Assessing the heterogeneity of in silico plasmid predictions based on whole-genome-sequenced clinical isolates.

High-throughput next-generation shotgun sequencing of pathogenic bacteria is growing in clinical relevance, especially for chromosomal DNA-based taxonomic identification and for antibiotic resistance prediction. Genetic exchange is facilitated for extrachromosomal DNA, e.g. plasmid-borne antibiotic resistance genes. Consequently, accurate identification of plasmids from whole-genome sequencing (WGS) data remains one of the major challenges for sequencing-based precision medicine in infectious diseases. Here, we assess the heterogeneity of four state-of-the-art tools (cBar, PlasmidFinder, plasmidSPAdes and Recycler) for the in silico prediction of plasmid-derived sequences from WGS data. Heterogeneity, sensitivity and precision were evaluated by reference-independent and reference-dependent benchmarking using 846 Gram-negative clinical isolates. Interestingly, the majority of predicted sequences were tool-specific, resulting in a pronounced heterogeneity across tools for the reference-independent assessment. In the reference-dependent assessment, sensitivity and precision values were found to substantially vary between tools and across taxa, with cBar exhibiting the highest median sensitivity (87.45%) but a low median precision (27.05%). Furthermore, integrating the individual tools into an ensemble approach showed increased sensitivity (95.55%) while reducing the precision (25.62%). CBar and plasmidSPAdes exhibited the strongest concordance with respect to identified antibiotic resistance factors. Moreover, false-positive plasmid predictions typically contained only few antibiotic resistance factors. In conclusion, while high degrees of heterogeneity and variation in sensitivity and precision were observed across the different tools and taxa, existing tools are valuable for investigating the plasmid-borne resistome. Nevertheless, additional studies on representative clinical data sets will be necessary to translate in silico plasmid prediction approaches from research to clinical application.

Comparing genome versus proteome-based identification of clinical bacterial isolates.

Whole-genome sequencing (WGS) is gaining importance in the analysis of bacterial cultures derived from patients with infectious diseases. Existing computational tools for WGS-based identification have, however, been evaluated on previously defined data relying thereby unwarily on the available taxonomic information.Here, we newly sequenced 846 clinical gram-negative bacterial isolates representing multiple distinct genera and compared the performance of five tools (CLARK, Kaiju, Kraken, DIAMOND/MEGAN and TUIT). To establish a faithful 'gold standard', the expert-driven taxonomy was compared with identifications based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis. Additionally, the tools were also evaluated using a data set of 200 Staphylococcus aureus isolates.CLARK and Kraken (with k =31) performed best with 626 (100%) and 193 (99.5%) correct species classifications for the gram-negative and S. aureus isolates, respectively. Moreover, CLARK and Kraken demonstrated highest mean F-measure values (85.5/87.9% and 94.4/94.7% for the two data sets, respectively) in comparison with DIAMOND/MEGAN (71 and 85.3%), Kaiju (41.8 and 18.9%) and TUIT (34.5 and 86.5%). Finally, CLARK, Kaiju and Kraken outperformed the other tools by a factor of 30 to 170 fold in terms of runtime.We conclude that the application of nucleotide-based tools using k-mers-e.g. CLARK or Kraken-allows for accurate and fast taxonomic characterization of bacterial isolates from WGS data. Hence, our results suggest WGS-based genotyping to be a promising alternative to the MS-based biotyping in clinical settings. Moreover, we suggest that complementary information should be used for the evaluation of taxonomic classification tools, as public databases may suffer from suboptimal annotations.

DNA methylation markers predict outcome in node-positive, estrogen receptor-positive breast cancer with adjuvant anthracycline-based chemotherapy.

