BACKGROUND: Plasma donor-derived cell-free DNA (dd-cfDNA) is used to screen for rejection in heart transplants. We launched the Trifecta-Heart study ( ClinicalTrials.gov No. NCT04707872), an investigator-initiated, prospective trial, to examine the correlations between genome-wide molecular changes in endomyocardial biopsies (EMBs) and plasma dd-cfDNA. The present report analyzes the correlation of plasma dd-cfDNA with gene expression in EMBs from 4 vanguard centers and compared these correlations with those in 604 kidney transplant biopsies in the Trifecta-Kidney study ( ClinicalTrials.gov No. NCT04239703). METHODS: We analyzed 137 consecutive dd-cfDNA-EMB pairs from 70 patients. Plasma %dd-cfDNA was measured by the Prospera test (Natera Inc), and gene expression in EMBs was assessed by Molecular Microscope Diagnostic System using machine-learning algorithms to interpret rejection and injury states. RESULTS: Top transcripts correlating with dd-cfDNA were related to genes increased in rejection such as interferon gamma-inducible genes (eg, HLA-DMA ) but also with genes induced by injury and expressed in macrophages (eg, SERPINA1 and HMOX1 ). In gene enrichment analysis, the top dd-cfDNA-correlated genes reflected inflammation and rejection pathways. Dd-cfDNA correlations with rejection genes in EMB were similar to those seen in kidney transplant biopsies, with somewhat stronger correlations for TCMR genes in hearts and ABMR genes in kidneys. However, the correlations with parenchymal injury-induced genes and macrophage genes were much stronger in hearts. CONCLUSIONS: In this first analysis of Trifecta-Heart study, dd-cfDNA correlates significantly with molecular rejection but also with injury and macrophage infiltration, reflecting the proinflammatory properties of injured cardiomyocytes. The relationship supports the utility of dd-cfDNA in clinical management of heart transplant recipients.

• MeSH
• Biomarkers blood   MeSH
• Biopsy MeSH
• Tissue Donors * MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Myocardium * pathology   metabolism   MeSH
• Predictive Value of Tests MeSH
• Prospective Studies MeSH
• Graft Rejection * genetics   immunology   pathology   blood   diagnosis   MeSH
• Aged MeSH
• Gene Expression Profiling MeSH
• Kidney Transplantation adverse effects   MeSH
• Heart Transplantation * adverse effects   MeSH
• Cell-Free Nucleic Acids * blood   genetics   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Observational Study MeSH
• Comparative Study MeSH
• Names of Substances
• Biomarkers MeSH
• Cell-Free Nucleic Acids * MeSH

BACKGROUND: All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels. RESULTS: This paper provides a theoretical consideration of the random effect of signal aggregation and its implications for correlation analysis and network inference. An attempt is made to quantitatively assess the magnitude of this effect from real data. Some preliminary ideas are offered to mitigate the consequences of random signal aggregation in the analysis of gene expression data. CONCLUSION: Resulting from the summation of expression intensities over a random number of individual cells, the observed signals may not adequately reflect the true dependence structure of intra-cellular gene expression levels needed as a source of information for network reconstruction. Whether the reported effect is extrime or not, the important point, is to reconize and incorporate such signal source for proper inference. The usefulness of inference on genetic regulatory structures from microarray data depends critically on the ability of investigators to overcome this obstacle in a scientifically sound way. REVIEWERS: This article was reviewed by Byung Soo KIM, Jeanne Kowalski and Geoff McLachlan.

• MeSH
• Humans MeSH
• Models, Genetic * MeSH
• Statistics, Nonparametric MeSH
• Oligonucleotide Array Sequence Analysis methods   statistics & numerical data   MeSH
• Gene Expression Profiling methods   statistics & numerical data   MeSH
• Computational Biology methods   statistics & numerical data   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH
• Research Support, N.I.H., Extramural MeSH