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Abstract We describe two clinical prenatal cases with rare de novo RIT1 variants, which showed more severe clinical manifestations than other Noonan Syndrome genotypes, resulting in fetal death. Extra attention is recommended when these variants are detected.

ABSTRACT Background Sufficient fetal fraction (FF) is crucial for quality control of NIPT (Non‐Invasive Prenatal Test) results. Different factors influencing bioinformatic estimation of FF should be considered when implementing NIPT. To what extent the total number of sequencing reads influences FF estimate has been unexplored. In this study, to test the robustness of SeqFF FF estimation and provide additional recommendations for NIPT analysis quality control, we compared the SeqFF FF estimates with two other methods and investigated how the number of sequencing reads and FF level affects the accuracy and precision of FF estimates. Methods WGS data of 516 NIPT samples from a prenatal screening program was obtained. Sample data were randomly downsampled by the read count, and FF was calculated by SeqFF software. Then, the outcome was compared with FF estimates from SNP‐ and chrY‐based methods. FF estimated with different read counts and FF levels were compared with FF at 30 M reads as a reference. Results SeqFF FF highly correlates with SNP‐ and chrY‐based FF estimates. Raising read count from 2 M to 10 M drastically increased the accuracy of FF estimates. After adding more reads, we saw a further improvement in FF accuracy, reaching a plateau at 20 M reads. Precision of SeqFF FF estimate is independent of FF level in the sample. Conclusion SeqFF is a robust method for FF estimation for both genders and for any FF level in range 2–13%. Accuracy of FF estimates highly depends on the read count. We recommend using no less than 10 M reads to achieve accurate FF estimates for NIPT analysis in clinical settings.

Cancer results from alterations at essential genomic sites and is characterized by uncontrolled cell proliferation, invasion and metastasis. Identification of driver genes of metastatic progression is essential, as metastases, not primary tumors, are fatal. To gain insight into the mutational concordance between different steps of malignant progression we performed exome sequencing and validation with targeted deep sequencing of successive steps of malignant progression from pre-invasive stages to asynchronous distant metastases in six breast cancer patients. Using the ratio of non-synonymous to synonymous mutations, a surprisingly large number of cancer driver genes, ranging between 3 and 145, were estimated to confer a selective advantage in the studied primary tumors. We report a substantial amount of metastasis specific mutations and a number of novel putative metastasis driver genes. Most notable are the DCC, ABCA13, TIAM2, CREBBP, BCL6B and ZNF185 genes, mainly mutated exclusively in metastases and highly likely driver genes of metastatic progression. We find different genes and pathways to be affected at different steps of malignant progression. The Adherens junction pathway is affected in four of the six studied patients and this pathway most likely plays a vital role in the metastatic process.

ABSTRACT The ability to change cell morphology is an advantageous characteristic adopted by multiple pathogenic bacteria in order to evade host immune detection and assault during infection. Uropathogenic Escherichia coli (UPEC) exhibits such cellular dynamics and has been shown to transition through a series of distinct morphological phenotypes during a urinary tract infection. Here, we report the first systematic spatio-temporal gene expression analysis of the UPEC transition through these phenotypes by using a flow chamber-based in vitro infection model that simulates conditions in the bladder. This analysis revealed a novel association between the cell division gene damX and reversible UPEC filamentation. We demonstrate a lack of reversible bacterial filamentation in a damX deletion mutant in vitro and absence of a filamentous response by this mutant in a murine model of cystitis. While deletion of damX abrogated UPEC filamentation and secondary surface colonization in tissue culture and in mouse infections, transient overexpression of damX resulted in reversible UPEC filamentation. In this study, we identify a hitherto-unknown damX-mediated mechanism underlying UPEC morphotypical switching. Murine infection studies showed that DamX is essential for establishment of a robust urinary tract infection, thus emphasizing its role as a mediator of virulence. Our study demonstrates the value of an in vitro methodology, in which uroepithelium infection is closely simulated, when undertaking targeted investigations that are challenging to perform in animal infection models. IMPORTANCE Urinary tract infections (UTIs) are most often caused by uropathogenic Escherichia coli (UPEC) and account for a considerable health care burden. UPEC exhibits a dynamic lifestyle in the course of infection, in which the bacterium transiently adopts alternative morphologies ranging from rod shaped to coccoid and filamentous, rendering it better at immune evasion and host epithelium adhesion. This penchant for morphotype switching might in large measure account for UPEC’s success as a pathogen. In aiming to uncover genes underlying the phenomenon of UPEC morphotype switching, this study identifies damX, a cell division gene, as a mediator of reversible filamentation during UTI. DamX-mediated filamentation represents an additional pathway for bacterial cell shape control, an alternative to SulA-mediated FtsZ sequestration during E. coli uropathogenesis, and hence represents a potential target for combating UTI.

Next generation sequencing is extensively applied to catalogue somatic mutations in cancer, in research settings and increasingly in clinical settings for molecular diagnostics, guiding therapy decisions. Somatic variant callers perform paired comparisons of sequencing data from cancer tissue and matched normal tissue in order to detect somatic mutations. The advent of many new somatic variant callers creates a need for comparison and validation of the tools, as no de facto standard for detection of somatic mutations exists and only limited comparisons have been reported. We have performed a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2 and Virmid for the detection of single nucleotide mutations and small deletions and insertions. We report a large variation in the number of calls from the nine somatic variant callers on the same sequencing data and highly variable agreement. Sequencing depth had markedly diverse impact on individual callers, as for some callers, increased sequencing depth highly improved sensitivity. For SNV calling, we report EBCall, Mutect, Virmid and Strelka to be the most reliable somatic variant callers for both exome sequencing and targeted deep sequencing. For indel calling, EBCall is superior due to high sensitivity and robustness to changes in sequencing depths.

Hereditary Haemorrhagic Telangiectasia (HHT) is an autosomal dominantly inherited vascular disease characterized by the presence of mucocutaneous telangiectasia and arteriovenous malformations in visceral organs. HHT is predominantly caused by mutations in ENG and ACVRL1, which both belong to the TGF-β signalling pathway. The exact mechanism of how haploinsufficiency of ENG and ACVRL1 leads to HHT manifestations remains to be identified. As long non-coding RNAs (lncRNAs) are increasingly recognized as key regulators of gene expression and constitute a sizable fraction of the human transcriptome, we wanted to assess whether lncRNAs play a role in the molecular pathogenesis of HHT manifestations. By microarray technology, we profiled lncRNA transcripts from HHT nasal telangiectasial and non-telangiectasial tissue using a paired design. The microarray probes were annotated using the GENCODE v.16 dataset, identifying 4,810 probes mapping to 2,811 lncRNAs. Comparing HHT telangiectasial tissue with HHT non-telangiectasial tissue, we identified 42 lncRNAs that are differentially expressed (q