Efficiency of DNA replication in the polymerase chain reaction

Massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS)

Chip scale DNA synthesis offers a high-throughput and cost-effective method for large-scale DNA-based information storage. Nevertheless, unbiased information retrieval from low-copy-number sequences remains a barricade that largely arises from the indispensable DNA amplification. Here, we devise a simulation-guided quantitative primer-template hybridization strategy to realize massively parallel homogeneous amplification of chip-scale DNA for DNA information storage (MPHAC-DIS). Using a fixed-energy primer design, we demonstrate the unbiasedness of MPHAC for amplifying 100,000-plex sequences. Simulations reveal that MPHAC achieves a fold-80 value of 1.0 compared to 3.2 with conventional fixed-length primers, lowering costs by up to four orders of magnitude through reduced over-sequencing. The MPHAC-DIS system using 35,406 encoded oligonucleotide allows simultaneous access of multimedia files including text, images, and videos with high decoding accuracy at very low sequencing depths. Specifically, even a ~ 1 ×  sequencing depth, with the combination of machine learning, results in an acceptable decoding accuracy of ~80%. The programmable and predictable MPHAC-DIS method thus opens new door for DNA-based large-scale data storage with potential industrial applications.

Establishing validated RT-qPCR workflow for the analysis of oligodendrocyte gene expression in the developing murine brain

Myelination is essential for the proper conduction of impulses across neuronal networks. Mature, myelinating glia differentiate from progenitor cells through distinct stages that correspond to oligodendrocyte-specific gene expression markers. Reverse transcription quantiatative PCR (RT-qPCR) is a common technique used to quantify gene expression across cell development; however, a lack of standardization and transparency in methodology may lead to irreproducible data. Here, we have designed and validated RT-qPCR assays for oligodendrocyte genes and reference genes in the developing C57BL6/J mouse brain that align with the MIQE guidelines, including quality controls for primer specificity, temperature dependence, and efficiency. A panel of eight commonly used reference genes was ranked using a series of reference gene stability methods that consistently identified Gapdh, Sdha, Hmbs, Hprt1, and Pgk1 as the top candidates for normalization across brain regions. In the cerebrum, myelin genes peaked in expression at postnatal day 21, which corresponds to the peak of developmental myelination. The gene expression patterns from the brain homogenate were in agreement with previously reported RNA-seq and microarray profiles from oligodendrocyte lineage cells. The validated RT-qPCR assays begin to build a framework for future investigation into the molecular mechanisms that regulate myelination in mouse models of brain development, aging, and disease.

Monitoring the Persistence of Pseudomonas sivasensis Strain CF10PS3 in Cereal Fields

The persistence and efficacy of biocontrol agents in agricultural fields are crucial for sustainable crop production. In this study, we investigated the persistence of the introduced bacterial strain Pseudomonas sivasensis CF10PS3 in the wheat phyllosphere using a novel qPCR probe protocol. The CF10PS3 strain, known for its in vitro biocontrol properties against wheat pathogens, was applied through foliar spray, and its persistence was monitored over 7 weeks. Our qPCR assays, designed to specifically detect CF10PS3, distinguished it from naturally occurring P. sivasensis strains, providing precise insights into its dynamics in the field. The experimental results indicated that CF10PS3 was already present on the wheat leaves before its application, suggesting its natural adaptation to the foliar environment. Following initial application, a significant increase in CF10PS3 was observed, though subsequent environmental factors such as rain and wind might have caused notable fluctuations in its population. Despite these variations, the introduced strain showed considerable persistence, with population levels significantly higher than those in untreated plots by the end of the study period. This research underscores the importance of understanding bacteria dynamics in the field, highlighting the influence of environmental conditions on their efficacy. The use of specific qPCR probes proved effective in monitoring introduced strains, offering valuable insights for optimizing biocontrol agent application strategies. Our findings contribute to the development of robust biocontrol methods, promoting sustainable agricultural practices and enhancing crop protection.

Novel and Sensitive Touchdown Polymerase Chain Reaction Assays for the Detection of Goat and Sheep Milk Adulteration with Cow Milk

Milk is the most consumed liquid food in the world due to its high nutritional value and relatively low cost, characteristics that make it vulnerable to adulteration. One of the most common types of milk adulteration involves the undeclared addition of cow's milk to milk from other mammalian species, such as goats, sheep, buffalo or donkeys. The incidence of such adulteration not only causes a crisis in terms of commercial market and consumer uncertainty but also poses a risk to public health, as allergies can be triggered by proteins in undeclared cow's milk. In this study, a specific qualitative touchdown (TD) PCR method was developed to detect the undeclared addition of cow's milk in goat and sheep milk based on the discrimination of the peak areas of the melting curves after the modification of bovine-specific primers. The developed methodology has high specificity for the DNA templates of other species, such as buffalos and donkeys, and is able to identify the presence of cow's milk down to 1%. Repeatability was tested at low bovine concentrations of 5% and 1% and resulted in %RSD values of 1.53-2.04 for the goat-cow assay and 2.49-7.16 for the sheep-cow assay, respectively. The application of this method to commercial goat milk samples indicated a high percentage of noncompliance in terms of labeling (50%), while a comparison of the results to rapid immunochromatographic and ELISA kits validated the excellent sensitivity and applicability of the proposed PCR methodology that was able to trace more adulterated samples. The developed assays offer the advantage of multiple detection in a single run, resulting in a cost- and time-efficient method. Future studies will focus on the applicability of these assays in dairy products such as cheese and yogurt.

Ligation-Based High-Performance Mimetic Enzyme Sensing Platform for Nucleic Acid Detection

G-quadruplex (G4)/hemin DNAzyme is a promising candidate to substitute horseradish peroxidase in biosensing systems, especially for the detection of nucleic acids. However, the relatively suboptimal catalytic capacity limits its potential applications. This makes it imperative to develop an ideal signal for the construction of highly sensitive biosensing platforms. Herein, we integrated a novel chimeric peptide-DNAzyme (CPDzyme) with the ligase chain reaction (LCR) for the cost-efficient and highly sensitive detection of nucleic acids. By employing microRNA (miRNA) and single-nucleotide polymorphism detection as the model, we designed a G4-forming sequence on the LCR probe with a terminally labeled amino group. Subsequently, asymmetric hemin with carboxylic arms allowed assembly with the LCR products and peptide to form CPDzyme, followed by the magnetic separation of the extraneous components and chemiluminescence detection. Compared with the conventional G4/hemin signaling-based method, the LCR-CPDzyme system demonstrated 3 orders of magnitude improved sensitivity, with accurate quantification of as low as 25 aM miRNA and differentiation of 0.1% of mutant DNA from the pool containing a large amount of wild-type DNA. The proposed LCR-CPDzyme strategy is a potentially powerful method for in vitro diagnostics and serves as a reference for the development of other ligation- or hybridization-based nucleic acid amplification assays.

Reducing Bias and Quantifying Uncertainty in Fluorescence Produced by PCR

We present a new approach for relating nucleic-acid content to fluorescence in a real-time Polymerase Chain Reaction (PCR) assay. By coupling a two-type branching process for PCR with a fluorescence analog of Beer's Law, the approach reduces bias and quantifies uncertainty in fluorescence. As the two-type branching process distinguishes between complementary strands of DNA, it allows for a stoichiometric description of reactions between fluorescent probes and DNA and can capture the initial conditions encountered in assays targeting RNA. Analysis of the expected copy-number identifies additional dynamics that occur at short times (or, equivalently, low cycle numbers), while investigation of the variance reveals the contributions from liquid volume transfer, imperfect amplification, and strand-specific amplification (i.e., if one strand is synthesized more efficiently than its complement). Linking the branching process to fluorescence by the Beer's Law analog allows for an a priori description of background fluorescence. It also enables uncertainty quantification (UQ) in fluorescence which, in turn, leads to analytical relationships between amplification efficiency (probability) and limit of detection. This work sets the stage for UQ-PCR, where both the input copy-number and its uncertainty are quantified from fluorescence kinetics.

A novel Gardnerella, Prevotella, and Lactobacillus standard that improves accuracy in quantifying bacterial burden in vaginal microbial communities

Bacterial vaginosis (BV) is the most common vaginal dysbiosis. In this condition, a polymicrobial biofilm develops on vaginal epithelial cells. Accurately quantifying the bacterial burden of the BV biofilm is necessary to further our understanding of BV pathogenesis. Historically, the standard for calculating total bacterial burden of the BV biofilm has been based on quantifying Escherichia coli 16S rRNA gene copy number. However, E. coli is improper for measuring the bacterial burden of this unique micro-environment. Here, we propose a novel qPCR standard to quantify bacterial burden in vaginal microbial communities, from an optimal state to a mature BV biofilm. These standards consist of different combinations of vaginal bacteria including three common BV-associated bacteria (BVAB) Gardnerella spp. (G), Prevotella spp. (P), and Fannyhessea spp. (F) and commensal Lactobacillus spp. (L) using the 16S rRNA gene (G:P:F:L, G:P:F, G:P:L and 1G:9L). We compared these standards to the traditional E. coli (E) reference standard using known quantities of mock vaginal communities and 16 vaginal samples from women. The E standard significantly underestimated the copy numbers of the mock communities, and this underestimation was significantly greater at lower copy numbers of these communities. The G:P:L standard was the most accurate across all mock communities and when compared to other mixed vaginal standards. Mixed vaginal standards were further validated with vaginal samples. This new G:P:L standard can be used in BV pathogenesis research to enhance reproducibility and reliability in quantitative measurements of BVAB, spanning from the optimal to non-optimal (including BV) vaginal microbiota.

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers

MOTIVATION

Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences.

RESULTS

We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/DormanLab/AmpliCI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Measuring the latent reservoir for HIV-1: Quantification bias in near full-length genome sequencing methods

Antiretroviral therapy (ART) effectively inhibits HIV-1 replication but is not curative due to the persistence of a latent viral reservoir in resting CD4⁺ T cells. This reservoir is a major barrier to cure. Sequencing studies have revealed that the population of proviruses persisting in ART-treated individuals is dominated by defective proviruses that cannot give rise to viral rebound due to fatal defects including large deletions and APOBEC3-mediated hypermutation. Near full genome sequencing (nFGS) of individual proviruses is used in reservoir assays to provide an estimate of the fraction of proviruses that are intact. nFGS methods rely on a long-distance outer PCR capturing most (~9 kb) of the genome, followed by nested inner PCRs. The outer PCR is carried out at limit dilution, and interpretation of the results is based on the assumption that all proviruses are quantitatively captured. Here, we evaluate nFGS methods using the intact proviral DNA assay (IPDA), a multiplex digital droplet PCR assay that quantitates intact and defective proviruses with single molecule sensitivity using only short, highly efficient amplicons. We analyzed proviral templates of known sequence to avoid the additional complication of sequence polymorphism. With the IPDA, we quantitated molecular yields at each step of nFGS methods. We demonstrate that nFGS methods are inefficient and miss ~70% of full-length proviruses due to amplification failure at the initial outer PCR step. In contrast, proviruses with large internal deletions encompassing 70% of the genome can be quantitatively amplified under the same conditions. Accurate measurement of the latent reservoir of HIV-1 is essential for evaluating the efficacy of cure strategies, and the bias against full length proviruses in nFGS methods must be considered.

Despite antiretroviral therapy, HIV-1 persists in a small population of resting memory CD4⁺ T cells carrying a latent viral genome. This latent reservoir is the major barrier to cure. Accurate reservoir assays are critical for evaluating therapies aimed at reducing the reservoir. Sequencing studies have shown that defective proviruses greatly outnumber the intact, replication-competent proviruses responsible for viral rebound, and reservoir assays that rely on near full-genome sequencing (nFGS) have provided important qualitative information about intact and defective proviruses. However, it is assumed that all proviruses are amplified with equal efficiency in nFGS methods, regardless of sequence length. Here, we rigorously measure the efficiency with which nFGS methods detect intact and defective proviruses using a highly efficient multiplex digital droplet PCR assay, the intact proviral DNA assay. This assay allows direct counting of input proviral template molecules as well as PCR amplified products generated with different nFGS methods. We determined that nFGS methods do not provide an accurate quantitative measure of intact proviruses. Only ~30% of intact proviruses were detected, while proviruses with large internal deletions were amplified at expected frequencies. Our study demonstrates that nFGS methods do not provide accurate quantitative information about the size and composition of the latent reservoir.

Kinetics of Isothermal Dumbbell Exponential Amplification: Effects of Mix Composition on LAMP and Its Derivatives

Loop-mediated isothermal amplification (LAMP) is an exponential amplification method of DNA strands that is more and more used for its high performances. Thanks to its high sensitivity and selectivity, LAMP found numerous applications from the detection of pathogens or viruses through their genome amplification to its incorporation as an amplification strategy in protein or miRNA biomarker quantification. The LAMP method is composed of two stages: the first one consists in the transformation of the DNA strands into dumbbell structures formed of two stems and loops thanks to four primers; then, in the second stage, only two primers are required to amplify the dumbbells exponentially in numerous hairpins of increasing lengths. In this paper, we propose a theoretical framework to analyze the kinetics of the second stage of LAMP, the isothermal dumbbell exponential amplification (IDEA) as function of the physico-chemical parameters of the amplification reaction. Dedicated experiments validate the models. We believe these results may help the optimization of LAMP performances by reducing the number of experiments necessary to find the best parameters.

Designer DNA biomolecules as a defined biomaterial for 3D bioprinting applications

DNA has excellent features such as the presence of functional and targeted molecular recognition motifs, tailorability, multifunctionality, high-precision molecular self-assembly, hydrophilicity, and outstanding biocompatibility. Due to these remarkable features, DNA has emerged as a leading next-generation biomaterial of choice to make hydrogels by self-assembly. In recent times, novel routes for the chemical synthesis of DNA, advances in tailorable designs, and affordable production ways have made DNA as a building block material for various applications. These advanced features have made researchers continuously explore the interesting properties of pure and hybrid DNA for 3D bioprinting and other biomedical applications. This review article highlights the topical advancements in the use of DNA as an ideal bioink for the bioprinting of cell-laden three-dimensional tissue constructs for regenerative medicine applications. Various bioprinting techniques and emerging design approaches such as self-assembly, nucleotide sequence, enzymes, and production cost to use DNA as a bioink for bioprinting applications are described. In addition, various types and properties of DNA hydrogels such as stimuli responsiveness and mechanical properties are discussed. Further, recent progress in the applications of DNA in 3D bioprinting are emphasized. Finally, the current challenges and future perspectives of DNA hydrogels in 3D bioprinting and other biomedical applications are discussed.

On the relation between input and output distributions of scRNA-seq experiments

MOTIVATION

Single-cell RNA sequencing determines RNA copy numbers per cell for a given gene. However, technical noise poses the question how observed distributions (output) are connected to their cellular distributions (input).

RESULTS

We model a single-cell RNA sequencing setup consisting of PCR amplification and sequencing, and derive probability distribution functions for the output distribution given an input distribution. We provide copy number distributions arising from single transcripts during PCR amplification with exact expressions for mean and variance. We prove that the coefficient of variation of the output of sequencing is always larger than that of the input distribution. Experimental data reveals the variance and mean of the input distribution to obey characteristic relations, which we specifically determine for a HeLa dataset. We can calculate as many moments of the input distribution as are known of the output distribution (up to all). This, in principle, completely determines the input from the output distribution.

AVAILABILITY AND IMPLEMENTATION

Source code freely available at https://github.com/danielschw188/InputOutputSCRNASeq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ultrasensitive Detection of Bacillus anthracis by Real-Time PCR Targeting a Polymorphism in Multi-Copy 16S rRNA Genes and Their Transcripts

The anthrax pathogen Bacillus anthracis poses a significant threat to human health. Identification of B. anthracis is challenging because of the bacterium's close genetic relationship to other Bacillus cereus group species. Thus, molecular detection is founded on species-specific PCR targeting single-copy genes. Here, we validated a previously recognized multi-copy target, a species-specific single nucleotide polymorphism (SNP) present in 2-5 copies in every B. anthracis genome analyzed. For this, a hydrolysis probe-based real-time PCR assay was developed and rigorously tested. The assay was specific as only B. anthracis DNA yielded positive results, was linear over 9 log10 units, and was sensitive with a limit of detection (LoD) of 2.9 copies/reaction. Though not exhibiting a lower LoD than established single-copy PCR targets (dhp61 or PL3), the higher copy number of the B. anthracis-specific 16S rRNA gene alleles afforded ≤2 unit lower threshold (Ct) values. To push the detection limit even further, the assay was adapted for reverse transcription PCR on 16S rRNA transcripts. This RT-PCR assay was also linear over 9 log10 units and was sensitive with an LoD of 6.3 copies/reaction. In a dilution series of experiments, the 16S RT-PCR assay achieved a thousand-fold higher sensitivity than the DNA-targeting assays. For molecular diagnostics, we recommend a real-time RT-PCR assay variant in which both DNA and RNA serve as templates (thus, no requirement for DNase treatment). This can at least provide results equaling the DNA-based implementation if no RNA is present but is superior even at the lowest residual rRNA concentrations.

A proof-of-principle study on implementing polymerase chain displacement reaction (PCDR) to improve forensic low-template DNA analysis

Polymerase chain reaction (PCR) plays an important role in forensic DNA analysis. However, the amplification of low-template DNA (LTDNA) samples usually encounters unsatisfactory results for the limited efficiency of PCR, which would interfere with the subsequent profile interpretation. Polymerase chain displacement reaction (PCDR) is a highly-efficient technique characterized by combining PCR and strand displacement reaction into a single PCDR cycle. This study explored the feasibility of PCDR for improving forensic LTDNA analysis. STR markers commonly used in forensic genetics were subjected to PCDR amplification and capillary electrophoresis detection. The results of singleplex reactions indicated that PCDR surpassed original PCR in efficiency for STR amplification. The average peak height of alleles in PCDR profiles was linearly correlated to the number of outer primers adopted for initiating the strand displacement process. Further, we assessed the multiplexing potential of PCDR by incorporating 17 STRs included in the expanded CODIS core loci and Amelogenin gene into a multiplex PCDR system. For pristine DNA templates ranged from 200 pg to 12.5 pg, the multiplex PCDR system consistently exhibited higher allele peak height as well as less allele dropout compared to the multiplex PCR references. Meanwhile, a significant reduction of stutter ratio was extensively observed in PCDR profiles. We also tested mock casework samples to verify the practical ability of multiplex PCDR for LTDNA detection. With DNA input varying from 48.1 pg to 6.6 pg, the multiplex PCDR system consistently obtained more allelic information than multiplex PCR methods. Our data collectively suggested that it is feasible to apply PCDR in forensic LTDNA analysis.

Evaluation of SYBR Green real time PCR for detecting SARS-CoV-2 from clinical samples

The pandemic caused by SARS-CoV-2 has triggered an extraordinary collapse of healthcare systems and hundred thousand of deaths worldwide. Following the declaration of the outbreak as a Public Health Emergency of International Concern by the World Health Organization (WHO) on January 30th, 2020, it has become imperative to develop diagnostic tools to reliably detect the virus in infected patients. Several methods based on real time reverse transcription polymerase chain reaction (RT-qPCR) for the detection of SARS-CoV-2 genomic RNA have been developed. In addition, these methods have been recommended by the WHO for laboratory diagnosis. Since most of these protocols are based on the use of fluorogenic probes and one-step reagents (cDNA synthesis followed by PCR amplification in the same tube), these techniques can be difficult to perform given the limited supply of reagents in low- and middle-income countries. In order to develop an inexpensive SARS-CoV-2 detection protocol using available resources we evaluated the SYBR Green based detection of SARS-CoV-2 to establish a suitable assay. To do so, we adapted one of the WHO recommended TaqMan-based one-step real time PCR protocols (from the University of Hong Kong) to SYBR Green. Our results indicate that SYBR-Green detection of ORF1b-nsp14 target represents a reliable cost-effective alternative to increase the testing capacity.

RNase H-dependent PCR enables highly specific amplification of antibody variable domains from single B-cells

Immunization-based antibody discovery platforms require robust and effective protocols for the amplification, cloning, expression, and screening of antibodies from large numbers of B-cells in order to effectively capture the diversity of an experienced Ig-repertoire. Multiplex PCR using a series of forward and reverse primers designed to recover antibodies from a range of different germline sequences is challenging because primer design requires the recovery of full length antibody sequences, low starting template concentrations, and the need for all the primers to function under the same PCR conditions. Here we demonstrate several advantages to incorporating RNase H2-dependent PCR (rh-PCR) into a high-throughput, antibody-discovery platform. Firstly, rh-PCR eliminated primer dimer synthesis to below detectable levels, thereby eliminating clones with a false positive antibody titer. Secondly, by increasing the specificity of PCR, the rh-PCR primers increased the recovery of cognate antibody variable regions from single B-cells, as well as downstream recombinant antibody titers. Finally, we demonstrate that rh-PCR primers provide a more homogeneous sample pool and greater sequence quality in a Next Generation Sequencing-based approach to obtaining DNA sequence information from large numbers of cloned antibody cognate pairs. Furthermore, the higher specificity of the rh-PCR primers allowed for a better match between native antibody germline sequences and the VL/VH fragments amplified from single B-cells.

Emulsion PCR (ePCR) as a Tool to Improve the Power of DGGE Analysis for Microbial Population Studies

To the authors’ knowledge, this is the first report of the use of emulsion-Polymerase chain reaction (e-PCR) coupled with denaturing gradient gel electrophoresis (DGGE) analysis. In the present work the effectiveness of ePCR in improving the power of the DGGE technique for microbial population studies was tested. Our results indicated that ePCR results in uniform amplification of several DNA molecules, overcoming the major limitations of conventional PCR, such as preferential amplification and DNA concentration dependence. Moreover, ePCR-DGGE resulted in higher sensitivity when compared to conventional PCR-DGGE methods used for studying microbial populations in a complex matrix. In fact, compared to conventional PCR, the DGGE profiles of ePCR products permitted the detection of a higher number of the species that were present in the tested sample.

Model-based Analysis for Real-time Label-free Ultraviolet Quantification of Ultrafast Plasmonic Polymerase Chain Reaction

A mathematical model for DNA quantification was calibrated using experimental results from real-time 260nm absorption measurements of plasmonic PCR thermocycling. The effect of different PCR parameters on template amplification was investigated using the calibrated model.

Quantifying molecular bias in DNA data storage

DNA has recently emerged as an attractive medium for archival data storage. Recent work has demonstrated proof-of-principle prototype systems; however, very uneven (biased) sequencing coverage has been reported, which indicates inefficiencies in the storage process. Deviations from the average coverage in the sequence copy distribution can either cause wasteful provisioning in sequencing or excessive number of missing sequences. Here, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that the two paramount sources of bias are the synthesis and amplification (PCR) processes. Based on these findings, we develop a statistical model for each molecular process as well as the overall process. We further use our model to explore the trade-offs between synthesis bias, storage physical density, logical redundancy, and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems.

Validating TrueAllele® Interpretation of DNA Mixtures Containing up to Ten Unknown Contributors

Most DNA evidence is a mixture of two or more people. Cybergenetics TrueAllele^® system uses Bayesian computing to separate genotypes from mixture data and compare genotypes to calculate likelihood ratio (LR) match statistics. This validation study examined the reliability of TrueAllele computing on laboratory‐generated DNA mixtures containing up to ten unknown contributors. Using log(LR) match information, the study measured sensitivity, specificity, and reproducibility. These reliability metrics were assessed under different conditions, including varying the number of assumed contributors, statistical sampling duration, and setting known genotypes. The main determiner of match information and variability was how much DNA a person contributed to a mixture. Observed contributor number based on data peaks gave better results than the number known from experimental design. The study found that TrueAllele is a reliable method for analyzing DNA mixtures containing up to ten unknown contributors.

Investigation of the Dominant Microbiota in Ready-to-Eat Grasshoppers and Mealworms and Quantification of Carbapenem Resistance Genes by qPCR

In this study, 30 samples of processed edible mealworms (Tenebrio molitor L.) and 30 samples of grasshoppers (Locusta migratoria migratorioides) were obtained from producers located in Europe (Belgium and the Netherlands) and Asia (Thailand) and subjected to PCR-DGGE analyses. The PCR-DGGE analyses showed that species in the genus Staphylococcus were predominant in the samples of mealworms from Belgium and grasshoppers from the Netherlands; species in the genus Bacillus were detected in the samples of mealworms and grasshoppers from Thailand. Moreover, Weissella cibaria/confusa/spp. was found in grasshoppers from Belgium. Since data concerning the role of novel foods such as edible insects in the dissemination of carbapenem resistance are currently lacking, the quantification of five carbapenemase encoding genes (bla NDM-1, bla VIM, bla GES, bla OXA-48, and bla KPC) by qPCR was also carried out in all the samples under study. The genes coding for GES and KPC were not detected in the analyzed samples. A very low frequency of bla OXA-48 (3%) and bla NDM-1 (10%) genes was detected among mealworms. In contrast, grasshoppers were characterized by a high incidence of the genes for OXA-48 and NDM-1, accounting for 57 and 27% of the overall grasshopper samples, respectively. The bla VIM gene was detected exclusively in two grasshopper samples from Thailand, showing only 7% positivity. The analysis of variance showed that all the effects (producers, species, and producers × species) were statistically significant for bla NDM-1, whereas for bla OXA-48 and bla VIM, no significant effects were detected for the same source of variation. Further studies are necessary to assess the possible role of edible insects as reservoirs for the resistance to carbapenems and to understand the correlation with the insect microbiota. Furthermore, an intensified surveillance plan examining the occurrence of carbapenemase encoding genes in the food chain and in environmental compartments is needed for a proper risk assessment. In such a context, the appropriate use of antimicrobials represents the main preventive action that should always be applied.

