Unveiling the functional heterogeneity of cytokine-primed human umbilical cord mesenchymal stem cells through single-cell RNA sequencing.

BACKGROUND: Mesenchymal stem cells (MSCs) hold immense promise for use in immunomodulation and regenerative medicine. However, their inherent heterogeneity makes it difficult to achieve optimal therapeutic outcomes for a specific clinical disease. Primed MSCs containing a certain cytokine can enhance their particular functions, thereby increasing their therapeutic potential for related diseases. Therefore, understanding the characteristic changes and underlying mechanisms of MSCs primed by various cytokines is highly important. RESULTS: In this study, we aimed to reveal the cellular heterogeneity, functional subpopulations, and molecular mechanisms of MSCs primed with IFN-γ, TNF-α, IL-4, IL-6, IL-15, and IL-17 using single-cell RNA sequencing (scRNA-seq). Our results demonstrated that cytokine priming minimized the heterogeneity of the MSC transcriptome, while the expression of MSC surface markers exhibited only slight changes. Notably, compared to IL-6, IL-15, and IL-17; IFN-γ, TNF-α, and IL-4 priming, which stimulated a significantly greater number of differentially expressed genes (DEGs). Functional analysis, which included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, indicated that IFN-γ, TNF-α, and IL-4-primed hUC-MSCs are involved in interferon-mediated immune-related processes, leukocyte migration, chemotaxis potential, and extracellular matrix and cell adhesion, respectively. Moreover, an investigation of various biological function scores demonstrated that IFN-γ-primed hUC-MSCs exhibit strong immunomodulatory ability, TNF-α-primed hUC-MSCs exhibit high chemotaxis potential, and IL-4-primed hUC-MSCs express elevated amounts of collagen. Finally, we observed that cytokine priming alters the distribution of functional subpopulations of MSCs, and these subpopulations exhibit various potential biological functions. Taken together, our study revealed the distinct regulatory effects of cytokine priming on MSC heterogeneity, biological function, and functional subpopulations at the single-cell level. CONCLUSIONS: These findings contribute to a comprehensive understanding of the inflammatory priming of MSCs, paving the way for their precise treatment in clinical applications.

Interleukin-18-primed human umbilical cord-mesenchymal stem cells achieve superior therapeutic efficacy for severe viral pneumonia via enhancing T-cell immunosuppression.

Coronavirus disease 2019 (COVID-19) treatments are still urgently needed for critically and severely ill patients. Human umbilical cord-mesenchymal stem cells (hUC-MSCs) infusion has therapeutic benefits in COVID-19 patients; however, uncertain therapeutic efficacy has been reported in severe patients. In this study, we selected an appropriate cytokine, IL-18, based on the special cytokine expression profile in severe pneumonia of mice induced by H1N1virus to prime hUC-MSCs in vitro and improve the therapeutic effect of hUC-MSCs in vivo. In vitro, we demonstrated that IL-18-primed hUC-MSCs (IL18-hUCMSC) have higher proliferative ability than non-primed hUC-MSCs (hUCMSCcon). In addition, VCAM-1, MMP-1, TGF-ß1, and some chemokines (CCL2 and CXCL12 cytokines) are more highly expressed in IL18-hUCMSCs. We found that IL18-hUCMSC significantly enhanced the immunosuppressive effect on CD3+ T-cells. In vivo, we demonstrated that IL18-hUCMSC infusion could reduce the body weight loss caused by a viral infection and significantly improve the survival rate. Of note, IL18-hUCMSC can also significantly attenuate certain clinical symptoms, including reduced activity, ruffled fur, hunched backs, and lung injuries. Pathologically, IL18-hUCMSC transplantation significantly enhanced the inhibition of inflammation, viral load, fibrosis, and cell apoptosis in acute lung injuries. Notably, IL18-hUCMSC treatment has a superior inhibitory effect on T-cell exudation and proinflammatory cytokine secretion in bronchoalveolar lavage fluid (BALF). Altogether, IL-18 is a promising cytokine that can prime hUC-MSCs to improve the efficacy of precision therapy against viral-induced pneumonia, such as COVID-19.

Extracting physicochemical features to predict protein secondary structure.

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

Modelling splice sites with locality-sensitive sequence features.

The splice sites are essential for pre-mRNA maturation and crucial for Splice Site Modelling (SSM); however, there are gaps between the splicing signals and the computationally identified sequence features. In this paper, the Locality Sensitive Features (LSFs) are proposed to reduce the gaps by homogenising their contexts. Under the skewness-kurtosis based statistics and data analysis, SSM attributed with LSFs is fulfilled by double-boundary outlier filters. The LSF-based SSM had been applied to six model organisms of diverse species; by the accuracy and Receiver Operating Characteristic (ROC) analysis, the promising results show the proposed methodology is versatile and robust for the splice-site classification. It is prospective the LSF-based SSM can serve as a new infrastructure for developing effective splice-site prediction methods and have the potential to be applied to other sequence prediction problems.

Intron identification approaches based on weighted features and fuzzy decision trees.

Current computational predictions of splice sites largely depend on the sequence patterns of known intronic sequence features (ISFs) described in the classical intron definition model (IDM). The computation-oriented IDM (CO-IDM) clearly provides more specific and concrete information for describing intron flanks of splice sites (IFSSs). In the paper, we proposed a novel approach of fuzzy decision trees (FDTs) which utilize (1) weighted ISFs of twelve uni-frame patterns (UFPs) and forty-five multi-frame patterns (MFPs) and (2) gain ratios to improve the performances in identifying an intron. First, we fuzzified extracted features from genomic sequences using membership functions with an unsupervised self-organizing map (SOM) technique. Then, we brought in different viewpoints of globally weighting and crossly referring in generating fuzzy rules, which are interpretable and useful for biologists to verify whether a sequence is an intron or not. Finally, the experimental results revealed the effectiveness of the proposed method in improving the identification accuracy. Besides, we also implemented an on-line intronic identifier to infer an unknown genomic sequence.

Finding new core promoter elements using backward-looking strategies.

Core Promoter Elements (CPEs) were key players in transcription initiation. Identifying CPEs is crucial for understanding gene expression. In this paper, a framework for finding new CPEs was proposed. An experiment was performed on the sequences of Eukaryotic Promoter Database (EPD). From the results, the known CPEs were all recovered; in addition, five new motifs were discovered in Drosophila and three in human. By comparing the results with currently known CPEs, it is shown that the proposed system is feasible and reliable, and these new CPEs are worth of further exploration.