Statistical algorithm for assuring similar efficiency in standards and samples for absolute quantification by real-time reverse transcription polymerase chain reaction

Roseburia Abundance Associates With Severity, Evolution and Outcome of Acute Ischemic Stroke

Stroke induces disorder of gut microbiota, however, whether this disorder differs according to stroke severity and its role in the evolution and outcome of stroke is currently unknown. Here we explored the composition and structure of fecal microbiome based on 68 acute ischemic stroke patients presenting with minor symptoms (admission National Institute of Health Stroke Scale (NIHSS) ≤ 3) and 67 patients with non-minor stroke (admission NIHSS 4-34) using high-throughput Illumina sequencing of the 16S rRNA. There was no significant difference in α-diversity indices, but the principal coordinate analysis of the microbiota indicated clear separation of the two groups. The significantly enriched butyrate-producing genus Roseburia in the minor stroke group was negatively correlated with fasting glucose, while the Erysipelotrichaceae incertae sedis abundant in non-minor stroke patients was positively correlated with stress hyperglycemia (i.e. fasting glucose/glycated hemoglobin ratio). Moreover, the relative abundance of genus Roseburia was also significantly associated with the dynamic changes of NIHSS score, as well as short-term and long-term functional outcomes. Our results suggested that stroke affects microbiota composition in a manner differentiated by stroke severity, and the enrichment of genus Roseburia may play a protective role in stroke evolution and outcome. Our findings strengthen the relevance of specific taxa for stroke severity that might allow targeted therapy in acute ischemic stroke.

Optimization of PCR for quantification of simian immunodeficiency virus genomic RNA in plasma of rhesus macaques (Macaca mulatta) using armored RNA

INTRODUCTION

Quantification of plasma viral load (PVL) is used to monitor disease progression in SIV-infected macaques. This study was aimed at optimizing of performance characteristics of the quantitative PCR (qPCR) PVL assay.

METHODS

The PVL quantification procedure was optimized by inclusion of an exogenous control hepatitis C virus armored RNA (aRNA), a plasma concentration step, extended digestion with proteinase K, and a second RNA elution step. Efficiency of viral RNA (vRNA) extraction was compared using several commercial vRNA extraction kits. Various parameters of qPCR targeting the gag region of SIVmac239, SIVsmE660, and the LTR region of SIVagmSAB were also optimized.

RESULTS

Modifications of the SIV PVL qPCR procedure increased vRNA recovery, reduced inhibition and improved analytical sensitivity. The PVL values determined by this SIV PVL qPCR correlated with quantification results of SIV RNA in the same samples using the 'industry standard' method of branched-DNA (bDNA) signal amplification.

CONCLUSIONS

Quantification of SIV genomic RNA in plasma of rhesus macaques using this optimized SIV PVL qPCR is equivalent to the bDNA signal amplification method, less costly and more versatile. Use of heterologous aRNA as an internal control is useful for optimizing performance characteristics of PVL qPCRs.

Overcoming inhibition in real-time diagnostic PCR

PCR is an important and powerful tool in several fields, including clinical diagnostics, food analysis, and forensic analysis. In theory, PCR enables the detection of one single cell or DNA molecule. However, the presence of PCR inhibitors in the sample affects the amplification efficiency of PCR, thus lowering the detection limit, as well as the precision of sequence-specific nucleic acid quantification in real-time PCR. In order to overcome the problems caused by PCR inhibitors, all the steps leading up to DNA amplification must be optimized for the sample type in question. Sampling and sample treatment are key steps, but most of the methods currently in use were developed for conventional diagnostic methods and not for PCR. Therefore, there is a need for fast, simple, and robust sample preparation methods that take advantage of the accuracy of PCR. In addition, the thermostable DNA polymerases and buffer systems used in PCR are affected differently by inhibitors. During recent years, real-time PCR has developed considerably and is now widely used as a diagnostic tool. This technique has greatly improved the degree of automation and reduced the analysis time, but has also introduced a new set of PCR inhibitors, namely those affecting the fluorescence signal. The purpose of this chapter is to view the complexity of PCR inhibition from different angles, presenting both molecular explanations and practical ways of dealing with the problem. Although diagnostic PCR brings together scientists from different diagnostic fields, end-users have not fully exploited the potential of learning from each other. Here, we have collected knowledge from archeological analysis, clinical diagnostics, environmental analysis, food analysis, and forensic analysis. The concept of integrating sampling, sample treatment, and the chemistry of PCR, i.e., pre-PCR processing, will be addressed as a general approach to overcoming real-time PCR inhibition and producing samples optimal for PCR analysis.

