The integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way. A method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability. This approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN). Our results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.

/pdf/the-rin-an-rna-integrity-number-for-assigning-integrity-2h6tnunr7z.pdf

The RIN: an RNA integrity number for assigning integrity values to RNA measurements

The real-time reverse transcription polymerase chain reaction (RT-qPCR) addresses the evident requirement for quantitative data analysis in molecular medicine, biotechnology, microbiology and diagnostics and has become the method of choice for the quantification of mRNA. Although it is often described as a "gold" standard, it is far from being a standard assay. The significant problems caused by variability of RNA templates, assay designs and protocols, as well as inappropriate data normalization and inconsistent data analysis, are widely known but also widely disregarded. As a first step towards standardization, we describe a series of RT-qPCR protocols that illustrate the essential technical steps required to generate quantitative data that are reliable and reproducible. We would like to emphasize, however, that RT-qPCR data constitute only a snapshot of information regarding the quantity of a given transcript in a cell or tissue. Any assessment of the biological consequences of variable mRNA levels must include additional information regarding regulatory RNAs, protein levels and protein activity. The entire protocol described here, encompassing all stages from initial assay design to reliable qPCR data analysis, requires approximately 15 h.

Quantification of mRNA using real-time RT-PCR

https://www.gene-quantification.de/fleige-pfaffl-rin-2006.pdf

RNA integrity and the effect on the real-time qRT-PCR performance

Deciphering the cellular and molecular interactions that drive disease within the tissue microenvironment holds promise for discovering drug targets of the future. In order to recapitulate the in vivo interactions thorough molecular analysis, one must be able to analyze specific cell populations within the context of their heterogeneous tissue microecology. Laser-capture microdissection (LCM) is a method to procure subpopulations of tissue cells under direct microscopic visualization. LCM technology can harvest the cells of interest directly or can isolate specific cells by cutting away unwanted cells to give histologically pure enriched cell populations. A variety of downstream applications exist: DNA genotyping and loss-of-heterozygosity (LOH) analysis, RNA transcript profiling, cDNA library generation, proteomics discovery and signal-pathway profiling. Herein we provide a thorough description of LCM techniques, with an emphasis on tips and troubleshooting advice derived from LCM users. The total time required to carry out this protocol is typically 1-1.5 h.

/pdf/laser-capture-microdissection-l74g2ra2vu.pdf

Laser-capture microdissection

Neurodegenerative disorders such as Parkinson and Alzheimer disease cause motor and cognitive dysfunction and belong to a heterogeneous group of common and disabling disorders. Although the complex molecular pathophysiology of neurodegeneration is largely unknown, major advances have been achieved by elucidating the genetic defects underlying mendelian forms of these diseases. This has led to the discovery of common pathophysiological pathways such as enhanced oxidative stress, protein misfolding and aggregation and dysfunction of the ubiquitin-proteasome system. Here, we describe loss-of-function mutations in a previously uncharacterized, predominantly neuronal P-type ATPase gene, ATP13A2, underlying an autosomal recessive form of early-onset parkinsonism with pyramidal degeneration and dementia (PARK9, Kufor-Rakeb syndrome). Whereas the wild-type protein was located in the lysosome of transiently transfected cells, the unstable truncated mutants were retained in the endoplasmic reticulum and degraded by the proteasome. Our findings link a class of proteins with unknown function and substrate specificity to the protein networks implicated in neurodegeneration and parkinsonism.

/pdf/hereditary-parkinsonism-with-dementia-is-caused-by-mutations-2d5jvj31b6.pdf

Hereditary parkinsonism with dementia is caused by mutations in ATP13A2, encoding a lysosomal type 5 P-type ATPase.

While it is universally accepted that intact RNA constitutes the best representation of the steady-state of transcription, there is no gold standard to define RNA quality prior to gene expression analysis. In this report, we evaluated the reliability of conventional methods for RNA quality assessment including UV spectroscopy and 28S:18S area ratios, and demonstrated their inconsistency. We then used two new freely available classifiers, the Degradometer and RIN systems, to produce user-independent RNA quality metrics, based on analysis of microcapillary electrophoresis traces. Both provided highly informative and valuable data and the results were found highly correlated, while the RIN system gave more reliable data. The relevance of the RNA quality metrics for assessment of gene expression differences was tested by Q-PCR, revealing a significant decline of the relative expression of genes in RNA samples of disparate quality, while samples of similar, even poor integrity were found highly comparable. We discuss the consequences of these observations to minimize artifactual detection of false positive and negative differential expression due to RNA integrity differences, and propose a scheme for the development of a standard operational procedure, with optional registration of RNA integrity metrics in public repositories of gene expression data.

Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces

Background: The molecular mechanisms underlying innate tumor drug resistance, a major obstacle to successful cancer therapy, remain poorly understood. In colorectal cancer (CRC), molecular studies have focused on drug-selected tumor cell lines or individual candidate genes using samples derived from patients already treated with drugs, so that very little data are available prior to drug treatment. Results: Transcriptional profiles of clinical samples collected from CRC patients prior to their exposure to a combined chemotherapy of folinic acid, 5-fluorouracil and irinotecan were established using microarrays. Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established that intrinsically differentiates, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses. Conclusion: Molecular interaction networks are described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations.

/pdf/deciphering-cellular-states-of-innate-tumor-drug-responses-1y88wzbcp4.pdf

Deciphering cellular states of innate tumor drug responses.

Background 
The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes.


Principal Findings 
Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP−] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters.


Conclusions 
Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator.

/pdf/a-functional-and-regulatory-network-associated-with-pip-4te6vezjcm.pdf

Virginie Boulanger

Papers

Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces

Deciphering cellular states of innate tumor drug responses.

A Functional and Regulatory Network Associated with PIP Expression in Human Breast Cancer