Effect of pooling samples on the efficiency of comparative studies using microarrays

qRAT: an R-based stand-alone application for relative expression analysis of RT-qPCR data

Background

Reverse transcription quantitative real-time PCR (RT-qPCR) is a well-established method for analysing gene expression. Most RT-qPCR experiments in the field of microbiology aim for the detection of transcriptional changes by relative quantification, which means the comparison of the expression level of a specific gene between different samples by the application of a calibration condition and internal reference genes. Due to the numerous data processing procedures and factors that can influence the final result, relative expression analysis and interpretation of RT-qPCR data are still not trivial and often necessitate the use of multiple separate software packages capable of performing specific functions.

Results

Here we present qRAT, a stand-alone desktop application based on R that automatically processes raw output data from any qPCR machine using well-established and state-of-the-art statistical and graphical techniques. The ability of qRAT to analyse RT-qPCR data was evaluated using two example datasets generated in our laboratory. The tool successfully completed the procedure in both cases, returning the expected results. The current implementation includes functionalities for parsing, filtering, normalizing and visualisation of relative RT-qPCR data, like the determination of the relative quantity and the fold change of differentially expressed genes as well as the correction of inter-plate variation for multiple-plate experiments.

Conclusion

qRAT provides a comprehensive, straightforward, and easy-to-use solution for the relative quantification of RT-qPCR data that requires no programming knowledge or additional software installation. All application features are available for free and without requiring a login or registration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04823-7.

Development of an HPTLC-based dynamic reference standard for the analysis of complex natural products using Jarrah honey as test sample

In this paper, we describe a novel approach to the development of a reference standard for the quality control of complex natural products, which will assist in the assessment of their authenticity and purity. The proposed method provides a template for the selection of samples, which can be pooled to obtain a reference standard. A shortfall of such an approach is, however, that the pooled sample is static in nature and therefore unable to capture difference in processing conditions or natural variations triggered by geographical or climatic impacts over time. To address this, the paper also outlines the development of a dynamic reference standard, which allows for ongoing adjustments to future variations. The method employs High-Performance Thin Layer Chromatography (HPTLC) derived extract profiles processed by multivariate analysis. The development of the dynamic reference standard is illustrated using honey, a complex natural matrix, as an example.

Temperature modulates immune gene expression in mosquitoes during arbovirus infection

The principal vector of dengue, Zika and chikungunya viruses is the mosquito Aedes aegypti, with its ability to transmit pathogens influenced by ambient temperature. We use chikungunya virus (CHIKV) to understand how the mosquito transcriptome responds to arbovirus infection at different ambient temperatures. We exposed CHIKV-infected mosquitoes to 18, 28 and 32°C, and found that higher temperature correlated with higher virus levels, particularly at 3 days post infection, but lower temperature resulted in reduced virus levels. RNAseq analysis indicated significantly altered gene expression levels in CHIKV infection. The highest number of significantly differentially expressed genes was observed at 28°C, with a more muted effect at the other temperatures. At the higher temperature, the expression of many classical immune genes, including Dicer-2, was not substantially altered in response to CHIKV. The upregulation of Toll, IMD and JAK-STAT pathways was only observed at 28°C. Functional annotations suggested that genes in immune response and metabolic pathways related to energy supply and DNA replication were involved in temperature-dependent changes. Time post infection also led to substantially different gene expression profiles, and this varied with temperature. In conclusion, temperature significantly modulates mosquito gene expression in response to infection, potentially leading to impairment of immune defences at higher temperatures.

Implementation of Antibody Rapid Diagnostic Testing versus Real-Time Reverse Transcription-PCR Sample Pooling in the Screening of COVID-19: a Case of Different Testing Strategies in Africa

Coronavirus disease 2019 (COVID-19) has wreaked havoc across the globe; although the number of cases in Africa remains lower than in other regions, it is on a gradual upward trajectory. To date, COVID-19 cases have been reported in 54 out of 55 African countries. However, due to limited severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) real-time reverse transcription-PCR (rRT-PCR) testing capacity and scarcity of testing reagents, it is probable that the total number of cases could far exceed published statistics. In this viewpoint, using Ghana, Malawi, South Africa, and Zimbabwe as examples of countries that have implemented different testing strategies, we argue that the implementation of sample pooling for rRT-PCR over antibody rapid diagnostic testing could have a greater impact in assessing disease burden. Sample pooling offers huge advantages compared to single test rRT-PCR, as it reduces diagnostic costs, personnel time, burnout, and analytical run times. Africa is already strained in terms of testing resources for COVID-19; hence, cheaper alternative ways need to be implemented to conserve resources, maximize mass testing, and reduce transmission in the wider population.

Protein array test detected three osteoporosis related plasma inflammatory cytokines in Chinese postmenopausal women

Inflammatory cytokines were involved in pathological conditions of osteoporosis (OP). However, the specific OP-associated inflammatory cytokines are still awaiting to be detected by using a systemic method. Herein, we adopted an extreme sampling scheme and examined inflammatory cytokines between subjects with low and high bone mineral density (BMD) through protein microarray. First, 8 candidate cytokines including B lymphocyte chemoattractant (BLC), osteopontin (OPN) and insulin-like growth factor-binding protein 4 (IGFBP4) were identified in the discovery extreme sampling subgroup. Then, the different expressions for BLC, OPN and IGFBP4 were validated and replicated in two independent extreme sampling subgroups. Further functional experiments showed that the cytokine BLC was involved in bone metabolism by inhibiting bone formation and promoting bone resorption. Together, this study further revealed that inflammatory cytokines were closely related with OP, and that they highlighted critical roles of BLC in the pathogenesis of OP.

On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

Background

In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets.

Result

The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined.

Conclusion

For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power.

A high-throughput method of analyzing multiple plant defensive compounds in minimized sample mass

PREMISE OF THE STUDY

Current methods for quantifying herbivore-induced alterations in plant biochemistry are often unusable by researchers due to practical constraints. We present a cost-effective, high-throughput protocol to quantify multiple biochemical responses from small plant tissue samples using spectrophotometric techniques.

