Benchmarking of two bioinformatic workflows for the analysis of whole-genome sequenced Staphylococcus aureus collected from patients with suspected sepsis.

BACKGROUND: The rapidly growing area of sequencing technologies, and more specifically bacterial whole-genome sequencing, could offer applications in clinical microbiology, including species identification of bacteria, prediction of genetic antibiotic susceptibility and virulence genes simultaneously. To accomplish the aforementioned points, the commercial cloud-based platform, 1928 platform (1928 Diagnostics, Gothenburg, Sweden) was benchmarked against an in-house developed bioinformatic pipeline as well as to reference methods in the clinical laboratory. METHODS: Whole-genome sequencing data retrieved from 264 Staphylococcus aureus isolates using the Illumina HiSeq X next-generation sequencing technology was used. The S. aureus isolates were collected during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital, in the western region of Sweden. The collected isolates were characterized according to accredited laboratory methods i.e., species identification by MALDI-TOF MS analysis and phenotypic antibiotic susceptibility testing (AST) by following the EUCAST guidelines. Concordance between laboratory methods and bioinformatic tools, as well as concordance between the bioinformatic tools was assessed by calculating the percent of agreement. RESULTS: There was an overall high agreement between predicted genotypic AST and phenotypic AST results, 98.0% (989/1006, 95% CI 97.3-99.0). Nevertheless, the 1928 platform delivered predicted genotypic AST results with lower very major error rates but somewhat higher major error rates compared to the in-house pipeline. There were differences in processing times i.e., minutes versus hours, where the 1928 platform delivered the results faster. Furthermore, the bioinformatic workflows showed overall 99.4% (1267/1275, 95% CI 98.7-99.7) agreement in genetic prediction of the virulence gene characteristics and overall 97.9% (231/236, 95% CI 95.0-99.2%) agreement in predicting the sequence types (ST) of the S. aureus isolates. CONCLUSIONS: Altogether, the benchmarking disclosed that both bioinformatic workflows are able to deliver results with high accuracy aiding diagnostics of severe infections caused by S. aureus. It also illustrates the need of international agreement on quality control and metrics to facilitate standardization of analytical approaches for whole-genome sequencing based predictions.

Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells.

Neuroblastoma is a childhood tumour that is responsible for approximately 15% of all childhood cancer deaths. Neuroblastoma tumours with amplification of the oncogene MYCN are aggressive, however, another aggressive subgroup without MYCN amplification also exists; rather, they have a deleted region at chromosome arm 11q. Twenty-six miRNAs are located within the breakpoint region of chromosome 11q and have been checked for a possible involvement in development of neuroblastoma due to the genomic alteration. Target genes of these miRNAs are involved in pathways associated with cancer, including proliferation, apoptosis and DNA repair. We could show that miR-548l found within the 11q region is downregulated in neuroblastoma cell lines with 11q deletion or MYCN amplification. In addition, we showed that the restoration of miR-548l level in a neuroblastoma cell line led to a decreased proliferation of these cells as well as a decrease in the percentage of cells in the S phase. We also found that miR-548l overexpression suppressed cell viability and promoted apoptosis, while miR-548l knockdown promoted cell viability and inhibited apoptosis in neuroblastoma cells. Our results indicate that 11q-deleted neuroblastoma and MYCN amplified neuroblastoma coalesce by downregulating miR-548l.

Verification of microRNA expression in human endometrial adenocarcinoma.

BACKGROUND: MicroRNAs are small non-coding RNAs that have been implicated in tumor initiation and progression. In a previous study we identified 138 miRNAs as differentially expressed in endometrial adenocarcinoma compared to normal tissues. One of these miRNAs was miRNA-34a, which regulates several genes involved in the Notch pathway, which is frequently altered in endometrial cancer. The aims of this study were to verify the differential expression of a subset of miRNAs and to scrutinize the regulatory role of mir-34a on the target genes NOTCH1 and DLL1. METHODS: Twenty-five miRNAs that were previously identified as differentially expressed were subjected to further analysis using qPCR. To investigate the regulation of NOTCH1 and DLL1 by mir-34a, we designed gain- and loss-of-function experiments in Ishikawa and HEK293 cell lines by transfection with a synthetic mir-34a mimic and a mir-34a inhibitor. RESULTS: Of the 25 validated miRNAs, seven were down-regulated and 18 were up-regulated compared to normal endometrium, which was fully consistent with our previous findings. In addition, the up-regulation of mir-34a led to a significant decrease in mRNA levels of NOTCH1 and DLL1, while down-regulation led to a significant increase in mRNA levels of these two genes. CONCLUSIONS: We verified both up-regulated and down-regulated miRNAs in the tumor samples, indicating various roles of microRNAs during tumor development. Mir-34a functions as a regulator by decreasing the expression of NOTCH1 and DLL1. Our study is the first to identify a correlation between mir-34a and its target genes NOTCH1 and DLL1 in endometrial adenocarcinoma.

miREC: a database of miRNAs involved in the development of endometrial cancer.

