Azorhizobium caulinodans c-di-GMP phosphodiesterase Chp1 involved in motility, EPS production, and nodulation of the host plant

Establishment of the rhizobia-legume symbiosis is usually accompanied by hydrogen peroxide (H2O2) production by the legume host at the site of infection, a process detrimental to rhizobia. In Azorhizobium caulinodans ORS571, deletion of chp1, a gene encoding c-di-GMP phosphodiesterase, led to increased resistance against H2O2 and to elevated nodulation efficiency on its legume host Sesbania rostrata. Three domains were identified in the Chp1: a PAS domain, a degenerate GGDEF domain, and an EAL domain. An in vitro enzymatic activity assay showed that the degenerate GGDEF domain of Chp1 did not have diguanylate cyclase activity. The phosphodiesterase activity of Chp1 was attributed to its EAL domain which could hydrolyse c-di-GMP into pGpG. The PAS domain functioned as a regulatory domain by sensing oxygen. Deletion of Chp1 resulted in increased intracellular c-di-GMP level, decreased motility, increased aggregation, and increased EPS (extracellular polysaccharide) production. H2O2-sensitivity assay showed that increased EPS production could provide ORS571 with resistance against H2O2. Thus, the elevated nodulation efficiency of the increment chp1 mutant could be correlated with a protective role of EPS in the nodulation process. These data suggest that c-di-GMP may modulate the A. caulinodans-S. rostrata nodulation process by regulating the production of EPS which could protect rhizobia against H2O2

OSlms: A Web Server to Evaluate the Prognostic Value of Genes in Leiomyosarcoma

The availability of transcriptome data and clinical annotation offers the opportunity to identify prognosis biomarkers in cancer. However, efficient online prognosis analysis tools are still lacking. Herein, we developed a user-friendly web server, namely Online consensus Survival analysis of leiomyosarcoma (OSlms), to centralize published gene expression data and clinical datasets of leiomyosarcoma (LMS) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSlms comprises of a total of 268 samples from three independent datasets, and employs the Kaplan Meier survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for LMS patients. Using OSlms, clinicians and basic researchers could determine the prognostic significance of genes of interests and get opportunities to identify novel potential important molecules for LMS. OSlms is free and publicly accessible at http://bioinfo.henu.edu.cn/LMS/LMSList.jsp

Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of preference drift. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 9 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec

High KRT8 Expression Independently Predicts Poor Prognosis for Lung Adenocarcinoma Patients

Keratin 8 (KRT8), a type II basic intermediate filament (IF) protein, is essential for the development and metastasis of various cancers. In this study, by analyzing RNA-seq data from the Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we have determined the expression profile of KRT8, and assessed its prognostic significance and the possible mechanism underlying the dysregulation. Our results showed that KRT8 mRNA expression was significantly up-regulated in both LUAD and LUSC tissues compared with normal lung tissues. The high KRT8 expression group for LUAD patients significantly reduced overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate analysis revealed that KRT8 expression was an independent prognostic indicator for poor OS and RFS in LUAD patients. However, KRT8 expression had no prognostic value in terms of OS and RFS for LUSC. By exploring DNA copy number alterations (CNAs) of the KRT8 gene in LUAD, we found that DNA low copy gain (+1 and +2) was associated with elevated KRT8 mRNA expression. From the above findings, we have deduced that KRT8 is aberrantly expressed in LUAD tissues and that its expression might independently predict poor OS and RFS for LUAD patients, but not for LUSC patients