METHODS AND RESULTS

Using Solanum lycopersicum and Medicago polymorpha leaves pre- and post-herbivory, we demonstrate that our protocol quantifies common plant defense responses: peroxidase production, polyphenol oxidase production, reactive oxygen species production, total protein production, and trypsin-like protease inhibition activity.

CONCLUSIONS

Current protocols can require 500 mg of tissue, but our assays detect activity in less than 10 mg. Our protocol takes two people 6 h to run any of the assays on 300 samples in triplicate, or all of the assays on 20 samples. Our protocol enables researchers to plan complex experiments that compare local versus systemic plant responses, quantify environmental and genetic variation, and measure population-level variation.

Sample Pooling and Inflammation Linked to the False Selection of Biomarkers for Neurodegenerative Diseases in Top-Down Proteomics: A Pilot Study

Proteomic technologies have been recently adapted to the new field of clinical proteomics. The origin of errors and biases has been well-identified in the pre-analytical steps, leading to the measurement of clinical analytes. One possible source of inadequacy in clinical proteomics is linked to sample pooling. This practice is usually related to low sample availability, variability, experiment time/cost. In this study, we first asked whether sample pooling in top–down proteomics is suitable to obtain a relevant biological average. Our second objective was to identify inflammatory biomarkers of outlier samples in our population of Creutzfeldt-Jakob disease patients. Our results demonstrated that, in a proteomics study, sample pooling as well as the inflammation status was an important source of errors: missed detection of biomarkers and false identification of others. Pooled samples were not equivalent to the average of biological values. In addition, this procedure reduced the statistical value of the identified biomarkers due to a stabilization of their standard deviation and rendered outlier samples difficult to detect. We identified serum amyloid A as a candidate biomarker of outlier samples. The presence of this protein, which could be explained by inflammatory processes, induced major modifications in the sample profiles.

Association of Elevated Urinary miR-126, miR-155, and miR-29b with Diabetic Kidney Disease

Effective diabetic kidney disease (DKD) biomarkers remain elusive, and urinary miRNAs represent a potential source of novel noninvasive disease sentinels. We profiled 754 miRNAs in pooled urine samples from DKD patients (n = 20), detecting significantly increased miR-126, miR-155, and miR-29b compared with controls (n = 20). These results were confirmed in an independent cohort of 89 DKD patients, 62 diabetic patients without DKD, and 41 controls: miR-126 (2.8-fold increase; P < 0.0001), miR-155 (1.8-fold increase; P < 0.001), and miR-29b (4.6-fold increase; P = 0.024). Combined receiver operating characteristic curve analysis resulted in an area under the curve of 0.8. A relative quantification threshold equivalent to 80% sensitivity for each miRNA gave a positive signal for 48% of DKD patients compared with 3.6% of diabetic patients without DKD. Laser-capture microdissection of renal biopsy specimens, followed by quantitative RT-PCR, detected miR-155 in glomeruli and proximal and distal tubules, whereas miR-126 and miR-29b were most abundant in glomerular extracts. Subsequent experiments showed miR-126 and miR-29b enrichment in glomerular endothelial cells (GEnCs) compared with podocytes, proximal tubular epithelial cells, and fibroblasts. Significantly increased miR-126 and miR-29b were detected in GEnC conditioned medium in response to tumor necrosis factor-α and transforming growth factor-β1, respectively. Our data reveal an altered urinary miRNA profile associated with DKD and link these variations to miRNA release from GEnCs.

Mouse-to-mouse variation in maturation heterogeneity of smooth muscle cells

Smooth muscle cell (SMC) heterogeneity plays an important role in vascular remodeling, a life-threatening hallmark of many vascular diseases. However, the characterization of SMCs at the single-cell level is stymied by drawbacks of contemporary single-cell protein measurements, including antibody probe cross-reactivity, chemical fixation artifacts, limited isoform-specific probes, low multiplexing and difficulty sampling cells with irregular morphologies. To scrutinize healthy vessels for subpopulations of SMCs with proliferative-like phenotypes, we developed a high-specificity, multiplexed single-cell immunoblotting cytometry tool for unfixed, uncultured primary cells. We applied our assay to demonstrate maturation stage profiling of aortic SMCs freshly isolated from individual mice. After ensuring unbiased sampling of SMCs (80-120 μm in length), we performed single-SMC electrophoretic protein separations, which resolve protein signal from off-target antibody binding, and immunoblotted for differentiation markers α-SMA, CNN-1 and SMMHC (targets ranging from 34 kDa to 227 kDa). We identified a subpopulation of immature-like SMCs, supporting the recently-established mechanism that only a subset of SMCs is responsible for vascular remodeling. Furthermore, the low sample requirements of our assay enable single-mouse resolution studies, which minimizes animal sacrifice and experimental costs while reporting animal-to-animal phenotypic variation, essential for achieving reproducibility and surmounting the drawbacks of pooling primary cells from different animals.

Development, validation and matrix effect of a QuEChERS method for the analysis of organochlorine pesticides in fish tissue

This study aims to develop and validate a method to determine OCPs in fish tissues, minimizing the consumption of sample and reagents, by using a modified QuEChERS along with ultrasound, d-SPE and gas chromatography with an electron capture detector (GC-ECD), refraining the pooling. Different factorial designs were employed to optimize the sample preparation phase. The validation method presented a recovery of around 77.3% and 110.8%, with RSD lower than 13% and the detection limits were between 0.24 and 2.88 μgkg^-1, revealing good sensitiveness and accuracy. The method was satisfactorily applied to the analysis of tissues from different species of fish and OCPs residues were detected. The proposed method was shown effective to determine OCPs low concentrations in fish tissues, using small sample mass (0.5 g), making the sample analyses viable without the need for grouping (pool).

