A Single-Tube Real-Time PCR Assay for Mycoplasma Detection as a Routine Quality Control of Cell Therapeutics

BACKGROUND: Contamination of cell culture and biological material by mollicute species is an important safety issue and requires testing. We have developed a singletube real-time polymerase chain reaction (PCR) assay for rapid detection of Mollicutes species stipulated by the European Pharmacopeia. METHODS: Primers and TaqMan probes (FAM- abeled) were deduced from 16S rDNA sequence alignment of 18 mollicutes species. A synthetic internal control (IC) DNA and an IC-specific TaqMan probe (VIC-labeled) were included. The analytical sensitivity of the assay was determined on DNA dilutions from 12 mollicute strains. Specificity was proven by the use of DNA from other bacteria. RESULTS: Analytical sensitivities of the PCR assay were in the range of 405–2,431 genomes/ml for 11 of the 12 tested mollicute DNA samples. The lowest sensitivity was found for Ureaplasma urealyticum (19,239 genomes/ml). Negative results for DNA samples from 3 different ubiquitous bacteria demonstrated the specificity of the PCR assay for Mollicutes. Direct testing of cell culture supernatants spiked with Mycoplasma orale revealed similar sensitivity compared to isolated DNA. CONCLUSION: Our single-tube real-time PCR assay with internal reaction control enables rapid and specific detection of mollicute contaminants. The test protocol is suitable for routine quality control of cell therapeutics

Molecular Screening for Vel- Blood Donors in Southwestern Germany

BACKGROUND: The SMIM1 protein carries the Vel blood group antigen, and homozygosity for a 17 bp deletion in the coding region of the SMIM1 gene represents the molecular basis of the Vel- blood group phenotype. We developed PCR-based methods for typing the SMIM1 17 bp (64-80del) gene deletion and performed a molecular screening for the Vel- blood type in German blood donors. METHODS: For SMIM1 genotyping, TaqMan-PCR and PCRSSP methods were developed and validated using reference samples. Both methods were used for screening of donors with blood group O from southwestern Germany. Heterozygotes and homozygotes for the SMIM1 64-80del allele were serologically typed for the Vel blood group antigen. In addition, the rs1175550 SNP in SMIM1 was typed and correlated to the results of the phenotyping. RESULTS: Both genotyping methods, TaqMan-PCR and PCR-SSP, represent reliable methods for the detection of the SMIM1 64-80del allele. Screening of 10,598 blood group O donors revealed 5 individuals homozygous for the deletional allele. They were confirmed Vel- by serological typing. Heterozygotes for the 64-80del allele showed different antigen expressions ranging from very weak to regular positive. CONCLUSION: Molecular screening of blood donors for the Vel- blood type is feasible and avoids the limitations of serological typing which might show false-negative results with heterozygous individuals. The identification of Vel- blood donors significantly contributes to the adequate blood supply of patients with anti-Vel

Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model

We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons (‘exuviae’) and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples