Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

The metabolic role of vitamin D in children's neurodevelopment: a network study

Neurodevelopmental disorders are rapidly increasing in prevalence and have been linked to various environmental risk factors. Mounting evidence suggests a potential role of vitamin D in child neurodevelopment, though the causal mechanisms remain largely unknown. Here, we investigate how vitamin D deficiency affects children's communication development, particularly in relation to Autism Spectrum Disorder (ASD). We do so by developing an integrative network approach that combines metabolomic profiles, clinical traits, and neurodevelopmental data from a pediatric cohort. Our results show that low levels of vitamin D are associated with changes in the metabolic networks of tryptophan, linoleic, and fatty acid metabolism. These changes correlate with distinct ASD-related phenotypes, including delayed communication skills and respiratory dysfunctions. Additionally, our analysis suggests the kynurenine and serotonin sub-pathways may mediate the effect of vitamin D on early life communication development. Altogether, our findings provide metabolome-wide insights into the potential of vitamin D as a therapeutic option for ASD and other communication disorders.

Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped

The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distinguishing lung sounds and supporting earlier detection of various respiratory pathologies. The frequency spread and the component magnitudes are revealed from the analysis of eighty-five bronchial (BS) and pleural rub (PS) lung sounds employing the power spectral density (PSD) plot and wavelet scalogram. The low-frequency spread, and persistence of the high-intensity frequency components are visible in BS sounds emanating from the uniform cross-sectional area of the trachea. The frictional rub between the pleurae causes a higher frequency spread of low-intensity intermittent frequency components in PS signals. From the complex networks of BS and PS, the extracted graph features are - graph density ([Formula: see text], transitivity ([Formula: see text], degree centrality ([Formula: see text]), betweenness centrality ([Formula: see text], eigenvector centrality ([Formula: see text]), and graph entropy (En). The high values of [Formula: see text] and [Formula: see text] show a strong correlation between distinct segments of the BS signal originating from a consistent cross-sectional tracheal diameter and, hence, the generation of high-intense low-spread frequency components. An intermittent low-intense and a relatively greater frequency spread in PS signal appear as high [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] values. With these complex network parameters as input attributes, the supervised machine learning techniques- discriminant analyses, support vector machines, k-nearest neighbors, and neural network pattern recognition (PRNN)- classify the signals with more than 90% accuracy, with PRNN having 25 neurons in the hidden layer achieving the highest (98.82%).

A pilot study suggests the correspondence between SAR202 bacteria and dissolved organic matter in the late stage of a year-long microcosm incubation

SAR202 bacteria are abundant in the marine environment and they have been suggested to contribute to the utilization of recalcitrant organic matter (RDOM) within the ocean's biogeochemical cycle. However, this functional role has only been postulated by metagenomic studies. During a one-year microcosm incubation of an open ocean microbial community with lysed Synechococcus and its released DOM, SAR202 became relatively more abundant in the later stage (after day 30) of the incubation. Network analysis illustrated a high degree of negative associations between SAR202 and a unique group of molecular formulae (MFs) in phase 2 (day 30 to 364) of the incubation, which is empirical evidence that SAR202 bacteria are major consumers of the more oxygenated, unsaturated, and higher-molecular-weight MFs. Further investigation of the SAR202-associated MFs suggested that they were potentially secondary products arising from initial heterotrophic activities following the amendment of labile Synechococcus-derived DOM. This pilot study provided a preliminary observation on the correspondence between SAR202 bacteria and more resistant DOM, further supporting the hypothesis that SAR202 bacteria play important roles in the degradation of RDOM and thus the ocean's biogeochemical cycle.

Large airway T cells in adults with former bronchopulmonary dysplasia

BACKGROUND

Bronchopulmonary Dysplasia (BPD) in infants born prematurely is a risk factor for chronic airway obstruction later in life. The distribution of T cell subtypes in the large airways is largely unknown.

OBJECTIVE

To characterize cellular and T cell profiles in the large airways of young adults with a history of BPD.

METHODS

Forty-three young adults born prematurely (preterm (n = 20), BPD (n = 23)) and 45 full-term-born (asthma (n = 23), healthy (n = 22)) underwent lung function measurements, and bronchoscopy with large airway bronchial wash (BW). T-cells subsets in BW were analyzed by immunocytochemistry.

RESULTS

The proportions of both lymphocytes and CD8 + T cells in BW were significantly higher in BPD (median, 6.6%, and 78.0%) when compared with asthma (3.4% and 67.8%, p = 0.002 and p = 0.040) and healthy (3.8% and 40%, p < 0.001 and p < 0.001). In all adults born prematurely (preterm and BPD), lymphocyte proportion correlated negatively with forced vital capacity (r= -0.324, p = 0.036) and CD8 + T cells correlated with forced expiratory volume in one second, FEV1 (r=-0.448, p = 0.048). Correlation-based network analysis revealed that lung function cluster and BPD-birth cluster were associated with lymphocytes and/or CD4 + and CD8 + T cells. Multivariate regression analysis showed that lymphocyte proportions and BPD severity qualified as independent factors associated with FEV1.

CONCLUSIONS

The increased cytotoxic T cells in the large airways in young adults with former BPD, suggest a similar T-cell subset pattern as in the small airways, resembling features of COPD. Our findings strengthen the hypothesis that mechanisms involving adaptive and innate immune responses are involved in the development of airway disease due to preterm birth.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Estrogen deficiency induces bone loss through the gut microbiota

Postmenopausal osteoporosis is a common bone metabolic disease, and gut microbiota (GM) imbalance plays an important role in the development of metabolic bone disease. Here, we show that ovariectomized mice had high levels of lipopolysaccharide in serum and gut microbiota dysbiosis through increases in luminal Firmicutes:Bacteroidetes ratio. We depleted the GM through antibiotic treatment and observed improvements in bone mass, bone microstructure, and bone strength in ovariectomized mice. Conversely, transplantation of GM adapted to ovariectomy induced bone loss. However, GM depletion reversed ovariectomy-induced gene expression in the tibia and increased periosteal bone formation. Furthermore, bioinformatics analysis revealed that the G-protein-coupled bile acid receptor (TGR5) and systemic inflammatory factors play key roles in bone metabolism. Silencing TGR5 expression through small interfering RNA (siRNA) in the local tibia and knockout of TGR5 attenuated the effects of GM depletion in ovariectomized mice, confirming these findings. Thus, this study highlights the critical role of the GM in inducing bone loss in ovariectomized mice and suggests that targeting TGR5 within the GM may have therapeutic potential for postmenopausal osteoporosis.