Longitudinal Multiplexed Measurement of Quantitative Proteomic Signatures in Mouse Lymphoma Models Using Magneto-Nanosensors

Cancer proteomics is the manifestation of relevant biological processes in cancer development. Thus, it reflects the activities of tumor cells, host-tumor interactions, and systemic responses to cancer therapy. To understand the causal effects of tumorigenesis or therapeutic intervention, longitudinal studies are greatly needed. However, most of the conventional mouse experiments are unlikely to accommodate frequent collection of serum samples with a large enough volume for multiple protein assays towards single-object analysis. Here, we present a technique based on magneto-nanosensors to longitudinally monitor the protein profiles in individual mice of lymphoma models using a small volume of a sample for multiplex assays. Methods: Drug-sensitive and -resistant cancer cell lines were used to develop the mouse models that render different outcomes upon the drug treatment. Two groups of mice were inoculated with each cell line, and treated with either cyclophosphamide or vehicle solution. Serum samples taken longitudinally from each mouse in the groups were measured with 6-plex magneto-nanosensor cytokine assays. To find the origin of IL-6, experiments were performed using IL-6 knock-out mice. Results: The differences in serum IL-6 and GCSF levels between the drug-treated and untreated groups were revealed by the magneto-nanosensor measurement on individual mice. Using the multiplex assays and mouse models, we found that IL-6 is secreted by the host in the presence of tumor cells upon the drug treatment. Conclusion: The multiplex magneto-nanosensor assays enable longitudinal proteomic studies on mouse tumor models to understand tumor development and therapy mechanisms more precisely within a single biological object.

pH Stress-Induced Cooperation between Rhodococcus ruber YYL and Bacillus cereus MLY1 in Biodegradation of Tetrahydrofuran

Microbial consortia consisting of cooperational strains exhibit biodegradation performance superior to that of single microbial strains and improved remediation efficiency by relieving the environmental stress. Tetrahydrofuran (THF), a universal solvent widely used in chemical and pharmaceutical synthesis, significantly affects the environment. As a refractory pollutant, THF can be degraded by some microbial strains under suitable conditions. There are often a variety of stresses, especially pH stress, that inhibit the THF-degradation efficiency of microbial consortia. Therefore, it is necessary to study the molecular mechanisms of microbial cooperational degradation of THF. In this study, under conditions of low pH (initial pH = 7.0) stress, a synergistic promotion of the THF degradation capability of the strain Rhodococcus ruber YYL was found in the presence of a non-THF degrading strain Bacillus cereus MLY1. Metatranscriptome analysis revealed that the low pH stress induced the strain YYL to up-regulate the genes involved in anti-oxidation, mutation, steroid and bile acid metabolism, and translation, while simultaneously down-regulating the genes involved in ATP production. In the co-culture system, strain MLY1 provides fatty acids, ATP, and amino acids for strain YYL in response to low pH stress during THF degradation. In return, YYL shares the metabolic intermediates of THF with MLY1 as carbon sources. This study provides the preliminary mechanism to understand how microbial consortia improve the degradation efficiency of refractory furan pollutants under environmental stress conditions.

A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in 'omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other 'omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during 'omics data processing and analysis, the TRF will increase confidence processing of 'omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory 'omics studies, and it can be executed using different commonly available software tools.

Transcriptome profiling of sulfate deprivation responses in two agarophytes Gracilaria changii and Gracilaria salicornia (Rhodophyta)

Seaweeds survive in marine waters with high sulfate concentration compared to those living at freshwater habitats. The cell wall polymer of Gracilaria spp. which supplies more than 50% of the world agar is heavily sulfated. Since sulfation reduces the agar quality, it is interesting to investigate the effects of sulfate deprivation on the sulfate contents of seaweed and agar, as well as the metabolic pathways of these seaweeds. In this study, two agarophytes G. changii and G. salicornia were treated under sulfate deprivation for 5 days. The sulfate contents in the seaweed/agar were generally lower in sulfate-deprivated samples compared to those in the controls, but the differences were only statistically significant for seaweed sample of G. changii and agar sample of G. salicornia. RNA sequencing (RNA-Seq) of sulfate-deprivated and untreated seaweed samples revealed 1,292 and 3,439 differentially expressed genes (DEGs; ≥1.5-fold) in sulfate-deprivated G. changii and G. salicornia, respectively, compared to their respective controls. Among the annotated DEGs were genes involved in putative agar biosynthesis, sulfur metabolism, metabolism of sulfur-containing amino acids, carbon metabolism and oxidative stress. These findings shed light on the sulfate deprivation responses in agarophytes and help to identify candidate genes involved in agar biosynthesis.

Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue

The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low versus high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism (e.g. AACS, ACOX1, ACLY) and red-ox pathways (e.g. CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response (e.g. IGTB2, IFIT3, LGALS1) and rennin angiotensin system (e.g. ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle (e.g. OTC, ARG1, CPS1), and amino acid biosynthesis (e.g. GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle (e.g. ARG1, CPS1), immune response/complement activation (e.g. C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling (e.g. EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis (e.g. YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.

Transcriptome Analysis and Differential Gene Expression on the Testis of Orange Mud Crab, Scylla olivacea, during Sexual Maturation

Adequate genetic information is essential for sustainable crustacean fisheries and aquaculture management. The commercially important orange mud crab, Scylla olivacea, is prevalent in Southeast Asia region and is highly sought after. Although it is a suitable aquaculture candidate, full domestication of this species is hampered by the lack of knowledge about the sexual maturation process and the molecular mechanisms behind it, especially in males. To date, data on its whole genome is yet to be reported for S. olivacea. The available transcriptome data published previously on this species focus primarily on females and the role of central nervous system in reproductive development. De novo transcriptome sequencing for the testes of S. olivacea from immature, maturing and mature stages were performed. A total of approximately 144 million high-quality reads were generated and de novo assembled into 160,569 transcripts with a total length of 142.2 Mb. Approximately 15–23% of the total assembled transcripts were annotated when compared to public protein sequence databases (i.e. UniProt database, Interpro database, Pfam database and Drosophila melanogaster protein database), and GO-categorised with GO Ontology terms. A total of 156,181 high-quality Single-Nucleotide Polymorphisms (SNPs) were mined from the transcriptome data of present study. Transcriptome comparison among the testes of different maturation stages revealed one gene (beta crystallin like gene) with the most significant differential expression—up-regulated in immature stage and down-regulated in maturing and mature stages. This was further validated by qRT-PCR. In conclusion, a comprehensive transcriptome of the testis of orange mud crabs from different maturation stages were obtained. This report provides an invaluable resource for enhancing our understanding of this species’ genome structure and biology, as expressed and controlled by their gonads.

