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The problem of designing an RNA sequence v that encodes for a given target protein w plays an important role in messenger RNA (mRNA) vaccine design. Due to codon degeneracy, there exist exponentially many RNA sequences for a single target protein. These candidate RNA sequences may adopt different secondary structure conformations with varying minimum free energy (MFE), affecting their thermodynamic stability and consequently mRNA half-life. In addition, species-specific codon usage bias, as measured by the codon adaptation index (CAI), also plays an essential role in translation efficiency. While previous works have focused on optimizing either MFE or CAI, more recent works have shown the merits of optimizing both objectives. Importantly, there is a trade-off between MFE and CAI, i.e. optimizing one objective is at the expense of the other. Here, we formulate the Pareto Optimal RNA Design problem, seeking the set of Pareto optimal solutions for which no other solution exists that is better in terms of both MFE and CAI. We introduce DERNA (DEsign RNA), which uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. DERNA uses dynamic programming to solve each convex combination in O(|w|³) time and O(|w|²) space. Compared to a previous approach that only optimizes MFE, we show on a benchmark dataset that DERNA obtains solutions with identical MFE but superior CAI. Additionally, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. Finally, we demonstrate our method’s potential for mRNA vaccine design using SARS-CoV-2 spike as the target protein. 

Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees.

We introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T.

https://github.com/elkebir-group/MCT.

Supplementary data are available at Bioinformatics online.

 

Peripheral blood mononuclear cells (PBMCs) play important roles in the pathogenesis of IgA nephropathy (IgAN). Our study aimed to provide a deep understanding of IgAN and focused on the dysregulation of hsa‐miR‐590‐3p and its target geneHMGB2in PBMCs. Three gene expression profile datasets (GSE14795, GSE73953 and GSE25590) were downloaded from the GEO database. The DEGs (differentially expressed genes)‐miRNA network that was associated with IgAN was constructed by Cytoscape, and HMGB2 and hsa‐miR‐590‐3p were selected for further exploration. The dual‐luciferase reporter system was utilized to verify their interaction. Then, the expression levels of HMGB2 and hsa‐miR‐590‐3p in PBMCs were detected by qPCR in another cohort, and the correlation of their expression levels with the clinical pathological manifestations and serum Gd‐IgA1(galactose‐deficient IgA1) levels was also investigated.HMGB2was identified as the target gene of hsa‐miR‐590‐3p. Furtherly, the elderly patients had higher HMGB2 expression levels than the expression levels of the younger patients. As the serum creatinine, serum BUN levels increased, the expression of HMGB2 decreased; Besides, the HMGB2 expression was positively correlated with serum complement 3(C3) levels, and it also had a negative correlation with the diastolic blood pressure, but not reach statistical significance. What is more, both hsa‐miR‐590‐3p and HMGB2 expression had a slight correlation tendency with serum Gd‐IgA1 levels in the whole population. In conclusion, HMGB2, the target gene of hsa‐miR‐590‐3p, was identified to correlate with the severity of IgAN, and this provides more clues for the pathogenesis of IgAN.