Assessing the accuracy of quantitative molecular microbial profiling

This is the final version of the article. Available from MDPI via the DOI in this record.The application of high-throughput sequencing in profiling microbial communities is providing an unprecedented ability to investigate microbiomes. Such studies typically apply one of two methods: amplicon sequencing using PCR to target a conserved orthologous sequence (typically the 16S ribosomal RNA gene) or whole (meta)genome sequencing (WGS). Both methods have been used to catalog the microbial taxa present in a sample and quantify their respective abundances. However, a comparison of the inherent precision or bias of the different sequencing approaches has not been performed. We previously developed a metagenomic control material (MCM) to investigate error when performing different sequencing strategies. Amplicon sequencing using four different primer strategies and two 16S rRNA regions was examined (Roche 454 Junior) and compared to WGS (Illumina HiSeq). All sequencing methods generally performed comparably and in good agreement with organism specific digital PCR (dPCR); WGS notably demonstrated very high precision. Where discrepancies between relative abundances occurred they tended to differ by less than twofold. Our findings suggest that when alternative sequencing approaches are used for microbial molecular profiling they can perform with good reproducibility, but care should be taken when comparing small differences between distinct methods. This work provides a foundation for future work comparing relative differences between samples and the impact of extraction methods. We also highlight the value of control materials when conducting microbial profiling studies to benchmark methods and set appropriate thresholds.The authors acknowledge funding from the European Metrology Research Programme joint research project “INFECT MET” (http://infectmet.lgcgroup.com) (an EMRP project, jointly funded by the EMRP participating countries within EURAMET and the European Union) and the UK National Measurement System for funding of this work and for the support of Thomas Laver by the BBSRC Industrial Case Studentship award BB/H016120/1

Instability of 8E5 calibration standard revealed by digital PCR risks inaccurate quantification of HIV DNA in clinical samples by qPCR

Establishing a cure for HIV is hindered by the persistence of latently infected cells which constitute the viral reservoir. Real-time qPCR, used for quantification of this reservoir by measuring HIV DNA, requires external calibration; a common choice of calibrator is the 8E5 cell line, which is assumed to be stable and to contain one HIV provirus per cell. In contrast, digital PCR requires no external calibration and potentially provides ‘absolute’ quantification. We compared the performance of qPCR and dPCR in quantifying HIV DNA in 18 patient samples. HIV DNA was detected in 18 by qPCR and in 15 by dPCR, the difference being due to the smaller sample volume analysed by dPCR. There was good quantitative correlation (R2 = 0.86) between the techniques but on average dPCR values were only 60% of qPCR values. Surprisingly, investigation revealed that this discrepancy was due to loss of HIV DNA from the 8E5 cell calibrant. 8E5 extracts from two other sources were also shown to have significantly less than one HIV DNA copy per cell and progressive loss of HIV from 8E5 cells during culture was demonstrated. We therefore suggest that the copy number of HIV in 8E5 extracts be established by dPCR prior to use as calibrator

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.

Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories

Five years MIQE guidelines: The case of the Arabian countries

The quantitative real time polymerase chain reaction (qPCR) has become a key molecular enabling technology with an immense range of research, clinical, forensic as well as diagnostic applications. Its relatively moderate instrumentation and reagent requirements have led to its adoption by numerous laboratories, including those located in the Arabian world, where qPCR, which targets DNA, and reverse transcription qPCR (RT-qPCR), which targets RNA, are widely used for region-specific biotechnology, agricultural and human genetic studies. However, it has become increasingly apparent that there are significant problems with both the quality of qPCR-based data as well as the transparency of reporting. This realisation led to the publication of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines in 2009 and their more widespread adoption in the last couple of years. An analysis of the performance of biomedical research in the Arabian world between 2001-2005 suggests that the Arabian world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Hence we have analysed specifically the quality of RT-qPCR-based peer-reviewed papers published since 2009 from Arabian researchers using a bespoke iOS/Android app developed by one of the authors. Our results show that compliance with 15 essential MIQE criteria was low (median of 40%, range 0-93%) and few details on RNA quality controls (22% compliance), assays design (12%), RT strategies (32%), amplification efficiencies (30%) and the normalisation process (3%). These data indicate that one of the reasons for the poor performance of Arabian world biomedical research may be the low standard of any supporting qPCR experiments and identify which aspects of qPCR experiments require significant improvements

Applicability of control materials to support gene promoter characterization and expression in engineered cells using digital PCR