Biochemical and proteomic analyses of the physiological response induced by individual housing in gilts provide new potential stress markers

Background

The objective assessment of animal stress and welfare requires proper laboratory biomarkers. In this work, we have analyzed the changes in serum composition in gilts after switching their housing, from pen to individual stalls, which is generally accepted to cause animal discomfort.

Results

Blood and saliva samples were collected a day before and up to four days after changing the housing system. Biochemical analyses showed adaptive changes in lipid and protein metabolism after the housing switch, whereas cortisol and muscular markers showed a large variability between animals. 2D-DIGE and iTRAQ proteomic approaches revealed variations in serum protein composition after changing housing and diet of gilts. Both techniques showed alterations in two main homeostatic mechanisms: the innate immune and redox systems. The acute phase proteins haptoglobin, apolipoprotein A-I and α1-antichymotrypsin 3, and the antioxidant enzyme peroxiredoxin 2 were found differentially expressed by 2D-DIGE. Other proteins related to the innate immune system, including lactotransferrin, protegrin 3 and galectin 1 were also identified by iTRAQ, as well as oxidative stress enzymes such as peroxiredoxin 2 and glutathione peroxidase 3. Proteomics also revealed the decrease of apolipoproteins, and the presence of intracellular proteins in serum, which may indicate physical injury to tissues.

Conclusions

Housing of gilts in individual stalls and diet change increase lipid and protein catabolism, oxidative stress, activate the innate immune system and cause a certain degree of tissue damage. We propose that valuable assays for stress assessment in gilts may be based on a score composed by a combination of salivary cortisol, lipid metabolites, innate immunity and oxidative stress markers and intracellular proteins.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-016-0887-1) contains supplementary material, which is available to authorized users.

Nutritional Geometric Profiles of Insulin/IGF Expression in Drosophila melanogaster

Insulin/IGF signaling (IIS) in Drosophila melanogaster is propagated by eight Drosophila insulin-like peptides (dilps) and is regulated by nutrition. To understand how dietary protein and sugar affect dilp expression, we followed the analytical concepts of the Nutritional Geometric Framework, feeding Drosophila adults media comprised of seven protein-to-carbohydrate ratios at four caloric concentrations. Transcript levels of all dilps and three IIS-regulated genes were measured. Each dilp presented a unique pattern upon a bivariate plot of sugar and protein. Dilp2 expression was greatest upon diets with low protein-to-carbohydrate ratio regardless of total caloric value. Dilp5 expression was highly expressed at approximately a 1:2 protein-to-carbohydrate ratio and its level increased with diet caloric content. Regression analysis revealed that protein-to-carbohydrate ratio and the interaction between this ratio and caloric content significantly affects dilp expression. The IIS-regulated transcripts 4eBP and InR showed strikingly different responses to diet composition: 4eBP was minimally expressed except when elevated at low caloric diets. InR expression increased with protein level, independent of caloric content. Values of published life history traits measured on similar diets revealed correlations between egg production and the expression of dilp8 4eBP, while low protein-to-carbohydrate ratio diets associated with long lifespan correlated with elevated dilp2. Analyzing how nutient composition associates with dilp expression and IIS reveals that nutritional status is modulated by different combinations of insulin-like peptides, and these features variously correlate to IIS-regulated life history traits.

Sources of Experimental Variation in 2-D Maps: The Importance of Experimental Design in Gel-Based Proteomics

The success of proteomic studies employing 2-D maps largely depends on the way surveys and experiments have been organized and performed. Planning gel-based proteomic experiments involves the selection of equipment, methodology, treatments, types and number of samples, experimental layout, and methods for data analysis. A good experimental design will maximize the output of the experiment while taking into account the biological and technical resources available. In this chapter we provide guidelines to assist proteomics researchers in all these choices and help them to design quantitative 2-DE experiments.

Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?

Molecular ecology has moved beyond the use of a relatively small number of markers, often noncoding, and it is now possible to use whole-genome measures of gene expression with microarrays and RNAseq (i.e. transcriptomics) to capture molecular response to environmental challenges. While transcriptome studies are shedding light on the mechanistic basis of traits as complex as personality or physiological response to catastrophic events, these approaches are still challenging because of the required technical expertise, difficulties with analysis and cost. Still, we found that in the last 10 years, 575 studies used microarrays or RNAseq in ecology. These studies broadly address three questions that reflect the progression of the field: (i) How much variation in gene expression is there and how is it structured? (ii) How do environmental stimuli affect gene expression? (iii) How does gene expression affect phenotype? We discuss technical aspects of RNAseq and microarray technology, and a framework that leverages the advantages of both. Further, we highlight future directions of research, particularly related to moving beyond correlation and the development of additional annotation resources. Measuring gene expression across an array of taxa in ecological settings promises to enrich our understanding of ecology and genome function.

