Heterogeneous expression of ACE2 and TMPRRS2 in mesenchymal stromal cells.

The outbreak of COVID-19 has become a serious public health emergency. The virus targets cells by binding the ACE2 receptor. After infection, the virus triggers in some humans an immune storm containing the release of proinflammatory cytokines and chemokines followed by multiple organ failure. Several vaccines are enrolled, but an effective treatment is still missing. Mesenchymal stem cells (MSCs) have shown to secrete immunomodulatory factors that suppress this cytokine storm. Therefore, MSCs have been suggested as a potential treatment option for COVID-19. We report here that the ACE2 expression is minimal or nonexistent in MSC derived from three different human tissue sources (adipose tissue, umbilical cord Wharton`s jelly and bone marrow). In contrast, TMPRSS2 that is implicated in SARS-CoV-2 entry has been detected in all MSC samples. These results are of particular importance for future MSC-based cell therapies to treat severe cases after COVID-19 infection.

Gene expression in chronic granulomatous disease and interferon-γ receptor-deficient cells treated in vitro with interferon-γ.

Interferon-γ (IFN-γ) plays an important role in innate and adaptive immunity against intracellular infections and is used clinically for the prevention and control of infections in chronic granulomatous disease (CGD) and inborn defects in the IFN-γ/interleukin (IL)-12 axis. Using transcriptome profiling (RNA-seq), we sought to identify differentially expressed genes, transcripts and exons in Epstein-Barr virus-transformed B lymphocytes (B-EBV) cells from CGD patients, IFN-γ receptor deficiency patients, and normal controls, treated in vitro with IFN-γ for 48 hours. Our results show that IFN-γ increased the expression of a diverse array of genes related to different cellular programs. In cells from normal controls and CGD patients, IFN-γ-induced expression of genes relevant to oxidative killing, nitric oxide synthase pathway, proteasome-mediated degradation, antigen presentation, chemoattraction, and cell adhesion. IFN-γ also upregulated genes involved in diverse stages of messenger RNA (mRNA) processing including pre-mRNA splicing, as well as others implicated in the folding, transport, and assembly of proteins. In particular, differential exon expression of WARS (encoding tryptophanyl-transfer RNA synthetase, which has an essential function in protein synthesis) induced by IFN-γ in normal and CGD cells suggests that this gene may have an important contribution to the benefits of IFN-γ treatment for CGD. Upregulation of mRNA and protein processing related genes in CGD and IFNRD cells could mediate some of the effects of IFN-γ treatment. These data support the concept that IFN-γ treatment may contribute to increased immune responses against pathogens through regulation of genes important for mRNA and protein processing.

SparkSeq: fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision.

UNLABELLED: Many time-consuming analyses of next -: generation sequencing data can be addressed with modern cloud computing. The Apache Hadoop-based solutions have become popular in genomics BECAUSE OF: their scalability in a cloud infrastructure. So far, most of these tools have been used for batch data processing rather than interactive data querying. The SparkSeq software has been created to take advantage of a new MapReduce framework, Apache Spark, for next-generation sequencing data. SparkSeq is a general-purpose, flexible and easily extendable library for genomic cloud computing. It can be used to build genomic analysis pipelines in Scala and run them in an interactive way. SparkSeq opens up the possibility of customized ad hoc secondary analyses and iterative machine learning algorithms. This article demonstrates its scalability and overall fast performance by running the analyses of sequencing datasets. Tests of SparkSeq also prove that the use of cache and HDFS block size can be tuned for the optimal performance on multiple worker nodes. AVAILABILITY AND IMPLEMENTATION: Available under open source Apache 2.0 license: https://bitbucket.org/mwiewiorka/sparkseq/.

Transcriptional analysis of South African cassava mosaic virus-infected susceptible and tolerant landraces of cassava highlights differences in resistance, basal defense and cell wall associated genes during infection.

