Anti-MMRN1 (aa 291-390) polyclonal antibody

The inbred mouse strain C57BL/6 is commonly used for the generation of transgenic mouse and is a well established strain in bone research. Different vendors supply different substrains of C57BL/6J as wild-type animals when genetic drift did not incur any noticeable phenotype. However, we sporadically observed drastic differences in the bone phenotype of WT C57BL/6J mice originating from different labs and speculated that these variations are attributable, at least in part, to the variation between C57BL/6J substrains, which is often overlooked. C57BL/6J-OlaHsd is a commonly used substrain that despite a well defined deletion in the alpha-synuclein (Snca) and multimerin-1 (Mmrn1) genes, was reported to display no obvious phenotype and is used as WT control. Here, we compared the bone phenotype of C57BL/6J-OlaHsd (6J-OLA) to C57BL/6J-RccHsd (6J-RCC) and to the original C57BL/6J (6J-JAX). Using CT analysis, we found that 6J-OLA mice display a significantly lower trabecular bone mass compared to 6J-RCC and 6J-JAX. PCR analysis revealed that both the Snca and Mmrn1 genes are expressed in bone tissue of 6J-RCC animals but not of 6J-OLA mutants, suggesting either one or both genes play a role in bone metabolism. In vitro analysis demonstrated increase in osteoclasts number and decreased osteoblast mineralization in cells derived from 6J-OLA compared with 6J-RCC. Our data may shed light on unexplained differences in basal bone measurements between different research centers and reiterate the importance of specifying the exact substrain type. In addition, our findings describe the physiological role for Mmrn1 and/or Snca in bone remodeling.

Background: Increasing studies have shown the important clinical role of immune and stromal cells in gastric cancer microenvironment. Based on information of immune and stromal cells in The Cancer Genome Atlas, this study aimed to construct a prognostic risk assessment model for gastric cancer. Material/Methods: Based on the immune/structural scores, differentially expressed genes (DEGs) were filtered and analyzed. Afterwards, DEGs associated with prognosis were screened and the risk assessment model was constructed in the training set. Moreover, the validity of the model was verified both in the testing set and the overall sample. Results: In this study, patients were divided into high-score and low-score groups based on immune/stromal score, and 919 DEGs were identified. By applying least absolute shrinkage and selection operator (LASSO) and Cox analysis, 10 mRNAs were selected to form a prognostic risk assessment model, risk score=(0.294*SLC17A9) + (-0.477*FERMT3) + (0.866*NRP1) + (0.350*MMRN1) + (0.381*RNASE1) + (0.189*TRIB3) + (0.230*PGAP3) + (0.087*MAGEA3) + (0.182*TACR2) + (0.368*CYP51A1). In the training set, the low-risk group divided by the model was found to have better overall survival, and the prediction efficiency of the model was demonstrated to be good. Multivariate Cox analysis indicated that the model could work as a prognostic factor independently. Similar results were shown in the testing group and overall patients cohort group. Finally, the risk assessment model and other clinical variables were integrated to construct a nomogram. Conclusions: In general, this study constructs a prognostic risk assessment model for gastric cancer, which could improve the prognosis stratification of patients combined with other clinical indicators.