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Lutter, D.

Publications and source records attributed to Lutter, D..

2 recordsLinked to original sources

Molecular classification of the placebo effect in nausea

Numerous studies have shown that the mere expectation improvement can alleviate symptoms in various conditions. These placebo effects often include reliable changes in central and peripheral organ systems. Here, we tested for the first time whether placebo effects can be monitored and predicted by plasma proteins. In a randomized controlled design, 90 healthy participants were exposed to a 20-min vection stimulus on two separate days and were randomly allocated to placebo treatment or no treatment on the second day. Significant placebo effects on nausea, motion sickness, and gastric activity could be verified. Using state-of-the-art proteomics, 74 differentially regulated proteins were identified in placebo-treated participants as compared to no-treatment controls. Gene ontology (GO) enrichment analyses of these proteins revealed acute-phase proteins as well as microinflammatory proteins to be reliable plasma correlates of the placebo effect. Regression analyses showed that day-adjusted scores of nausea indices in the placebo group were predictable by the identified GO protein signatures. We next identified specific plasma proteins, for which a significant amount of variance could be explained by the experimental factors sex, group, nausea, or their interactions. GO enrichment analyses of these proteins identified grooming behavior as a prominent hit, based on neurexin-1 (NRXN1) and contactin-associated protein-like 4 (CNTNAP4). Finally, Receiver Operator Characteristics (ROC) allowed to identify specific plasma proteins differentiating placebo responders from non-responders. These comprised immunoglobulins (IGHM, IGKV1D-16, IGHV3-23, IGHG1) and MASP2, related to regulation of complement activation, as well as proteins involved in oxidation reduction processes (QSOX1, CP TXN). This proof-of-concept study indicates that plasma proteomics are a promising tool to identify molecular correlates and predictors of the placebo effect in humans.

neuroscience

Correlation guided Network Integration (CoNI) reveals novel genetic regulators of hepatic metabolism

ABSTRACTThe steadily increasing amount of newly generated omics data of various types from genomics to metabolomics is a chance and a challenge to systems biology. To fully use its potential, one key is the meaningful integration of different types of omics. We here present a fully unsupervised and versatile correlation-based method, termed Correlation guided Network Integration (CoNI), to integrate multi-omics data into a hypergraph structure that allows for identification of effective regulators. Our approach further unravels single transcripts mapped to specific densely connected metabolic sub-graphs or pathways. By applying our method on transcriptomics and metabolomics data from murine livers under standard chow or high-fat-diet, we isolated eleven genes with a regulatory effect on hepatic metabolism. Subsequent in vitro and ex vivo experiments in human liver cells and human obtained liver biopsies validated seven candidates including INHBE and COBLL1, to alter lipid metabolism and to correlate with diabetes related traits such as overweight, hepatic fat content and insulin resistance (HOMA-IR). Last, we successfully applied our methods to an independent data-set to confirm its versatile and transferable character.Competing Interest StatementMatthias H. Tschoep is a scientific advisor to Novo Nordisk, and ERX. Jerzy Adamski is a scientific advisor to Biocrates Life Sciences AG. View Full Text

bioinformatics