BACKGROUND: Cassava mosaic disease is caused by several distinct geminivirus species, including South African cassava mosaic virus-[South Africa:99] (SACMV). To date, there is limited gene regulation information on viral stress responses in cassava, and global transcriptome profiling in SACMV-infected cassava represents an important step towards understanding natural host responses to plant geminiviruses. RESULTS: A RNA-seq time course (12, 32 and 67 dpi) study, monitoring gene expression in SACMV-challenged susceptible (T200) and tolerant (TME3) cassava landraces, was performed using the Applied Biosystems (ABI) SOLiD next-generation sequencing platform. The multiplexed paired end sequencing run produced a total of 523 MB and 693 MB of paired-end reads for SACMV-infected susceptible and tolerant cDNA libraries, respectively. Of these, approximately 50.7% of the T200 reads and 55.06% of TME3 reads mapped to the cassava reference genome available in phytozome. Using a log2 fold cut-off (p<0.05), comparative analysis between the six normalized cDNA libraries showed that 4181 and 1008 transcripts in total were differentially expressed in T200 and TME3, respectively, across 12, 32 and 67 days post infection, compared to mock-inoculated. The number of responsive transcripts increased dramatically from 12 to 32 dpi in both cultivars, but in contrast, in T200 the levels did not change significantly at 67 dpi, while in TME3 they declined. GOslim functional groups illustrated that differentially expressed genes in T200 and TME3 were overrepresented in the cellular component category for stress-related genes, plasma membrane and nucleus. Alterations in the expression of other interesting genes such as transcription factors, resistance (R) genes, and histone/DNA methylation-associated genes, were observed. KEGG pathway analysis uncovered important altered metabolic pathways, including phenylpropanoid biosynthesis, sucrose and starch metabolism, and plant hormone signalling. CONCLUSIONS: Molecular mechanisms for TME3 tolerance are proposed, and differences in patterns and levels of transcriptome profiling between T200 and TME3 with susceptible and tolerant phenotypes, respectively, support the hypothesis that viruses rearrange their molecular interactions in adapting to hosts with different genetic backgrounds.

A comprehensive look at transcription factor gene expression changes in colorectal adenomas.

BACKGROUND: Biological processes are controlled by transcription networks. Expression changes of transcription factor (TF) genes in precancerous lesions are therefore crucial events in tumorigenesis. Our aim was to obtain a comprehensive picture of these changes in colorectal adenomas. METHODS: Using a 3-pronged selection procedure, we analyzed transcriptomic data on 34 human tissue samples (17 adenomas and paired samples of normal mucosa, all collected with ethics committee approval and written, informed patient consent) to identify TFs with highly significant tumor-associated gene expression changes whose potential roles in colorectal tumorigenesis have been under-researched. Microarray data were subjected to stringent statistical analysis of TF expression in tumor vs. normal tissues, MetaCore-mediated identification of TF networks displaying enrichment for genes that were differentially expressed in tumors, and a novel quantitative analysis of the publications examining the TF genes' roles in colorectal tumorigenesis. RESULTS: The 261 TF genes identified with this procedure included DACH1, which plays essential roles in the proper proliferation and differentiation of retinal and leg precursor cell populations in Drosophila melanogaster. Its possible roles in colorectal tumorigenesis are completely unknown, but it was found to be markedly overexpressed (mRNA and protein) in all colorectal adenomas and in most colorectal carcinomas. However, DACH1 expression was absent in some carcinomas, most of which were DNA mismatch-repair deficient. When networks were built using the set of TF genes identified by all three selection procedures, as well as the entire set of transcriptomic changes in adenomas, five hub genes (TGFB1, BIRC5, MYB, NR3C1, and TERT) where identified as putatively crucial components of the adenomatous transformation process. CONCLUSION: The transcription-regulating network of colorectal adenomas (compared with that of normal colorectal mucosa) is characterized by significantly altered expression of over 250 TF genes, many of which have never been investigated in relation to colorectal tumorigenesis.

Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage.

The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers.

Leonhard Med, a trusted research environment for processing sensitive research data.

This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a "bare-metal" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.

rnaSeqMap: a Bioconductor package for RNA sequencing data exploration.

BACKGROUND: The throughput of commercially available sequencers has recently significantly increased. It has reached the point where measuring the RNA expression by the depth of coverage has become feasible even for largest genomes. The development of software tools is constantly following the progress of biological hardware. In particular, as RNA sequencing software can be regarded genome browsers, exon junction tools and statistical tools operating on counts of reads in predefined regions. The library rnaSeqMap, freely available via Bioconductor, is an RNA sequencing software which is independent of any biological hardware platform. It is based upon standard Bioconductor infrastructure for sequencing data and includes several novel features focused on deeper understanding of coverage expression profiles and discovery of novel transcription regions. RESULTS: rnaSeqMap is a toolbox for analyses that may be performed with the use of gene annotations or alternatively, in an unsupervised mode, on any genomic region to find novel or non-standard transcripts. The data back-end may be a MySQL database or a set of files in standard BAM format. The processing in R can be run on a machine without any particular hardware requirements, and scales linearly with the number of genomic loci and number of samples analyzed. The main features of rnaSeqMap include coverage operations, discovering irreducible regions of high expression, significance search and splicing analyses with nucleotide granularity. CONCLUSIONS: This software may be used for a range of applications related to RNA sequencing by building customized analysis pipelines. The applicability and precision is expected to increase in parallel with the progress of the genome coverage in sequencers.

SwissPKcdw - A clinical data warehouse for the optimization of pediatric dosing regimens.

Clinical trials have been performed mainly in adults and accordingly the necessary information is lacking for pediatric patients, especially regarding dosage recommendation for approved drugs. This gap in information could be filled with results from pharmacokinetic (PK) modeling, based on data collected in daily clinical routine. In order to make this data accessible and usable for research, the Swiss Pharmacokinetics Clinical Data Warehouse (SwissPKcdw ) project has been set up, including a clinical data warehouse (CDW) and the regulatory framework for data transfer and use within. Embedded into the secure BioMedIT network, the CDW can connect to various data providers and researchers in order to collaborate on the data securely. Due to its modularity, partially containerized deployment and open-source software, each of the components can be extended, modified, and re-used for similar projects that require integrated data management, data analysis, and web tools in a secure scientific data and information technology (IT) environment. Here, we describe a collaborative and interprofessional effort to implement the aforementioned infrastructure between several partners from medical health care and academia. Furthermore, we describe a real-world use case where blood samples from pediatric patients were analyzed for the presence of genetic polymorphisms and the results were aggregated and further analyzed together with the health-related patient data in the SwissPKcdw .

Eight-channel iTRAQ enables comparison of the activity of six leukemogenic tyrosine kinases.

There are a number of leukemogenic protein-tyrosine kinases (PTKs) associated with leukemic transformation. Although each is linked with a specific disease their functional activity poses the question whether they have a degree of commonality in their effects upon target cells. Exon array analysis of the effects of six leukemogenic PTKs (BCR/ABL, TEL/PDGFRbeta, FIP1/PDGFRalpha, D816V KIT, NPM/ALK, and FLT3ITD) revealed few common effects on the transcriptome. It is apparent, however, that proteome changes are not directly governed by transcriptome changes. Therefore, we assessed and used a new generation of iTRAQ tagging, enabling eight-channel relative quantification discovery proteomics, to analyze the effects of these six leukemogenic PTKs. Again these were found to have disparate effects on the proteome with few common targets. BCR/ABL had the greatest effect on the proteome and had more effects in common with FIP1/PDGFRalpha. The proteomic effects of the four type III receptor kinases were relatively remotely related. The only protein commonly affected was eosinophil-associated ribonuclease 7. Five of six PTKs affected the motility-related proteins CAPG and vimentin, although this did not correspond to changes in motility. However, correlation of the proteomics data with that from the exon microarray not only showed poor levels of correlation between transcript and protein levels but also revealed alternative patterns of regulation of the CAPG protein by different oncogenes, illustrating the utility of such a combined approach.

X:Map: annotation and visualization of genome structure for Affymetrix exon array analysis.

