Narrow Results

Next-generation sequencing has provided rapid collection and quantification of ‘big’ biological data. In particular, multi-omics and integration of different molecular data such as miRNA and mRNA can provide important insights to disease classification and processes. There is a need for computational methods that can correctly model and interpret these relationships, and handle the difficulties of large-scale data. In this study, we develop a novel method of representing miRNA-mRNA interactions to classify cancer. Specifically, graphs are designed to account for the interactions and biological communication between miRNAs and mRNAs, using message-passing and attention mechanisms. Patient-matched miRNA and mRNA expression data is obtained from The Cancer Genome Atlas for 12 cancers, and targeting information is incorporated from TargetScan. A Graph Transformer Network (GTN) is selected to provide high interpretability of classification through self-attention mechanisms. The GTN is able to classify the 12 different cancers with an accuracy of 93.56% and is compared to a Graph Convolutional Network, Random Forest, Support Vector Machine, and Multilayer Perceptron. While the GTN does not outperform all of the other classifiers in terms of accuracy, it allows high interpretation of results. Multi-omics models are compared and generally outperform their respective single-omics performance. Extensive analysis of attention identifies important targeting pathways and molecular biomarkers based on integrated miRNA and mRNA expression.

In an organism, genes encode proteins, some of which in turn regulate other genes. Such interactions work in highly structured but incredibly complex ways, and make up a genetic regulatory network. Recently, nonlinear delay differential equations have been proposed for describing genetic regulatory networks in the state-space form. In this paper, we study stability properties of genetic regulatory networks with time delays, by the notion of delay-independent stability. We first present necessary and sufficient conditions for delay-independent local stability of genetic regulatory networks with a single time delay, and then extend the main result to genetic regulatory networks with multiple time delays. To illustrate the main theory, we analyze delay-independent stability of three genetic regulatory networks in E. coli or zebra fish. For E. coli, an autoregulatory network and a repressilatory network are analyzed. The results show that these two genetic regulatory networks with parameters in the physiological range are delay-independently robustly stable. For zebra fish, an autoregulatory network for the gene her1 is analyzed. The result shows that delay-independent stability of this network depends on the initial number of protein molecules, which is in agreement with the existing biological knowledge. The theories presented in this paper provide a very useful complement to the previous work and a framework for further studying the stability of more complex genetic regulatory networks.

Objectives: To investigate the risk of flare-ups after COVID-19 vaccination in patients with rheumatic disease. Methods: A total of 1,617 patients with rheumatic diseases were identified from three rheumatology clinics. Patients were interviewed for demographic data, disease activity, and vaccination status. Disease flare-up was determined clinically by independent rheumatologists. Change of serum markers and medications were retrieved from medical records. The risk of exacerbation of rheumatic disease, change in serum markers, and escalation of rheumatic medications between vaccinated and nonvaccinated patients were determined using Cox, linear, and logistic regression models, respectively. Possible confounding factors were also taken into consideration. Results: Among 562 (34.76%) patients who received COVID-19 vaccination, rheumatic disease (HR = 2.10, $P<$ 0.001), inflammatory arthritis (HR = 2.71, $P<$ 0.001), rheumatoid arthritis (RA) (HR = 2.03, $P$ = 0.002), spondyloarthritis (SpA) (HR = 4.78, $P<$ 0.001), autoimmune disease (HR = 1.77, $P$ = 0.01), and systemic lupus erythematosus (SLE) (HR = 1.99, $P$ = 0.02) were associated with postvaccination clinical flare-up. Adult Still’s disease (B = 12.76, $P$ = 0.03) was associated with increased serum C-reactive protein (CRP). No association was found between vaccination and escalation of rheumatic medication. Subgroup analyses showed that only the mRNA vaccine was associated with flare-ups. Conclusion: COVID-19 vaccination was associated with minor disease flare-up but not escalation of rheumatic medications. In the absence of absolute contraindications, COVID-19 vaccination is recommended in patients with rheumatic disease.

KEY MESSAGES

1. Vaccination is effective in the prevention of morbidity due to COVID-19 in patients with autoimmune diseases.

2. The mRNA vaccine was associated with mild rheumatic disease flare-up.

3. Inactivated virus vaccine is preferable to mRNA vaccine in patients with active autoimmune disease.