Survey of CF mutations in the clinical laboratory

BACKGROUND: Since it is impossible to sequence the complete CFTR gene routinely, clinical laboratories must rely on test systems that screen for a panel of the most frequent mutations causing disease in a high percentage of patients. Thus, in a cohort of 257 persons that were referred to our laboratory for analysis of CF gene mutations, reverse line probe assays for the most common CF mutations were performed. These techniques were evaluated as routine first-line analyses of the CFTR gene status. METHODS: DNA from whole blood specimens was extracted and subjected to PCR amplification of 9 exons and 6 introns of the CFTR gene. The resulting amplicons were hybridised to probes for CF mutations and polymorphisms, immobilised on membranes supplied by Roche Molecular Systems, Inc. and Innogenetics, Inc.. Denaturing gradient gel electrophoresis and sequencing of suspicious fragments indicating mutations were done with CF exon and intron specific primers. RESULTS: Of the 257 persons tested over the last three years (referrals based on 1) clinical symptoms typical for/indicative of CF, 2) indication for in vitro fertilisation, and 3) gene status determination because of anticipated parenthood and partners or relatives affected by CF), the reverse line blots detected heterozygote or homozygote mutations in the CFTR gene in 68 persons (26%). Eighty-three percent of those affected were heterozygous (47 persons) or homozygous (10 persons) for the ΔF508 allele. The only other CF-alleles that we found with these tests were the G542X allele (3 persons), the G551D allele (3 persons), the 3849+10kb C-T allele (2 persons) the R117H allele (2 persons) and the 621+1G-T allele (1 person). Of the fifteen IVS8-5T-polymorphisms detected in intron 8, seven (47%) were found in males referred to us from IVF clinics. These seven 5T-alleles were all coupled with a heterozygous ΔF508 allele, they make up 35% of the males with fertility problems (20 men) referred to us. CONCLUSIONS: In summary, the frequency of CF chromosomes in the cohort examined with these tests was 26%, with the ΔF508 allele affecting 83% of the CF chromosomes. It is a substantial improvement for routine CF diagnostics to have available a test system for 30 mutations plus the polypyrimidine length variants in intron 8. Our results show that this test system allows a routine first-line analyses of the CFTR gene status

Different higher order kinematics between star-forming and quiescent galaxies based on the SAMI, MAGPI, and LEGA-C surveys

We present the first statistical study of spatially integrated non-Gaussian stellar kinematics spanning 7 Gyr in cosmic time. We use deep, rest-frame optical spectroscopy of massive galaxies (stellar mass ⁠) at redshifts z = 0.05, 0.3, and 0.8 from the SAMI, MAGPI, and LEGA-C surveys, to measure the excess kurtosis h4 of the stellar velocity distribution, the latter parametrized as a Gauss–Hermite series. We find that at all redshifts where we have large enough samples, h4 anticorrelates with the ratio between rotation and dispersion, highlighting the physical connection between these two kinematic observables. In addition, and independently from the anticorrelation with rotation-to-dispersion ratio, we also find a correlation between h4 and M⋆, potentially connected to the assembly history of galaxies. In contrast, after controlling for mass, we find no evidence of independent correlation between h4 and aperture velocity dispersion or galaxy size. These results hold for both star-forming and quiescent galaxies. For quiescent galaxies, h4 also correlates with projected shape, even after controlling for the rotation-to-dispersion ratio. At any given redshift, star-forming galaxies have lower h4 compared to quiescent galaxies, highlighting the link between kinematic structure and star-forming activity

Evolution in the orbital structure of quiescent galaxies from MAGPI, LEGA-C, and SAMI surveys: direct evidence for merger-driven growth over the last 7 Gyr

