Complexity and network dynamics in physiological adaptation: An integrated view

Optimizing parameter search for community detection in time-evolving networks of complex systems

Network representations have been effectively employed to analyze complex systems across various areas and applications, leading to the development of network science as a core tool to study systems with multiple components and complex interactions. There is a growing interest in understanding the temporal dynamics of complex networks to decode the underlying dynamic processes through the temporal changes in network structures. Community detection algorithms, which are specialized clustering algorithms, have been instrumental in studying these temporal changes. They work by grouping nodes into communities based on the structure and intensity of network connections over time, aiming to maximize the modularity of the network partition. However, the performance of these algorithms is highly influenced by the selection of resolution parameters of the modularity function used, which dictate the scale of the represented network, in both size of communities and the temporal resolution of the dynamic structure. The selection of these parameters has often been subjective and reliant on the characteristics of the data used to create the network. Here, we introduce a method to objectively determine the values of the resolution parameters based on the elements of self-organization and scale-invariance. We propose two key approaches: (1) minimization of biases in spatial scale network characterization and (2) maximization of scale-freeness in temporal network reconfigurations. We demonstrate the effectiveness of these approaches using benchmark network structures as well as real-world datasets. To implement our method, we also provide an automated parameter selection software package that can be applied to a wide range of complex systems.

Metacode: One code to rule them all

The code of codes or metacode is a microcosm where biological layers, as well as their codes, interact together allowing the continuity of information flow in organisms by increasing biological entities' complexity. Through this novel organic code, biological systems scale towards niches with higher informatic freedom building structures that increase the entropy in the universe. Code biology has developed a novel informational framework where biological entities strive themselves through the information flow carried out through organic codes consisting of two molecular or functional landscapes intertwined through arbitrary linkages via an adaptor whose nature is autonomous from molecular determinism. Here we will integrate genomic and epigenomic codes according to the evidence released in ENCODE (phase 3), psychENCODE and GTEx project, outlining the principles of the metacode, to address the continuous nature of biological systems and their inter-layered information flow. This novel complex metacode maps from very constrained sets of elements (i.e., regulation sites modulating gene expression) to new ones with greater freedom of decoding (i.e., a continuous cell phenotypic space). This leads to a new domain in code biology where biological systems are informatic attractors that navigate an energy metaspace through a complexity-noise balance, stalling in emergent niches where organic codes take meaning.

Genes, Environments, and Time: The Biology of Adversity and Resilience

Exposures to adverse environments, both psychosocial and physicochemical, are prevalent and consequential across a broad range of childhood populations. Such adversity, especially early in life, conveys measurable risk to learning and behavior and to the foundations of both mental and physical health. Using an interactive gene-environment-time (GET) framework, we survey the independent and interactive roles of genetic variation, environmental context, and developmental timing in light of advances in the biology of adversity and resilience, as well as new discoveries in biomedical research. Drawing on this rich evidence base, we identify 4 core concepts that provide a powerful catalyst for fresh thinking about primary health care for young children: (1) all biological systems are inextricably integrated, continuously "reading" and adapting to the environment and "talking back" to the brain and each other through highly regulated channels of cross-system communication; (2) adverse environmental exposures induce alterations in developmental trajectories that can lead to persistent disruptions of organ function and structure; (3) children vary in their sensitivity to context, and this variation is influenced by interactions among genetic factors, family and community environments, and developmental timing; and (4) critical or sensitive periods provide unmatched windows of opportunity for both positive and negative influences on multiple biological systems. These rapidly moving frontiers of investigation provide a powerful framework for new, science-informed thinking about health promotion and disease prevention in the early childhood period.

Body Reprogramming: Reframing the Fibromyalgia narrative and providing an integrative therapeutic model

We propose a theory known as the Hyland model to help conceptualise Fibromyalgia within a complex adaptive control system. A fundamental assumption is that symptom generating mechanisms are causally connected, forming a network that has emergent properties. An illness narrative has been developed which has a ‘goodness of fit’ with the lived experience of those with Fibromyalgia. The theory guides management within the clinical setting and incorporates current evidence-based therapeutic strategies, within a multi-modal intervention described as ‘Body Reprogramming’. This intervention focuses on non-pharmacological and lifestyle-based considerations. The theoretical framework also helps explain why modest therapeutic effects are gained from current pharmacological options.

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.

Epigenetic Inheritance Underlying Pulmonary Arterial Hypertension

In pulmonary arterial hypertension (PAH), the Warburg effect (glycolytic shift) and mitochondrial fission are determinants of phenotype alterations characteristic of the disease, such as proliferation, apoptosis resistance, migration, endothelial-mesenchymal transition, and extracellular matrix stiffness. Current therapies, focusing largely on vasodilation and antithrombotic protection, do not restore these aberrant phenotypes suggesting that additional pathways need be targeted. The multifactorial nature of PAH suggests epigenetic changes as potential determinants of vascular remodeling. Transgenerational epigenetic changes induced by hypoxia can result in permanent changes early in fetal development increasing PAH risk in adulthood. Unlike genetic mutations, epigenetic changes are pharmacologically reversible, making them an attractive target as therapeutic strategies for PAH. This review offers a landscape of the most current clinical, epigenetic-sensitive changes contributing to PAH vascular remodeling both in early and later life, with a focus on a network medicine strategy. Furthermore, we discuss the importance of the application (from morphogenesis to disease onset) of molecular network-based algorithms to dissect PAH molecular pathobiology. Additionally, we suggest an integrated network-based program for clinical disease gene discovery that may reveal novel biomarkers and novel disease targets, thus offering a truly innovative path toward redefining and treating PAH, as well as facilitating the trajectory of a comprehensive precision medicine approach to PAH.

A quantitative method for estimating the adaptedness in a physiological study

Background

Existed mathematical models of individual adaptation are mostly reductionist by nature. Researchers usually a priori consider the subject adapted basing only on the fact of continued or prolonged influence of the harmful factor. This paper describes a method that allows assessing the physiological adaptedness to experimental challenges on the basis of holistic approach and quantitative criteria.

Methods

The suggested method comprises simple equations and incorporates into the model an indicator that differentiates functions in regard to their significance for determining physiological adaptedness considered as an outcome of the adaptive process.

Results

The proposed empirical model affords the possibility of comparing subjects in respect to their resistance to several loads. Physiological parameters were differentiated with regard to their significance for assessing adaptedness. Two examples of animal adaptation to exercise after physical training and plant adaptogen administration are considered.

Conclusion

The calculated index of adaptedness is useful in that it replaces wordy descriptions of large tables that reveal alterations in numerous parameters of many subjects under study.

Genetic and epigenetic-sensitive regulatory network in immune response: a putative link between HLA-G and diabetes

INTRODUCTION

Human leukocyte antigen-G (HLA-G) gene encodes for a tolerogenic molecule constitutively expressed in human pancreas and upregulated upon inflammatory signals. The 14 bp INS/DEL polymorphism in the 3'UTR of HLA-G may influence the susceptibility for diabetes and coronary heart diseases (CHD), thus suggesting a novel candidate gene. DNA hypomethylation at HLA-G promoter may be a putative useful clinical biomarker for CHD onset. Upregulation of soluble HLA-G isoform (sHLA-G) was detected in prediabetic and diabetic subjects, suggesting a putative role in metabolic dysfunctions.

AREAS COVERED

We conducted a scoping literature review of genetic and epigenetic-sensitive mechanisms regulating HLA-G in diabetes. English-language manuscripts published between 1997 and 2019, were identified through PubMed, Google Scholar, and Web of Science database searches. After selecting 14 original articles representing case-control studies, we summarized and critically evaluated their main findings.

