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limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

/pdf/limma-powers-differential-expression-analyses-for-rna-4yl7ae3arh.pdf

limma powers differential expression analyses for RNA-sequencing and microarray studies

When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/

https://journal.embnet.org/index.php/embnetjournal/article/download/200/458

Cutadapt removes adapter sequences from high-throughput sequencing reads

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.

/pdf/accurate-normalization-of-real-time-quantitative-rt-pcr-data-34rwzsvnu5.pdf

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Ongoing beta cell death in type 1 diabetes (T1D) can be detected using biomarkers selectively discharged by dying beta cells into plasma. microRNA-375 (miR-375) ranks among the top biomarkers based on studies in animal models and human islet transplantation. Our objective was to identify additional microRNAs that are co-released with miR-375 proportionate to the amount of beta cell destruction. RT-PCR profiling of 733 microRNAs in a discovery cohort of T1D patients 1 h before/after islet transplantation indicated increased plasma levels of 22 microRNAs. Sub-selection for beta cell selectivity resulted in 15 microRNAs that were subjected to double-blinded multicenter analysis. This led to the identification of eight microRNAs that were consistently increased during early graft destruction: besides miR-375, these included miR-132/204/410/200a/429/125b, microRNAs with known function and enrichment in beta cells. Their potential clinical translation was investigated in a third independent cohort of 46 transplant patients by correlating post-transplant microRNA levels to C-peptide levels 2 months later. Only miR-375 and miR-132 had prognostic potential for graft outcome, and none of the newly identified microRNAs outperformed miR-375 in multiple regression. In conclusion, this study reveals multiple beta cell-enriched microRNAs that are co-released with miR-375 and can be used as complementary biomarkers of beta cell death.

/pdf/the-microrna-landscape-of-acute-beta-cell-destruction-in-13pbkt55z8.pdf

The microRNA Landscape of Acute Beta Cell Destruction in Type 1 Diabetic Recipients of Intraportal Islet Grafts

A brief commentary on "Chromosomal aberrations in neuroblastoma cell lines identified by cross species color banding and chromosome painting".

Taking advantage of the high specificity of double-dye hydrolysis-locked nucleic acid (LNA) probes (Ugozzoli et al, 2004), we developed a qualitative three-colour real-time polymerase chain reaction (PCR) assay for the genotyping of sickle cell disease (SCD). The term SCD encompasses a group of disorders associated with mutations in the Haemoglobin Beta gene (HBB). The most common forms are defined by the presence, in codon 6, of an E6V mutation (GAG > GTG; Haemoglobin S; Hb S) on both alleles (sickle cell anaemia) and co-inheritance of Hb S and Hb C (E6K mutation; GAG > AAG; Hb SC disease). The highest prevalence of SCD is found in subSaharan Africa (sickle-cell gene carrier prevalence varying between 5% and 40% of the population), and most affected children die in the early years of life without being diagnosed or because of non-adapted treatments (Weatherall & Clegg, 2001). The methodological approach presented here combined the use of a primer pair designed to amplify the region of interest in the HBB gene and three double-dye probes containing LNA nucleotides. Each probe was labelled with a different fluorochrome to enable the different alleles to be discriminated (Fig 1). Primers were designed with the freeware meltcalc, ver. 2.0 (http://www.meltcalc.de). The Tm of each double-dye LNA probe was calculated by using the Exiqon Tm prediction tool (http://lna-tm.com). Samples from patients that were homozygous for each of the three aforementioned alleles were used to demonstrate the specificity of the probes (Fig 1). We used unique PCR conditions on the ABI 7500 Fast machine (Applied Biosystems, Foster City, CA, USA), which generated specific results for each probe in <40 min (see Table SI, Fig S1). A cohort of 37 genomic DNA representing the different possible genotypes was used to compare our new technique with either a PCR followed by restriction endonuclease cleavage to detect the A and S alleles (Saiki et al, 1988), or an allele-specific PCR designed to detect A and C alleles (Fischel-Ghodsian et al, 1990). A 100% concordance was observed between our technology and the two published methods. Moreover, the deduced genotypes matched perfectly with the available phenotypic analysis (Gulbis et al, 2006). To investigate the sensitivity of this analysis, we tested different amounts of genomic DNA per PCR reaction. The results were perfectly interpretable from 100 to 1 ng of starting material (data not shown), and enabled the testing of prenatal diagnosis samples even when there was scarce fetal tissue (demonstrated on 18 gDNA obtained from fetal samples; data not shown). Finally, to demonstrate the robustness of our approach, we tested 10 representative samples on currently available instru-

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2141.2006.06447.x

Rapid and easy prenatal diagnosis of sickle cell anemia using double-dye LNA probe technology.

Neuroblastoma is one of the most common and most deadly childhood tumors. There is an unmet need to develop new therapeutic modalities for this malignancy that preferentially should be guided by our increasing knowledge of the biology of neuroblastoma. Proliferation and survival of neuroblastoma cells is critically dependent on suppression of the activity of the tumor suppressor protein p53, which is often mediated by increased activity of the MDM2 oncoprotein. Accordingly, small-molecule inhibitors of the interaction between MDM2 and p53 may provide a useful therapeutic option for the treatment of neuroblastoma by restoring the potent antitumor activity of wild-type p53. One of the most promising classes of selective inhibitors of the p53–MDM2 interaction are the nutlins, which have been extensively studied over the last years in several tumor types, including neuroblastoma. We discuss here preclinical data that support the notion that nutlin drugs may offer therapeutic benefit for children with neuroblastoma, on condition that wild-type p53 is present.

https://biblio.ugent.be/publication/4228985/file/4228986.pdf

Selective inhibition of the p53–MDM2 interaction by nutlin drugs: a new therapeutic perspective for neuroblastoma

Biomedical researchers are moving towards high-throughput screening, as this allows for automatization, better reproducibility and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time consuming, and error-prone. Therefore, several tools have been developed to aid researchers in this data analysis, but they focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens together. To meet these needs, we developed HTSplotter, available as web tool and Python module, that performs automatic data analysis and visualisation of either endpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements in order to identify experiment type and controls. After appropriate data normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism by the Bliss independence method. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, convenience and time over existing tools.

https://www.biorxiv.org/content/biorxiv/early/2021/09/10/2021.09.06.459128.full.pdf

Jo Vandesompele

Papers

The microRNA Landscape of Acute Beta Cell Destruction in Type 1 Diabetic Recipients of Intraportal Islet Grafts

A brief commentary on "Chromosomal aberrations in neuroblastoma cell lines identified by cross species color banding and chromosome painting".

Rapid and easy prenatal diagnosis of sickle cell anemia using double-dye LNA probe technology.

Selective inhibition of the p53–MDM2 interaction by nutlin drugs: a new therapeutic perspective for neuroblastoma

HTSplotter: an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening