High-throughput Expression Profiling Research Articles

SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling

Introduction: Laser capture microdissection (LCM) facilitates procurement of defined cell populations for study in the context of histopathology. The morphologic assessment step in the LCM procedure is time consuming and tedious, thus restricting the utility of the technology for large applications. Results: Here, we describe the use of Spatially Invariant Vector Quantization (SIVQ) for histological analysis within LCM. Using SIVQ, we selected vectors as morphologic predicates that were representative of normal epithelial or cancer cells and then searched for phenotypically similar cells across entire tissue sections. The selected cells were subsequently auto-microdissected and the recovered RNA was analyzed by expression microarray. Gene expression profiles from SIVQ–LCM and standard LCM-derived samples demonstrated highly congruous signatures, confirming the equivalence of the differing microdissection methods. Conclusion: SIVQ–LCM improves the work-flow of microdissection in two significant ways. First, the process is transformative in that it shifts the pathologist’s role from technical execution of the entire microdissection to a limited-contact supervisory role, enabling large-scale extraction of tissue by expediting subsequent semi-autonomous identification of target cell populations. Second, this work-flow model provides an opportunity to systematically identify highly constrained cell populations and morphologically consistent regions within tissue sections. Integrating SIVQ with LCM in a single environment provides advanced capabilities for efficient and high-throughput histological-based molecular studies.

Functional integration of a metabolic network model and expression data without arbitrary thresholding.

Flux balance analysis (FBA) has been used extensively to analyze genome-scale, constraint-based models of metabolism in a variety of organisms. The predictive accuracy of such models has recently been improved through the integration of high-throughput expression profiles of metabolic genes and proteins. However, extensions of FBA often require that such data be discretized a priori into sets of genes or proteins that are either 'on' or 'off'. This procedure requires selecting relatively subjective expression thresholds, often requiring several iterations and refinements to capture the expression dynamics and retain model functionality. We present a method for mapping expression data from a set of environmental, genetic or temporal conditions onto a metabolic network model without the need for arbitrary expression thresholds. Metabolic Adjustment by Differential Expression (MADE) uses the statistical significance of changes in gene or protein expression to create a functional metabolic model that most accurately recapitulates the expression dynamics. MADE was used to generate a series of models that reflect the metabolic adjustments seen in the transition from fermentative- to glycerol-based respiration in Saccharomyces cerevisiae. The calculated gene states match 98.7% of possible changes in expression, and the resulting models capture functional characteristics of the metabolic shift. MADE is implemented in Matlab and requires a mixed-integer linear program solver. Source code is freely available at http://www.bme.virginia.edu/csbl/downloads/.

Phenocopy – A Strategy to Qualify Chemical Compounds during Hit-to-Lead and/or Lead Optimization

A phenocopy is defined as an environmentally induced phenotype of one individual which is identical to the genotype-determined phenotype of another individual. The phenocopy phenomenon has been translated to the drug discovery process as phenotypes produced by the treatment of biological systems with new chemical entities (NCE) may resemble environmentally induced phenotypic modifications. Various new chemical entities exerting inhibition of the kinase activity of Transforming Growth Factor β Receptor I (TGF-βR1) were qualified by high-throughput RNA expression profiling. This chemical genomics approach resulted in a precise time-dependent insight to the TGF-β biology and allowed furthermore a comprehensive analysis of each NCE's off-target effects. The evaluation of off-target effects by the phenocopy approach allows a more accurate and integrated view on optimized compounds, supplementing classical biological evaluation parameters such as potency and selectivity. It has therefore the potential to become a novel method for ranking compounds during various drug discovery phases.

Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data

AbstractRecent developments in extracting and processing biological and clinical data are allowing quantitative approaches to studying living systems. High-throughput sequencing, expression profiles, proteomics, and electronic health records are some examples of such technologies. Extracting meaningful information from those technologies requires careful analysis of the large volumes of data they produce. In this note, we present a set of distributions that commonly appear in the analysis of such data. These distributions present some interesting features: they are discontinuous in the rational numbers, but continuous in the irrational numbers, and possess a certain self-similar (fractal-like) structure. The first set of examples which we present here are drawn from a high-throughput sequencing experiment. Here, the self-similar distributions appear as part of the evaluation of the error rate of the sequencing technology and the identification of tumorogenic genomic alterations. The other examples are obtained from risk factor evaluation and analysis of relative disease prevalence and co-mordbidity as these appear in electronic clinical data. The distributions are also relevant to identification of subclonal populations in tumors and the study of the evolution of infectious diseases, and more precisely the study of quasi-species and intrahost diversity of viral populations.