Molecular staging of node negative patients with colorectal cancer

Metastatic disease is the principle cause of death from colorectal cancer. In that context, the most significant indicator of overall survival and therapeutic response to adjuvant chemotherapy is the presence of metastatic tumor cells in regional lymph nodes. Although histopathologic analysis of lymph nodes is central to all colorectal cancer staging paradigms, its prognostic and predictive value is limited. Indeed, about 30% of patients with histopathology-negative lymph nodes (pN0) die from metastatic disease, reflected by microscopic lymph node metastases that are overlooked by standard techniques. These unrecognized tumor cells are especially important when considering racial disparities in outcomes in colorectal cancer patients, where blacks with lymph node-negative disease have the largest discrepancies in outcomes, with more than 40% excess mortality compared to Caucasian patients. However, the significance of tumor cells in regional lymph nodes remains uncertain, and approximately 50% of colorectal cancer patients with nodal metastases detected by histopathology remain free of recurrent disease. Accurate identification of occult metastases in regional lymph nodes, and defining their value as prognostic markers of recurrence risk and predictive markers of response to adjuvant chemotherapy remains one challenge in the management of colorectal cancer patients. Guanylyl cyclase C (GUCY2C), a receptor which is expressed primarily in intestinal cells normally, but is universally over-expressed by colorectal cancer cells, has been validated to detect prognostically significant occult metastases using quantitative RT-PCR (RT-qPCR). Biomarker validation was achieved through a prospective, multicenter, blinded clinical trial. In that trial, occult tumor burden estimated across all regional lymph nodes by GUCY2C RT-qPCR predicted clinical outcomes, identifying node-negative patients with a low (near zero) risk, and those with >80% risk, of developing disease recurrence. Moreover, there was disproportionately higher occult tumor burden in black, compared to white, patients which contributes to racial disparities in outcomes in colorectal cancer. The diagnostic paradigm quantifying occult tumor burden using GUCY2C qRT-PCR is positioned to reduce racial disparities in colorectal cancer mortality.

Guanylyl cyclase C as a biomarker in colorectal cancer

While histologic assessment of nodes is a component of all colon cancer staging paradigms, approximately 30% of patients with histology-negative nodes (pN0) die of disseminated disease reflected by occult nodal metastases. Undetected metastases are particularly important when considering racial disparities in colon cancer, where black subjects with pN0 disease exhibit the greatest differences in outcomes, with >40% excess mortality. Recently, guanylyl cyclase C (GCC), a protein normally restricted to intestinal cells, but universally expressed by colorectal cancer cells, was validated for detecting occult metastases. Indeed, occult tumor burden across regional lymph nodes estimated by GCC quantitative reverse transcription PCR identifies pN0 patients with near zero risk, and those with >80% risk, of unfavorable outcomes. Disproportionately high occult tumor burden in black patients underlies racial disparities in stage-specific mortality. These studies position the platform encompassing quantification of occult tumor burden by GCC quantitative reverse transcription PCR for translation, as a detect-treat paradigm to reduce racial disparities in colon cancer mortality.

Simulation of between repeat variability in real time PCR reactions

While many decisions rely on real time quantitative PCR (qPCR) analysis few attempts have hitherto been made to quantify bounds of precision accounting for the various sources of variation involved in the measurement process. Besides influences of more obvious factors such as camera noise and pipetting variation, changing efficiencies within and between reactions affect PCR results to a degree which is not fully recognized. Here, we develop a statistical framework that models measurement error and other sources of variation as they contribute to fluorescence observations during the amplification process and to derived parameter estimates. Evaluation of reproducibility is then based on simulations capable of generating realistic variation patterns. To this end, we start from a relatively simple statistical model for the evolution of efficiency in a single PCR reaction and introduce additional error components, one at a time, to arrive at stochastic data generation capable of simulating the variation patterns witnessed in repeated reactions (technical repeats). Most of the variation in C(q) values was adequately captured by the statistical model in terms of foreseen components. To recreate the dispersion of the repeats' plateau levels while keeping the other aspects of the PCR curves within realistic bounds, additional sources of reagent consumption (side reactions) enter into the model. Once an adequate data generating model is available, simulations can serve to evaluate various aspects of PCR under the assumptions of the model and beyond.

Validation of kinetics similarity in qPCR

Quantitative real-time PCR (qPCR) is the method of choice for specific and sensitive quantification of nucleic acids. However, data validation is still a major issue, partially due to the complex effect of PCR inhibition on the results. If undetected PCR inhibition may severely impair the accuracy and sensitivity of results. PCR inhibition is addressed by prevention, detection and correction of PCR results. Recently, a new family of computational methods for the detection of PCR inhibition called kinetics outlier detection (KOD) emerged. KOD methods are based on comparison of one or a few kinetic parameters describing a test reaction to those describing a set of reference reactions. Modern KOD can detect PCR inhibition reflected by shift of the amplification curve by merely half a cycle with specificity and sensitivity >90%. Based solely on data analysis, these tools complement measures to improve and control pre-analytics. KOD methods do not require labor and materials, do not affect the reaction accuracy and sensitivity and they can be automated for fast and reliable quantification. This review describes the background of KOD methods, their principles, assumptions, strengths and limitations. Finally, the review provides recommendations how to use KOD and how to evaluate its performance.