The use of standardized components and processes in engineering underpins the design-build-test model, and the engineering of biological systems is no different. Substantial efforts to standardize both the components and the methods to validate the engineered biological systems is ongoing. This study has developed a panel of control materials encoding the commonly used reporter genes GFP and RFP as DNA or RNA molecules. Each panel contained up to six samples with increasingly small copy number differences between the two reporter genes that ranged from 1- to 2-fold differences. These copy number differences represent the magnitude of changes that may need to be measured to validate an engineered system. Using digital PCR (dPCR), we demonstrated that it is possible to quantify changes in both gene and gene transcript numbers both within and between samples down to 1.05-fold. We corroborated these findings using a simple gene circuit within a bacterial model to demonstrate that dPCR was able to precisely identify small changes in gene expression of two transcripts in response to promoter stimulation. Finally, we used our findings to highlight sources of error that can contributed to the measurement uncertainty in the measurement of small ratios in biological systems. Together, the development of a panel of control materials and validation of a high accuracy method for the measurement of small changes in gene expression, this study can contribute to the engineering biology “toolkit” of methods and materials to support the current standardization efforts

Quantitative methods to monitor RNA biomarkers in myotonic dystrophy

Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are human neuromuscular disorders associated with mutations of simple repetitive sequences in afected genes. The abnormal expansion of CTG repeats in the 3′-UTR of the DMPK gene elicits DM1, whereas elongated CCTG repeats in intron 1 of ZNF9/CNBP triggers DM2. Pathogenesis of both disorders is manifested by nuclear retention of expanded repeat containing RNAs and aberrant alternative splicing. The precise determination of absolute numbers of mutant RNA molecules is important for a better understanding of disease complexity and for accurate evaluation of the efficacy of therapeutic drugs. We present two quantitative methods, Multiplex Ligation-Dependent Probe Amplifcation and droplet digital PCR, for studying the mutant DMPK transcript (DMPKexpRNA) and the aberrant alternative splicing in DM1 and DM2 human tissues and cells. We demonstrate that in DM1, the DMPKexpRNA is detected in higher copy number than its normal counterpart. Moreover, the absolute number of the mutant transcript indicates its low abundance with only a few copies per cell in DM1 fibroblasts. Most importantly, in conjunction with fuorescence in-situ hybridization experiments, our results suggest that in DM1 fibroblasts, the vast majority of nuclear RNA foci consist of a few molecules of DMPKexpRNA

Analytic Metaphysics versus Naturalized Metaphysics: The Relevance of Applied Ontology

The relevance of analytic metaphysics has come under criticism: Ladyman & Ross, for instance, have suggested do discontinue the field. French & McKenzie have argued in defense of analytic metaphysics that it develops tools that could turn out to be useful for philosophy of physics. In this article, we show first that this heuristic defense of metaphysics can be extended to the scientific field of applied ontology, which uses constructs from analytic metaphysics. Second, we elaborate on a parallel by French & McKenzie between mathematics and metaphysics to show that the whole field of analytic metaphysics, being useful not only for philosophy but also for science, should continue to exist as a largely autonomous field

Selection of optimal reference genes for normalization in quantitative RT-PCR

<p>Abstract</p> <p>Background</p> <p>Normalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.</p> <p>Results</p> <p>We propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.</p> <p>Conclusions</p> <p>The proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.</p

Implications of Storing Urinary DNA from Different Populations for Molecular Analyses

Molecular diagnosis using urine is established for many sexually transmitted diseases and is increasingly used to diagnose tumours and other infectious diseases. Storage of urine prior to analysis, whether due to home collection or bio-banking, is increasingly advocated yet no best practice has emerged. Here, we examined the stability of DNA in stored urine in two populations over 28 days.Urine from 40 (20 male) healthy volunteers from two populations, Italy and Zambia, was stored at four different temperatures (RT, 4 degrees C, -20 degrees C & -80 degrees C) with and without EDTA preservative solution. Urines were extracted at days 0, 1, 3, 7 and 28 after storage. Human DNA content was measured using multi-copy (ALU J) and single copy (TLR2) targets by quantitative real-time PCR. Zambian and Italian samples contained comparable DNA quantity at time zero. Generally, two trends were observed during storage; no degradation, or rapid degradation from days 0 to 7 followed by little further degradation to 28 days. The biphasic degradation was always observed in Zambia regardless of storage conditions, but only twice in Italy.Site-specific differences in urine composition significantly affect the stability of DNA during storage. Assessing the quality of stored urine for molecular analysis, by using the type of strategy described here, is paramount before these samples are used for molecular prognostic monitoring, genetic analyses and disease diagnosis

Implications of storing urinary DNA from different populations for molecular analyses.

Molecular diagnosis using urine is established for many sexually transmitted diseases and is increasingly used to diagnose tumours and other infectious diseases. Storage of urine prior to analysis, whether due to home collection or bio-banking, is increasingly advocated yet no best practice has emerged. Here, we examined the stability of DNA in stored urine in two populations over 28 days