Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.

As a tumor-associated antigen and a surface marker of breast cancer stem cells (BCSCs), epithelial cell adhesion molecule (EpCAM) plays an important role in not only cell adhesion, morphogenesis, metastases but also carcinogenesis. A non-synonymous C/T polymorphism (rs1126497) in exon3 of EpCAM causes a transition of 115 amino acid from Met to Thr. Another polymorphism (A/G, rs1421) in the 3'UTR causes loss of has-miR-1183 binding. A multiple independent case-control analysis was performed to assess the association between EpCAM genotypes and breast cancer risk. We observed that the variant EpCAM genotype (rs1126497 CT, and TT) was associated with substantially increased risk of breast cancer. Genotyping a total of 1643 individuals with breast cancer and 1818 control subjects in Eastern and Southern Chinese populations showed that rs1126497 CT + TT genotype had an odd ratio of 1.40 (95% confidence interval, 1.16-1.57) for developing breast cancer compared with CC genotype. The allele T increases the risk of breast cancer in a dose-dependent response manner (P (trend) < 0.001). Moreover, compared to breast cancer patients carrying the CC genotype, the EpCAM SNP rs1126497 CT or TT carrier was significantly associated with early breast cancer onset (P = 0.0023). However, no significant difference was found in genotype frequencies at the rs1421 A/G site between cases and controls. These findings suggest that M115T polymorphism in EpCAM may be a genetic modifier for developing breast cancer.

Cellular senescence represents the state of irreversible cell cycle arrest during cell division. Cellular senescence not only plays a role in diverse biological events such as embryogenesis, tissue regeneration and repair, ageing and tumour occurrence prevention, but it is also involved in many cardiovascular, renal and liver diseases through the senescence-associated secretory phenotype (SASP). This review summarizes the molecular mechanisms underlying cellular senescence and its possible effects on a variety of renal diseases. We will also discuss the therapeutic approaches based on the regulation of senescent and SASP blockade, which is considered as a promising strategy for the management of renal diseases. K E Y W O R D Sacute renal injury, cellular senescence, diabetic nephropathy, glomerulonephritis, kidney transplanation, renal diseases, renal fibrosis, senescence-associated secretory phynotype

Background Chimeric antigen receptor T‐cell (CAR‐T) therapy for acute myeloid leukaemia (AML) has thus far been elusive, in part due to target restriction and phenotypic heterogeneity of AML cells. Mutations of the FMS‐like tyrosine kinase 3 (FLT3) and DNA methyltransferase 3A (DNMT3A) genes are common driver mutations that present with a poor prognosis in AML patients. We found that AML patients with FLT3 or DNMT3A mutations had higher expression of CD44 isoform 6 (CD44v6) compared to normal specimens. Therefore, we intended to demonstrate CD44v6 could be a specific option for AML with FLT3 or DNMT3A mutations. Methods Internal tandem duplication (ITD) mutations of FLT3 (FLT3/ITD) knock‐in clone and DNMT3A‐R882H mutant clones of SKM‐1 cells were generated using CRISPR/Cas9 and lentiviral transfection, respectively. CD44v6 CAR‐T cells were constructed by transfecting T cells with lentivirus containing CD44v6 CAR. CD44v6 expression in AML cell lines, AML patients and healthy donors was evaluated by flow cytometry. DNA methylation assays were used to analyse the mechanisms of FLT3 and DNMT3A mutations affecting CD44v6 expression. Results Aberrant overexpression of CD44v6 was observed in AML cell lines with FLT3 or DNMT3A mutations compared to the wild‐type SKM‐1 or K562 cells. AML patients with FLT3 or DNMT3A mutations had higher expression of CD44v6 compared to normal specimens. Then we constructed CD44v6 CAR‐T cells and found that CD44v6 CAR‐T specifically lysed CD44v6+ cells, accompanied by cytokines release. No significant killing effect was observed from CD44v6‐ AML cells and normal cells after co‐culture with CD44v6 CAR‐T. These results were also observed in vivo. Furthermore, we found that FLT3 or DNMT3A mutations induced CD44v6 overexpression by downregulating the CpG methylation of CD44 promoter. Conclusions Collectively, CD44v6 is a promising target of CAR‐T for AML patients with FLT3 or DNMT3A mutations.

Apolipoprotein is a kind of protein which can transport the lipids through the lymphatic and circulatory systems. The abnormal expression level of apolipoprotein always causes angiocardiopathy. Thus, correct recognition of apolipoprotein from proteomic data is very crucial to the comprehension of cardiovascular system and drug design. This study is to develop a computational model to predict apolipoproteins. In the model, the apolipoproteins and non-apolipoproteins were collected to form benchmark dataset. On the basis of the dataset, we extracted the g-gap dipeptide composition information from residue sequences to formulate protein samples. To exclude redundant information or noise, the analysis of various (ANOVA)-based feature selection technique was proposed to find out the best feature subset. The support vector machine (SVM) was selected as discrimination algorithm. Results show that 96.2% of sensitivity and 99.3% of specificity were achieved in five-fold cross-validation. These findings open new perspectives to improve apolipoproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease.