Differential Correlation Network Research Articles

DSTC-Net: differential spatio-temporal correlation network for similar action recognition

DSTC-Net: differential spatio-temporal correlation network for similar action recognition

Association of circulating long-chain free fatty acids and incident diabetes risk among normoglycemic Chinese adults: a prospective nested case–control study

BackgroundLong-chain free fatty acids (FFAs) are associated with risk of incident diabetes. However, a comprehensive assessment of the associations in normoglycemic populations is lacking. ObjectivesOur study aimed to comprehensively investigate the prospective associations and patterns of FFA profiles with diabetes risk among normoglycemic Chinese adults. MethodsThis is a prospective nested case–control study from the China Cardiometabolic Disease and Cancer Cohort (4C) study. We quantitatively measured 53 serum FFAs using a targeted metabolomics approach in 1707 incident diabetes subjects and 1707 propensity score–matched normoglycemic controls. Conditional logistic regression models were employed to estimate odds ratios (ORs) for associations. Least Absolute Shrinkage and Selection Operator (LASSO) penalty regression and quantile g-computation (qg-comp) analyses were implemented to estimate the association between multi-FFA exposures and incident diabetes. ResultsThe majority of odd-chain FFAs exhibited an inverse association with incident diabetes, wherein the ORs per SD increment of all 7 saturated fatty acids (SFAs), monounsaturated fatty acid (MUFA) 15:1, and polyunsaturated fatty acid (PUFA) 25:2 were ranging from 0.79 to 0.88 (95% CIs ranging between 0.71 and 0.97). Even-chain FFAs comprised 99.3% of total FFAs and displayed heterogeneity with incident diabetes. SFAs with 18–26 carbon atoms are inversely linked to incident diabetes, with ORs ranging from 0.81 to 0.86 (95% CIs ranging between 0.73 and 0.94). MUFAs 26:1 (OR: 0.85; 95% CI: 0.76, 0.94), PUFAs 20:4 (OR: 0.84; 95% CI: 0.75, 0.94), and 24:2 (OR: 0.87; 95% CI: 0.78, 0.97) demonstrated significant associations. In multi-FFA exposure model, 24 FFAs were significantly associated with incident diabetes, most of which were consistent with univariate results. The mixture OR was 0.78 (95% CI: 0.61, 0.99; P = 0.04159). Differential correlation network analysis revealed pre-existing perturbations in intraclass and interclass FFA coregulation before diabetes onset. ConclusionsThese findings underscore the variations in diabetes risk associated with FFAs across chain length and unsaturation degree, highlighting the importance of recognizing FFA subtypes in the pathogenesis of diabetes.

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

Gut ecological networks reveal associations between bacteria, exercise, and clinical profile in non-alcoholic fatty liver disease patients.

Gut microbial dysbiosis has been observed in non-alcoholic fatty liver disease (NAFLD). The beneficial impact of exercise, the recommended lifestyle change for NAFLD patients, might be mediated by the gut microbiome (GM). However, the exact taxonomic and functional signatures associated with the host's clinical and biochemical improvement during exercise in NAFLD patients have not been elucidated yet. To investigate the impact of exercise on GM and reveal GM structures associated with NAFLD improvement during exercise. Stool samples from a 12-week randomized controlled exercise study on NAFLD subjects (N = 39) were retrieved, and shotgun metagenomics was performed at baseline and endpoint. Differential correlation network and enrichment analysis were applied to characterize the GM taxonomic and functional changes during the exercise intervention and to associate GM changes with biomarkers of liver status and metabolic dysregulation. Network analysis demonstrated that exercise induced significant changes in the bacterial interactome, which were associated with waist circumference; resting metabolic rate; plasma fasting concentrations of triglyceride, glucose, insulin, and glycated hemoglobin A1c; and homeostasis model assessment for insulin resistance. Grouping the patients in the exercise group (N = 20) as responders (N = 13) and non-responders (N = 7) using their intrahepatic lipid content (IHL) change allowed us to identify bacteria consortia contributing to the levels of alanine fermentation, methanol-, creatinine-, and protocatechuate degradation and as a result to the plasma concentrations of liver injury markers alanine transaminase, gamma-glutamyl transaminase, and aspartate transaminase. We showed that even though exercise has not a significant impact on the alpha- and beta-diversity of NAFLD patients, it leads to a significant re-structuring of the gut bacteria interactome and that specific structural changes can be linked with improvements in IHL. IMPORTANCE Our study is applying a community-based approach to examine the influence of exercise on gut microbiota (GM) and discover GM structures linked with NAFLD improvements during exercise. The majority of microbiome research has focused on finding specific species that may contribute to the development of human diseases. However, we believe that complex diseases, such as NAFLD, would be more efficiently treated using consortia of species, given that bacterial functionality is based not only on its own genetic information but also on the interaction with other microorganisms. Our results revealed that exercise significantly changes the GM interaction and that structural alterations can be linked with improvements in intrahepatic lipid content and metabolic functions. We believe that the identification of these characteristics in the GM enhances the development of exercise treatment for NAFLD and will attract general interest in this field.

Altered Serum Bile Acid Profile Associated with Chronic Allograft Dysfunction in Kidney Transplant Recipients.

BACKGROUND Chronic allograft dysfunction (CAD) is the leading cause of graft loss among kidney transplant recipients (KTRs). Bile acids (BAs) play an important role in regulating inflammatory process, which is the major contributor to the development of CAD. The aim of this study was to evaluate the association between BAs metabolic dysregulation and CAD in KTRs. MATERIAL AND METHODS Fifteen serum BA species were determined in 43 healthy controls (HCs) and 131 KTRs by UPLC-MS/MS. KTRs were grouped into stable renal function (STA) and CAD1 and CAD2 groups based on eGFR levels. Circulating CYP7A1, CYP7B1, CYP27A1, and SLCO2B1 mRNA levels were determined by RT-PCR. RESULTS Total BA concentrations were comparable among the 4 groups. However, KTRs showed significantly different BAs profiling compared to HCs. KTRs with severe CAD (CAD2) had significantly lower unconjugated BAs and secondary BAs (SBAs) compared to the other 3 groups. KTRs had significantly lower SBAs/primary BAs (PBAs) ratios than HCs, which were comparable among the 3 KTR groups. Conjugated/unconjugated BAs ratios increased significantly with the deterioration of allograft function, which was further confirmed by correlation analysis. Differential correlation network analysis revealed that perturbations in intraclass and interclass BA coregulation existed during CAD progression. Moreover, relative gene expressions of CYP7B1 and CYP27A1 were positively correlated with eGFR. CONCLUSIONS BA species profiling, but not total BA concentrations, was significantly altered in KTRs with CAD. The shifts from unconjugated BAs toward conjugated BAs, SBAs toward PBAs, and distinct pairwise BAs coregulation patterns were the main characteristics of KTRs with CAD.

A Lipid Signature with Perturbed Triacylglycerol Co-Regulation, Identified from Targeted Lipidomics, Predicts Risk for Type 2 Diabetes and Mediates the Risk from Adiposity in Two Prospective Cohorts of Chinese Adults.

The roles of individual and co-regulated lipid molecular species in the development of type 2 diabetes (T2D) and mediation from metabolic risk factors remain unknown. We conducted profiling of 166 plasma lipid species in 2 nested case-control studies within 2 independent cohorts of Chinese adults, the Dongfeng-Tongji and the Jiangsu non-communicable disease cohorts. After 4.61 (0.15) and 7.57 (1.13) years' follow-up, 1039 and 520 eligible participants developed T2D in these 2 cohorts, respectively, and controls were 1:1 matched to cases by age and sex. We found 27 lipid species, including 10 novel ones, consistently associated with T2D risk in the 2 cohorts. Differential correlation network analysis revealed significant correlations of triacylglycerol (TAG) 50:3, containing at least one oleyl chain, with 6 TAGs, at least 3 of which contain the palmitoyl chain, all downregulated within cases relative to controls among the 27 lipids in both cohorts, while the networks also both identified the oleyl chain-containing TAG 50:3 as the central hub. We further found that 13 of the 27 lipids consistently mediated the association between adiposity indicators (body mass index, waist circumference, and waist-to-height ratio) and diabetes risk in both cohorts (all P < 0.05; proportion mediated: 20.00%, 17.70%, and 17.71%, and 32.50%, 28.73%, and 33.86%, respectively). Our findings suggested notable perturbed co-regulation, inferred from differential correlation networks, between oleyl chain- and palmitoyl chain-containing TAGs before diabetes onset, with the oleyl chain-containing TAG 50:3 at the center, and provided novel etiological insight regarding lipid dysregulation in the progression from adiposity to overt T2D.

