Insights from Systems Biology in Physiological Studies: Learning from Context

Silva, Pedro Henrique Imenez; Melo, Diogo; Mendonça, Pedro

doi:10.1159/000478648

Cited by 5 publications

(3 citation statements)

References 72 publications

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“…This sort of interaction between different loci leading to different phenotypes, called epistasis, is central to our understanding of how a molecular network is coordinated [44]. Given the natural properties of complex networks and the ubiquitous intermolecular dependence in gene regulation and biochemical and metabolic systems, joint actions are the norm rather than the exception in inherited complex traits.…”

Section: Discussionmentioning

confidence: 99%

Genetic Variations of Oxidative Stress Related Genes ALOX5, ALOX5AP and MPO Modulate Ischemic Stroke Susceptibility Through Main Effects and Epistatic Interactions in a Chinese Population

Liu

Song

et al. 2017

Cell Physiol Biochem

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Background/Aims: To investigate the roles of the oxidative stress related-genes ALOX5, ALOX5AP and MPO in ischemic stroke susceptibility in the Han Chinese population. Methods: A total of 351 ischemic stroke patients and 417 controls were recruited. The ALOX5 rs10900213, ALOX5AP rs4293222 and MPO rs2107545 gene polymorphisms were genotyped. Results: We identified that rs2107545 of MPO gene was significantly associated with ischemic stroke susceptibility after adjusting for covariates. Furthermore, we also considered the likely complexity of oxidative stress and inflammatory process in stroke by assessing the combined effects of multiple genes. Generalized multifactor dimensionality reduction (GMDR) analysis revealed that the combination of ALOX5 rs10900213, ALOX5AP rs4293222 and MPO rs2107545 was significantly associated with increased risk of ischemic stroke (P=0.0040, OR (95% CI) =1.991 (1.241 to 3.195)). Additionally, the MPO rs2107545 genotype was significantly associated with clinical outcomes at 6 months after discharge from the hospital. Conclusion: Our study revealed that epistatic interaction among the ALOX5, ALOX5AP and MPO genes played a significant role in vulnerability to ischemic stroke. Furthermore, these results also suggest that the rs2107545 of MPO gene can be used as a biomarker for the susceptibility and prognosis of ischemic stroke patients.

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Section: Discussionmentioning

confidence: 99%

Genetic Variations of Oxidative Stress Related Genes ALOX5, ALOX5AP and MPO Modulate Ischemic Stroke Susceptibility Through Main Effects and Epistatic Interactions in a Chinese Population

Liu

Song

et al. 2017

Cell Physiol Biochem

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“…Systems biology has been used to develop integrative models 16 in multiple sclerosis (MS) 17 and amyotrophic lateral sclerosis (ALS). 18 Normally, the spinal cord serves as a multi-organ physiological adaptive, and stabilizing network.…”

Section: Current Lack Of Integration Of Multi-system Effects Of Spina...mentioning

confidence: 99%

Development of a Systems Medicine Approach to Spinal Cord Injury

Guest

Kelly-Hedrick

Williamson

et al. 2023

Journal of Neurotrauma

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Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic influences for which data integration methods are lacking. Infections, pressure sores, and heterotopic ossification are known recovery modifiers. However, the molecular pathobiology of the disease-modifying factors altering the neurological recovery-chronic syndrome trajectory is mainly unknown, with significant data gaps between intensive early treatment and chronic phases. Changes in organ function such as gut dysbiosis, adrenal dysregulation, fatty liver, muscle loss, and autonomic dysregulation disrupt homeostasis, generating progression-driving allostatic load. Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations. Due to many interacting variables in individuals, substantiating the effects of treatments to improve neurological outcomes is difficult. Acute injury outcome predictors, including blood and cerebrospinal fluid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes. In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories. This study examines the limitations of current classifications of SCI and how these can evolve through systems medicine.

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“…Due to the large dimensionality of gene expression data, often composed of several thousands of gene expression measures, a major tool in the analysis of co-expression is gene clustering: separating the genes into related groups, which can then be explored separately (D'haeseleer, 2005). This drastically reduces the number of genes we need to consider at the same time and allows for the identification of hubs or centrally connected genes that can be used to inform further experimental validation (Imenez Silva et al, 2017;Langfelder & Horvath, 2008).…”

Section: Introductionmentioning

confidence: 99%

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

Melo

Pallares

Ayroles

2023

Preprint

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Finding communities in gene co-expression networks is a common first step toward extracting biological insight from such complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask the question: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

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Insights from Systems Biology in Physiological Studies: Learning from Context

Cited by 5 publications

References 72 publications

Genetic Variations of Oxidative Stress Related Genes ALOX5, ALOX5AP and MPO Modulate Ischemic Stroke Susceptibility Through Main Effects and Epistatic Interactions in a Chinese Population

Genetic Variations of Oxidative Stress Related Genes ALOX5, ALOX5AP and MPO Modulate Ischemic Stroke Susceptibility Through Main Effects and Epistatic Interactions in a Chinese Population

Development of a Systems Medicine Approach to Spinal Cord Injury

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

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