Optimizing mycobacteria molecular diagnostics: No decontamination! Human DNA depletion? Greener storage at 4 °C!

Introduction: Tuberculosis (TB) is an infectious disease caused by the group of bacterial pathogens Mycobacterium tuberculosis complex (MTBC) and is one of the leading causes of death worldwide. Timely diagnosis and treatment of drug-resistant TB is a key pillar of WHO's strategy to combat global TB. The time required to carry out drug susceptibility testing (DST) for MTBC via the classic culture method is in the range of weeks and such delays have a detrimental effect on treatment outcomes. Given that molecular testing is in the range of hours to 1 or 2 days its value in treating drug resistant TB cannot be overstated. When developing such tests, one wants to optimize each step so that tests are successful even when confronted with samples that have a low MTBC load or contain large amounts of host DNA. This could improve the performance of the popular rapid molecular tests, especially for samples with mycobacterial loads close to the limits of detection. Where optimizations could have a more significant impact is for tests based on targeted next generation sequencing (tNGS) which typically require higher quantities of DNA. This would be significant as tNGS can provide more comprehensive drug resistance profiles than the relatively limited resistance information provided by rapid tests. In this work we endeavor to optimize pre-treatment and extraction steps for molecular testing. Methods: We begin by choosing the best DNA extraction device by comparing the amount of DNA extracted by five commonly used devices from identical samples. Following this, the effect that decontamination and human DNA depletion have on extraction efficiency is explored. Results: The best results were achieved (i.e., the lowest Ct values) when neither decontamination nor human DNA depletion were used. As expected, in all tested scenarios the addition of decontamination to our workflow substantially reduced the yield of DNA extracted. This illustrates that the standard TB laboratory practice of applying decontamination, although being vital for culture-based testing, can negatively impact the performance of molecular testing. As a complement to the above experiments, we also considered the best Mycobacterium tuberculosis DNA storage method to optimize molecular testing carried out in the near- to medium-term. Comparing Ct values following three-month storage at 4 °C and at -20 °C and showed little difference between the two. Discussion: In summary, for molecular diagnostics aimed at mycobacteria this work highlights the importance of choosing the right DNA extraction device, indicates that decontamination causes significant loss of mycobacterial DNA, and shows that samples preserved for further molecular testing can be stored at 4 °C, just as well at -20 °C. Under our experimental settings, human DNA depletion gave no significant improvement in Ct values for the detection of MTBC.

RNA transcription and degradation of Alu retrotransposons depends on sequence features and evolutionary history.

Alu elements are one of the most successful groups of RNA retrotransposons and make up 11% of the human genome with over 1 million individual loci. They are linked to genetic defects, increases in sequence diversity, and influence transcriptional activity. Still, their RNA metabolism is poorly understood yet. It is even unclear whether Alu elements are mostly transcribed by RNA Polymerase II or III. We have conducted a transcription shutoff experiment by α-amanitin and metabolic RNA labeling by 4-thiouridine combined with RNA fragmentation (TT-seq) and RNA-seq to shed further light on the origin and life cycle of Alu transcripts. We find that Alu RNAs are more stable than previously thought and seem to originate in part from RNA Polymerase II activity, as previous reports suggest. Their expression however seems to be independent of the transcriptional activity of adjacent genes. Furthermore, we have developed a novel statistical test for detecting the expression of quantitative trait loci in Alu elements that relies on the de Bruijn graph representation of all Alu sequences. It controls for both statistical significance and biological relevance using a tuned k-mer representation, discovering influential sequence features missed by regular motif search. In addition, we discover several point mutations using a generalized linear model, and motifs of interest, which also match transcription factor-binding motifs.

Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imaging.

MOTIVATION: Recent imaging technologies allow for high-throughput tracking of cells as they migrate, divide, express fluorescent markers and change their morphology. The interpretation of these data requires unbiased, efficient statistical methods that model the dynamics of cell phenotypes. RESULTS: We introduce treeHFM, a probabilistic model which generalizes the theory of hidden Markov models to tree structured data. While accounting for the entire genealogy of a cell, treeHFM categorizes cells according to their primary phenotypic features. It models all relevant events in a cell's life, including cell division, and thereby enables the analysis of event order and cell fate heterogeneity. Simulations show higher accuracy in predicting correct state labels when modeling the more complex, tree-shaped dependency of samples over standard HMM modeling. Applying treeHFM to time lapse images of hematopoietic progenitor cell differentiation, we demonstrate that progenitor cells undergo a well-ordered sequence of differentiation events. AVAILABILITY AND IMPLEMENTATION: The treeHFM is implemented in C++. We provide wrapper functions for the programming languages R (CRAN package, https://CRAN.R-project.org/package=treeHFM) and Matlab (available at Mathworks Central, http://se.mathworks.com/matlabcentral/fileexchange/57575-treehfml). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

G-EYE advanced colonoscopy for improved polyp detection rates - a randomized tandem pilot study with different endoscopists.