PURPOSE: We have shown that DNA methylation of the PITX2 gene predicts risk of distant recurrence in steroid hormone receptor-positive, node-negative breast cancer. Here, we present results from a multicenter study investigating whether PITX2 and other candidate DNA methylation markers predict outcome in node-positive, estrogen receptor-positive, HER-2-negative breast cancer patients who received adjuvant anthracycline-based chemotherapy. EXPERIMENTAL DESIGN: Using a microarray platform, we analyzed DNA methylation in regulatory regions of PITX2 and 60 additional candidate genes in 241 breast cancer specimens. Using Cox regression analysis, we assessed the predictive power of the individual marker/marker panel candidates. Clinical endpoints were time to distant metastasis, disease-free survival, and overall survival. A nested bootstrap/cross-validation strategy was applied to identify and validate marker panels. RESULTS: DNA methylation of PITX2 and 14 other genes was correlated with clinical outcome. In multivariate models, each methylation marker added significant information to established clinical factors. A four-marker panel including PITX2, BMP4, FGF4, and C20orf55 was identified that resulted in improvement of outcome prediction compared with PITX2 alone. CONCLUSIONS: This study provides further evidence for the PITX2 biomarker, which has now been successfully confirmed to predict outcome among different breast cancer patient populations. We further identify new DNA methylation biomarkers, three of which can be combined into a panel with PITX2 to increase the outcome prediction performance in our anthracycline-treated primary breast cancer population. Our results show that a well-defined panel of DNA methylation markers enables outcome prediction in lymph node-positive, HER-2-negative breast cancer patients treated with anthracycline-based chemotherapy.

Connexin43-dependent mechanism modulates renin secretion and hypertension.

To investigate the function of Cx43 during hypertension, we studied the mouse line Cx43KI32 (KI32), in which the coding region of Cx32 replaces that of Cx43. Within the kidneys of homozygous KI32 mice, Cx32 was expressed in cortical and medullary tubules, as well as in some extra- and intraglomerular vessels, i.e., at sites where Cx32 and Cx43 are found in WT mice. Under such conditions, renin expression was much reduced compared with that observed in the kidneys of WT and heterozygous KI32 littermates. After exposure to a high-salt diet, all mice retained a normal blood pressure. However, whereas the levels of renin were significantly reduced in the kidneys of WT and heterozygous KI32 mice, reaching levels comparable to those observed in homozygous littermates, they were not further affected in the latter animals. Four weeks after the clipping of a renal artery (the 2-kidney, 1-clip [2K1C] model), 2K1C WT and heterozygous mice showed an increase in blood pressure and in the circulating levels of renin, whereas 2K1C homozygous littermates remained normotensive and showed unchanged plasma renin activity. Hypertensive, but not normotensive, mice also developed cardiac hypertrophy. The data indicate that replacement of Cx43 by Cx32 is associated with decreased expression and secretion of renin, thus preventing the renin-dependent hypertension that is normally induced in the 2K1C model.

Clinical Resistome Screening of 1,110 Escherichia coli Isolates Efficiently Recovers Diagnostically Relevant Antibiotic Resistance Biomarkers and Potential Novel Resistance Mechanisms.

Multidrug-resistant pathogens represent one of the biggest global healthcare challenges. Molecular diagnostics can guide effective antibiotics therapy but relies on validated, predictive biomarkers. Here we present a novel, universally applicable workflow for rapid identification of antimicrobial resistance (AMR) biomarkers from clinical Escherichia coli isolates and quantitatively evaluate the potential to recover causal biomarkers for observed resistance phenotypes. For this, a metagenomic plasmid library from 1,110 clinical E. coli isolates was created and used for high-throughput screening to identify biomarker candidates against Tobramycin (TOB), Ciprofloxacin (CIP), and Trimethoprim-Sulfamethoxazole (TMP-SMX). Identified candidates were further validated in vitro and also evaluated in silico for their diagnostic performance based on matched genotype-phenotype data. AMR biomarkers recovered by the metagenomics screening approach mechanistically explained 77% of observed resistance phenotypes for Tobramycin, 76% for Trimethoprim-Sulfamethoxazole, and 20% Ciprofloxacin. Sensitivity for Ciprofloxacin resistance detection could be improved to 97% by complementing results with AMR biomarkers that are undiscoverable due to intrinsic limitations of the workflow. Additionally, when combined in a multiplex diagnostic in silico panel, the identified AMR biomarkers reached promising positive and negative predictive values of up to 97 and 99%, respectively. Finally, we demonstrate that the developed workflow can be used to identify potential novel resistance mechanisms.

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.

Comparative DNA methylation analysis in normal and tumour tissues and in cancer cell lines using differential methylation hybridisation.

Immortalized human cancer cell lines are widely used as tools and model systems in cancer research but their authenticity with regard to primary tissues remains a matter of debate. We have used differential methylation hybridisation to obtain comparative methylation profiles from normal and tumour tissues of lung and colon, and permanent cancer cell lines originally derived from these tissues. Average methylation differences only larger than 25% between sample groups were considered for the profiles and with this criterion approximately 1000 probesets, around 2% of the sites represented on the array, indicated differential methylation between normal lung and primary lung cancer tissue, and approximately 700 probesets between normal colon and primary colon cancer tissue. Both hyper- and hypomethylation was found to differentiate normal tissue from cancer tissue. The profiles obtained from these tissue comparisons were found to correspond largely to those from the corresponding cancer cell lines, indicating that the cell lines represent the methylation pattern of the primary tissue rather well. Moreover, the cancer specific profiles were found to be very similar for the two tumour types studied. Tissue specific differential methylation between lung and colon tissues, in contrast, was found to be preserved to a larger extent only in the malignant tissue, but was not preserved well in the cancer cell lines studied. Overall, our data therefore provide further evidence that permanent cell lines are good model systems for cancer specific methylation patterns, but deviate with regard to tissue-specific methylation.

Identifying DNA methylation biomarkers of cancer drug response.

In the last few years, DNA methylation has become one of the most studied gene regulation mechanisms in carcinogenesis as a result of the cumulative evidence produced by the scientific community. Moreover, advances in the technologies that allow detection of DNA methylation in a variety of analytes have opened the possibility of developing methylation-based tests. A number of studies have provided evidence that specific methylation changes can alter the response to different therapeutic agents in cancer and, therefore, be useful biomarkers. For example, the association of the methylation status of DNA repair genes such as MGMT and MLH1 illustrate the two main mechanisms of response to DNA damaging agents. Loss of methylation of MGMT, and the subsequent increase in gene expression, leads to a reduction in response to alkylating agents as a result of enhanced repair of drug-induced DNA damage. Conversely, the increase in methylation of MLH1 and its resulting loss of expression has been consistently observed in drug-resistant tumor cells. MLH1 encodes a mismatch repair enzyme activated in response to DNA damage; activation of MLH1 also induces apoptosis of tumor cells, and thus loss of its expression leads to resistance to DNA-damaging agents. Other methylation-regulated genes that could serve as biomarkers in cancer therapy include drug transporters, genes involved in microtubule formation and stability, and genes related to hormonal therapy response. These methylation markers have potential applications for disease prognosis, treatment response prediction, and the development of novel treatment strategies.

Epigenomics AG.

Earlier diagnosis and a more individualized choice of treatment options has the potential to greatly improve the outcome of life-threatening diseases. DNA methylation has proven to be a rich source of biomarkers for diagnosis, prognosis and drug response prediction in cancer and other diseases. Epigenomics AG makes use of DNA methylation biomarkers to develop in vitro diagnostic test products. The product pipeline comprises screening tests for the early detection of cancer in body fluids and molecular pathology tests on routinely available tissue samples for cancer prognosis. In collaborations with pharmaceutical and biotechnology companies, Epigenomics provides access to its broad range of technologies for the support of drug development and commercialization by patient stratification and drug response prediction.

DNA methylation markers - an opportunity to further individualize therapy in breast cancer?