Global expression profiling of rice microRNAs by one-tube stem-loop reverse transcription quantitative PCR revealed important roles of microRNAs in abiotic stress responses

MicroRNAs are a class of endogenous small RNA molecules (20-24 nucleotides) that have pivotal roles in regulating gene expression mostly at posttranscriptional levels in plants. Plant microRNAs have been implicated in the regulation of diverse biological processes including growth and stress responses. However, the information about microRNAs in regulating abiotic stress responses in rice is limited. We optimized a one-tube stem-loop reverse transcription quantitative PCR (ST-RT qPCR) for high-throughput expression profiling analysis of microRNAs in rice under normal and stress conditions. The optimized ST-RT qPCR method was as accurate as small RNA gel blotting and was more convenient and time-saving than other methods in quantifying microRNAs. With this method, 41 rice microRNAs were quantified for their relative expression levels after drought, salt, cold, and abscisic acid (ABA) treatments. Thirty-two microRNAs showed induced or suppressed expression after stress or ABA treatment. Further analysis suggested that stress-responsive cis-elements were enriched in the promoters of stress-responsive microRNA genes. The expressions of five and seven microRNAs were significantly affected in the rice plant with defects in stress tolerance regulatory genes OsSKIPa and OsbZIP23, respectively. Some of the predicted target genes of these microRNAs were also related to abiotic stresses. We conclude that ST-RT qPCR is an efficient and reliable method for expression profiling of microRNAs and a significant portion of rice microRNAs participate in abiotic stress response and regulation.

CD14, a key candidate gene associated with a specific immune response to cockroach

Sensitization to cockroach allergen is one of the strongest predictors of asthma morbidity, especially among African Americans. Our aims were to determine the genomic basis of cockroach sensitization and the specific response to cockroach antigen. We investigated the Th1/Th2 cytokine profile of co-cultured plasmacytoid dendritic cells (pDCs) and CD4+ T cells and the 'transcript signature' of the immune response to cockroach antigen using high-throughput expression profiling of co-cultured cells. We observed significantly elevated levels of IL-13, IL-10, and TNF-alpha, but undetectable levels of IL-12p70 and IFN-alpha, when cultures were exposed to crude cockroach antigen. A significant difference was observed for IL-13 between cockroach-allergic and non-allergic individuals (P=0.039). Microarray analyses demonstrated a greater response at 48 h compared with 4 h, with 50 genes being uniquely expressed in cockroach antigen-treated cells, including CD14, S100A8, CCL8, and IFI44L. The increased CD14 expression was further observed in purified pDCs, human monocytic THP-1 cells, and the supernatant of co-cultured pDCs and CD4+ T cells on exposure to cockroach extract. Furthermore, the most differential expression of CD14 between cockroach allergy and non-cockroach allergy was only observed among individuals with the CC 'high-risk' genotype of the CD14-260C/T. Ingenuity Pathways Analysis analyses suggested the IFN signalling as the most significant canonical pathway. Our results suggest that these differentially expressed genes, particularly CD14, and genes in the IFN signalling pathway may be important candidates for further investigation of their role in the immune response to cockroach allergen.

Microarray Profiling Reveals That Placental Transcriptomes of Early-onset HELLP Syndrome and Preeclampsia Are Similar

