Mast Cell-Specific Expression of Human Siglec-8 in Conditional Knock-in Mice

Abstract

Sialic acid-binding Ig-like lectin 8 (Siglec-8) is expressed on the surface of human eosinophils, mast cells, and basophils—cells that participate in allergic and other diseases. Ligation of Siglec-8 by specific glycan ligands or antibodies triggers eosinophil death and inhibits mast cell degranulation; consequences that could be leveraged as treatment. However, Siglec-8 is not expressed in murine and most other species, thus limiting preclinical studies in vivo. Based on a ROSA26 knock-in vector, a construct was generated that contains the CAG promoter, a LoxP-floxed-Neo-STOP fragment, and full-length Siglec-8 cDNA. Through homologous recombination, this Siglec-8 construct was targeted into the mouse genome of C57BL/6 embryonic stem (ES) cells, and chimeric mice carrying the ROSA26-Siglec-8 gene were generated. After cross-breeding to mast cell-selective Cre-recombinase transgenic lines (CPA3-Cre, and Mcpt5-Cre), the expression of Siglec-8 in different cell types was determined by RT-PCR and flow cytometry. Peritoneal mast cells (dual FcεRI+ and c-Kit+) showed the strongest levels of surface Siglec-8 expression by multicolor flow cytometry compared to expression levels on tissue-derived mast cells. Siglec-8 was seen on a small percentage of peritoneal basophils, but not other leukocytes from CPA3-Siglec-8 mice. Siglec-8 mRNA and surface protein were also detected on bone marrow-derived mast cells. Transgenic expression of Siglec-8 in mice did not affect endogenous numbers of mast cells when quantified from multiple tissues. Thus, we generated two novel mouse strains, in which human Siglec-8 is selectively expressed on mast cells. These mice may enable the study of Siglec-8 biology in mast cells and its therapeutic targeting in vivo.

Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non‐model Organisms

“…The latter are more frequently used when genomic data from closely related species are unavailable, but not always. It seems that some researchers tend to overestimate the prediction accuracy of such software, to a degree that experimentally non‐validated miRNA targets are speculated to control various biological processes . This is probably due to the misconception that the parameters that these software programs take into account (e.g., computation of miRNA–mRNA favorable thermodynamics and secondary structure of mRNA) enable us to efficiently eliminate most false positives.…”

“…It seems that speculations about miRNA–target regulation, based on prediction alone are less frequent in highly studied model organisms as experimental support can be obtained relatively easily. However, when it comes to non‐model organisms, recent publications continue with bold speculations regarding the putative role of miRNAs that regulate targets that were chosen from a predicted list without any experimental support . A disturbing fact is that in non‐model organisms the search is often made by assuming miRNA–target recognition rules that might not reflect the biology of the studied organisms .…”

“…Notably, faulty target prediction is not restricted only to non‐bilaterian animals and lineages where the mechanism is unknown as some studies in bilaterian non‐model organisms are suffering from the same problems . These studies searched for seed‐matching targets, which is indeed the relevant mechanism for bilaterians; however, their conclusions about the importance of miRNA‐related targets are most likely wrong because of the reasons discussed in the previous chapter.…”

“…Studies of miRNAs often do not stop at lists of predicted targets. Many perform functional annotation on the predicted targets, followed by Gene Ontology (GO) enrichment analysis for finding biological pathways regulated by certain miRNAs . When significant enrichment for biological processes is detected, the level of confidence in the “newly discovered” miRNA‐regulated pathway is increased.…”

“…In summary, we discussed major problems related to computational predictions of miRNA targets and their regulatory pathways. The high level of noise in lists of predicted targets (Figures and ), the failure to consider the type of the relevant miRNA–target interaction, and the pitfalls of using GO enrichment analyses on predicted targets, altogether strongly suggest that miRNA‐regulated pathways being reported without experimental support are false.…”