Detection and quantification of functional genes of cellulose- degrading, fermentative, and sulfate-reducing bacteria and methanogenic archaea

Cellulose degradation, fermentation, sulfate reduction, and methanogenesis are microbial processes that coexist in a variety of natural and engineered anaerobic environments. Compared to the study of 16S rRNA genes, the study of the genes encoding the enzymes responsible for these phylogenetically diverse functions is advantageous because it provides direct functional information. However, no methods are available for the broad quantification of these genes from uncultured microbes characteristic of complex environments. In this study, consensus degenerate hybrid oligonucleotide primers were designed and validated to amplify both sequenced and unsequenced glycoside hydrolase genes of cellulose-degrading bacteria, hydA genes of fermentative bacteria, dsrA genes of sulfate-reducing bacteria, and mcrA genes of methanogenic archaea. Specificity was verified in silico and by cloning and sequencing of PCR products obtained from an environmental sample characterized by the target functions. The primer pairs were further adapted to quantitative PCR (Q-PCR), and the method was demonstrated on samples obtained from two sulfate-reducing bioreactors treating mine drainage, one lignocellulose based and the other ethanol fed. As expected, the Q-PCR analysis revealed that the lignocellulose-based bioreactor contained higher numbers of cellulose degraders, fermenters, and methanogens, while the ethanol-fed bioreactor was enriched in sulfate reducers. The suite of primers developed represents a significant advance over prior work, which, for the most part, has targeted only pure cultures or has suffered from low specificity. Furthermore, ensuring the suitability of the primers for Q-PCR provided broad quantitative access to genes that drive critical anaerobic catalytic processes.

Quality control for quantitative PCR based on amplification compatibility test

Quantitative qPCR is a routinely used method for the accurate quantification of nucleic acids. Yet it may generate erroneous results if the amplification process is obscured by inhibition or generation of aberrant side-products such as primer dimers. Several methods have been established to control for pre-processing performance that rely on the introduction of a co-amplified reference sequence, however there is currently no method to allow for reliable control of the amplification process without directly modifying the sample mix. Herein we present a statistical approach based on multivariate analysis of the amplification response data generated in real-time. The amplification trajectory in its most resolved and dynamic phase is fitted with a suitable model. Two parameters of this model, related to amplification efficiency, are then used for calculation of the Z-score statistics. Each studied sample is compared to a predefined reference set of reactions, typically calibration reactions. A probabilistic decision for each individual Z-score is then used to identify the majority of inhibited reactions in our experiments. We compare this approach to univariate methods using only the sample specific amplification efficiency as reporter of the compatibility. We demonstrate improved identification performance using the multivariate approach compared to the univariate approach. Finally we stress that the performance of the amplification compatibility test as a quality control procedure depends on the quality of the reference set.

Molecular staging estimates occult tumor burden in colorectal cancer

Tumor cells in regional lymph nodes are a key prognostic marker of survival and predictive marker of response to adjuvant chemotherapy in colorectal cancer. However, clinicopathologic techniques to detect lymph node metastases remain imperfect, and approximately 30% of patients with lymph nodes negative by histology (pN0) develop recurrent disease, reflecting occult metastases that escape detection. These observations underscore an unmet clinical need for accurate approaches to identify occult nodal metastases in colorectal cancer patients. GUCY2C is a receptor whose expression normally is restricted to intestinal epithelial cells, but is universally overexpressed by colorectal cancer cells. A prospective, multicenter, blinded clinical trial established the prognostic utility of GUCY2C qRT-PCR to detect occult nodal metastases in pN0 colorectal cancer patients. Molecular staging revealed that approximately 13% of pN0 patients were free of cancer cells, while approximately 87% had GUCY2C results that suggested occult metastases. The presence of occult nodal metastases was the most powerful independent predictor of time to recurrence and disease-free survival. These observations establish the utility of molecular detection of occult nodal metastases for assessing prognostic risk in pN0 colorectal cancer patients. Advancing GUCY2C into staging paradigms in clinical laboratories will require validation in independent patient populations, definition of the relationship between the quantity of occult tumor metastases and risk, and determination of the utility of GUCY2C qRT-PCR to identify pN0 patients who might benefit from adjuvant chemotherapy.

Relative quantification based on logistic models for individual polymerase chain reactions

The quantitative real-time reverse transcription polymerase chain reaction (RT-PCR) technology measures molecular variations in specific biomarkers. Relative quantification determines the target expression relative to an external standard or reference sample and should be adjusted for the PCR efficiencies actually achieved. More accurate methods of estimating PCR efficiency require a number of serial dilutions of the target sample, which is not generally feasible for clinical specimens. Alternatively, the efficiency of a single reaction may be estimated by considering kinetic data from this reaction. The current methods of estimating individual reaction efficiency require finding its exponential phase, which may affect the accuracy and precision of efficiency estimates. Thus, a model adequately representing all available kinetic RT-PCR data is preferable, but no such model is currently in use for relative quantification. In this work, we use a logistic model for all kinetic data from each RT-PCR and propose a new method of efficiency-adjusted relative quantification based on the estimates from the fitted logistic models. This method allows incorporating multiple replicates and possibly multiple reference ('housekeeping') genes for estimating relative expression and corresponding confidence interval. Real kinetic RT-PCR data are used to compare the proposed and standard methods. The methods are applied to the clinical data from the ongoing study of guanylyl cyclase C as a biomarker for colorectal cancer.

ProCAT: a data analysis approach for protein microarrays

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.