A new surveillance and response tool: risk map of infected Oncomelania hupensis detected by Loop-mediated isothermal amplification (LAMP) from pooled samples

Although schistosomiasis remains a serious health problem worldwide, significant achievements in schistosomiasis control has been made in the People's Republic of China. The disease has been eliminated in five out of 12 endemic provinces, and the prevalence in remaining endemic areas is very low and is heading toward elimination. A rapid and sensitive method for monitoring the distribution of infected Oncomelania hupensis is urgently required. We applied a loop-mediated isothermal amplification (LAMP) assay targeting 28S rDNA for the rapid and effective detection of Schistosoma japonicum DNA in infected and prepatent infected O. hupensis snails. The detection limit of the LAMP method was 100 fg of S. japonicum genomic DNA. To promote the application of the approach in the field, the LAMP assay was used to detect infection in pooled samples of field-collected snails. In the pooled sample detection, snails were collected from 28 endemic areas, and 50 snails from each area were pooled based on the maximum pool size estimation, crushed together and DNA was extracted from each pooled sample as template for the LAMP assay. Based on the formula for detection from pooled samples, the proportion of positive pooled samples and the positive proportion of O. hupensis detected by LAMP of Xima village reached 66.67% and 1.33%, while those of Heini, Hongjia, Yangjiang and Huangshan villages were 33.33% and 0.67%, and those of Tuanzhou and Suliao villages were 16.67% and 0.33%, respectively. The remaining 21 monitoring field sites gave negative results. A risk map for the transmission of schistosomiasis was constructed using ArcMap, based on the positive proportion of O. hupensis infected with S. japonicum, as detected by the LAMP assay, which will form a guide for surveillance and response strategies in high risk areas.

Quantifying mRNA and microRNA with qPCR in cervical carcinogenesis: a validation of reference genes to ensure accurate data

A number of recent studies have catalogued global gene expression patterns in a panel of normal, tumoral cervical tissues so that potential biomarkers can be identified. The qPCR has been one of the most widely used technologies for detecting these potential biomarkers. However, few studies have investigated a correct strategy for the normalization of data in qPCR assays for cervical tissues. The aim of this study was to validate reference genes in cervical tissues to ensure accurate quantification of mRNA and miRNA levels in cervical carcinogenesis. For this purpose, some issues for obtaining reliable qPCR data were evaluated such as the following: geNorm analysis with a set of samples which meet all of the cervical tissue conditions (Normal + CIN1 + CIN2 + CIN3 + Cancer); the use of individual Ct values versus pooled Ct values; and the use of a single (or multiple) reference genes to quantify mRNA and miRNA expression levels. Two different data sets were put on the geNorm to assess the expression stability of the candidate reference genes: the first dataset comprised the quantities of the individual Ct values; and the second dataset comprised the quantities of the pooled Ct values. Moreover, in this study, all the candidate reference genes were analyzed as a single “normalizer”. The normalization strategies were assessed by measuring p16^INK4a and miR-203 transcripts in qPCR assays. We found that the use of pooled Ct values, can lead to a misinterpretation of the results, which suggests that the maintenance of inter-individual variability is a key factor in ensuring the reliability of the qPCR data. In addition, it should be stressed that a proper validation of the suitability of the reference genes is required for each experimental setting, since the indiscriminate use of a reference gene can also lead to discrepant results.

Pooling of porcine fecal samples for quantification of Lawsonia intracellularis by real-time polymerase chain reaction

Procedures in which biological specimens are mixed and tested as 1 sample (pooling) have been applied for various biological specimens and laboratory examinations. The objective of the current study was to investigate agreement between laboratory testing of fecal pools and theoretical values obtained by averaging test results from individual fecal samples in relation to a quantitative polymerase chain reaction (qPCR) test for Lawsonia intracellularis. Ten diarrheic and 10 normal fecal samples were submitted from each of 43 Danish swine herds (n = 860 fecal samples). Pools (n = 43), each containing 20 individual fecal samples from the same herd, were prepared in the laboratory by pooling 10% fecal phosphate buffered saline solutions. All pools and individual fecal samples were subjected to qPCR testing for L. intracellularis. The theoretical number of L. intracellularis in the pools was calculated as the mean number of bacteria from the 20 individual fecal samples contributing to each pool. Agreement between the laboratory testing of pools and theoretical calculations based on individual sample results was evaluated. Pooling resulted in fewer L. intracellularis-positive herds (41.9%) compared with testing 20 fecal samples (53.5%). Agreement between the laboratory and the theoretical pools for dichotomized test results was 100% (95% confidence interval: 91.8-100%). For the quantitative test results, Lin concordance correlation coefficient was 0.997. The mean difference between the laboratory testing and the theoretical values was not different from zero (mean difference = 0.039 log₁₀ bacteria/g feces; P = 0.26).

Proteomic and functional consequences of hexokinase deficiency in glucose-repressible Kluyveromyces lactis

The analysis of glucose signaling in the Crabtree-positive eukaryotic model organism Saccharomyces cerevisiae has disclosed a dual role of its hexokinase ScHxk2, which acts as a glycolytic enzyme and key signal transducer adapting central metabolism to glucose availability. In order to identify evolutionarily conserved characteristics of hexokinase structure and function, the cellular response of the Crabtree-negative yeast Kluyveromyces lactis to rag5 null mutation and concomitant deficiency of its unique hexokinase KlHxk1 was analyzed by means of difference gel electrophoresis. In total, 2,851 fluorescent spots containing different protein species were detected in the master gel representing all of the K. lactis proteins that were solubilized from glucose-grown KlHxk1 wild-type and mutant cells. Mass spectrometric peptide analysis identified 45 individual hexokinase-dependent proteins related to carbohydrate, short-chain fatty acid and tricarboxylic acid metabolism as well as to amino acid and protein turnover, but also to general stress response and chromatin remodeling, which occurred as a consequence of KlHxk1 deficiency at a minimum 3-fold enhanced or reduced level in the mutant proteome. In addition, three proteins exhibiting homology to 2-methylcitrate cycle enzymes of S. cerevisiae were detected at increased concentrations, suggesting a stimulation of pyruvate formation from amino acids and/or fatty acids. Experimental validation of the difference gel electrophoresis approach by post-lysis dimethyl labeling largely confirmed the abundance changes detected in the mutant proteome via the former method. Taking into consideration the high proportion of identified hexokinase-dependent proteins exhibiting increased proteomic levels, KlHxk1 is likely to have a repressive function in a multitude of metabolic pathways. The proteomic alterations detected in the mutant classify KlHxk1 as a multifunctional enzyme and support the view of evolutionary conservation of dual-role hexokinases even in organisms that are less specialized than S. cerevisiae in terms of glucose utilization.