Metabolic Reprogramming and Its Relationship to Survival in Hepatocellular Carcinoma.

Hepatocarcinogenesis is frequently accompanied by substantial metabolic reprogramming to maximize the growth and proliferation of cancer cells. In this study, we carried out a comprehensive study of metabolomics and lipidomics profiles combined with gene expression analysis to characterize the metabolic reprogramming in hepatocellular carcinoma (HCC). Compared with adjacent noncancerous liver tissue, the enhanced aerobic glycolysis and de novo lipogenesis (DNL) and the repressed urea cycle were underscored in HCC tissue. Furthermore, multiscale embedded correlation analysis was performed to construct differential correlation networks and reveal pathologically relevant molecule modules. The obtained hub nodes were further screened according to the maximum biochemical diversity and the least intraclass correlation. Finally, a panel of ornithine, FFA 18:1, PC O-32:1 and TG (18:1_17:1_18:2) was generated to achieve the prognostic risk stratification of HCC patients (p < 0.001 by log-rank test). Altogether, our findings suggest that the metabolic dysfunctions of HCC detected via metabolomics and lipidomics would contribute to a better understanding of clinical relevance of hepatic metabolic reprogramming and provide potential sources for the identification of therapeutic targets and the discovery of biomarkers.

Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.

Differential correlation network analysis of rectal transcriptomes reveals cystic fibrosis-related disturbance.

Background: Intestinal pathology in cystic fibrosis (CF) remains mechanistically underexplored. Aim: We hypothesized that differential correlation network analysis of expression data would reveal hub genes of CF-related disturbance in the large bowel. Materials & methods: Transcriptomes of 29 rectal tissue samples were accessed at ArrayExpress (E-GEOD-15568 by Stanke et al.). Results: We identified 279 transcript pairs differentially correlating in CF and controls, including: ESRRA and RPL18 (rCF=0.55; rcontrols=-0.68; padj=1.60×10-100), SRP14 and FAU (rCF=-0.69; rcontrols=0.48; padj=2.99×10-90), SRP14 and GDI2 (rCF=-0.34; rcontrols=0.60; padj=1.05×10-78). The genes related to membrane protein targeting (q=8.34×10-14) and one cluster was clearly linked to male infertility. Conclusion:FAU, SRP14and GDI2 may be involved in a compensatory protein trafficking mechanism in CF rectum, highlighting their potential therapeutic value.

Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics

Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.

CorDiffViz: an R package for visualizing multi-omics differential correlation networks

BackgroundDifferential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.ResultsWe present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html.ConclusionsOur software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

Identification of HCC-Related Genes Based on Differential Partial Correlation Network.

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, but its pathogenesis is still unclear. As the disease is involved in multiple biological processes, systematic identification of disease genes and module biomarkers can provide a better understanding of disease mechanisms. In this study, we provided a network-based approach to integrate multi-omics data and discover disease-related genes. We applied our method to HCC data from The Cancer Genome Atlas (TCGA) database and obtained a functional module with 15 disease-related genes as network biomarkers. The results of classification and hierarchical clustering demonstrate that the identified functional module can effectively distinguish between the disease and the control group in both supervised and unsupervised methods. In brief, this computational method to identify potential functional disease modules could be useful to disease diagnosis and further mechanism study of complex diseases.

Lipidomic Network of Mild Cognitive Impairment from the Mayo Clinic Study of Aging.

