The pathogenesis of systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are greatly influenced by different immune cells. Nowadays both T-cell receptor (TCR) and B-cell receptor (BCR) sequencing technology have emerged with the maturity of NGS technology. However, both SLE and RA peripheral blood TCR or BCR repertoire sequencing remains lacking because repertoire sequencing is an expensive assay and consumes valuable tissue samples. This study used computational methods TRUST4 to construct TCR repertoire and BCR repertoire from bulk RNA-seq data of both SLE and RA patients’ peripheral blood and analyzed the clonality and diversity of the immune repertoire between the two diseases. Although the functions of immune cells have been studied, the mechanism is still complicated. Differentially expressed genes in each immune cell type and cell–cell interactions between immune cell clusters have not been covered. In this work, we clustered eight immune cell subsets from original scRNA-seq data and disentangled the characteristic alterations of cell subset proportion under both SLE and RA conditions. The cell–cell communication analysis tool CellChat was also utilized to analyze the influence of MIF family and GALECTIN family cytokines, which were reported to regulate SLE and RA, respectively. Our findings correspond to previous findings that MIF increases in the serum of SLE patients. This work proved that the presence of LGALS9, PTPRC and CD44 in platelets could serve as a clinical indicator of rheumatoid arthritis. Our findings comprehensively illustrate dynamic alterations in immune cells during pathogenesis of SLE and RA. This work identified specific V genes and J genes in TCR and BCR that could be used to expand our understanding of SLE and RA. These findings provide a new insight inti the diagnosis and treatment of the two autoimmune diseases.

Litsea Lam. is an ecological and economic important genus of the “core Lauraceae” group in the Lauraceae. The few studies to date on the comparative chloroplast genomics and phylogenomics of Litsea have been conducted as part of other studies on the Lauraceae. Here, we sequenced the whole chloroplast genome sequence of Litsea auriculata, an endangered tree endemic to eastern China, and compared this with previously published chloroplast genome sequences of 11 other Litsea species. The chloroplast genomes of the 12 Litsea species ranged from 152,132 (L. szemaois) to 154,011 bp (L. garrettii) and exhibited a typical quadripartite structure with conserved genome arrangement and content, with length variations in the inverted repeat regions (IRs). No codon usage preferences were detected within the 30 codons used in the chloroplast genomes, indicating a conserved evolution model for the genus. Ten intergenic spacers (psbE–petL, trnH–psbA, petA–psbJ, ndhF–rpl32, ycf4–cemA, rpl32–trnL, ndhG–ndhI, psbC–trnS, trnE–trnT, and psbM–trnD) and five protein coding genes (ndhD, matK, ccsA, ycf1, and ndhF) were identified as divergence hotspot regions and DNA barcodes of Litsea species. In total, 876 chloroplast microsatellites were located within the 12 chloroplast genomes. Phylogenetic analyses conducted using the 51 additional complete chloroplast genomes of “core Lauraceae” species demonstrated that the 12 Litsea species grouped into four sub-clades within the Laurus-Neolitsea clade, and that Litsea is polyphyletic and closely related to the genera Lindera and Laurus. Our phylogeny strongly supported the monophyly of the following three clades (Laurus–Neolitsea, Cinnamomum–Ocotea, and Machilus–Persea) among the above investigated “core Lauraceae” species. Overall, our study highlighted the taxonomic utility of chloroplast genomes in Litsea, and the genetic markers identified here will facilitate future studies on the evolution, conservation, population genetics, and phylogeography of L. auriculata and other Litsea species.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

Background: Kinases are a type of enzymes which can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates. Kinase activities could be utilized to be represented as specific biomarkers of specific cancer types. Nowadays novel algorithms have already been developed to compute kinase activities from phosphorylated proteomics data. However, phosphorylated proteomics sequencing could be costly expensive and need valuable samples. Moreover,not methods which could achieve kinase activities from bulk RNA-sequence data have been developed. Here we propose KBPRNA, a general computational framework for extracting specific kinase activities from bulk RNA-sequencing data in cancer samples. KBPRNA also achieves better performance in predicting kinase activities from bulk RNA-sequence data under cancer conditions benchmarking against other models. Results: In this study, we used LINCS-L1000 dataset which was used to be reported as efficient gene signatures in defining bulk RNA-seq data as input dataset of KBPRNA. Also, we utilized eXtreme Gradient Boosting (XGboost) as the main algorithm to extract valuable information to predict kinase activities. This model outperforms other methods such as linear regression and random forest in predicting kinase activities from bulk RNA-seq data. KBPRNA integrated tissue samples coming from breast invasive carcinoma, hepatocellular carcinoma, lung squamous cell carcinoma, Glioblastoma multiforme and Uterine Corpus Endometrial Carcinoma. It was found that KBPRNA achieved good performance with an average R score above threshold of 0.5 in kinase activity prediction. Conclusions: Model training and testing process showed that KBPRNA outperformed other machine learning methods in predicting kinase activities coming from various cancer types’ tissue samples. This model could be utilized to approximate basic kinase activities and link it with specific biological functions, which in further promoted the progress of cancer identification and prognosis.