Genetic architecture of tuber-bound free amino acids in potato and effect of growing environment on the amino acid content

Free amino acids in potato tubers contribute to their nutritional value and processing quality. Exploring the natural variation in their accumulation in tubers across diverse genetic backgrounds is critical to potato breeding programs aiming to enhance or partition their distribution effectively. This study assessed variation in the tuber-bound free amino acids in a diversity panel of tetraploid potato clones developed and maintained by the Texas A&M Potato Breeding Program to explore their genetic basis and to obtain genomic-estimated breeding values for applied breeding purposes. Free amino acids content was evaluated in tubers of 217 tetraploid potato clones collected from Dalhart, Texas in 2019 and 2020, and Springlake, Texas in 2020. Most tuber amino acids were not affected by growing location, except histidine and proline, which were significantly lower (- 59.0%) and higher (+ 129.0%), respectively, at Springlake, Texas (a location that regularly suffers from abiotic stresses, mainly high-temperature stress). Single nucleotide polymorphism markers were used for genome-wide association studies and genomic selection of clones based on amino acid content. Most amino acids showed significant variations among potato clones and moderate to high heritabilities. Principal component analysis separated fresh from processing potato market classes based on amino acids distribution patterns. Genome-wide association studies discovered 33 QTL associated with 13 free amino acids. Genomic-estimated breeding values were calculated and are recommended for practical potato breeding applications to select parents and advance clones with the desired free amino acid content.

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

The metabolic role of vitamin D in children's neurodevelopment: a network study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with various proposed environmental risk factors and a rapidly increasing prevalence. Mounting evidence suggests a potential role of vitamin D deficiency in ASD pathogenesis, though the causal mechanisms remain largely unknown. Here we investigate the impact of vitamin D on child neurodevelopment through an integrative network approach that combines metabolomic profiles, clinical traits, and neurodevelopmental data from a pediatric cohort. Our results show that vitamin D deficiency is associated with changes in the metabolic networks of tryptophan, linoleic, and fatty acid metabolism. These changes correlate with distinct ASD-related phenotypes, including delayed communication skills and respiratory dysfunctions. Additionally, our analysis suggests the kynurenine and serotonin sub-pathways may mediate the effect of vitamin D on early childhood communication development. Altogether, our findings provide metabolome-wide insights into the potential of vitamin D as a therapeutic option for ASD and other communication disorders.

Understanding ayahuasca effects in major depressive disorder treatment through in vitro metabolomics and bioinformatics

Emerging insights from metabolomic-based studies of major depression disorder (MDD) are mainly related to biochemical processes such as energy or oxidative stress, in addition to neurotransmission linked to specific metabolite intermediates. Hub metabolites represent nodes in the biochemical network playing a critical role in integrating the information flow in cells between metabolism and signaling pathways. Limited technical-scientific studies have been conducted to understand the effects of ayahuasca (Aya) administration in the metabolism considering MDD molecular context. Therefore, this work aims to investigate an in vitro primary astrocyte model by untargeted metabolomics of two cellular subfractions: secretome and intracellular content after pre-defined Aya treatments, based on DMT concentration. Mass spectrometry (MS)-based metabolomics data revealed significant hub metabolites, which were used to predict biochemical pathway alterations. Branched-chain amino acid (BCAA) metabolism, and vitamin B6 and B3 metabolism were associated to Aya treatment, as "housekeeping" pathways. Dopamine synthesis was overrepresented in the network results when considering the lowest tested DMT concentration (1 µmol L^-1). Building reaction networks containing significant and differential metabolites, such as nicotinamide, L-DOPA, and L-leucine, is a useful approach to guide on dose decision and pathway selection in further analytical and molecular studies.

Structural brain signatures of frailty, defined as accumulation of self-reported health deficits in older adults

Background

Frailty in older adults has been associated with reduced brain health. However, structural brain signatures of frailty remain understudied. Our aims were: (1) Explore associations between a frailty index (FI) and brain structure on magnetic resonance imaging (MRI). (2) Identify the most important FI features driving the associations.

Methods

We designed a cross-sectional observational study from a population-based study (The Irish Longitudinal Study on Aging: TILDA). Participants aged ≥50 years who underwent the wave 3 MRI sub-study were included. We measured cortex, basal ganglia, and each of the Desikan-Killiany regional volumes. Age-and sex-adjusted correlations were performed with a 32-item self-reported FI that included conditions commonly tested for frailty in research and clinical settings. A graph theory analysis of the network composed by each FI item and cortex volume was performed. White matter fiber integrity was quantified using diffusion tensor imaging (DTI).

Results

In 523 participants (mean age 69, 49% men), lower cortex and thalamic volumes were independently associated with higher FI. Sensory and functional difficulties, diabetes, polypharmacy, knee pain, and self-reported health were the main FI associations with cortex volume. In the network analysis, cortex volume had a modest influence within the frailty network. Regionally, higher FI was significantly associated with lower volumes in both orbitofrontal and temporal cortices. DTI analyses revealed inverse associations between the FI and the integrity of some association bundles.

Conclusion

The FI used had a recognizable but subtle structural brain signature in this sample. Only some FI deficits were directly associated with cortex volume, suggesting scope for developing FIs that include metrics more specifically related with brain health in future aging neuroscience studies.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Analysis of the Arabidopsis coilin mutant reveals a positive role of AtCOILIN in plant immunity

Biogenesis of ribonucleoproteins occurs in dynamic subnuclear compartments called Cajal bodies (CBs). COILIN is a critical scaffolding component essential for CB formation, composition, and activity. We recently showed that Arabidopsis (Arabidopsis thaliana) AtCOILIN is phosphorylated in response to bacterial elicitor treatment. Here, we further investigated the role of AtCOILIN in plant innate immunity. Atcoilin mutants are compromised in defense responses to bacterial pathogens. Besides confirming a role of AtCOILIN in alternative splicing (AS), Atcoilin showed differential expression of genes that are distinct from those of AS, including factors involved in RNA biogenesis, metabolism, plant immunity, and phytohormones. Atcoilin mutant plants have reduced levels of defense phytohormones. As expected, the mutant plants were more sensitive to the necrotrophic fungal pathogen Botrytis cinerea. Our findings reveal an important role for AtCOILIN in innate plant immunity.

Functional Complex Networks Based on Operational Architectonics: Application on Electroencephalography-Brain-computer Interface for Imagined Speech

A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEGs, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.

Effects of long-term exercise and a high-fat diet on synovial fluid metabolomics and joint structural phenotypes in mice: an integrated network analysis

OBJECTIVE

To explore how systemic factors that modify knee osteoarthritis risk are connected to 'whole-joint' structural changes by evaluating the effects of high-fat diet and wheel running exercise on synovial fluid (SF) metabolomics.

METHODS

Male mice were fed a defined control or high-fat (60% kcal fat) diet from 6 to 52 weeks of age, and half the animals were housed with running wheels from 26 to 52 weeks of age (n = 9-13 per group). Joint tissue structure and osteoarthritis pathology were evaluated by histology and micro-computed tomography. Systemic metabolic and inflammatory changes were evaluated by body composition, glucose tolerance testing, and serum biomarkers. SF metabolites were analyzed by high performance-liquid chromatography mass spectrometry. We built correlation-based network models to evaluate the connectivity between systemic and local metabolic biomarkers and osteoarthritis structural pathology within each experimental group.

RESULTS

High-fat diet caused moderate osteoarthritis, including cartilage pathology, synovitis and increased subchondral bone density. In contrast, voluntary exercise had a negligible effect on these joint structure components. 1,412 SF metabolite features were detected, with high-fat sedentary mice being the most distinct. Diet and activity uniquely altered SF metabolites attributed to amino acids, lipids, and steroids. Notably, high-fat diet increased network connections to systemic biomarkers such as interleukin-1β and glucose intolerance. In contrast, exercise increased local joint-level network connections, especially among subchondral bone features and SF metabolites.

CONCLUSION

Network mapping showed that obesity strengthened SF metabolite links to blood glucose and inflammation, whereas exercise strengthened SF metabolite links to subchondral bone structure.

Reversal of alopecia areata, osteoporosis follow treatment with activation of Tgr5 in mice

BACKGROUND

Alopecia areata is an autoimmune hair loss disease with infiltration of pro-inflammatory cells into hair follicles. The role of Tgr5 in dermatitis has attracted considerable attention. The present study aimed to investigate the effect of Tgr5 in the development of Alopecia areata.

METHODS

The study utilized a comparison control group design with four groups of wild-type group, wild-type+INT777 group, Tgr5-/- group, and Tgr5-/-+INT777 group. The mice were treated with INT777 (30 mg/kg/day) or the carrier solution (DMSO) intraperitoneally for 7 weeks, and the back skin was collected and analyzed by histology and immunohistochemistry staining. The lumbar vertebrae 4 has also been analyzed by DXA and Micro-CT.

RESULTS

Tgr5-/- mice displayed the decreasingly significant in hair area and length, skin thickness, and the ratio of anagen and telogen, collagen, and mast cell number and loss the bone mass than WT group. After treating with INT777, the appearance of alopecia areata and bone microstructure has improved. Immunohistochemistry and qPCR analysis showed that activation of Tgr5 can down-regulate the express of JAK1, STAT3, IL-6, TNF-α, and VEGF.

CONCLUSION

These findings indicate that activation of Tgr5 mediated amelioration of alopecia areata and osteoporosis by down-regulated JAK1-STAT3 signaling pathway.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BACKGROUND

The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

RESULTS

We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.

CONCLUSIONS

SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Description of Microbial Communities of Phosphate Mine Wastes in Morocco, a Semi-Arid Climate, Using High-Throughput Sequencing and Functional Prediction

Soil microbiota are vital for successful revegetation, as they play a critical role in nutrient cycles, soil functions, and plant growth and health. A rehabilitation scenario of the abandoned Kettara mine (Morocco) includes covering acidic tailings with alkaline phosphate mine wastes to limit water infiltration and hence acid mine drainage. Revegetation of phosphate wastes is the final step to this rehabilitation plan. However, revegetation is hard on this type of waste in semi-arid areas and only a few plants managed to grow naturally after 5 years on the store-and-release cover. As we know that belowground biodiversity is a key component for aboveground functioning, we sought to know if any structural problem in phosphate waste communities could explain the almost absence of plants. To test this hypothesis, bacterial and archaeal communities present in these wastes were assessed by 16S rRNA metabarcoding. Exploration of taxonomic composition revealed a quite diversified community assigned to 19 Bacterial and two Archaeal phyla, similar to other studies, that do not appear to raise any particular issues of structural problems. The dominant sequences belonged to Proteobacteria, Bacteroidetes, Actinobacteria, and Gemmatimonadetes and to the genera Massilia, Sphingomonas, and Adhaeribacter. LEfSe analysis identified 19 key genera, and metagenomic functional prediction revealed a broader phylogenetic range of taxa than expected, with all identified genera possessing at least one plant growth-promoting trait. Around 47% of the sequences were also related to genera possessing strains that facilitate plant development under biotic and environmental stress conditions, such as drought and heat.

