Comparative analysis of false discovery rate methods in constructing metabolic association networks

Correlation of blood-based immune molecules with cardiac gene expression profiles reveals insights into Chagas cardiomyopathy pathogenesis

Introduction

Understanding compartmentalized immune responses in target organs is crucial for elucidating the pathogenesis of various diseases. However, obtaining samples from affected vital organs often poses safety challenges. In this study, we aimed to investigate potential correlations between the levels of disease-associated immune molecules in the bloodstream with their gene expression profiles in the hearts of patients suffering from Chagas Cardiomyopathy (CCC). This debilitating and often fatal condition is caused by infection with the protozoan Trypanosoma cruzi.

Methods

Blood samples were analyzed using the Bio-Plex platform. Gene Expression Omnibus (GEO) database was used to determine gene expression profile in heart tissue from CCC and non-Chagas controls (CTRL).

Results

Elevated levels of inflammatory cytokines were detected in the plasma of CCC patients, and these levels correlated with clinical indicators of deteriorating cardiac function. Notably, 75% of the soluble factors assessed in the plasma exhibited a consistent relationship with their gene expression levels in the cardiac tissue of CCC patients. Analysis of interactions and signaling pathways related to these molecules revealed an overrepresentation of inflammatory pathways in both blood and heart compartments. Moreover, we identified that differentially expressed genes in CCC cardiac tissue were primarily associated with T-cell signaling pathways and correlated with the presence of CD8+ T cells in the myocardium.

Discussion

Our findings establish a strong correlation between relevant immune molecules and their signaling pathways in both the blood and heart tissue in CCC. This validates the use of blood as a non-invasive medium for understanding immunopathology and identifying markers for cardiac dysfunction in Chagas disease.

Large-Scale Simultaneous Inference with Hypothesis Testing: Multiple Testing Procedures in Practice

A statistical hypothesis test is one of the most eminent methods in statistics. Its pivotal role comes from the wide range of practical problems it can be applied to and the sparsity of data requirements. Being an unsupervised method makes it very flexible in adapting to real-world situations. The availability of high-dimensional data makes it necessary to apply such statistical hypothesis tests simultaneously to the test statistics of the underlying covariates. However, if applied without correction this leads to an inevitable increase in Type 1 errors. To counteract this effect, multiple testing procedures have been introduced to control various types of errors, most notably the Type 1 error. In this paper, we review modern multiple testing procedures for controlling either the family-wise error (FWER) or the false-discovery rate (FDR). We emphasize their principal approach allowing categorization of them as (1) single-step vs. stepwise approaches, (2) adaptive vs. non-adaptive approaches, and (3) marginal vs. joint multiple testing procedures. We place a particular focus on procedures that can deal with data with a (strong) correlation structure because real-world data are rarely uncorrelated. Furthermore, we also provide background information making the often technically intricate methods accessible for interdisciplinary data scientists.

Genetic Association with Subgingival Bacterial Colonization in Chronic Periodontitis

Chronic periodontitis is the most prevalent form of inflammatory destructive bone disease and has been affecting humans since antiquity. Evidence suggest that genetic factors can highly influence periodontitis risk, modulating disease elements such as the susceptibility to microbial colonization and the nature of subsequent host-microbe interaction. Several single-nucleotide polymorphisms (SNPs) have been associated with the occurrence of periodontitis, but the full range of genetic influence in periodontitis outcomes remains to be determined. In this context, this study comprises an analysis of possible correlation between periodontitis-related genetic variants with changes in the subgingival microbiological pattern performed in a Brazilian population (n = 167, comprising 76 chronic periodontitis patients and 91 healthy subjects). For the genetic characterization, 19 candidate SNPs were selected based on the top hits of previous large genome wide association studies (GWAS), while the subgingival microbiota was characterized for the presence and relative quantity of 40 bacterial species by DNA-DNA checkerboard. The case/control association test did not demonstrate a significant effect of the target SNPs with the disease phenotype. The polymorphism rs2521634 proved significantly associated with Tannerella forsythia, Actinomyces gerencseriae, Fusobacterium periodonticum, and Prevotella nigrescens; rs10010758 and rs6667202 were associated with increased counts of Porphyromonas gingivalis; and rs10043775 proved significantly associated with decreased counts of Prevotella intermedia. In conclusion, we present strong evidence supporting a direct connection between the host’s genetic profile, specifically rs2521634, rs10010758, rs6667202, and rs10043775 polymorphisms, and the occurrence of chronic periodontitis-associated bacteria.

Longitudinal Metabolite Profiling of Cerebrospinal Fluid in Normal Pressure Hydrocephalus Links Brain Metabolism with Exercise-Induced VEGF Production and Clinical Outcome

Idiopathic normal pressure hydrocephalus is a neurological disease caused by abnormal cerebrospinal fluid flow and presents with symptoms such as dementia. Current therapy involves the removal of excess cerebrospinal fluid by shunting. Not all patients respond to this therapy and biomarkers are needed that could facilitate the characterization of patients likely to benefit from this treatment. Here, we measure brain metabolism in normal pressure hydrocephalus patients by performing a novel longitudinal metabolomic profiling study of cerebrospinal fluid. We find that the levels of brain metabolites correlate with clinical parameters, the amount of vascular endothelial growth factor in the cerebrospinal fluid, and environmental stimuli such as exercise. Metabolomic analysis of normal pressure hydrocephalus patients provides insight into changes in brain metabolism that accompany cerebrospinal fluid disorders and may facilitate the development of new biomarkers for this condition.

Progressive Changes in the Plasma Metabolome during Malnutrition in Juvenile Pigs

Severe acute malnutrition (SAM) is one of the leading nutrition-related causes of death in children under five years of age. The clinical features of SAM are well documented, but a comprehensive understanding of the development from a normal physiological state to SAM is lacking. Characterizing the temporal metabolomic change may help to understand the disease progression and to define nutritional rehabilitation strategies. Using a piglet model we hypothesized that a progressing degree of malnutrition induces marked plasma metabolite changes. Four-week-old weaned pigs were fed a nutrient-deficient maize diet (MAL) or nutritionally optimized reference diet (REF) for 7 weeks. Plasma collected weekly was subjected to LC-MS for a nontargeted profiling of metabolites with abundance differentiation. The MAL pigs showed markedly reduced body-weight gain and lean-mass proportion relative to the REF pigs. Levels of eight essential and four nonessential amino acids showed a time-dependent deviation in the MAL pigs from that in the REF. Choline metabolites and gut microbiomic metabolites generally showed higher abundance in the MAL pigs. The results demonstrated that young malnourished pigs had a profoundly perturbed metabolism, and this provides basic knowledge about metabolic changes during malnourishment, which may be of help in designing targeted therapeutic foods for refeeding malnourished children.