An array-based melt curve analysis method for the identification and classification of closely related pathogen strains

PCR-based techniques are widely used to identify disease causing bacterial and viral pathogens, especially in point-of-care or near-patient clinical settings that require rapid results and sample-to-answer workflows. However, such techniques often fail to differentiate between closely related species that have highly variable genomes. Here, a homogenous (closed-tube) pathogen identification and classification method is described that combines PCR amplification, array-based amplicon sequence verification, and real-time detection using an inverse fluorescence fluorescence-resonance energy transfer technique. The amplification is designed to satisfy the inclusivity criteria and create ssDNA amplicons, bearing a nonradiating quencher moiety at the 5'-terminus, for all the related species. The array includes fluorescent-labeled probes which preferentially capture the variants of the amplicons and classify them through solid-phase thermal denaturing (melt curve) analysis. Systematic primer and probe design algorithms and empirical validation methods are presented and successfully applied to the challenging example of identification of, and differentiation between, closely related human rhinovirus and human enterovirus strains.

Modeling bias and variation in the stochastic processes of small RNA sequencing

The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data.

Talking the talk, but not walking the walk: RT-qPCR as a paradigm for the lack of reproducibility in molecular research

Poorly executed and inadequately reported molecular measurement methods are amongst the causes underlying the lack of reproducibility of much biomedical research. Although several high impact factor journals have acknowledged their past failure to scrutinise adequately the technical soundness of manuscripts, there is a perplexing reluctance to implement basic corrective measures. The reverse transcription real-time quantitative PCR (RT-qPCR) is probably the most straightforward measurement technique available for RNA quantification and is widely used in research, diagnostic, forensic and biotechnology applications. Despite the impact of the minimum information for the publication of quantitative PCR experiments (MIQE) guidelines, which aim to improve the robustness and the transparency of reporting of RT-qPCR data, we demonstrate that elementary protocol errors, inappropriate data analysis and inadequate reporting continue to be rife and conclude that the majority of published RT-qPCR data are likely to represent technical noise.

Molecular diagnosis of the tick-borne pathogen Anaplasma marginale in cattle blood samples from Nigeria using qPCR

Tick-borne diseases (TBDs) are some of the most important animal health and management problems in Africa, including Nigeria. This study aims to determine the prevalence of an important TBD, anaplasmosis, in a North-central region of Nigeria. Blood samples were collected from cattle and stored on Whatman FTA^® cards. Information on village, age and sex associated with each cattle was also recorded. The packed red blood cell volume (PCV) for each blood sample was determined. After DNA extraction, pathogen presence was evaluated by TaqMan^® based qPCR of which 75.9 % of the cattle tested positive for Anaplasma marginale. Statistical analysis revealed that the presence of A. marginale infection differed significantly between cattle age groups. However, there was no significant difference in the prevalence of this pathogen between the sexes or among cattle grouped by PCV level. Finally, using a highly sensitive molecular method our pioneer study contributes to the improvement of the current knowledge regarding tick-borne pathogens that seriously affect animal health in specific areas of Nigeria.

Characterization of degradation and heterozygote balance by simulation of the forensic DNA analysis process

Simulation experiments were used to show the impact of varying extraction efficiency, aliquot proportion, and PCR efficiency on the heterozygote balance of a range of diploid and haploid cells. Reducing either parameters introduces variance. It is well-known that the variance in heterozygote balance increases as the amount of DNA is reduced. Surprisingly the distribution is in fact diamond shaped — the variance start to decrease at very low amounts of DNA. Simulations suggest that pristine diluted DNA is an acceptable approximation in validations to infer heterozygote balance. However, the difference in distribution of the variance between diploid and haploid cell types may, under some circumstances, need to be considered in statistical models. Finally, we exemplify how simulations can be used to predict the outcome of PCR for degraded samples. Visualizing the predicted DNA profile as an electropherogram can help to identify the best approach for sample processing.

Onchocerciasis transmission in Ghana: the human blood index of sibling species of the Simulium damnosum complex

Background

Vector-biting behaviour is important for vector-borne disease (VBD) epidemiology. The proportion of blood meals taken on humans (the human blood index, HBI), is a component of the biting rate per vector on humans in VBD transmission models. Humans are the definitive host of Onchocerca volvulus, but the simuliid vectors feed on a range of animals and HBI is a key indicator of the potential for human onchocerciasis transmission. Ghana has a diversity of Simulium damnosum complex members, which are likely to vary in their HBIs, an important consideration for parameterization of onchocerciasis control and elimination models.

Methods

Host-seeking and ovipositing S. damnosum (sensu lato) (s.l.) were collected from seven villages in four Ghanaian regions. Taxa were morphologically and molecularly identified. Blood meals from individually stored blackfly abdomens were used for DNA profiling, to identify previous host choice. Household, domestic animal, wild mammal and bird surveys were performed to estimate the density and diversity of potential blood hosts of blackflies.

Results

A total of 11,107 abdomens of simuliid females (which would have obtained blood meal(s) previously) were tested, with blood meals successfully amplified in 3,772 (34 %). A single-host species was identified in 2,857 (75.7 %) of the blood meals, of which 2,162 (75.7 %) were human. Simulium soubrense Beffa form, S. squamosum C and S. sanctipauli Pra form were the most anthropophagic (HBI = 0.92, 0.86 and 0.70, respectively); S. squamosum E, S. yahense and S. damnosum (sensu stricto) (s.s.)/S. sirbanum were the most zoophagic (HBI = 0.44, 0.53 and 0.63, respectively). The degree of anthropophagy decreased (but not statistically significantly) with increasing ratio of non-human/human blood hosts. Vector to human ratios ranged from 139 to 1,198 blackflies/person.

Conclusions

DNA profiling can successfully identify blood meals from host-seeking and ovipositing blackflies. Host choice varies according to sibling species, season and capture site/method. There was no evidence that HBI is vector and/or host density dependent. Transmission breakpoints will vary among locations due to differing cytospecies compositions and vector abundances.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1703-2) contains supplementary material, which is available to authorized users.

Contamination Controls for Analysis of Root Canal Samples by Molecular Methods: An Overlooked and Unsolved Problem

INTRODUCTION

It has been almost 20 years since molecular methods were first described for the analysis of root canal microbial flora. Contamination control samples are essential to establish DNA decontamination before taking root canal samples, and this review assessed those studies.

METHODS

Using PubMed, a search was conducted for studies using molecular microbial analysis for the investigation of endodontic samples. Studies were grouped according to the cleaning protocol, acquisition methods, and processing of control samples taken to check for contamination.