Lipid alterations contribute to Alzheimer's disease (AD) pathogenesis. Lipidomics studies could help systematically characterize such alterations and identify potential biomarkers. To identify lipids associated with mild cognitive impairment and amyloid-β deposition, and to examine lipid correlation patterns within phenotype groupsMethods:Eighty plasma lipids were measured using mass spectrometry for 1,255 non-demented participants enrolled in the Mayo Clinic Study of Aging. Individual lipids associated with mild cognitive impairment (MCI) were first identified. Correlation network analysis was then performed to identify lipid species with stable correlations across conditions. Finally, differential correlation network analysis was used to determine lipids with altered correlations between phenotype groups, specifically cognitively unimpaired versus MCI, and with elevated brain amyloid versus without. Seven lipids were associated with MCI after adjustment for age, sex, and APOE4. Lipid correlation network analysis revealed that lipids from a few species correlated well with each other, demonstrated by subnetworks of these lipids. 177 lipid pairs differently correlated between cognitively unimpaired and MCI patients, whereas 337 pairs of lipids exhibited altered correlation between patients with and without elevated brain amyloid. In particular, 51 lipid pairs showed correlation alterations by both cognitive status and brain amyloid. Interestingly, the lipids central to the network of these 51 lipid pairs were not significantly associated with either MCI or amyloid, suggesting network-based approaches could provide biological insights complementary to traditional association analyses. Our attempt to characterize the alterations of lipids at network-level provides additional insights beyond individual lipids, as shown by differential correlations in our study.

Association of Serum Bile Acids Profile and Pathway Dysregulation With the Risk of Developing Diabetes Among Normoglycemic Chinese Adults: Findings From the 4C Study

Comprehensive assessment of serum bile acids (BAs) aberrations before diabetes onset remains inconclusive. We examined the association of serum BA profile and coregulation with the risk of developing type 2 diabetes mellitus (T2DM) among normoglycemic Chinese adults. We tested 23 serum BA species in subjects with incident diabetes (n = 1,707) and control subjects (n = 1,707) matched by propensity score (including age, sex, BMI, and fasting glucose) from the China Cardiometabolic Disease and Cancer Cohort (4C) Study, which was composed of 54,807 normoglycemic Chinese adults with a median follow-up of 3.03 years. Multivariable-adjusted odds ratios (ORs) for associations of BAs with T2DM were estimated using conditional logistic regression. In multivariable-adjusted logistic regression analysis, per SD increment of unconjugated primary and secondary BAs were inversely associated with incident diabetes, with an OR (95% CI) of 0.89 (0.83-0.96) for cholic acid, 0.90 (0.84-0.97) for chenodeoxycholic acid, and 0.90 (0.83-0.96) for deoxycholic acid (P < 0.05 and false discovery rate <0.05). On the other hand, conjugated primary BAs (glycocholic acid, taurocholic acid, glycochenodeoxycholic acid, taurochenodeoxycholic acid, and sulfated glycochenodeoxycholic acid) and secondary BA (tauroursodeoxycholic acid) were positively related with incident diabetes, with ORs ranging from 1.11 to 1.19 (95% CIs ranging between 1.05 and 1.28). In a fully adjusted model additionally adjusted for liver enzymes, HDL cholesterol, diet, 2-h postload glucose, HOMA-insulin resistance, and waist circumference, the risk estimates were similar. Differential correlation network analysis revealed that perturbations in intraclass (i.e., primary and secondary) and interclass (i.e., unconjugated and conjugated) BA coregulation preexisted before diabetes onset. These findings reveal novel changes in BAs exist before incident T2DM and support a potential role of BA metabolism in the pathogenesis of diabetes.