Network Analysis in Systems Epidemiology

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

Sex Differences in the Physiological Network of Healthy Young Subjects

Within human physiology, systemic interactions couple physiological variables to maintain homeostasis. These interactions change according to health status and are modified by factors such as age and sex. For several physiological processes, sex-based distinctions in normal physiology are present and defined in isolation. However, new methodologies are indispensable to analyze system-wide properties and interactions with the objective of exploring differences between sexes. Here we propose a new method to construct complex inferential networks from a normalization using the clinical criteria for health of physiological variables, and the correlations between anthropometric and blood tests biomarkers of 198 healthy young participants (117 women, 81 men, from 18 to 27 years old). Physiological networks of men have less correlations, displayed higher modularity, higher small-world index, but were more vulnerable to directed attacks, whereas networks of women were more resilient. The networks of both men and women displayed sex-specific connections that are consistent with the literature. Additionally, we carried out a time-series study on heart rate variability (HRV) using Physionet's Fantasia database. Autocorrelation of HRV, variance, and Poincare's plots, as a measure of variability, are statistically significant higher in young men and statistically significant different from young women. These differences are attenuated in older men and women, that have similar HRV distributions. The network approach revealed differences in the association of variables related to glucose homeostasis, nitrogen balance, kidney function, and fat depots. The clusters of physiological variables and their roles within the network remained similar regardless of sex. Both methodologies show a higher number of associations between variables in the physiological system of women, implying redundant mechanisms of control and simultaneously showing that these systems display less variability in time than those of men, constituting a more resilient system.

iMAP: A Web Server for Metabolomics Data Integrative Analysis

Metabolomics data analysis depends on the utilization of bioinformatics tools. To meet the evolving needs of metabolomics research, several integrated platforms have been developed. Our group has developed a desktop platform IP4M (integrated Platform for Metabolomics Data Analysis) which allows users to perform a nearly complete metabolomics data analysis in one-stop. With the extensive usage of IP4M, more and more demands were raised from users worldwide for a web version and a more customized workflow. Thus, iMAP (integrated Metabolomics Analysis Platform) was developed with extended functions, improved performances, and redesigned structures. Compared with existing platforms, iMAP has more methods and usage modes. A new module was developed with an automatic pipeline for train-test set separation, feature selection, and predictive model construction and validation. A new module was incorporated with sufficient editable parameters for network construction, visualization, and analysis. Moreover, plenty of plotting tools have been upgraded for highly customized publication-ready figures. Overall, iMAP is a good alternative tool with complementary functions to existing metabolomics data analysis platforms. iMAP is freely available for academic usage at https://imap.metaboprofile.cloud/ (License MPL 2.0).

A framework for stability-based module detection in correlation graphs

Graphs can be used to represent the direct and indirect relationships between variables, and elucidate complex relationships and interdependencies. Detecting structure within a graph is a challenging problem. This problem is studied over a range of fields and is sometimes termed community detection, module detection, or graph partitioning. A popular class of algorithms for module detection relies on optimizing a function of modularity to identify the structure. In practice, graphs are often learned from the data, and thus prone to uncertainty. In these settings, the uncertainty of the network structure can become exaggerated by giving unreliable estimates of the module structure. In this work, we begin to address this challenge through the use of a nonparametric bootstrap approach to assessing the stability of module detection in a graph. Estimates of stability are presented at the level of the individual node, the inferred modules, and as an overall measure of performance for module detection in a given graph. Furthermore, bootstrap stability estimates are derived for complexity parameter selection that ultimately defines a graph from data in a way that optimizes stability. This approach is utilized in connection with correlation graphs but is generalizable to other graphs that are defined through the use of dissimilarity measures. We demonstrate our approach using a broad range of simulations and on a metabolomics dataset from the Beijing Olympics Air Pollution study. These approaches are implemented using bootcluster package that is available in the R programming language.

Inferring ecosystem networks as information flows

The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.

Gut microbiome dysbiosis alleviates the progression of osteoarthritis in mice

Gut microbiota dysbiosis has been studied under the pathological conditions of osteoarthritis (OA). However, the effect of antibiotic-induced gut flora dysbiosis on OA remains incompletely understood at present. Herein, we used a mouse (8 weeks) OA model of destabilization of the medial meniscus (DMM) and gut microbiome dysbiosis induced by antibiotic treatment with ampicillin and neomycin for 8 weeks. The results show that antibiotic-induced intestinal microbiota dysbiosis reduced the serum level of lipopolysaccharide (LPS) and the inflammatory response, such as suppression of the levels of tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6), which can lead to decreased matrix metalloprotease-13 (MMP-13) expression and improvement of OA after joint injury. In addition, trabecular thickness (Tb.Th) and osteophyte scores were increased significantly in antibiotic-induced male mice compared with female mice. We further used network correlation analysis to verify the effect of gut microbiota dysbiosis on OA. Therefore, the present study contributes to our understanding of the gut-joint axis in OA and reveals the relationship between the inflammatory response, sex and gut microbiota, which may provide new strategies to prevent the symptoms and long-term sequelae of OA. Conclusion: Our data showed that gut microbiome dysbiosis alleviates the progression of OA.

Physiological Network From Anthropometric and Blood Test Biomarkers

Currently, research in physiology focuses on molecular mechanisms underlying the functioning of living organisms. Reductionist strategies are used to decompose systems into their components and to measure changes of physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence -and collapse- of biological functions of the organism as a whole is often a less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers. We employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p-value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p-value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether Complex Inference Networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for determination and quantification of health and disease.

Proximal colon-derived O-glycosylated mucus encapsulates and modulates the microbiota

Colon mucus segregates the intestinal microbiota from host tissues, but how it organizes to function throughout the colon is unclear. In mice, we found that colon mucus consists of two distinct O-glycosylated entities of Muc2: a major form produced by the proximal colon, which encapsulates the fecal material including the microbiota, and a minor form derived from the distal colon, which adheres to the major form. The microbiota directs its own encapsulation by inducing Muc2 production from proximal colon goblet cells. In turn, O-glycans on proximal colon-derived Muc2 modulate the structure and function of the microbiota as well as transcription in the colon mucosa. Our work shows how proximal colon control of mucin production is an important element in the regulation of host-microbiota symbiosis.

Mitochondria and cellular redox state on the route from ageing to Alzheimer's disease

Several theories have been postulated, trying to explain why and how living organisms age. Despite some controversies and still huge open questions, a growing body of evidence suggest alterations of mitochondrial functionality and redox-homeostasis occur during the ageing process. Oxidative damage and mitochondrial dysfunction do not represent the cause of ageing per se but they have to be analyzed within the complexity of those series of processes occurring during lifespan. The establishment of a crosstalk among them is a shared common feature of many chronic age-related diseases, including neurodegenerative disorders, for which ageing is a major risk factor. The challenge is to understand when and how the interplay between these two systems move towards from normal ageing process to a pathological phenotype. Here in this review, we discuss the crosstalk between mitochondria and cytosolic-ROS. Furthermore, through a visual data mining approach, we attempt to describe the dynamic interplay between mitochondria and cellular redox state on the route from ageing to an AD phenotype.

Metabolic Physiological Networks: The Impact of Age

Metabolic homeostasis emerges from the interplay between several feedback systems that regulate the physiological variables related to energy expenditure and energy availability, maintaining them within a certain range. Although it is well known how each individual physiological system functions, there is little research focused on how the integration and adjustment of multiple systems results in the generation of metabolic health. The aim here was to generate an integrative model of metabolism, seen as a physiological network, and study how it changes across the human lifespan. We used data from a transverse, community-based study of an ethnically and educationally diverse sample of 2572 adults. Each participant answered an extensive questionnaire and underwent anthropometric measurements (height, weight, and waist), fasting blood tests (glucose, HbA1c, basal insulin, cholesterol HDL, LDL, triglycerides, uric acid, urea, and creatinine), along with vital signs (axillar temperature, systolic, and diastolic blood pressure). The sample was divided into 6 groups of increasing age, beginning with less than 25 years and increasing by decades up to more than 65 years. In order to model metabolic homeostasis as a network, we used these 15 physiological variables as nodes and modeled the links between them, either as a continuous association of those variables, or as a dichotomic association of their corresponding pathological states. Weight and overweight emerged as the most influential nodes in both types of networks, while high betweenness parameters, such as triglycerides, uric acid and insulin, were shown to act as gatekeepers between the affected physiological systems. As age increases, the loss of metabolic homeostasis is revealed by changes in the network’s topology that reflect changes in the system−wide interactions that, in turn, expose underlying health stages. Hence, specific structural properties of the network, such as weighted transitivity, i.e., the density of triangles in the network, can provide topological indicators of health that assess the whole state of the system. Overall, our findings show the importance of visualizing health as a network of organs and/or systems, and highlight the importance of triglycerides, insulin, uric acid and glucose as key biomarkers in the prevention of the development of metabolic disorders.

A Shift in Glycerolipid Metabolism Defines the Follicular Fluid of IVF Patients with Unexplained Infertility

Follicular fluid (FF) constitutes the microenvironment of the developing oocyte. We recently characterized its lipid composition and found lipid signatures of positive pregnancy outcome after in vitro fertilization (IVF). In the current study, we aimed to test the hypothesis that unexplained female infertility is related to lipid metabolism, given the lipid signature of positive-outcome IVF patients we previously found. Assuming that FF samples from IVF patients with male factor infertility can represent a non-hindered metabolic microenvironment, we compared them to FF taken from women with unexplained infertility. FF from patients undergoing IVF was examined for its lipid composition. We found highly increased triacylglycerol levels, with a lower abundance of monoacylglycerols, phospholipids and sphingolipids in the FF of patients with unexplained infertility. The alterations in the lipid class accumulation were independent of the body mass index (BMI) and were altogether kept across the age groups. Potential lipid biomarkers for pregnancy outcomes showed a highly discriminative abundance in the FF of unexplained infertility patients. Lipid abundance distinguished IVF patients with unrecognized infertility and provided a potential means for the evaluation of female fertility.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

mGWAS Uncovers Gln-Glucosinolate Seed-Specific Interaction and its Role in Metabolic Homeostasis

Gln is a key player in plant metabolism. It is one of the major free amino acids that is transported into the developing seed and is central for nitrogen metabolism. However, Gln natural variation and its regulation and interaction with other metabolic processes in seeds remain poorly understood. To investigate the latter, we performed a metabolic genome-wide association study (mGWAS) of Gln-related traits measured from the dry seeds of the Arabidopsis (Arabidopsis thaliana) diversity panel using all potential ratios between Gln and the other members of the Glu family as traits. This semicombinatorial approach yielded multiple candidate genes that, upon further analysis, revealed an unexpected association between the aliphatic glucosinolates (GLS) and the Gln-related traits. This finding was confirmed by an independent quantitative trait loci mapping and statistical analysis of the relationships between the Gln-related traits and the presence of specific GLS in seeds. Moreover, an analysis of Arabidopsis mutants lacking GLS showed an extensive seed-specific impact on Gln levels and composition that manifested early in seed development. The elimination of GLS in seeds was associated with a large effect on seed nitrogen and sulfur homeostasis, which conceivably led to the Gln response. This finding indicates that both Gln and GLS play key roles in shaping the seed metabolic homeostasis. It also implies that select secondary metabolites might have key functions in primary seed metabolism. Finally, our study shows that an mGWAS performed on dry seeds can uncover key metabolic interactions that occur early in seed development.

The complex response of free and bound amino acids to water stress during the seed setting stage in Arabidopsis

Free amino acids (FAAs) and protein-bound amino acids (PBAAs) in seeds play an important role in seed desiccation, longevity, and germination. However, the effect that water stress has on these two functional pools, especially when imposed during the crucial seed setting stage is unclear. To better understand these effects, we exposed Arabidopsis plants at the seed setting stage to a range of water limitation and water deprivation conditions and then evaluated physiological, metabolic, and proteomic parameters, with special focus on FAAs and PBAAs. We found that in response to severe water limitation, seed yield decreased, while seed weight, FAA, and PBAA content per seed increased. Nevertheless, the composition of FAAs and PBAAs remained unaltered. In response to severe water deprivation, however, both seed yield and weight were reduced. In addition, major alterations were observed in both FAA and proteome compositions, which indicated that both osmotic adjustment and proteomic reprogramming occurred in these naturally desiccation-tolerant organs. However, despite the major proteomic alteration, the PBAA composition did not change, suggesting that the proteomic reprogramming was followed by a proteomic rebalancing. Proteomic rebalancing has not been observed previously in response to stress, but its occurrence under stress strongly suggests its natural function. Together, our data show that the dry seed PBAA composition plays a key role in seed fitness and therefore is rigorously maintained even under severe water stress, while the FAA composition is more plastic and adaptable to changing environments, and that both functional pools are distinctly regulated.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Can metabolic tightening and expansion of co-expression network play a role in stress response and tolerance?