RESULTS

Of 136 studies applying molecular analysis to root canal samples, 21 studies performed surface cleaning and checking nucleotide decontamination with contamination control samples processed by polymerase chain reaction. Only 1 study described disinfection, sampling from the access cavity, and processing by polymerase chain reaction and reported the result; that study reported that all samples contained contaminating bacterial DNA.

CONCLUSIONS

Cleaning, disinfection, and checking for contamination are basic scientific prerequisites for this type of investigation; yet, this review identifies it as an overlooked issue. On the basis of this review, we call for improved scientific practice in this field.

Empirical insights into the stochasticity of small RNA sequencing

The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data.

Inclusion probability for DNA mixtures is a subjective one-sided match statistic unrelated to identification information

Background:

DNA mixtures of two or more people are a common type of forensic crime scene evidence. A match statistic that connects the evidence to a criminal defendant is usually needed for court. Jurors rely on this strength of match to help decide guilt or innocence. However, the reliability of unsophisticated match statistics for DNA mixtures has been questioned.

Materials and Methods:

The most prevalent match statistic for DNA mixtures is the combined probability of inclusion (CPI), used by crime labs for over 15 years. When testing 13 short tandem repeat (STR) genetic loci, the CPI^-1 value is typically around a million, regardless of DNA mixture composition. However, actual identification information, as measured by a likelihood ratio (LR), spans a much broader range. This study examined probability of inclusion (PI) mixture statistics for 517 locus experiments drawn from 16 reported cases and compared them with LR locus information calculated independently on the same data. The log(PI^-1) values were examined and compared with corresponding log(LR) values.

Results:

The LR and CPI methods were compared in case examples of false inclusion, false exclusion, a homicide, and criminal justice outcomes. Statistical analysis of crime laboratory STR data shows that inclusion match statistics exhibit a truncated normal distribution having zero center, with little correlation to actual identification information. By the law of large numbers (LLN), CPI^-1 increases with the number of tested genetic loci, regardless of DNA mixture composition or match information. These statistical findings explain why CPI is relatively constant, with implications for DNA policy, criminal justice, cost of crime, and crime prevention.

Conclusions:

Forensic crime laboratories have generated CPI statistics on hundreds of thousands of DNA mixture evidence items. However, this commonly used match statistic behaves like a random generator of inclusionary values, following the LLN rather than measuring identification information. A quantitative CPI number adds little meaningful information beyond the analyst's initial qualitative assessment that a person's DNA is included in a mixture. Statistical methods for reporting on DNA mixture evidence should be scientifically validated before they are relied upon by criminal justice.

Computational analysis of stochastic heterogeneity in PCR amplification efficiency revealed by single molecule barcoding

The polymerase chain reaction (PCR) is one of the most widely used techniques in molecular biology. In combination with High Throughput Sequencing (HTS), PCR is widely used to quantify transcript abundance for RNA-seq, and in the context of analysis of T and B cell receptor repertoires. In this study, we combine DNA barcoding with HTS to quantify PCR output from individual target molecules. We develop computational tools that simulate both the PCR branching process itself, and the subsequent subsampling which typically occurs during HTS sequencing. We explore the influence of different types of heterogeneity on sequencing output, and compare them to experimental results where the efficiency of amplification is measured by barcodes uniquely identifying each molecule of starting template. Our results demonstrate that the PCR process introduces substantial amplification heterogeneity, independent of primer sequence and bulk experimental conditions. This heterogeneity can be attributed both to inherited differences between different template DNA molecules, and the inherent stochasticity of the PCR process. The results demonstrate that PCR heterogeneity arises even when reaction and substrate conditions are kept as constant as possible, and therefore single molecule barcoding is essential in order to derive reproducible quantitative results from any protocol combining PCR with HTS.

Real-time cdPCR opens a window into events occurring in the first few PCR amplification cycles

Polymerase chain reaction (PCR) end-point limiting dilution techniques, collectively termed "digital PCR (dPCR)", have been proposed as providing a potentially primary method for DNA quantification. We are evaluating several commercially available dPCR systems for use in certifying mass concentration in human genomic DNA reference materials. To better understand observed anomalies among results from chamber- and droplet-dPCR (cdPCR and ddPCR) systems, we have developed a graphical tool for evaluating and documenting the performance of PCR assays in real-time cdPCR systems: the ogive plot, the cumulative distribution of crossing threshold values. The ogive structure appears to embed information about early amplification events. We have successfully simulated ogives observed with different assays and reaction conditions using a four-stage amplification model parameterized by the probability of creating an intact 1) first generation "long" amplicon of indeterminate length from an original DNA target, 2) second generation defined-length amplicon from a long amplicon, and 3) defined-length amplicon from another defined-length amplicon. We are using insights from this model to optimize dPCR assay design and reaction conditions and to help validate assays proposed for use in value-assigning DNA reference materials.

Sources of PCR-induced distortions in high-throughput sequencing data sets

PCR permits the exponential and sequence-specific amplification of DNA, even from minute starting quantities. PCR is a fundamental step in preparing DNA samples for high-throughput sequencing. However, there are errors associated with PCR-mediated amplification. Here we examine the effects of four important sources of error—bias, stochasticity, template switches and polymerase errors—on sequence representation in low-input next-generation sequencing libraries. We designed a pool of diverse PCR amplicons with a defined structure, and then used Illumina sequencing to search for signatures of each process. We further developed quantitative models for each process, and compared predictions of these models to our experimental data. We find that PCR stochasticity is the major force skewing sequence representation after amplification of a pool of unique DNA amplicons. Polymerase errors become very common in later cycles of PCR but have little impact on the overall sequence distribution as they are confined to small copy numbers. PCR template switches are rare and confined to low copy numbers. Our results provide a theoretical basis for removing distortions from high-throughput sequencing data. In addition, our findings on PCR stochasticity will have particular relevance to quantification of results from single cell sequencing, in which sequences are represented by only one or a few molecules.

Sequence-dependent biophysical modeling of DNA amplification

A theoretical framework for prediction of the dynamic evolution of chemical species in DNA amplification reactions, for any specified sequence and operating conditions, is reported. Using the polymerase chain reaction (PCR) as an example, we developed a sequence- and temperature-dependent kinetic model for DNA amplification using first-principles biophysical modeling of DNA hybridization and polymerization. We compare this kinetic model with prior PCR models and discuss the features of our model that are essential for quantitative prediction of DNA amplification efficiency for arbitrary sequences and operating conditions. Using this model, the kinetics of PCR is analyzed. The ability of the model to distinguish between the dynamic evolution of distinct DNA sequences in DNA amplification reactions is demonstrated. The kinetic model is solved for a typical PCR temperature protocol to motivate the need for optimization of the dynamic operating conditions of DNA amplification reactions. It is shown that amplification efficiency is affected by dynamic processes that are not accurately represented in the simplified models of DNA amplification that form the basis of conventional temperature cycling protocols. Based on this analysis, a modified temperature protocol that improves PCR efficiency is suggested. Use of this sequence-dependent kinetic model in a control theoretic framework to determine the optimal dynamic operating conditions of DNA amplification reactions, for any specified amplification objective, is discussed.

How good is a PCR efficiency estimate: Recommendations for precise and robust qPCR efficiency assessments

We have examined the imprecision in the estimation of PCR efficiency by means of standard curves based on strategic experimental design with large number of technical replicates. In particular, how robust this estimation is in terms of a commonly varying factors: the instrument used, the number of technical replicates performed and the effect of the volume transferred throughout the dilution series. We used six different qPCR instruments, we performed 1–16 qPCR replicates per concentration and we tested 2–10 μl volume of analyte transferred, respectively. We find that the estimated PCR efficiency varies significantly across different instruments. Using a Monte Carlo approach, we find the uncertainty in the PCR efficiency estimation may be as large as 42.5% (95% CI) if standard curve with only one qPCR replicate is used in 16 different plates. Based on our investigation we propose recommendations for the precise estimation of PCR efficiency: (1) one robust standard curve with at least 3–4 qPCR replicates at each concentration shall be generated, (2) the efficiency is instrument dependent, but reproducibly stable on one platform, and (3) using a larger volume when constructing serial dilution series reduces sampling error and enables calibration across a wider dynamic range.

Dynamics and control of DNA sequence amplification

DNA amplification is the process of replication of a specified DNA sequence in vitro through time-dependent manipulation of its external environment. A theoretical framework for determination of the optimal dynamic operating conditions of DNA amplification reactions, for any specified amplification objective, is presented based on first-principles biophysical modeling and control theory. Amplification of DNA is formulated as a problem in control theory with optimal solutions that can differ considerably from strategies typically used in practice. Using the Polymerase Chain Reaction as an example, sequence-dependent biophysical models for DNA amplification are cast as control systems, wherein the dynamics of the reaction are controlled by a manipulated input variable. Using these control systems, we demonstrate that there exists an optimal temperature cycling strategy for geometric amplification of any DNA sequence and formulate optimal control problems that can be used to derive the optimal temperature profile. Strategies for the optimal synthesis of the DNA amplification control trajectory are proposed. Analogous methods can be used to formulate control problems for more advanced amplification objectives corresponding to the design of new types of DNA amplification reactions.

Stochastic sampling effects in STR typing: Implications for analysis and interpretation

The analysis and interpretation of forensic STR typing results can become more complicated when reduced template amounts are used for PCR amplification due to increased stochastic effects. These effects are typically observed as reduced heterozygous peak-height balance and increased frequency of undetected alleles (allelic "dropout"). To investigate the origins of these effects, a study was performed using the AmpFlSTR(®) Identifiler Plus(®) and MiniFiler(®) kits to amplify replicates from a dilution series of NIST Human DNA Quantitation Standard (SRM(®) 2372A). The resulting amplicons were resolved and detected on two different genetic analyzer platforms, the Applied Biosystems 3130xL and 3500 analyzers. Results from our study show that the four different STR/genetic analyzer combinations exhibited very similar peak-height ratio statistics when normalized for the amount of template DNA in the PCR. Peak-height ratio statistics were successfully modeled using the Poisson distribution to simulate pre-PCR stochastic sampling of the alleles, confirming earlier explanations that sampling is the primary source for peak-height imbalance in reduced template dilutions. In addition, template-based pre-PCR sampling simulations also successfully predicted allelic dropout frequencies, as modeled by logistic regression methods, for the low-template DNA dilutions. We discuss the possibility that an accurately quantified DNA template might be used to characterize the linear signal response for data collected using different STR kits or genetic analyzer platforms, so as to provide a standardized approach for comparing results obtained from different STR/CE combinations and to aid in validation studies.

TrueAllele casework on Virginia DNA mixture evidence: computer and manual interpretation in 72 reported criminal cases

Mixtures are a commonly encountered form of biological evidence that contain DNA from two or more contributors. Laboratory analysis of mixtures produces data signals that usually cannot be separated into distinct contributor genotypes. Computer modeling can resolve the genotypes up to probability, reflecting the uncertainty inherent in the data. Human analysts address the problem by simplifying the quantitative data in a threshold process that discards considerable identification information. Elevated stochastic threshold levels potentially discard more information. This study examines three different mixture interpretation methods. In 72 criminal cases, 111 genotype comparisons were made between 92 mixture items and relevant reference samples. TrueAllele computer modeling was done on all the evidence samples, and documented in DNA match reports that were provided as evidence for each case. Threshold-based Combined Probability of Inclusion (CPI) and stochastically modified CPI (mCPI) analyses were performed as well. TrueAllele's identification information in 101 positive matches was used to assess the reliability of its modeling approach. Comparison was made with 81 CPI and 53 mCPI DNA match statistics that were manually derived from the same data. There were statistically significant differences between the DNA interpretation methods. TrueAllele gave an average match statistic of 113 billion, CPI averaged 6.68 million, and mCPI averaged 140. The computer was highly specific, with a false positive rate under 0.005%. The modeling approach was precise, having a factor of two within-group standard deviation. TrueAllele accuracy was indicated by having uniformly distributed match statistics over the data set. The computer could make genotype comparisons that were impossible or impractical using manual methods. TrueAllele computer interpretation of DNA mixture evidence is sensitive, specific, precise, accurate and more informative than manual interpretation alternatives. It can determine DNA match statistics when threshold-based methods cannot. Improved forensic science computation can affect criminal cases by providing reliable scientific evidence.

Emergence of fractals in aggregation with stochastic self-replication

We propose and investigate a simple model which describes the kinetics of aggregation of Brownian particles with stochastic self-replication. An exact solution and the scaling theory are presented alongside numerical simulation which fully support all theoretical findings. In particular, we show analytically that the particle size distribution function exhibits dynamic scaling and we verify it numerically using the idea of data collapse. Furthermore, the conditions under which the resulting system emerges as a fractal are found, the fractal dimension of the system is given, and the relationship between this fractal dimension and a conserved quantity is pointed out.

A new method for quantitative real-time polymerase chain reaction data analysis

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantification method that has been extensively used in biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle method and linear and nonlinear model-fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence can hardly be accurate and therefore can distort results. We propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtract the fluorescence in the former cycle from that in the latter cycle, transforming the n cycle raw data into n-1 cycle data. Then, linear regression is applied to the natural logarithm of the transformed data. Finally, PCR amplification efficiencies and the initial DNA molecular numbers are calculated for each reaction. This taking-difference method avoids the error in subtracting an unknown background, and thus it is more accurate and reliable. This method is easy to perform, and this strategy can be extended to all current methods for PCR data analysis.

Migration of CD11b+ accessory cells during murine lung regeneration

In many mammalian species, the removal of one lung leads to growth of the remaining lung to near-baseline levels. In studying post-pneumonectomy mice, we used morphometric measures to demonstrate neoalveolarization within 21 days of pneumonectomy. Of note, the detailed histology during this period demonstrated no significant pulmonary inflammation. To identify occult blood-borne cells, we used a parabiotic model (wild-type/GFP) of post-pneumonectomy lung growth. Flow cytometry of post-pneumonectomy lung digests demonstrated a rapid increase in the number of cells expressing the hematopoietic membrane molecule CD11b; 64.5% of the entire GFP(+) population were CD11b(+). Fluorescence microscopy demonstrated that the CD11b(+) peripheral blood cells migrated into both the interstitial tissue and alveolar airspace compartments. Pneumonectomy in mice deficient in CD11b (CD18(-/-) mutants) demonstrated near-absent leukocyte migration into the airspace compartment (p<.001) and impaired lung growth as demonstrated by lung weight (p<.05) and lung volume (p<.05). Transcriptional activity of the partitioned CD11b(+) cells demonstrated significantly increased transcription of Angpt1, Il1b, and Mmp8, Mmp9, Ncam1, Sele, Sell, Selp in the alveolar airspace and Adamts2, Ecm1, Egf, Mmp7, Npr1, Tgfb2 in the interstitial tissue (>4-fold regulation; p<.05). These data suggest that blood-borne CD11b(+) cells represent a population of accessory cells contributing to post-pneumonectomy lung growth.

Alveolar epithelial dynamics in postpneumonectomy lung growth

The intimate anatomic and functional relationship between epithelial cells and endothelial cells within the alveolus suggests the likelihood of a coordinated response during postpneumonectomy lung growth. To define the population dynamics and potential contribution of alveolar epithelial cells to alveolar angiogenesis, we studied alveolar Type II and I cells during the 21 days after pneumonectomy. Alveolar Type II cells were defined and isolated by flow cytometry using a CD45(-) , MHC class II(+) , phosphine(+) phenotype. These phenotypically defined alveolar Type II cells demonstrated an increase in cell number after pneumonectomy; the increase in cell number preceded the increase in Type I (T1α(+) ) cells. Using a parabiotic wild type/GFP pneumonectomy model, <3% of the Type II cells and 1% of the Type I cells were positive for GFP-a finding consistent with the absence of a blood-borne contribution to alveolar epithelial cells. The CD45(-) , MHC class II(+) , phosphine(+) Type II cells demonstrated the active transcription of angiogenesis-related genes both before and after pneumonectomy. When the Type II cells on Day 7 after pneumonectomy were compared to nonsurgical controls, 10 genes demonstrated significantly increased expression (P<0.05). In contrast to the normal adult Type II cells, there was notable expression of inflammation-associated genes (Ccl2, Cxcl2, Ifng) as well as genes associated with epithelial growth (Ereg, Lep). Together, the data suggest an active contribution of local alveolar Type II cells to alveolar growth.

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.

Alveolar macrophage dynamics in murine lung regeneration

In most mammalian species, the removal of one lung results in dramatic compensatory growth of the remaining lung. To investigate the contribution of alveolar macrophages (AMs) to murine post-pneumonectomy lung growth, we studied bronchoalveolar lavage (BAL)-derived AM on 3, 7, 14 and 21 days after left pneumonectomy. BAL demonstrated a 3.0-fold increase in AM (CD45(+), CD11b(-), CD11c(+), F4/80(+), Gr-1(-)) by 14 days after pneumonectomy. Cell cycle flow cytometry of the BAL-derived cells demonstrated an increase in S + G2 phase cells on days 3 (11.3 ± 2.7%) and 7 (12.1 ± 1.8%) after pneumonectomy. Correspondingly, AM demonstrated increased expression of VEGFR1 and MHC class II between days 3 and 14 after pneumonectomy. To investigate the potential contribution of peripheral blood cells to this AM population, parabiotic mice (wild-type/GFP) underwent left pneumonectomy. Analysis of GFP(+) cells in the post-pneumonectomy lung demonstrated that by day 14, less than 1% of the AM population were derived from the peripheral blood. Finally, AM gene transcription demonstrated a significant shift from decreased transcription of angiogenesis-related genes on day 3 to increased transcription on day 7 after pneumonectomy. The increased number of locally proliferating AM, combined with their growth-related gene transcription, suggests that AM actively participate in compensatory lung growth.

Robust quantification of polymerase chain reactions using global fitting

Background

Quantitative polymerase chain reactions (qPCR) are used to monitor relative changes in very small amounts of DNA. One drawback to qPCR is reproducibility: measuring the same sample multiple times can yield data that is so noisy that important differences can be dismissed. Numerous analytical methods have been employed that can extract the relative template abundance between samples. However, each method is sensitive to baseline assignment and to the unique shape profiles of individual reactions, which gives rise to increased variance stemming from the analytical procedure itself.

Principal Findings

We developed a simple mathematical model that accurately describes the entire PCR reaction profile using only two reaction variables that depict the maximum capacity of the reaction and feedback inhibition. This model allows quantification that is more accurate than existing methods and takes advantage of the brighter fluorescence signals from later cycles. Because the model describes the entire reaction, the influences of baseline adjustment errors, reaction efficiencies, template abundance, and signal loss per cycle could be formalized. We determined that the common cycle-threshold method of data analysis introduces unnecessary variance because of inappropriate baseline adjustments, a dynamic reaction efficiency, and also a reliance on data with a low signal-to-noise ratio.

Significance

Using our model, fits to raw data can be used to determine template abundance with high precision, even when the data contains baseline and signal loss defects. This improvement reduces the time and cost associated with qPCR and should be applicable in a variety of academic, clinical, and biotechnological settings.

DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: combining quantitative data for greater identification information

Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells ('binomial sampling') from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function. As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50±35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. Finally, we discuss the implications of this work for routine forensic practice.