We present the first study of spatially integrated higher-order stellar kinematics over cosmic time. We use deep rest-frame optical spectroscopy of quiescent galaxies at redshifts z = 0.05, 0.3, and 0.8 from the SAMI, MAGPI, and LEGA-C surveys to measure the excess kurtosis h4 of the stellar velocity distribution, the latter parametrized as a Gauss-Hermite series. Conservatively using a redshift-independent cut in stellar mass (⁠⁠) and matching the stellar-mass distributions of our samples, we find 7σ evidence of h4 increasing with cosmic time, from a median value of 0.019 ± 0.002 at z = 0.8 to 0.059 ± 0.004 at z = 0.06. Alternatively, we use a physically motivated sample selection based on the mass distribution of the progenitors of local quiescent galaxies as inferred from numerical simulations; in this case, we find 10σ evidence. This evolution suggests that, over the last 7 Gyr, there has been a gradual decrease in the rotation-to-dispersion ratio and an increase in the radial anisotropy of the stellar velocity distribution, qualitatively consistent with accretion of gas-poor satellites. These findings demonstrate that massive galaxies continue to accrete mass and increase their dispersion support after becoming quiescent

A targeted amplicon next-generation sequencing assay for tryptase genotyping to support personalized therapy in mast cell-related disorders.

Tryptase, the most abundant mast cell granule protein, is elevated in severe asthma patients independent of type 2 inflammation status. Higher active β tryptase allele counts are associated with higher levels of peripheral tryptase and lower clinical benefit from anti-IgE therapies. Tryptase is a therapeutic target of interest in severe asthma and chronic spontaneous urticaria. Active and inactive allele counts may enable stratification to assess response to therapies in asthmatic patient subpopulations. Tryptase gene loci TPSAB1 and TPSB2 have high levels of sequence identity, which makes genotyping a challenging task. Here, we report a targeted next-generation sequencing (NGS) assay and downstream bioinformatics analysis for determining polymorphisms at tryptase TPSAB1 and TPSB2 loci. Machine learning modeling using multiple polymorphisms in the tryptase loci was used to improve the accuracy of genotyping calls. The assay was tested and qualified on DNA extracted from whole blood of healthy donors and asthma patients, achieving accuracy of 96%, 96% and 94% for estimation of inactive α and βΙΙΙFS tryptase alleles and α duplication on TPSAB1, respectively. The reported NGS assay is a cost-effective method that is more efficient than Sanger sequencing and provides coverage to evaluate known as well as unreported tryptase polymorphisms

Summary of datasets for development and validation of the tryptase PCR NGS assay pipeline.

Samples from healthy donors and asthma patients were included. Technical replicates were run on samples from DS2. Samples were selected based on tryptase genotype and population diversity. DS3 includes subjects from a diverse population of African, American, East Asian, European and South Asian ancestry.</p

Technical reproducibility of the PCR NGS tryptase genotyping workflow.

Genotypes reported by the workflow are highly concordant between two technical replicates of sample set DS2.</p

Schematic of tryptase gene cluster, PCR NGS analysis and tryptase genotyping workflow.

a) A schematic of the Tryptase locus is shown. Tryptase locus on chromosome 16 comprises a highly homologous cluster of genes: TPSAB1, TPSB2, TPSD1 and TPSG1. TPSAB1 and TPSD1 are located on the positive strand of DNA, while TPSB2 and TPSG1 are on the negative strand. A consensus sequence that was generated for alignment of tryptase short reads in the PCR NGS workflow matches TPSAB1, TPSB2 and TPSD1 with high identity scores. Primers were designed to capture TPSAB1 and TPSB2, and exclude TPSD1 and TPSG1, resulting in two amplicons of 689 bps within TPSAB1 and TPSB2. Various tryptase alleles expressed by different tryptase gene loci are shown. Inactive forms are highlighted in orange. b) The reads are first mapped to the tryptase consensus sequence. Fractional abundance of SNPs with sufficient sequencing coverage is calculated and reported as input features for developing the tryptase genotyping model. Highly correlated SNPs are removed from the feature set to reduce the number of redundant features. Data is split into training and testing sets and cross-validation and hyperparameter tuning are performed to optimize the model performance, while reducing model complexity and the risk of overfitting. The model is externally validated on the hold-out set. The colored SNPs represent the polymorphisms in tryptase that distinguish various alleles. bp: base pair. ML: machine learning.</p

Summary of tryptase genotyping models, SNPs and model performance.

Summary of tryptase genotyping models, SNPs and model performance.</p

Accuracy of the PCR NGS tryptase genotyping workflow.

The genotyping workflow achieves an estimated accuracy of 95% across roughly 130 samples analyzed in 4 batches.</p