Background The involvement of the placenta in the pathogenesis of preeclampsia and HELLP syndrome is well established, and placental lesions are also similar in these two syndromes. Here we aimed to examine the placental transcriptome and to identify candidate biomarkers in early-onset preeclampsia and HELLP syndrome. Methods Placental specimens were obtained at C-sections from women with early-onset preeclampsia and HELLP syndrome, and from controls who delivered preterm or at term. After histopathological examination, fresh-frozen placental specimens were used for microarray profiling and validation by qRT-PCR. Differential expression was analysed using log–linear models while adjusting for gestational age. Gene ontology and pathway analyses were used to interpret gene expression changes. Tissue microarrays were constructed from paraffin-embedded placental specimens and immunostained. Results Placental gene expression was gestational age-dependent among preterm and term controls. Out of the 350 differentially expressed genes in preeclampsia and 554 genes in HELLP syndrome, 224 genes (including LEP, CGB, LHB, INHA, SIGLEC6, PAPPA2, TREM1, and FLT1) changed in the same direction (elevated or reduced) in both syndromes. Many of these encode proteins that have been implicated as biomarkers for preeclampsia. Enrichment analyses revealed similar biological processes, cellular compartments and biological pathways enriched in early-onset preeclampsia and HELLP syndrome; however, some processes and pathways (e.g., cytokine–cytokine receptor interaction) were over-represented only in HELLP syndrome. Conclusion High-throughput transcriptional and tissue microarray expression profiling revealed that placental transcriptomes of early-onset preeclampsia and HELLP syndrome largely overlap, underlying a potential common cause and pathophysiologic processes in these syndromes. However, gene expression changes may also suggest a more severe placental pathology and pronounced inflammatory response in HELLP syndrome than in preeclampsia.

Use of genomic signatures to predict response to histone deacetylase inhibitors and correlation with tumor subtypes and molecular pathways.

e13596 Background: Histone deacetylase (HDAC) inhibitors are a promising class of cancer drugs. The ability to predict which tumors are likely to respond helps in planning clinical trials and delivering treatment. Methods: We developed gene expression signatures using cell lines before and after treatment with valproic acid (VPA), trichostatin A (TSA), and suberoylanilide hydroxamic acid (SAHA). Gene expression signatures for pathway activation were developed using induction of genes regulating epigenetic processes (HDAC1, HDAC4, SIRT1, DNMT2, and EZH2). Using logistic regression we predicted drug sensitivity and epigenetic signaling pathways in subtypes of cancers using data from 32 public datasets. Prediction of pathway activity and drug responsiveness was validated using human tumor cells grown in 3D culture or mouse xenografts. Results: Pathway activation patterns and drug sensitivity vary across tumor types and subtypes. Thyroid and GI cancers and mesothelioma are predicted to have highest response to HDAC inhibitors (p<0.0001). There were significant differences in HDAC pathway activity and drug response within tumor subtypes. For example, glioblastoma, particularly Freije class HC2A and HC2B, are more likely to be sensitive to HDAC inhibitors than lower grade gliomas, which have lower HDAC4 and EZH2 activation (p<0.0001). Basal and HER2-positive breast tumors have higher HDAC4 activity and sensitivity to HDAC inhibitors than ER-positive tumors (p<0.0005). We saw correlation between components of the epigenetic machinery. HDAC inhibitor sensitivity correlates with MYC and EZH2 activation, particularly in ovarian tumors, mesotheliomas, basal breast cancer, and gliomas (p<0.001). Preclinical drug studies in cell lines and patient tumors grown in 3D culture and orthotopic models validate our genomic analyses. Conclusions: Epigenetic pathway activity and sensitivity to epigenetic modifiers vary across tumor types and subtypes. High-throughput expression profiling can impact cancer drug selection. Pre-identification of patient response may improve response rates and assist in identifying optimal inclusion criteria for clinical trials. No significant financial relationships to disclose.

Abstract 1143: Translational control analysis by translationally active RNA capture/microarray analysis (TrIP-Chip)

Abstract We have developed a new approach to systematically study post-transcriptional regulation in a small number of cells. Actively translating mRNAs are associated with polysomes and the newly synthesized peptide chains are closely associated with molecular chaperones such as hsp70s, which assist in the proper folding of nascent polypeptides into higher ordered structures. These chaperones provide an anchor with which to separate actively translating mRNAs associated with polysomes from free mRNAs. Affinity capture beads were developed to capture hsp70 chaperones associated with the polysome complexes. The isolated actively translating mRNAs were used for high throughput expression profiling analysis. Feasibility was demonstrated using an in vitro translation system with known translationally regulated mRNA transcript thymidylate synthase (TS). We further developed the approach using HCT-116 colon cancer cells with both TS and p53 as positive controls. The steady state levels of TS and p53 mRNAs were unaltered after 5-fluorouracil (5-FU) treatment as assessed by real time qRT-PCR analysis. In contrast, the protein expression and polysome-associated mRNA levels of both genes were increased. These differences in translational rate were revealed with our new approach from 500 cells. This technology has the potential to make investigation of translational control feasible with limited quantities of clinical specimens. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1143.

