50. ASSESSMENT OF MICROBIAL CONTAMINATION TO ELIMINATE ARTIFACTS IN SEQUENCING-BASED PGT-A/PGT-SR. (August 2019)

• Record Type: Journal Article
• Authors: Altarescu, G.; Granit, R.; Dror, T.; Kirshberg, S.; Rosen, T.; Shaviv, S.; Zeligson, S.; Beeri, R.; Renbaum, P.; Zeevi, D.A.
• Is Part Of: Reproductive biomedicine online. Volume 39(2019)Supplement 1
• Page Start: e57
• Publication Date: 2019-08
• Subjects: PGT-S -- PGT-A -- metagenomics -- quality contro; Human reproductive technology -- Periodicals; Human embryo -- Periodicals; Reproduction -- Periodicals; 616.692
• DOI: 10.1016/j.rbmo.2019.04.103
• Languages: English
• ISSNs: 1472-6483

Abstract

Abstract : Introduction: Low pass high throughput sequencing has become a choice method for obtaining chromosome copy number information from biopsies of pre-implantation embryos. In addition to large copy number variants (CNVs) in human embryo samples, data sets from these routine procedures also provide information regarding the microbial content of each specimen and it's derived sequencing library. The aim of this study was to probe raw sequencing data from clinical PGT-A/PGT-SR cycles for microbial contamination which might explain artifactual or difficult to interpret CNV results in contaminated samples. Materials & Methods: Between January 2018 and July 2018, PGT-A/PGT-SR was performed on a total of 448 blastomere/blastocyst biopsies at Shaare Zedek Medical Center, Jerusalem, Israel. Library prep and sequencing was performed using the VeriSeq kit (Illumina) according to the manufacturer's protocol. Derivative log ratio (DLR) noise measurements as well as whole chromosomal and segmental CNVs were identified and reported using BlueFuse software. To assess microbial contamination, raw fastq files from each sequencing library were input to PathoScope, a metagenomics suite, for non-human read parsing and microbe identification. Both bacterial and DNA viral taxonomes were fed to PathoScope for non-human read mapping. Results: An average of 0.18%+/-0.76% of total reads per sequencing data set, mapped to bacterial/viral genomes, and a positive correlation was observed between qualitative copy number noise measurement (DLR) and microbial sequence contamination. Notably, 6 of the most heavily contaminated samples (>0.9% non-human read fraction) presented with over-representation of sequencing reads mapping to the Streptococcus suis genome. In depth post-hoc analysis indicated that sequencing libraries from all 6 samples were prepared from the same index PCR primer which had likely been contaminated by the manufacturer during or shortly after oligo synthesis. Conclusions: Microbial contamination is a genuine threat to the quality of molecular analyses such as PGT-A and PGT-SR. This study draws attention to the prospect that clinical labs, conducting sequencing-based PGT-A/PGT-SR, consider quality control measures to control for artifactual results that may arise from data sets that are contaminated by genomes of foreign organisms.