Over the last few decades, a wealth of treatment options have become available for breast cancer. To specifically direct those therapies to patients with the highest need who will receive the greatest benefit, biomarkers are urgently needed. Two specific needs seem to be most pressing: first is the need for prognostic markers, which would determine which group of patients may recover without adjuvant chemotherapy. Second, predictive markers for specific treatments, such as different endocrine treatments, chemotherapies or targeted drugs, are expected to play a major role in the near future. Ideally, such markers should be strong single markers, or low-complexity marker panels containing only a few markers, to allow for easier assay development and improved reproducibility. The possibility to measure the marker(s) in formalin-fixed specimens would greatly facilitate integration into routine clinical practice. A common and early event in breast cancer is aberrant DNA methylation within gene regulatory regions, affecting a variety of genes with different functions. Data from recently published studies indicate that altered DNA methylation carries prognostic as well as predictive information in breast cancer. Together with the technical advantages of a DNA-based marker, DNA methylation may well constitute the ideal biomarker to further individualize breast cancer treatment. Here the recent literature is reviewed and the most interesting markers, which have the potential to significantly change breast cancer treatment and, therefore, warrant further systematic clinical validation, are highlighted.

Expression of the mouse gap junction gene Gjb3 is regulated by distinct mechanisms in embryonic stem cells and keratinocytes.

Connexins are the protein subunits of gap junction channels and are expressed in a highly regulated temporal and spatial pattern in embryonic development and adult life, with most cell types expressing more than one isoform. Connexin31 (Cx31) is encoded by the gene Gjb3 and expressed throughout mouse development n a complex pattern; in adult mice it becomes restricted to the granular layer of epidermis, testis, and placenta. In placenta, lack of Cx31 leads to transient dysmorphogenesis affecting embryonic survival. Here we have analyzed the structure of mouse Gjb3 as well as its transcriptional regulation by transient transfection of reporter gene constructs in HM1 mouse embryonic stem cells and a mouse keratinocytederived cell line, Hel37, as model systems for early development and skin, respectively. Like most connexin genes, Gjb3 is composed of two exons, the second of which contains the whole coding region and is separated from the first exon by an intron of 2.3 kb. Expression in keratinocytes is regulated by a basal promoter extending to 561 bp upstream of exon 1 in conjunction with a regulatory region between upstream positions 561 and 841. In contrast, expression of Gjb3 in embryonic stem cells depended on the basal promoter together with the intron. The enhancing effect of the intron was found only in embryonic stem cells and depended on its native position and the integrity of the splice sites. Thus, expression of Gjb3 in keratinocytes and embryonic stem cells is regulated by different cis-regulatory elements and differs in its requirements for the intron in situ.

Replacement by a lacZ reporter gene assigns mouse connexin36, 45 and 43 to distinct cell types in pancreatic islets.

Transcripts of three connexin isoforms (Cx36, Cx43 and Cx45) have been reported in rodent pancreatic islets, but the precise distribution of the cognate proteins is still unknown. We determined expression of Cx36 in a cell-autonomous manner using mice with a targeted replacement of the Cx36 coding region by a lacZ reporter gene. For cell-autonomous monitoring of Cx43 expression, we used the Cre/loxP system: Mice carrying the Cx43 coding region flanked by loxP sites (floxed) also carried an embedded lacZ gene that is activated after Cre-mediated recombination in cells with transcriptional activity of the Cx43 gene. Deletion of the Cx43 coding region in beta-cells did not result in the activation of the embedded lacZ reporter gene. Instead, Cx43 expression was found in endothelial cells of the islets of Langerhans in mice with endothelium-specific deletion. Ubiquitous deletion of Cx43 led to a similar endothelial lacZ expression, but again, activity of the reporter gene was not detected in beta-cells. Mice with targeted replacement of the Cx45 coding region by lacZ showed a vascular expression similar to Cx43. The data show that native insulin-producing cells express a connexin isoform (Cx36) which differs from those (Cx43 and Cx45) expressed by vascular islet cells.