Affymetrix exon arrays aim to target every known and predicted exon in the human, mouse or rat genomes, and have reporters that extend beyond protein coding regions to other areas of the transcribed genome. This combination of increased coverage and precision is important because a substantial proportion of protein coding genes are predicted to be alternatively spliced, and because many non-coding genes are known also to be of biological significance. In order to fully exploit these arrays, it is necessary to associate each reporter on the array with the features of the genome it is targeting, and to relate these to gene and genome structure. X:Map is a genome annotation database that provides this information. Data can be browsed using a novel Google-maps based interface, and analysed and further visualized through an associated BioConductor package. The database can be found at http://xmap.picr.man.ac.uk.

Temporal activation of WNT/ß-catenin signaling is sufficient to inhibit SOX10 expression and block melanoma growth.

Despite advances in the systemic treatment of patients with metastatic melanoma using immune checkpoint and tyrosine kinase inhibitors (TKI), the majority of stage IV melanoma patients eventually succumb to the disease. We have previously identified the transcription factor Sox10 as a crucial player in melanoma, yet the underlying molecular mechanisms mediating Sox10-dependent tumorigenesis remain largely uncharacterized. Here, we show that MEK and RAF inhibitors do not suppress levels of SOX10 protein in patient-derived cells in vitro, as well as in melanoma patients in vivo. In a search for pharmacological inhibitors of SOX10, we performed a mass spectrometry-based screen in human melanoma cells. Subsequent analysis revealed that SOX10 directly interacts with ß-catenin, which is a key mediator of canonical Wnt/ß-catenin signaling. We demonstrate that inhibitors of glycogen synthase kinase 3 alpha/beta (GSK3α/ß) efficiently abrogate SOX10 protein in human melanoma cells in vitro and in melanoma mouse models in vivo. The mechanism of action of GSK3-mediated SOX10 suppression is transcription-independent and relies on the presence of a proteasome degradable form of ß-catenin. Taken together, we provide evidence that activation of canonical Wnt signaling has a profound effect on melanoma growth and is able to counteract Sox10-dependent melanoma maintenance both in vitro and in vivo.

Exon level integration of proteomics and microarray data.

BACKGROUND: Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. RESULTS: Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. CONCLUSION: We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.

Genes sharing the protein family domain decrease the performance of classification with RNA-seq genomic signatures.

BACKGROUND: The experience with running various types of classification on the CAMDA neuroblastoma dataset have led us to the conclusion that the results are not always obvious and may differ depending on type of analysis and selection of genes used for classification. This paper aims in pointing out several factors that may influence the downstream machine learning analysis. In particular those factors are: type of the primary analysis, type of the classifier and increased correlation between the genes sharing a protein domain. They influence the analysis directly, but also interplay between them may be important. We have compiled the gene-domain database and used it for analysis to see the differences between the genes that share a domain versus the rest of the genes in the datasets. RESULTS: The major findings are: pairs of genes that share a domain have an increased Spearman's correlation coefficients of counts; genes sharing a domain are expected to have a lower predictive power due to increased correlation. For most of the cases it can be seen with the higher number of misclassified samples; classifiers performance may vary depending on a method, still in most cases using genes sharing a domain in the training set results in a higher misclassification rate; increased correlation in genes sharing a domain results most often in worse performance of the classifiers regardless of the primary analysis tools used, even if the primary analysis alignment yield varies. CONCLUSIONS: The effect of sharing a domain is likely more a results of real biological co-expression than just sequence similarity and artifacts of mapping and counting. Still, this is more difficult to conclude and needs further research. The effect is interesting itself, but we also point out some practical aspects in which it may influence the RNA sequencing analysis and RNA biomarker use. In particular it means that a gene signature biomarker set build out of RNA-sequencing results should be depleted for genes sharing common domains. It may cause to perform better when applying classification. REVIEWERS: This article was reviewed by Dimitar Vassiliev and Susmita Datta.

Molecular insights into Cassava brown streak virus susceptibility and resistance by profiling of the early host response.

Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) are responsible for significant cassava yield losses in eastern sub-Saharan Africa. To study the possible mechanisms of plant resistance to CBSVs, we inoculated CBSV-susceptible and CBSV-resistant cassava varieties with a mixed infection of CBSVs using top-cleft grafting. Transcriptome profiling of the two cassava varieties was performed at the earliest time point of full infection (28 days after grafting) in the susceptible scions. The expression of genes encoding proteins in RNA silencing, salicylic acid pathways and callose deposition was altered in the susceptible cassava variety, but transcriptional changes were limited in the resistant variety. In total, the expression of 585 genes was altered in the resistant variety and 1292 in the susceptible variety. Transcriptional changes led to the activation of ß-1,3-glucanase enzymatic activity and a reduction in callose deposition in the susceptible cassava variety. Time course analysis also showed that CBSV replication in susceptible cassava induced a strong up-regulation of RDR1, a gene previously reported to be a susceptibility factor in other potyvirus-host pathosystems. The differences in the transcriptional responses to CBSV infection indicated that susceptibility involves the restriction of callose deposition at plasmodesmata. Aniline blue staining of callose deposits also indicated that the resistant variety displays a moderate, but significant, increase in callose deposition at the plasmodesmata. Transcriptome data suggested that resistance does not involve typical antiviral defence responses (i.e. RNA silencing and salicylic acid). A meta-analysis of the current RNA-sequencing (RNA-seq) dataset and selected potyvirus-host and virus-cassava RNA-seq datasets revealed that the conservation of the host response across pathosystems is restricted to genes involved in developmental processes.

High correspondence between Affymetrix exon and standard expression arrays.

Exon arrays aim to provide comprehensive gene expression data at the level of individual exons, similar to that provided on a per-gene basis by existing expression arrays. This report describes the performance of Affymetrix GeneChip Human Exon 1.0 ST array by using replicated RNA samples from two human cell lines, MCF7 and MCF10A, hybridized both to Exon 1.0 ST and to HG-U133 Plus2 arrays. Cross-comparison between array types requires an appropriate mapping to be found between individual probe sets. Three possible mappings were considered, reflecting different strategies for dealing with probe sets that target different parts of the same transcript. Irrespective of the mapping used, Exon 1.0 ST and HG-U133 Plus2 arrays show a high degree of correspondence. More than 80% of HG-U133 Plus2 probe sets may be mapped to the Exon chip, and fold changes are found well preserved for over 96% of those probe sets detected present. Since HG-U133 Plus2 arrays have already been extensively validated, these results lend a significant degree of confidence to exon arrays.

Benchmarking distributed data warehouse solutions for storing genomic variant information.

Database URL: https://github.com/ZSI-Bio/variantsdwh.

Pramel7 mediates ground-state pluripotency through proteasomal-epigenetic combined pathways.

Naive pluripotency is established in preimplantation epiblast. Embryonic stem cells (ESCs) represent the immortalization of naive pluripotency. 2i culture has optimized this state, leading to a gene signature and DNA hypomethylation closely comparable to preimplantation epiblast, the developmental ground state. Here we show that Pramel7 (PRAME-like 7), a protein highly expressed in the inner cell mass (ICM) but expressed at low levels in ESCs, targets for proteasomal degradation UHRF1, a key factor for DNA methylation maintenance. Increasing Pramel7 expression in serum-cultured ESCs promotes a preimplantation epiblast-like gene signature, reduces UHRF1 levels and causes global DNA hypomethylation. Pramel7 is required for blastocyst formation and its forced expression locks ESCs in pluripotency. Pramel7/UHRF1 expression is mutually exclusive in ICMs whereas Pramel7-knockout embryos express high levels of UHRF1. Our data reveal an as-yet-unappreciated dynamic nature of DNA methylation through proteasome pathways and offer insights that might help to improve ESC culture to reproduce in vitro the in vivo ground-state pluripotency.

Corrigendum: Pramel7 mediates ground-state pluripotency through proteasomal-epigenetic combined pathways.

This corrects the article DOI: 10.1038/ncb3554.

Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations.

BACKGROUND: Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels. RESULTS: An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur. CONCLUSION: Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data.