Abstract 1149: Genetic analysis of single cells

Abstract We are developing highly sensitive and quantitative genomic technologies for genetic and expression analysis of single cells or trace amount of DNA/RNA materials, using microarray and next-generation sequencing platforms. We have tested various Multiple Displacement Amplification (MDA)-based protocols under a variety of reaction conditions. With our current protocol and a 300K-SNP chip readout, we were able to obtain 88.3% and 93.9% call rate, and 97.4% and 99.9% call accuracy with direct cell lysis from 1 cell and 5 cells, respectively. We are also developing RNA amplification methods for high-throughput expression profiling of single cells. With our current protocol, we were able to generate reproducible expression profiles, R2 = 0.73 and 0.77, using 10 pg and 50 pg total RNA input, respectively. In addition, the profiles correlated well with those obtained with standard 100 ng total RNA input, R2 = 0.61 and 0.77, respectively. Our data show that sequencing of single-cell transcriptomes can clearly distinguish embryonic stem cells from embryonic fibroblasts and tumor cells. We are currently using these technologies to study medical specimens such as circulating tumor cells and cancer stem cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1149.

Translational control analysis by translationally active RNA capture/microarray analysis (TrIP–Chip)

We have developed a new approach to systematically study post-transcriptional regulation in a small number of cells. Actively translating mRNAs are associated with polysomes and the newly synthesized peptide chains are closely associated with molecular chaperones such as hsp70s, which assist in the proper folding of nascent polypeptides into higher ordered structures. These chaperones provide an anchor with which to separate actively translating mRNAs associated with polysomes from free mRNAs. Affinity capture beads were developed to capture hsp70 chaperones associated with the polysome complexes. The isolated actively translating mRNAs were used for high-throughput expression profiling analysis. Feasibility was demonstrated using an in vitro translation system with known translationally regulated mRNA transcript thymidylate synthase (TS). We further developed the approach using HCT-116 colon cancer cells with both TS and p53 as positive controls. The steady-state levels of TS and p53 mRNAs were unaltered after 5-fluorouracil treatment as assessed by real-time qRT-PCR analysis. In contrast, the protein expression and polysome-associated mRNA levels of both genes were increased. These differences in translational rate were revealed with our new approach from 500 cells. This technology has the potential to make investigation of translational control feasible with limited quantities of clinical specimens.

Applications of New Sequencing Technologies for Transcriptome Analysis

Transcriptome analysis has been a key area of biological inquiry for decades. Over the years, research in the field has progressed from candidate gene-based detection of RNAs using Northern blotting to high-throughput expression profiling driven by the advent of microarrays. Next-generation sequencing technologies have revolutionized transcriptomics by providing opportunities for multidimensional examinations of cellular transcriptomes in which high-throughput expression data are obtained at a single-base resolution.

The Widely Used Diagnostics “DNA Microarray”-A Review

Problem statement: DNA microarray technique is one of the latest advances in the field of molecular biology and medicine. It is a multiplex technique used in combination of bioinformatics and statistical data analysis. Since, 1995, the technique offers the possibility of conducting tens or hundreds of thousands of simultaneous hybridizations. Approach: This increased experimental efficiency permits high throughput and whole genome expression profiling of pathogens and hosts. Results: Microarray technologies are rapidly advancing with numerous applications in gene expression, genotyping, pharmaco-genomics, proteomics and cell biology, in infectious diseases recognizing the causative agent, molecular typing, in studying the interactions between causative agents and host cells, cancer biology, genetics, determining the presence of a pathogen in food samples, to characterize microbial isolates, identifying the presence of virulence factors or microbial resistance genes, to detect microbial mutations associated with resistance to antiretroviral drugs, simultaneously detect and discriminate several viruses, to study the contaminants in cultures, such as mycoplasma, yeasts, fungi, exogenous and endogenous viruses and prions from both animal and human origin so and so forth. Conclusion: This article reviewed the applicability of this technique elaborately in some of the important areas of biological sciences.

Recent advances in genetic predisposition to clinical acute lung injury

It has been well established that acute lung injury (ALI), and the more severe presentation of acute respiratory distress syndrome (ARDS), constitute complex traits characterized by a multigenic and multifactorial etiology. Identification and validation of genetic variants contributing to disease susceptibility and severity has been hampered by the profound heterogeneity of the clinical phenotype and the role of environmental factors, which includes treatment, on outcome. The critical nature of ALI and ARDS, compounded by the impact of phenotypic heterogeneity, has rendered the amassing of sufficiently powered studies especially challenging. Nevertheless, progress has been made in the identification of genetic variants in select candidate genes, which has enhanced our understanding of the specific pathways involved in disease manifestation. Identification of novel candidate genes for which genetic association studies have confirmed a role in disease has been greatly aided by the powerful tool of high-throughput expression profiling. This article will review these studies to date, summarizing candidate genes associated with ALI and ARDS, acknowledging those that have been replicated in independent populations, with a special focus on the specific pathways for which candidate genes identified so far can be clustered.

PAVE: Program for assembling and viewing ESTs

BackgroundNew sequencing technologies are rapidly emerging. Many laboratories are simultaneously working with the traditional Sanger ESTs and experimenting with ESTs generated by the 454 Life Science sequencers. Though Sanger ESTs have been used to generate contigs for many years, no program takes full advantage of the 5' and 3' mate-pair information, hence, many tentative transcripts are assembled into two separate contigs. The new 454 technology has the benefit of high-throughput expression profiling, but introduces time and space problems for assembling large contigs.ResultsThe PAVE (Program for Assembling and Viewing ESTs) assembler takes advantage of the 5' and 3' mate-pair information by requiring that the mate-pairs be assembled into the same contig and joined by n's if the two sub-contigs do not overlap. It handles the depth of 454 data sets by "burying" similar ESTs during assembly, which retains the expression level information while circumventing time and space problems. PAVE uses MegaBLAST for the clustering step and CAP3 for assembly, however it assembles incrementally to enforce the mate-pair constraint, bury ESTs, and reduce incorrect joins and splits. The PAVE data management system uses a MySQL database to store multiple libraries of ESTs along with their metadata; the management system allows multiple assemblies with variations on libraries and parameters. Analysis routines provide standard annotation for the contigs including a measure of differentially expressed genes across the libraries. A Java viewer program is provided for display and analysis of the results. Our results clearly show the benefit of using the PAVE assembler to explicitly use mate-pair information and bury ESTs for large contigs.ConclusionThe PAVE assembler provides a software package for assembling Sanger and/or 454 ESTs. The assembly software, data management software, Java viewer and user's guide are freely available.

MicroRNAs and Cancer ¿ The Search Begins!

For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer.

MicroRNAs and Cancer—The Search Begins!

For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer.

Assessment of Microrna Expression in Mantle Cell Lymphoma Using High Throughput Techniques

Mantle cell lymphoma (MCL) accounts for 3–10% of all lymphomas and demonstrates a poor clinical response to current therapeutic approaches, with a median survival of 3–5 years. The natural history of MCL is heterogeneous and not well-defined by standard clinical markers as patients may die within months of diagnosis or experience long-term survival. There remains a need for reliable biomarkers of MCL prognosis. To date, global gene expression signatures have not been determined for formalin-fixed, paraffin-embedded (FFPE) MCL samples due to difficulties isolating full-length mRNA transcripts from FFPE tissues. Examining microRNA expression in FFPE samples may circumvent this problem, as this population of RNAs remains intact during processing of FFPE samples. MicroRNAs (miRs) are small, non-coding RNAs, which regulate gene expression by inhibiting mRNA translation. Although miR expression signatures have been derived for other hematological malignancies, assessment of miR expression in patients with MCL has yet to be undertaken. We hypothesize that different pathological subtypes of MCL have unique miR expression signatures, distinct from miR expression profiles of other B-cell non-Hodgkin lymphomas. Our objectives were two-fold:To determine and validate miR expression in different pathological subtypes of MCL; and,To compare miR expression in MCL to other B-cell non-Hodgkin lymphomas.Total RNA was extracted from FFPE samples [17 conventional MCL, 11 blastoid MCL, 4 follicular lymphomas (Grades 1, 2, 3a, and 3b), 1 nodal marginal zone lymphoma, 1 small lymphocytic lymphoma (SLL/CLL) and 3 benign, reactive lymph nodes (normal controls)] using the RecoverAll kit for FFPE tissues (Ambion). RNA was subjected to quantitative real-time PCR (qRT-PCR) for 365 miRs and 3 endogenous control small nucleolar RNAs to obtain miR expression profiles using the TaqMan Low Density Array (TLDA) v1.0 platform (MicroFluidic card, Applied Biosystems). TLDAs were run on the ABI7900 HT analyzer with TLDA upgrade and analysed with RQ Manager software provided by Applied Biosystems. Expression profiles were correlated to pathological subtype, and hierarchical clustering, principal component analysis (PCA), and ANOVA were performed using Partek software [Partek Genomics Suite for Gene Expression Data]. Results indicate that miR expression profiles differ between B-cell, non-Hodgkin lymphoma and MCL samples. Clustering analysis and PCA both demonstrated different profiles between MCL and B-cell, non-Hodgkin lymphomas. Although PCA did not demonstrate significant differences between the conventional and blastoid MCL samples, a set of fifteen miRs may be able to distinguish these two groups of MCL, since these 15 miRs are relatively upregulated in blastoid MCL in comparison to conventional MCL. In addition, PCA revealed five (3 conventional MCL samples and 2 blastoid samples), which did not cluster with their respective groups. These five samples were from patients known to have progressive disease, indicating that such patients may have different miR expression profiles compared to patients with non-progressive disease. We conclude that high-throughput miR expression profiles can be generated from FFPE samples in B-cell non-Hodgkin lymphomas and that miR expression profiles for MCL samples differ from those of other B-cell non-Hodgkin lymphomas. Blastoid and conventional MCL samples may not have significantly differing profiles, however a set of 15 miRs appears to be able to distinguish between these two groups. Of note, samples from patients with known progressive disease have significantly different profiles from those with non-progressive disease.