Plants respond and adapt to changes in their environment by employing a wide variety of genetic, molecular, and biochemical mechanisms. When so doing, they trigger large-scale rearrangements at the metabolic and transcriptional levels. The dynamics and patterns of these rearrangements and how they govern a stress response is not clear. In this opinion, we discuss a plant's response to stress from the perspective of the metabolic gene co-expression network and its rearrangement upon stress. As a case study, we use publicly available expression data of Arabidopsis thaliana plants exposed to heat and drought stress to evaluate and compare the co-expression networks of metabolic genes. The analysis highlights that stress conditions can lead to metabolic tightening and expansion of the co-expression network. We argue that this rearrangement could play a role in a plant's response to stress and thus may be an additional tool to assess and understand stress tolerance/sensitivity. Additional studies are needed to evaluate the metabolic network in response to multiple stresses at various intensities and across different genetic backgrounds (e.g., intra- and inter-species, sensitive and tolerant eco/genotypes).

Correlation network analysis shows divergent effects of a long-term, high-fat diet and exercise on early stage osteoarthritis phenotypes in mice

BACKGROUND

Obesity increases knee osteoarthritis (OA) risk through metabolic, inflammatory, and biomechanical factors, but how these systemic and local mediators interact to drive OA pathology is not well understood. We tested the effect of voluntary running exercise after chronic diet-induced obesity on knee OA-related cartilage and bone pathology in mice. We then used a correlation-based network analysis to identify systemic and local factors associated with early-stage knee OA phenotypes among the different diet and exercise groups.

METHODS

Male C57BL/6J mice were fed a defined control (10% kcal fat) or high fat (HF) (60% kcal fat) diet from 6 to 37 weeks of age. At 25 weeks, one-half of the mice from each diet group were housed in cages with running wheels for the remainder of the study. Histology, micro computed tomography, and magnetic resonance imaging were used to evaluate changes in joint tissue structure and OA pathology. These local variables were then compared to systemic metabolic (body mass, body fat, and glucose tolerance), inflammatory (serum adipokines and inflammatory mediators), and functional (mechanical tactile sensitivity and grip strength) outcomes using a correlation-based network analysis. Diet and exercise effects were evaluated by two-way analysis of variance.

RESULTS

An HF diet increased the infrapatellar fat pad size and posterior joint osteophytes, and wheel running primarily altered the subchondral cortical and trabecular bone. Neither HF diet nor exercise altered average knee cartilage OA scores compared to control groups. However, the coefficient of variation was ≥25% for many outcomes, and some mice in both diet groups developed moderate OA (≥33% maximum score). This supported using correlation-based network analyses to identify systemic and local factors associated with early-stage knee OA phenotypes. In wheel-running cohorts, an HF diet reduced the network size compared to the control diet group despite similar running distances, suggesting that diet-induced obesity dampens the effects of exercise on systemic and local OA-related factors. Each of the 4 diet and activity groups showed mostly unique networks of local and systemic factors correlated with early-stage knee OA.

CONCLUSION

Despite minimal group-level effects of chronic diet-induced obesity and voluntary wheel running on knee OA pathology under the current test durations, diet and exercise substantially altered the relationships among systemic and local variables associated with early-stage knee OA. These results suggest that distinct pre-OA phenotypes may exist prior to the development of disease.

miR-543 regulates the epigenetic landscape of myelofibrosis by targeting TET1 and TET2

Myelofibrosis (MF) is a myeloproliferative neoplasm characterized by cytopenia and extramedullary hematopoiesis, resulting in splenomegaly. Multiple pathological mechanisms (e.g., circulating cytokines and genetic alterations, such as JAKV617F mutation) have been implicated in the etiology of MF, but the molecular mechanism causing resistance to JAK2V617F inhibitor therapy remains unknown. Among MF patients who were treated with the JAK inhibitor ruxolitinib, we compared noncoding RNA profiles of ruxolitinib therapy responders versus nonresponders and found miR-543 was significantly upregulated in nonresponders. We validated these findings by reverse transcription-quantitative PCR. in this same cohort, in 2 additional independent MF patient cohorts from the United States and Romania, and in a JAK2V617F mouse model of MF. Both in vitro and in vivo models were used to determine the underlying molecular mechanism of miR-543 in MF. Here, we demonstrate that miR-543 targets the dioxygenases ten-eleven translocation 1 (TET1) and 2 (TET2) in patients and in vitro, causing increased levels of global 5-methylcytosine, while decreasing the acetylation of histone 3, STAT3, and tumor protein p53. Mechanistically, we found that activation of STAT3 by JAKs epigenetically controls miR-543 expression via binding the promoter region of miR-543. Furthermore, miR-543 upregulation promotes the expression of genes related to drug metabolism, including CYP3A4, which is involved in ruxolitinib metabolism. Our findings suggest miR-543 as a potentially novel biomarker for the prognosis of MF patients with a high risk of treatment resistance and as a potentially new target for the development of new treatment options.

Transcriptomic data-driven discovery of global regulatory features of rice seeds developing under heat stress

Plants respond to abiotic stressors through a suite of strategies including differential regulation of stress-responsive genes. Hence, characterizing the influences of the relevant global regulators or on stress-related transcription factors is critical to understand plant stress response. Rice seed development is highly sensitive to elevated temperatures. To elucidate the extent and directional hierarchy of gene regulation in rice seeds under heat stress, we developed and implemented a robust multi-level optimization-based algorithm called Minimal Regulatory Network identifier (MiReN). MiReN could predict the minimal regulatory relationship between a gene and its potential regulators from our temporal transcriptomic dataset. MiReN predictions for global regulators including stress-responsive gene Slender Rice 1 (SLR1) and disease resistance gene XA21 were validated with published literature. It also predicted novel regulatory influences of other major regulators such as Kinesin-like proteins KIN12C and STD1, and WD repeat-containing protein WD40. Out of the 228 stress-responsive transcription factors identified, we predicted de novo regulatory influences on three major groups (MADS-box M-type, MYB, and bZIP) and investigated their physiological impacts during stress. Overall, MiReN results can facilitate new experimental studies to enhance our understanding of global regulatory mechanisms triggered during heat stress, which can potentially accelerate the development of stress-tolerant cultivars.

