Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction in graph structures. In the pursuit of predicting gene expression, an endeavor shared by predecessors like the L1000 and Enformer methods, we introduce a novel spatial graph-neural network (GNN) approach. This innovative strategy incorporates graph features, encompassing both regulatory and structural elements. The regulatory elements include pair-wise gene correlation, biological pathways, protein-protein interaction networks, and transcription factor regulation. The spatial structural elements include chromosomal distance, histone modification and Hi-C inferred 3D genomic features. Principal Node Aggregation models, validated independently, emerge as frontrunners, demonstrating superior performance compared to traditional regression and other deep learning models. By embracing the spatial GNN paradigm, our method significantly advances the description of the intricate network of gene interactions, surpassing the performance, predictable scope, and initial requirements set by previous methods.

Deep neural network based tissue deconvolution of circulating tumor cell RNA.

Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis.

Serum Metabolites Associated with Blood Pressure in Chronic Kidney Disease Patients.

Blood pressure is one of the most basic health screenings and it has a complex relationship with chronic kidney disease (CKD). Controlling blood pressure for CKD patients is crucial for curbing kidney function decline and reducing the risk of cardiovascular disease. Two independent CKD cohorts, including matched controls (discovery n = 824; validation n = 552), were recruited. High-throughput metabolomics was conducted with the patients' serum samples using mass spectrometry. After controlling for CKD severity and other clinical hypertension risk factors, we identified ten metabolites that have significant associations with blood pressure. The quantitative importance of these metabolites was verified in a fully connected neural network model. Of the ten metabolites, seven have not previously been associated with blood pressure. The metabolites that had the strongest positive association with blood pressure were aspartylglycosamine (p = 4.58 × 10-5), fructose-1,6-diphosphate (p = 1.19 × 10-4) and N-Acetylserine (p = 3.27 × 10-4). Three metabolites that were negatively associated with blood pressure (phosphocreatine, p = 6.39 × 10-3; dodecanedioic acid, p = 0.01; phosphate, p = 0.04) have been reported previously to have beneficial effects on hypertension. These results suggest that intake of metabolites as supplements may help to control blood pressure in CKD patients.