BACKGROUND: Endometrial cancer (EC) is the most frequently diagnosed gynecological malignancy and the fourth most common cancer diagnosis overall among women. As with many other forms of cancer, it has been shown that certain miRNAs are differentially expressed in EC and these miRNAs are believed to play important roles as regulators of processes involved in the development of the disease. With the rapidly growing number of studies of miRNA expression in EC, there is a need to organize the data, combine the findings from experimental studies of EC with information from various miRNA databases, and make the integrated information easily accessible for the EC research community. FINDINGS: The miREC database is an organized collection of data and information about miRNAs shown to be differentially expressed in EC. The database can be used to map connections between miRNAs and their target genes in order to identify specific miRNAs that are potentially important for the development of EC. The aim of the miREC database is to integrate all available information about miRNAs and target genes involved in the development of endometrial cancer, and to provide a comprehensive, up-to-date, and easily accessible source of knowledge regarding the role of miRNAs in the development of EC. Database URL: http://www.mirecdb.org . CONCLUSIONS: Several databases have been published that store information about all miRNA targets that have been predicted or experimentally verified to date. It would be a time-consuming task to navigate between these different data sources and literature to gather information about a specific disease, such as endometrial cancer. The miREC database is a specialized data repository that, in addition to miRNA target information, keeps track of the differential expression of genes and miRNAs potentially involved in endometrial cancer development. By providing flexible search functions it becomes easy to search for EC-associated genes and miRNAs from different starting points, such as differential expression and genomic loci (based on genomic aberrations).

MicroRNA expression in human endometrial adenocarcinoma.

BACKGROUND: MicroRNAs are small non-coding RNAs that play crucial roles in the pathogenesis of different cancer types. The aim of this study was to identify miRNAs that are differentially expressed in endometrial adenocarcinoma compared to healthy endometrium. These miRNAs can potentially be used to develop a panel for classification and prognosis in order to better predict the progression of the disease and facilitate the choice of treatment strategy. METHODS: Formalin fixed paraffin embedded endometrial tissue samples were collected from the Örebro university hospital. QPCR was used to quantify the expression levels of 742 miRNAs in 30 malignant and 20 normal endometrium samples. After normalization of the qPCR data, miRNAs differing significantly in expression between normal and cancer samples were identified, and hierarchical clustering analysis was used to identify groups of miRNAs with coordinated expression profiles. RESULTS: In comparisons between endometrial adenocarcinoma and normal endometrium samples 138 miRNAs were found to be significantly differentially expressed (p < 0.001) among which 112 miRNAs have not been previous reported for endometrial adenocarcinoma. CONCLUSION: Our study shows that several miRNAs are differentially expressed in endometrial adenocarcinoma. These identified miRNA hold great potential as target for classification and prognosis of this disease. Further analysis of the differentially expressed miRNA and their target genes will help to derive new biomarkers that can be used for classification and prognosis of endometrial adenocarcinoma.

Validation of suitable endogenous control genes for quantitative PCR analysis of microRNA gene expression in a rat model of endometrial cancer.

BACKGROUND: MicroRNAs are small RNA molecules that negatively regulate gene expression by translational inhibition or mRNA cleavage. The discovery that abnormal expression of particular miRNAs contributes to human disease, including cancer, has spurred growing interest in analysing expression profiles of these molecules. Quantitative polymerase chain reaction is frequently used for quantification of miRNA expression due to its sensitivity and specificity. To minimize experimental error in this system an appropriate endogenous control gene must be chosen. An ideal endogenous control gene should be expressed at a constant level across all samples and its expression stability should be unaffected by the experimental procedure. RESULTS: The expression and validation of candidate control genes (4.5S RNA(H) A, Y1, 4.5S RNA(H) B, snoRNA, U87 and U6) was examined in 21 rat cell lines to establish the most suitable endogenous control for miRNA analysis in a rat model of cancer. The stability of these genes was analysed using geNorm and NormFinder algorithms. U87 and snoRNA were identified as the most stable control genes, while Y1 was least stable. CONCLUSION: This study identified the control gene that is most suitable for normalizing the miRNA expression data in rat. That reference gene will be useful when miRNAs expression are analyzed in order to find new miRNA markers for endometrial cancer in rat.