Atopic dermatitis displays stable and dynamic skin transcriptome signatures

Skin transcriptome studies in atopic dermatitis (AD) showed broad dysregulation as well as "improvement" under therapy. These observations were mainly made in trials and based on microarray data. Our aim was to explore the skin transcriptome and the impact of systemic treatment in patients of the TREATgermany registry. Biopsy specimens from 59 patients with moderate-to-severe AD before and 30 patients 12 weeks after start of systemic treatment (dupilumab [n= 22] or cyclosporine [n= 8]) and from 31 healthy controls were subjected to mRNA sequencing. Differential expression, pathway enrichment, correlation, and coexpression network analysis were conducted. Both lesional and nonlesional skin showed a stable "core" signature characterized by disturbed epidermal differentiation and activation of IL-31/IL-1 signaling. Asecond dynamic signature showed progressive enrichment for type 2 inflammation, TH17 signaling, and natural killer cell function. Markers correlated with disease activity have functions in epidermal barrier properties and immune modulation. IL4RA was among the top 3 central dysregulated genes. Cyclosporine led to a more pronounced global transcriptome reversion and normalized TH17 cell/IL23 signaling, whereas dupilumab led to a stronger increase in level of epidermal differentiation markers. Both treatments strongly decreased levels of type 2 markers, but overall the residual profile was still profoundly different from that of healthy skin. Lower levels of IL4RA and IL13 and high IL36A expression were related to a stronger clinical response to dupilumab. The AD core signature is characterized by dysregulation of genes related to keratinocyte differentiation and itch signaling. Adynamic signature reflects progressive immune responses dominated by type 2 cytokines with an additional role of TH17 and natural killer cell signaling.

Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis

SummaryThe coronavirus disease 2019 (COVID-19) pandemic presents an unprecedented threat to global public health. Herein, we utilized a combination of targeted and untargeted tandem mass spectrometry to analyze the plasma lipidome and metabolome in mild, moderate, and severe COVID-19 patients and healthy controls. A panel of 10 plasma metabolites effectively distinguished COVID-19 patients from healthy controls (AUC = 0.975). Plasma lipidome of COVID-19 resembled that of monosialodihexosyl ganglioside (GM3)-enriched exosomes, with enhanced levels of sphingomyelins (SMs) and GM3s, and reduced diacylglycerols (DAGs). Systems evaluation of metabolic dysregulation in COVID-19 was performed using multiscale embedded differential correlation network analyses. Using exosomes isolated from the same cohort, we demonstrated that exosomes of COVID-19 patients with elevating disease severity were increasingly enriched in GM3s. Our work suggests that GM3-enriched exosomes may partake in pathological processes related to COVID-19 pathogenesis and presents the largest repository on the plasma lipidome and metabolome distinct to COVID-19.

High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults.

Comprehensive assessment of serum lipidomic aberrations before type 2 diabetes mellitus (T2DM) onset has remained lacking in Han Chinese. We evaluated changes in lipid coregulation antecedent to T2DM and identified novel lipid predictors for T2DM in individuals with normal glucose regulation (NGR). In the discovery study, we tested 667 baseline serum lipids in subjects with incident diabetes and propensity score-matched control subjects (n = 200) from a prospective cohort comprising 3,821 Chinese adults with NGR. In the validation study, we tested 250 lipids in subjects with incident diabetes and matched control subjects (n = 724) from a pooled validation cohort of 14,651 individuals with NGR covering five geographical regions across China. Differential correlation network analyses revealed perturbed lipid coregulation antecedent to diabetes. The predictive value of a serum lipid panel independent of serum triglycerides and 2-h postload glucose was also evaluated. At the level of false-discovery rate <0.05, 38 lipids, including triacylglycerols (TAGs), lyso-phosphatidylinositols, phosphatidylcholines, polyunsaturated fatty acid (PUFA)-plasmalogen phosphatidylethanolamines (PUFA-PEps), and cholesteryl esters, were significantly associated with T2DM risk in the discovery and validation cohorts. A preliminary study found most of the lipid predictors were also significantly associated with the risk of prediabetes. Differential correlation network analysis revealed that perturbations in intraclass (i.e., non-PUFA-TAG and PUFA-TAGs) and interclass (i.e., TAGs and PUFA-PEps) lipid coregulation preexisted before diabetes onset. Our lipid panel further improved prediction of incident diabetes over conventional clinical indices. These findings revealed novel changes in lipid coregulation existing before diabetes onset and expanded the current panel of serum lipid predictors for T2DM in normoglycemic Chinese individuals.

Metabolomic signature of exposure and response to citalopram/escitalopram in depressed outpatients

Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD17) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD17 scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine–homogentisic acid and methionine–tyrosine interactions associated with HRSD17. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.

New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fisher’s Z-transformation test is usually used. However, the Fisher’s Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fisher’s Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.

Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females.

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females.