Microarray RNA transcriptional profiling: Part I. Platforms, experimental design and standardization

This review summarizes, in a balanced and comprehensive manner, the various components of microarrays and their types, substrate architecture, platforms for microarray probe implementation, standardizations and confounders. The review is intended to familiarize the beginner with the principles of experimental design and the selection of an appropriate microarray platform. This parallel technology has revolutionized transcriptomic approaches to data profiling and has a major role in the identification of expressed genes, classification and diagnosis studies. The technology is still evolving and guidelines for standardization and reporting have been developed and are being improved.

Array-based methods for diagnosis and prevention of transplant rejection

DNA microarray is a microhybridization-based assay that is used to simultaneously study the expression of thousands of genes, thus providing a global view of gene expression in a tissue sample. This powerful technique has been adopted by many biomedical disciplines and will likely have a profound impact on the diagnosis, treatment and prognosis of human diseases. This review article presents an overview of the application of microarray technology to the field of solid-organ transplantation.

Identification and confirmation of haptoglobin as a potential serum biomarker in hypertrophic cardiomyopathy using proteomic approaches

INTRODUCTION

Aiming at identifying biomarkers for hypertrophic cardiomyopathy (HCM), the serum proteome was explored through a two-dimensional gel-based proteomic approach (2D-DIGE) coupled with mass spectrometry and database interrogation.

METHODS

Serum samples from 20 male HCM patients and their sex- and age-matched controls were cleaned from interfering components. Patients and controls were pooled in five matched groups with the same age, and proteins extracts from each pool were labelled with cyanine dyes. Then, gel images were analysed using a fluorescence scanner and proteins were identified. Tryptic peptides were analysed by capillary reversed-phase liquid chromatography coupled online with tandem mass spectrometry (MS/MS).

RESULTS

Four different proteins were observed to be differentially expressed between HCM patients and their matched controls. Of them, decreases in haptoglobin levels were confirmed to be associated with HCM in an independent set of 181 consecutive HCM patients from our monographic clinic and 114 controls with similar age and sex using a nephelometer-based technique. Moreover, a significant negative correlation was observed between haptoglobin and subaortic gradient, thus highlighting the role of haptoglobin in HCM.

CONCLUSION

All these observations point out the utility of the 2D-DIGE proteomic strategy for the identification of serum proteins indicative of the presence of cardiac injury.

Urinary proteome analysis of irritable bowel syndrome (IBS) symptom subgroups

Irritable bowel syndrome (IBS) is a functional gastrointestinal (GI) disorder characterized by chronic abdominal pain associated with alterations in bowel function. Given the heterogeneity of the symptoms, multiple pathophysiologic factors are suspected to play a role. We classified women with IBS into four subgroups based on distinct symptom profiles. In-depth shotgun proteomic analysis was carried out to profile the urinary proteomes to identify possible proteins associated with these subgroups. First void urine samples with urine creatinine level≥100 mg/dL were used after excluding samples that tested positive for blood. Urine from 10 subjects representing each symptom subgroup was pooled for proteomic analysis. The urine proteome was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a data-independent method known as Precursor Acquisition Independent From Ion Count (PAcIFIC) that allowed extended detectable dynamic range. Differences in protein quantities were determined by peptide spectral counting followed by validation of select proteins with ELISA or a targeted single reaction monitoring (LC-SRM/MS) approach. Four IBS symptom subgroups were selected: (1) Constipation, (2) Diarrhea+Low Pain, (3) Diarrhea+High Pain, and (4) High Pain+High Psychological Distress. A fifth group consisted of Healthy Control subjects. From comparisons of quantitative spectral counting data among the symptom subgroups and controls, a total of 18 proteins that showed quantitative differences in relative abundance and possible physiological relevance to IBS were selected for further investigation. Three of the 18 proteins were chosen for validation by either ELISA or SRM. An elevated expression of gelsolin (GSN) was associated with the high pain groups. Trefoil Factor 3 (TFF3) levels were higher in IBS groups compared to controls. In this study, the IBS patients subclassified by predominant symptoms showed differences in urine proteome levels. Proteins showing distinctive changes are involved in homeostasis of intestinal function and inflammatory response. These findings warrant future studies with larger, independent cohorts to enable more extensive assessment and validation of urinary protein markers as a diagnostic tool in adults with IBS.

Assessment of skewed exposure in case-control studies with pooling

Pooling-based strategies that combine samples from multiple participants for laboratory assays have been proposed for epidemiologic investigations of biomarkers to address issues including cost, efficiency, detection, and when minimal sample volume is available. A modification of the standard logistic regression model has been previously described to allow use with pooled data; however, this model makes assumptions regarding exposure distribution and logit-linearity of risk (i.e., constant odds ratio) that can be violated in practice. We were motivated by a nested case-control study of miscarriage and inflammatory factors with highly skewed distributions to develop a more flexible model for analysis of pooled data. Using characteristics of the gamma distribution and the relation between models of binary outcome conditional on exposure and of exposure conditional on outcome, we use a modified logistic regression to accommodate nonlinearity because of unequal shape parameters in gamma distributed exposure for cases and controls. Using simulations, we compare our approach with existing methods for logistic regression for pooled data considering: (1) constant and dose-dependent effects; (2) gamma and log-normal distributed exposure; (3) effect size; and (4) the proportions of biospecimens pooled. We show that our approach allows estimation of odds ratios that vary with exposure level, yet has minimal loss of efficiency compared with existing approaches when exposure effects are dose-invariant. Our model performed similarly to a maximum likelihood estimation approach in terms of bias and efficiency, and provides an easily implemented approach for estimation with pooled biomarker data when effects may not be constant across exposure.

