recovery and may inform the development of empirical genetic-related studies for TBI. Future studies that combine systems biology methodology and genomic, proteomic, and epigenetic approaches are needed in TBI.

The Complex Cardiac Atherosclerotic Disorder: The Elusive Role of Genetics and the New Consensus of Systems Biology Approach

<p>The present technological status of genetics and genomics or the genome-wide association studies (GWAS) are insufficient to explain complex diseases like atherosclerosis and coronary artery disease (CAD). It appears that the genetic risk variants of atherosclerosis are activated concurrently with functionally active specific environmental risk factors. With the systems biology methodological approach the atherosclerotic process and CAD are better explained and studied as a unified entity with significant clinical consequences.</p><p>Systems biology is an alternative approach for the study of atherosclerosis and CAD. With the systems biology approach the follow-up of the atherosclerotic process requires four conceptual areas of study: 1) the two potential directions, the bottom-up direction (functional composition from genes to phenotypes) and the top-down direction (functional decomposition from phenotypes to genes); 2) the four disciplines or levels of complexity: the genomic, the cellular, the modular and the model (clinical phenotype) level; 3) the concept of network construction; 4) the atherosclerotic plaque development and progression across all levels of complexity.</p><p>The systems biology methodology is holistic in conception. The proposed systems patterns are able to follow up the progressive nature of atherosclerosis and to explain the appearance of the clinical cardiovascular phenotypes. The phenotypes of CAD are integrated clinical wholes that determine through constrains and therapeutic procedures the behavior of the biological parts in the lower levels of complexity. This way of thinking is leading from genomics, through networks, to the mainstay of clinical cardiology. </p>

A Systems Biology Methodology Combining Transcriptome and Interactome Datasets to Assess the Implications of Cytokinin Signaling for Plant Immune Networks.

Cytokinins (CKs) play an important role in plant growth and development. Also, several studies highlight the modulatory implications of CKs for plant-pathogen interaction. However, the underlying mechanisms of CK mediating immune networks in plants are still not fully understood. A detailed analysis of high-throughput transcriptome (RNA-Seq and microarrays) datasets under modulated conditions of plant CKs and its mergence with cellular interactome (large-scale protein-protein interaction data) has the potential to unlock the contribution of CKs to plant defense. Here, we specifically describe a detailed systems biology methodology pertinent to the acquisition and analysis of various omics datasets that delineate the role of plant CKs in impacting immune pathways in Arabidopsis.

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology

Pharmacological and genetic rodent models of schizophrenia play an important role in the drug discovery pipeline, but quantifying the molecular similarity of such models with the underlying human pathophysiology has proved difficult. We developed a novel systems biology methodology for the direct comparison of anterior prefrontal cortex tissue from four established glutamatergic rodent models and schizophrenia patients, enabling the evaluation of which model displays the greatest similarity to schizophrenia across different pathophysiological characteristics of the disease.Liquid chromatography coupled tandem mass spectrometry (LC-MSE) proteomic profiling was applied comparing healthy and “disease state” in human post-mortem samples and rodent brain tissue samples derived from models based on acute and chronic phencyclidine (PCP) treatment, ketamine treatment or NMDA receptor knockdown. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of five functional domains of the disease such as “development and differentiation”, which were represented across all four rodent models and were thus subsequently used for cross-species comparison. Kernel-based machine learning techniques quantified that the chronic PCP model represented schizophrenia brain changes most closely for four of these functional domains.This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of schizophrenia most closely, providing an indication of face validity as well as potential guidance in the refinement of construct and predictive validity. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating functional dimensions of behaviour with distinct biological processes.

Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.

High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype–phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.