BACKGROUND AND AIMS: The most commonly missed polyps in colonoscopy are those located behind haustral folds. The G-EYE system is a standard colonoscope consisting of re-processable balloon at its distal tip. The G-EYE balloon improves the detection of polyps by straightening the haustral folds. In our back-to-back tandem study, we aimed to determine whether and to what extent the G-EYE system could reduce adenoma miss rates in screening colonoscopy. METHODS: Patients referred to colonoscopy were randomized into 2 groups. Group A underwent a standard colonoscopy (SC) followed by balloon colonoscopy (BC), and Group B underwent BC followed by SC. In this randomized tandem study, the investigator's level of training and the endoscopists themselves were changed after each withdrawal. Each endoscopist was blinded to the results of the first withdrawal. RESULTS: Fifty-eight patients were enrolled and randomized into 2 groups with similar baseline characteristics. Nine patients were excluded from the study. Twenty-five patients underwent SC followed by BC while 24 underwent BC followed by SC. The adenoma miss rate for SC was 41 %, with an additional detection rate of 69 % for BC (ratio 1.69). The overall miss rate for polyps was 60 % for SC, with an additional detection rate of 150 % for BC (ratio 2.5). Experienced investigators who used BC were able to identify an additional 7 polyps while inexperienced investigators. CONCLUSIONS: Although our results could not clearly confirm that BC improves adenoma detection, the investigator's experience appears to be a major determinant of the adenoma detection rate.

Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model.

Hidden Markov models (HMMs) have been extensively used to dissect the genome into functionally distinct regions using data such as RNA expression or DNA binding measurements. It is a challenge to disentangle processes occurring on complementary strands of the same genomic region. We present the double-stranded HMM (dsHMM), a model for the strand-specific analysis of genomic processes. We applied dsHMM to yeast using strand specific transcription data, nucleosome data, and protein binding data for a set of 11 factors associated with the regulation of transcription.The resulting annotation recovers the mRNA transcription cycle (initiation, elongation, termination) while correctly predicting strand-specificity and directionality of the transcription process. We find that pre-initiation complex formation is an essentially undirected process, giving rise to a large number of bidirectional promoters and to pervasive antisense transcription. Notably, 12% of all transcriptionally active positions showed simultaneous activity on both strands. Furthermore, dsHMM reveals that antisense transcription is specifically suppressed by Nrd1, a yeast termination factor.

A novel test for independence derived from an exact distribution of ith nearest neighbours.

Dependence measures and tests for independence have recently attracted a lot of attention, because they are the cornerstone of algorithms for network inference in probabilistic graphical models. Pearson's product moment correlation coefficient is still by far the most widely used statistic yet it is largely constrained to detecting linear relationships. In this work we provide an exact formula for the [Formula: see text]th nearest neighbor distance distribution of rank-transformed data. Based on that, we propose two novel tests for independence. An implementation of these tests, together with a general benchmark framework for independence testing, are freely available as a CRAN software package (http://cran.r-project.org/web/packages/knnIndep). In this paper we have benchmarked Pearson's correlation, Hoeffding's D, dcor, Kraskov's estimator for mutual information, maximal information criterion and our two tests. We conclude that no particular method is generally superior to all other methods. However, dcor and Hoeffding's D are the most powerful tests for many different types of dependence.

Efficacy of SpyGlass(TM)-directed biopsy compared to brush cytology in obtaining adequate tissue for diagnosis in patients with biliary strictures.

AIM: To evaluate the diagnostic yield (inflammatory activity) and efficiency (size of the biopsy specimen) of SpyGlass(TM)-guided biopsy vs standard brush cytology in patients with and without primary sclerosing cholangitis (PSC). METHODS: At the University Medical Center Mainz, Germany, 35 consecutive patients with unclear biliary lesions (16 patients) or long-standing PSC (19 patients) were screened for the study. All patients underwent a physical examination, lab analyses, and abdominal ultrasound. Thirty-one patients with non-PSC strictures or with PSC were scheduled to undergo endoscopic retrograde cholangiography (ERC) and subsequent peroral cholangioscopy (POC). Standard ERC was initially performed, and any lesions or strictures were localized. POC was performed later during the same session. The Boston Scientific SpyGlass System(TM) (Natick, MA, United States) was used for choledochoscopy. The biliary tree was visualized, and suspected lesions or strictures were biopsied, followed by brush cytology of the same area. The study endpoints (for both techniques) were the degree of inflammation, tissue specimen size, and the patient populations (PSC vs non-PSC). Inflammatory changes were divided into three categories: none, low activity, and high activity. The specimen quantity was rated as low, moderate, or sufficient. RESULTS: SpyGlass(TM) imaging and brush cytology with material retrieval were performed in 29 of 31 (93.5%) patients (23 of the 29 patients were male). The median patient age was 45 years (min, 20 years; max, 76 years). Nineteen patients had known PSC, and 10 showed non-PSC strictures. No procedure-related complications were encountered. However, for both methods, tissues could only be retrieved from 29 patients. In cases of inflammation of the biliary tract, the diagnostic yield of the SpyGlass(TM)-directed biopsies was greater than that using brush cytology. More tissue material was obtained for the biopsy method than for the brush cytology method (P = 0.021). The biopsies showed significantly more inflammatory characteristics and greater inflammatory activity compared to the cytological investigation (P = 0.014). The greater quantity of tissue samples proved useful for both PSC and non-PSC patients. CONCLUSION: SpyGlass(TM) imaging can be recommended for proper inflammatory diagnosis in PSC patients. However, its value in diagnosing dysplasia was not addressed in this study and requires further investigation.

Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction.

MOTIVATION: For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. RESULTS: In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics online. CONTACT: duemcke@mpipz.mpg.de or tresch@mpipz.mpg.de SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinfomatics online.

One Hand Clapping: detection of condition-specific transcription factor interactions from genome-wide gene activity data.

We present One Hand Clapping (OHC), a method for the detection of condition-specific interactions between transcription factors (TFs) from genome-wide gene activity measurements. OHC is based on a mapping between transcription factors and their target genes. Given a single case-control experiment, it uses a linear regression model to assess whether the common targets of two arbitrary TFs behave differently than expected from the genes targeted by only one of the TFs. When applied to osmotic stress data in S. cerevisiae, OHC produces consistent results across three types of expression measurements: gene expression microarray data, RNA Polymerase II ChIP-chip binding data and messenger RNA synthesis rates. Among the eight novel, condition-specific TF pairs, we validate the interaction between Gcn4p and Arr1p experimentally. We apply OHC to a large gene activity dataset in S. cerevisiae and provide a compendium of condition-specific TF interactions.

Measurement of genome-wide RNA synthesis and decay rates with Dynamic Transcriptome Analysis (DTA).

Standard transcriptomics measures total cellular RNA levels. Our understanding of gene regulation would be greatly improved if we could measure RNA synthesis and decay rates on a genome-wide level. To that end, the Dynamic Transcriptome Analysis (DTA) method has been developed. DTA combines metabolic RNA labeling with standard transcriptomics to measure RNA synthesis and decay rates in a precise and non-perturbing manner. Here, we present the open source R/Bioconductor software package DTA. It implements all required bioinformatics steps that allow the accurate absolute quantification and comparison of RNA turnover.

Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast.

To obtain rates of mRNA synthesis and decay in yeast, we established dynamic transcriptome analysis (DTA). DTA combines non-perturbing metabolic RNA labeling with dynamic kinetic modeling. DTA reveals that most mRNA synthesis rates are around several transcripts per cell and cell cycle, and most mRNA half-lives range around a median of 11 min. DTA can monitor the cellular response to osmotic stress with higher sensitivity and temporal resolution than standard transcriptomics. In contrast to monotonically increasing total mRNA levels, DTA reveals three phases of the stress response. During the initial shock phase, mRNA synthesis and decay rates decrease globally, resulting in mRNA storage. During the subsequent induction phase, both rates increase for a subset of genes, resulting in production and rapid removal of stress-responsive mRNAs. During the recovery phase, decay rates are largely restored, whereas synthesis rates remain altered, apparently enabling growth at high salt concentration. Stress-induced changes in mRNA synthesis rates are predicted from gene occupancy with RNA polymerase II. DTA-derived mRNA synthesis rates identified 16 stress-specific pairs/triples of cooperative transcription factors, of which seven were known. Thus, DTA realistically monitors the dynamics in mRNA metabolism that underlie gene regulatory systems.