A correlation-based network for biomarker discovery in obesity with metabolic syndrome

Background

Obesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarker profile has been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to identify existing patterns of immune-microbiome interactions in obesity.

Results

The current study performed correlation-based network analysis on five different datasets obtained from 11 obese with metabolic syndrome (MetS) and 12 healthy weight men. These datasets included: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese with MetS group had a denser network (total number of edges, n = 369) compared to the healthy network (n = 299). Within the obese with MetS network, biomarkers from the immune cell abundance group was found to be correlated to biomarkers from all four other datasets. Conversely in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. These observations suggest high involvement of immune cells in obese with MetS individuals. There were also three key hubs found among immune cells in the obese with MetS networks involving regulatory T cells, neutrophil and cytotoxic cell abundance. No hubs were present in the healthy network.

Conclusion

These results suggest a more complex interaction of inflammatory markers in obesity, with high connectivity of immune cells in the obese with MetS network compared to the healthy network. Three key hubs were identified in the obese with MetS network, involving Treg, neutrophils and cytotoxic cell abundance. Compared to a t-test, the network approach offered more meaningful results when comparing obese with MetS and healthy weight individuals, demonstrating its superiority in exploratory analysis.

Application of correlation analysis in cytology: Opportunities to study specific activity of follicular thyrocytes

The task of biomedical diagnostics is to determine the dependence of the conclusion/diagnosis on the sets of parameters that characterize the state of the biosystem/patient. The performed analytical review of modern scientific literature permitted us to determine that Bayesian, regression and correlation analyzes and logical programming are most often used for biomedical diagnostics purposes. At the same time, their informativeness can only be realized for the solution of those diagnostic tasks in which quantitative parameters are analyzed. Qualitative and binary information provides an opportunity to find out more about features of the biosystem’s state. However, its use is limited, since the results obtained are presented in words (that is, in a linguistic form) that cannot be processed by means of traditional (digital) mathematical analysis. The objective of this work was determining the capabilities of the mathematical apparatus to deepen the study of hormonopoiesis in the thyroid gland. The object of the study was electron micrographs of ultrathin tissue sections, its subject was the features of correlations between ultrastructural cell elements which carry out the processes of synthesis and secretion in follicular thyrocytes. In the context of studying the features of synthetic and secretory activity of follicular thyrocytes of the thyroid glands in white male rats, it was shown that the objectification of non-numerical information about the state of cells allows us to use linguistic information about changes in their morphofunctional state. Implementation of correlation analysis for studying the relationships and interdependencies between organelles which implement synthesis and secretion of the hormonal product in the main structural unit of the thyroid gland – follicular thyrocyte – allows us to determine, study, analyze and generalize peculiarities of both changes in individual ultrastructures and their functional complexes (clusters) in response to the actions of various factors and to trace the interdependencies and mutual interactions existing between them, as well as to deepen the idea of the intimate mechanism features of the specific directions of follicular thyrocyte activity, which substantially expands the research platform in cytophysiology and cytomorphology.

Comparison of metabolite networks from four German population-based studies

Background

Metabolite networks are suggested to reflect biological pathways in health and disease. However, it is unknown whether such metabolite networks are reproducible across different populations. Therefore, the current study aimed to investigate similarity of metabolite networks in four German population-based studies.

Methods

One hundred serum metabolites were quantified in European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (n = 2458), EPIC-Heidelberg (n = 812), KORA (Cooperative Health Research in the Augsburg Region) (n = 3029) and CARLA (Cardiovascular Disease, Living and Ageing in Halle) (n = 1427) with targeted metabolomics. In a cross-sectional analysis, Gaussian graphical models were used to construct similar networks of 100 edges each, based on partial correlations of these metabolites. The four metabolite networks of the top 100 edges were compared based on (i) common features, i.e. number of common edges, Pearson correlation (r) and hamming distance (h); and (ii) meta-analysis of the four networks.

Results

Among the four networks, 57 common edges and 66 common nodes (metabolites) were identified. Pairwise network comparisons showed moderate to high similarity (r = 63–0.96, h = 7–72), among the networks. Meta-analysis of the networks showed that, among the 100 edges and 89 nodes of the meta-analytic network, 57 edges and 66 metabolites were present in all the four networks, 58–76 edges and 75–89 nodes were present in at least three networks, and 63–84 edges and 76–87 edges were present in at least two networks. The meta-analytic network showed clear grouping of 10 sphingolipids, 8 lyso-phosphatidylcholines, 31 acyl-alkyl-phosphatidylcholines, 30 diacyl-phosphatidylcholines, 8 amino acids and 2 acylcarnitines.

Conclusions

We found structural similarity in metabolite networks from four large studies. Using a meta-analytic network, as a new approach for combining metabolite data from different studies, closely related metabolites could be identified, for some of which the biological relationships in metabolic pathways have been previously described. They are candidates for further investigation to explore their potential role in biological processes.

GC-MS metabolic profiling reveals fructose-2,6-bisphosphate regulates branched chain amino acid metabolism in the heart during fasting

INTRODUCTION

As an insulin sensitive tissue, the heart decreases glucose usage during fasting. This response is mediated, in part, by decreasing phosphofructokinase-2 (PFK-2) activity and levels of its product fructose-2,6-bisphosphate. However, the importance of fructose-2,6-bisphosphate in the fasting response on other metabolic pathways has not been evaluated.

OBJECTIVES

The goal of this study is to determine how sustaining cardiac fructose-2,6-bisphosphate levels during fasting affects the metabolomic profile.

METHODS

Control and transgenic mice expressing a constitutively active form of PFK-2 (Glyco^Hi) were subjected to either 12-h fasting or regular feeding. Animals (n = 4 per group) were used for whole-heart extraction, followed by gas chromatography-mass spectrometry metabolic profiling and multivariate data analysis.

RESULTS

Principal component analysis displayed differences between Control and Glyco^Hi groups under both fasting and fed conditions while a clear response to fasting was observed only for Control animals. However, pathway analysis revealed that these smaller changes in the Glyco^Hi group were significantly associated with branched-chain amino acid (BCAA) metabolism (~ 40% increase in all BCAAs). Correlation network analysis demonstrated clear differences in response to fasting between Control and Glyco^Hi groups amongst most parameters. Notably, fasting caused an increase in network density in the Control group from 0.12 to 0.14 while the Glyco^Hi group responded oppositely (0.17-0.15).