EXPERT COMMENTARY

Although epigenetic modifications are involved in the onset of hyperglycemic conditions evolving into diabetes and CHD, it is still difficult to obtain simple and useful clinical biomarkers. Inflammatory-induced KDM6A/INF-β/HLA-G axis might be a part of the epigenetic network leading to overexpression of HLA-G at pancreatic level. Network medicine may show whether HLA-G is involved in diabetes and CHD.

Una visión compleja sobre la etiología de las enfermedades

A pesar de que el objetivo de las investigaciones en ciencias médicas es alcanzar un mayor conocimiento de cómo el cuerpo y su funcionamiento se relacionan con los patrones disfuncionales y cómo estos generan patologías, la mayoría de los esfuerzos se centran en preguntas usando datos cada vez más detallados. Sin embargo, podría ser posible abordar con éxito a los usuarios mediante una mirada más amplia de mecanismos corporales desde una perspectiva global y pensando en cómo las disfunciones o patologías pueden influir desencadenando otros problemas.El cuerpo se puede entender como un sistema o una red compleja en la que los patrones disfuncionales surgen de la interacción entre múltiples niveles físicos y funcionales. El logro de un mayor progreso con los usuarios dependerá, en lo fundamental, de las propiedades y relaciones de las patologías, disfunciones y herramientas que están disponibles o se deban desarrollar con el fin de estudiar los mecanismos de patología-disfunción.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

ECG-derived spatial QRS-T angle is associated with ICD implantation, mortality and heart failure admissions in patients with LV systolic dysfunction

Background

Increased spatial QRS-T angle has been shown to predict appropriate implantable cardioverter defibrilIator (ICD) therapy in patients with left ventricular systolic dysfunction (LVSD). We performed a retrospective cohort study in patients with left ventricular ejection fraction (LVEF) 31–40% to assess the relationship between the spatial QRS-T angle and other advanced ECG (A-ECG) as well as echocardiographic metadata, with all-cause mortality or ICD implantation for secondary prevention.

Methods

534 patients ≤75 years of age with LVEF 31–40% were identified through an echocardiography reporting database. Digital 12-lead ECGs were retrospectively matched to 295 of these patients, for whom echocardiographic and A-ECG metadata were then generated. Data mining was applied to discover novel ECG and echocardiographic markers of risk. Machine learning was used to develop a model to predict possible outcomes.

Results

49 patients (17%) had events, defined as either mortality (n = 16) or ICD implantation for secondary prevention (n = 33). 72 parameters (58 A-ECG, 14 echocardiographic) were univariately different (p<0.05) in those with vs. without events. After adjustment for multiplicity, 24 A-ECG parameters and 3 echocardiographic parameters remained different (p<2x10^-3). These included the posterior-to-leftward QRS loop ratio from the derived vectorcardiographic horizontal plane (previously associated with pulmonary artery pressure, p = 2x10^-6); spatial mean QRS-T angle (134 vs. 112°, p = 1.6x10^-4); various repolarisation vectors; and a previously described 5-parameter A-ECG score for LVSD (p = 4x10^-6) that also correlated with echocardiographic global longitudinal strain (R² = - 0.51, P < 0.0001). A spatial QRS-T angle >110° had an adjusted HR of 3.4 (95% CI 1.6 to 7.4) for secondary ICD implantation or all-cause death and adjusted HR of 4.1 (95% CI 1.2 to 13.9) for future heart failure admission. There was a loss of complexity between A-ECG and echocardiographic variables with an increasing degree of disease.

Conclusion

Spatial QRS-T angle >110° was strongly associated with arrhythmic events and all-cause death. Deep analysis of global ECG and echocardiographic metadata revealed underlying relationships, which otherwise would not have been appreciated. Delivered at scale such techniques may prove useful in clinical decision making in the future.

Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations

BACKGROUND

Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability "Prakriti" in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium.

METHODS

Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates.

RESULTS

Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz. CYP2C19, CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts.

CONCLUSION

Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods' performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost.

Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?

Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.