Effects of pooling samples on the performance of classification algorithms: a comparative study

A pooling design can be used as a powerful strategy to compensate for limited amounts of samples or high biological variation. In this paper, we perform a comparative study to model and quantify the effects of virtual pooling on the performance of the widely applied classifiers, support vector machines (SVMs), random forest (RF), k-nearest neighbors (k-NN), penalized logistic regression (PLR), and prediction analysis for microarrays (PAMs). We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. All investigated classifiers yield lower misclassification rates with smaller pool sizes. RF mainly outperforms other investigated algorithms, while accuracy levels are comparable among all the remaining ones. Guidelines are derived to identify an optimal pooling scheme for obtaining adequate predictive power and, hence, to motivate a study design that meets best experimental objectives and budgetary conditions, including time constraints.

Identification of a biomarker panel for colorectal cancer diagnosis

BACKGROUND

Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries.

METHODS

A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables.

RESULTS

After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples.

CONCLUSIONS

We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

Towards accurate estimation of the proportion of true null hypotheses in multiple testing

BACKGROUND

Biomedical researchers are now often faced with situations where it is necessary to test a large number of hypotheses simultaneously, eg, in comparative gene expression studies using high-throughput microarray technology. To properly control false positive errors the FDR (false discovery rate) approach has become widely used in multiple testing. The accurate estimation of FDR requires the proportion of true null hypotheses being accurately estimated. To date many methods for estimating this quantity have been proposed. Typically when a new method is introduced, some simulations are carried out to show the improved accuracy of the new method. However, the simulations are often very limited to covering only a few points in the parameter space.

RESULTS

Here I have carried out extensive in silico experiments to compare some commonly used methods for estimating the proportion of true null hypotheses. The coverage of these simulations is unprecedented thorough over the parameter space compared to typical simulation studies in the literature. Thus this work enables us to draw conclusions globally as to the performance of these different methods. It was found that a very simple method gives the most accurate estimation in a dominantly large area of the parameter space. Given its simplicity and its overall superior accuracy I recommend its use as the first choice for estimating the proportion of true null hypotheses in multiple testing.

poolMC: smart pooling of mRNA samples in microarray experiments

BACKGROUND

Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements.

RESULTS

A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment.

CONCLUSIONS

The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size.

Hybrid pooled-unpooled design for cost-efficient measurement of biomarkers

Evaluating biomarkers in epidemiological studies can be expensive and time consuming. Many investigators use techniques such as random sampling or pooling biospecimens in order to cut costs and save time on experiments. Commonly, analyses based on pooled data are strongly restricted by distributional assumptions that are challenging to validate because of the pooled biospecimens. Random sampling provides data that can be easily analyzed. However, random sampling methods are not optimal cost-efficient designs for estimating means. We propose and examine a cost-efficient hybrid design that involves taking a sample of both pooled and unpooled data in an optimal proportion in order to efficiently estimate the unknown parameters of the biomarker distribution. In addition, we find that this design can be used to estimate and account for different types of measurement and pooling error, without the need to collect validation data or repeated measurements. We show an example where application of the hybrid design leads to minimization of a given loss function based on variances of the estimators of the unknown parameters. Monte Carlo simulation and biomarker data from a study on coronary heart disease are used to demonstrate the proposed methodology.

Characterizing populations of individuals using pooled samples

Biomonitoring involves the assessment of human or animal populations by measuring organic or biological compounds or their metabolites in the body fluids or tissues of individuals in those populations. Pooling samples before making analytical measurements can reduce the costs of biomonitoring by reducing the number of analyses. By proper choice of pooled-sample design, population means can be estimated without measuring individual samples. I present a statistical method for characterizing an entire population distribution of such compounds by exploiting the theoretic relationship between interindividual-sample variance and the variation between pooled samples. I use simulation experiments to determine an optimum pooled-sample design as a function of the number of subpopulations and the number of available samples. Using pooled samples to characterize populations is not only more cost-efficient, but also in some cases it can lead to more precise and less biased parameter estimation than that occurs with individual samples.

Investigating sample pooling strategies for DIGE experiments to address biological variability

If biological questions are to be answered using quantitative proteomics, it is essential to design experiments which have sufficient power to be able to detect changes in expression. Sample subpooling is a strategy that can be used to reduce the variance but still allow studies to encompass biological variation. Underlying sample pooling strategies is the biological averaging assumption that the measurements taken on the pool are equal to the average of the measurements taken on the individuals. This study finds no evidence of a systematic bias triggered by sample pooling for DIGE and that pooling can be useful in reducing biological variation. For the first time in quantitative proteomics, the two sources of variance were decoupled and it was found that technical variance predominates for mouse brain, while biological variance predominates for human brain. A power analysis found that as the number of individuals pooled increased, then the number of replicates needed declined but the number of biological samples increased. Repeat measures of biological samples decreased the numbers of samples required but increased the number of gels needed. An example cost benefit analysis demonstrates how researchers can optimise their experiments while taking into account the available resources.

Uncovering the Arabidopsis thaliana nectary transcriptome: investigation of differential gene expression in floral nectariferous tissues

BACKGROUND

Many flowering plants attract pollinators by offering a reward of floral nectar. Remarkably, the molecular events involved in the development of nectaries, the organs that produce nectar, as well as the synthesis and secretion of nectar itself, are poorly understood. Indeed, to date, no genes have been shown to directly affect the de novo production or quality of floral nectar. To address this gap in knowledge, the ATH1 Affymetrix GeneChip array was used to systematically investigate the Arabidopsis nectary transcriptome to identify genes and pathways potentially involved in nectar production.