CONCLUSIONS

Elevated cardiac PFK-2 activity during fasting selectively increases BCAAs levels and decreases global changes in metabolism.

Integrating Multiple Interaction Networks for Gene Function Inference

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

Independent effects of dietary fat and sucrose content on chondrocyte metabolism and osteoarthritis pathology in mice

Obesity is one of the most significant risk factors for knee osteoarthritis. However, therapeutic strategies to prevent or treat obesity-associated osteoarthritis are limited because of uncertainty about the etiology of disease, particularly with regard to metabolic factors. High-fat-diet-induced obese mice have become a widely used model for testing hypotheses about how obesity increases the risk of osteoarthritis, but progress has been limited by variation in disease severity, with some reports concluding that dietary treatment alone is insufficient to induce osteoarthritis in mice. We hypothesized that increased sucrose content of typical low-fat control diets contributes to osteoarthritis pathology and thus alters outcomes when evaluating the effects of a high-fat diet. We tested this hypothesis in male C57BL/6J mice by comparing the effects of purified diets that independently varied sucrose or fat content from 6 to 26 weeks of age. Outcomes included osteoarthritis pathology, serum metabolites, and cartilage gene and protein changes associated with cellular metabolism and stress-response pathways. We found that the relative content of sucrose versus cornstarch in low-fat iso-caloric purified diets caused substantial differences in serum metabolites, joint pathology, and cartilage metabolic and stress-response pathways, despite no differences in body mass or body fat. We also found that higher dietary fat increased fatty acid metabolic enzymes in cartilage. The findings indicate that the choice of control diets should be carefully considered in mouse osteoarthritis studies. Our study also indicates that altered cartilage metabolism might be a contributing factor to how diet and obesity increase the risk of osteoarthritis.

Summary: The contribution of metabolic factors to obesity-associated knee osteoarthritis is uncertain. Here, we show how dietary fat and sucrose independently alter cartilage metabolic enzymes and osteoarthritis pathophysiology in mice.

Using microRNA Networks to Understand Cancer

Human cancers are characterized by deregulated expression of multiple microRNAs (miRNAs), involved in essential pathways that confer the malignant cells their tumorigenic potential. Each miRNA can regulate hundreds of messenger RNAs (mRNAs), while various miRNAs can control the same mRNA. Additionally, many miRNAs regulate and are regulated by other species of non-coding RNAs, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs). For this reason, it is extremely difficult to predict, study, and analyze the precise role of a single miRNA involved in human cancer, considering the complexity of its connections. Focusing on a single miRNA molecule represents a limited approach. Additional information could come from network analysis, which has become a common tool in the biological field to better understand molecular interactions. In this review, we focus on the main types of networks (monopartite, association networks and bipartite) used for analyzing biological data related to miRNA function. We briefly present the important steps to take when generating networks, illustrating the theory with published examples and with future perspectives of how this approach can help to better select miRNAs that can be therapeutically targeted in cancer.

Circulating miRNAs in sepsis-A network under attack: An in-silico prediction of the potential existence of miRNA sponges in sepsis

Biomarkers based on the molecular mechanism of sepsis are important for timely diagnosis and treatment. A large panel of small non-coding microRNAs was reported to modulate the immune response in sepsis but have not been tested in clinical practice. Large-scale identification of microRNA networks in sepsis might reveal a new biological mechanism that can be also targeted by gene therapy. Therefore, the main objective of this study is to perform a comparison of the miRNA network between septic patients and healthy controls. We used the previously measured levels of expression of 16 different circulating human and viral microRNAs in plasma from 99 septic patients and 53 healthy controls. We used three different computational methods to find correlations between the expressions of microRNAs and to build microRNA networks for the two categories, septic patients and healthy controls. We found that the microRNA network of the septic patients is significantly less connected when compared to miRNA network of the healthy controls (21 edges vs 52 edges, P < 0.0001). We hypothesize that several microRNAs (miR-16, miR-29a, miR-146, miR-155, and miR-182) are being sponged in sepsis explaining the loss of connection in the septic patient miRNA network. This was specific for sepsis, as it did not occur in other conditions characterized by an increased inflammatory response such as in post-surgery patients. Using several target prediction instruments, we predicted potential common sponges for the miRNA network in sepsis from several signaling pathways. Understanding the dynamics of miRNA network in sepsis is useful to explain the molecular pathophysiology of sepsis and for designing therapeutic strategies that target essential components of the immune response pathways.

Network-Guided GWAS Improves Identification of Genes Affecting Free Amino Acids

Amino acids are essential for proper growth and development in plants. Amino acids serve as building blocks for proteins but also are important for responses to stress and the biosynthesis of numerous essential compounds. In seed, the pool of free amino acids (FAAs) also contributes to alternative energy, desiccation, and seed vigor; thus, manipulating FAA levels can significantly impact a seed's nutritional qualities. While genome-wide association studies (GWAS) on branched-chain amino acids have identified some regulatory genes controlling seed FAAs, the genetic regulation of FAA levels, composition, and homeostasis in seeds remains mostly unresolved. Hence, we performed GWAS on 18 FAAs from a 313-ecotype Arabidopsis (Arabidopsis thaliana) association panel. Specifically, GWAS was performed on 98 traits derived from known amino acid metabolic pathways (approach 1) and then on 92 traits generated from an unbiased correlation-based metabolic network analysis (approach 2), and the results were compared. The latter approach facilitated the discovery of additional novel metabolic interactions and single-nucleotide polymorphism-trait associations not identified by the former approach. The most prominent network-guided GWAS signal was for a histidine (His)-related trait in a region containing two genes: a cationic amino acid transporter (CAT4) and a polynucleotide phosphorylase resistant to inhibition with fosmidomycin. A reverse genetics approach confirmed CAT4 to be responsible for the natural variation of His-related traits across the association panel. Given that His is a semiessential amino acid and a potent metal chelator, CAT4 orthologs could be considered as candidate genes for seed quality biofortification in crop plants.