High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA

MicroRNAs (miRNAs) are an emerging class of small non-coding RNAs implicated in a wide variety of cellular processes. Research in this field is accelerating, and the growing number of miRNAs emphasizes the need for high-throughput and sensitive detection methods. Here we present the successful evaluation of the Megaplex reverse transcription format of the stem-loop primer-based real-time quantitative polymerase chain reaction (RT-qPCR) approach to quantify miRNA expression. The Megaplex reaction provides simultaneous reverse transcription of 450 mature miRNAs, ensuring high-throughput detection. Further, the introduction of a complementary DNA pre-amplification step significantly reduces the amount of input RNA needed, even down to single-cell level. To evaluate possible pre-amplification bias, we compared the expression of 384 miRNAs in three different cancer cell lines with Megaplex RT, with or without an additional pre-amplification step. The normalized Cq values of all three sample pairs showed a good correlation with maintenance of differential miRNA expression between the cell lines. Moreover, pre-amplification using 10 ng of input RNA enabled the detection of miRNAs that were undetectable when using Megaplex alone with 400 ng of input RNA. The high specificity of RT-qPCR together with a superior sensitivity makes this approach the method of choice for high-throughput miRNA expression profiling.

Genetic polymorphism and pathogenesis of benign prostatic hyperplasia

There is apparent uncertainty in etiology and pathogenesis of Benign Prostatic Hyperplasia. Though several hypothesis put forwarded, like other multifactorial complex diseases, BPH still lacks an integrated model of pathogenesis and progression. BPH is a common problem of aged men due uncontrollable non-cancerous prostatic growth. A wide variety of genetic factors have been associated with tissue hyperplasia in general. Androgen related genes and metabolism genes are closely associated with growth and function of prostate. Recent emergence of BPH as inflammation-mediated disease also opens new avenues in search for genetic factors of BPH. Knowledge of gene polymorphisms may be an important aid in genetic screening of risk of disease involved and the understanding of complex interactions involved in genesis and progression of BPH. Till to date there is no report of high penetrance genetic marker in BPH. There is immense possibility of evolving a model of low penetrance markers of BPH and with this prospect a more exhaustive search for all BPH associated polymorphism in larger population is needed. This strategy combined with high-throughput expression profiling and linkage studies of genes for establishing a clear picture of pathophysiology and risk factor of a complex disease like BPH. In this study, we have reviewed a variety of genetic polymorphisms in relation to their possible role in benign prostatic hyperplasia. We have included associations identified in molecular epidemiology studies and the consistency of findings reported to date. Suggestions for further research are also offered. Keyword: Polymorphism, Benign Prostatic Hyperplasia, Pathogenesis