RESULTS

In this study, we identified a large number of genes differentially expressed between secretory lateral nectaries and non-secretory median nectary tissues, as well as between mature lateral nectaries (post-anthesis) and immature lateral nectaries (pre-anthesis). Expression within nectaries was also compared to thirteen non-nectary reference tissues, from which 270 genes were identified as being significantly upregulated in nectaries. The expression patterns of 14 nectary-enriched genes were also confirmed via RT PCR. Upon looking into functional groups of upregulated genes, pathways involved in gene regulation, carbohydrate metabolism, and lipid metabolism were particularly enriched in nectaries versus reference tissues.

CONCLUSION

A large number of genes preferentially expressed in nectaries, as well as between nectary types and developmental stages, were identified. Several hypotheses relating to mechanisms of nectar production and regulation thereof are proposed, and provide a starting point for reverse genetics approaches to determine molecular mechanisms underlying nectar synthesis and secretion.

Statistical design of quantitative mass spectrometry-based proteomic experiments

We review the fundamental principles of statistical experimental design, and their application to quantitative mass spectrometry-based proteomics. We focus on class comparison using Analysis of Variance (ANOVA), and discuss how randomization, replication and blocking help avoid systematic biases due to the experimental procedure, and help optimize our ability to detect true quantitative changes between groups. We also discuss the issues of pooling multiple biological specimens for a single mass analysis, and calculation of the number of replicates in a future study. When applicable, we emphasize the parallels between designing quantitative proteomic experiments and experiments with gene expression microarrays, and give examples from that area of research. We illustrate the discussion using theoretical considerations, and using real-data examples of profiling of disease.

Proteomic analysis of circulating monocytes in Chinese premenopausal females with extremely discordant bone mineral density

Osteoporosis (OP) is a major public health problem, mainly characterized by low bone mineral density (BMD). Circulating monocytes (CMCs) may serve as progenitors of osteoclasts and produce a wide variety of factors important to bone metabolism. However, the specific action mechanism of CMCs in the pathogenesis of OP is far from clear. We performed a comparative protein expression profiling study of CMCs in Chinese premenopausal females with extremely discordant BMD, identified a total of 38 differentially expressed proteins, and confirmed with Western blotting five proteins: ras suppressor protein1 (RSU1), gelsolin (GSN), manganese-containing superoxide dismutase (SOD2), glutathione peroxidase 1(GPX1), and prolyl 4-hydroxylase beta subunit (P4HB). These proteins might affect CMCs' trans-endothelium, differentiation, and/or downstream osteoclast functions, thus contribute to differential osteoclastogenesis and finally lead to BMD variation. The findings promote our understanding of the role of CMCs in BMD determination, and provide an insight into the pathogenesis of human OP.

Calprotectin is released from human skeletal muscle tissue during exercise

Skeletal muscle has been identified as a secretory organ. We hypothesized that IL-6, a cytokine secreted from skeletal muscle during exercise, could induce production of other secreted factors in skeletal muscle. IL-6 was infused for 3 h into healthy young males (n = 7) and muscle biopsies obtained at time points 0, 3 and 6 h in these individuals and in resting controls. Affymetrix microarray analysis of gene expression changes in skeletal muscle biopsies identified a small set of genes changed by IL-6 infusion. RT-PCR validation confirmed that S100A8 and S100A9 mRNA were up-regulated 3-fold in skeletal muscle following IL-6 infusion compared to controls. Furthermore, S100A8 and S100A9 mRNA levels were up-regulated 5-fold in human skeletal muscle following cycle ergometer exercise for 3 h at approximately 60% of in young healthy males (n = 8). S100A8 and S100A9 form calprotectin, which is known as an acute phase reactant. Plasma calprotectin increased 5-fold following acute cycle ergometer exercise in humans, but not following IL-6 infusion. To identify the source of calprotectin, healthy males (n = 7) performed two-legged dynamic knee extensor exercise for 3 h with a work load of approximately 50% of peak power output and arterial-femoral venous differences were obtained. Arterial plasma concentrations for calprotectin increased 2-fold compared to rest and there was a net release of calprotectin from the working muscle. In conclusion, IL-6 infusion and muscle contractions induce expression of S100A8 and S100A9 in skeletal muscle. However, IL-6 alone is not a sufficient stimulus to facilitate release of calprotectin from skeletal muscle.

Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ)

We assess the reliability of isobaric-tags for relative and absolute quantitation (iTRAQ), based on different types of replicate analyses taking into account technical, experimental, and biological variations. In total, 10 iTRAQ experiments were analyzed across three domains of life involving Saccharomyces cerevisiae KAY446, Sulfolobus solfataricus P2, and Synechocystis sp. PCC 6803. The coverage of protein expression of iTRAQ analysis increases as the variation tolerance increases. In brief, a cutoff point at +/-50% variation (+/-0.50) would yield 88% coverage in quantification based on an analysis of biological replicates. Technical replicate analysis produces a higher coverage level of 95% at a lower cutoff point of +/-30% variation. Experimental or iTRAQ variations exhibit similar behavior as biological variations, which suggest that most of the measurable deviations come from biological variations. These findings underline the importance of replicate analysis as a validation tool and benchmarking technique in protein expression analysis.

Gene copy number changes in dermatofibrosarcoma protuberans – a fine-resolution study using array comparative genomic hybridization

Dermatofibrosarcoma protuberans (DFSP) is a rare, slow-growing, low-grade dermal tumor. Cytogenetic and FISH studies have revealed that the chromosomal rearrangements characteristic of DFSP tumors involve both translocations and the formation of a supernumerary ring derived from chromosomes 17 and 22. The t(17;22) (q22;q13.1) translocation generates a gene fusion between COL1A1 and PDGFB, which serves as a diagnostic marker of DFSP. In the present study we performed array-CGH (aCGH) analysis on ten DFSP tumors. The COL1A1 region at 17q was gained in 71% (5/7) of the samples and the PDGFB region at 22q was gained in 43% (3/7) of the individual samples. In addition to the 17q and 22q gains, altogether 17 minimal common regions of gain and one region of loss were detected.