Björn Rotter

SuperSAGE evidence for CD14++CD16+ monocytes as a third monocyte subset

Monocytes are a heterogeneous cell population with subset-specific functions and phenotypes. The differential expression of CD14 and CD16 distinguishes classical CD14(++)CD16(-), intermediate CD14(++)CD16(+), and nonclassical CD14(+)CD16(++) monocytes. Current knowledge on human monocyte heterogeneity is still incomplete: while it is increasingly acknowledged that CD14(++)CD16(+) monocytes are of outstanding significance in 2 global health issues, namely HIV-1 infection and atherosclerosis, CD14(++)CD16(+) monocytes remain the most poorly characterized subset so far. We therefore developed a method to purify the 3 monocyte subsets from human blood and analyzed their transcriptomes using SuperSAGE in combination with high-throughput sequencing. Analysis of 5 487 603 tags revealed unique identifiers of CD14(++)CD16(+) monocytes, delineating these cells from the 2 other monocyte subsets. Gene Ontology (GO) enrichment analysis suggests diverse immunologic functions, linking CD14(++)CD16(+) monocytes to Ag processing and presentation (eg, CD74, HLA-DR, IFI30, CTSB), to inflammation and monocyte activation (eg, TGFB1, AIF1, PTPN6), and to angiogenesis (eg, TIE2, CD105). In conclusion, we provide genetic evidence for a distinct role of CD14(++)CD16(+) monocytes in human immunity. After CD14(++)CD16(+) monocytes have earlier been discussed as a potential therapeutic target in inflammatory diseases, we are hopeful that our data will spur further research in the field of monocyte heterogeneity.

SuperSAGE: the drought stress-responsive transcriptome of chickpea roots

BackgroundDrought is the major constraint to increase yield in chickpea (Cicer arietinum). Improving drought tolerance is therefore of outmost importance for breeding. However, the complexity of the trait allowed only marginal progress. A solution to the current stagnation is expected from innovative molecular tools such as transcriptome analyses providing insight into stress-related gene activity, which combined with molecular markers and expression (e)QTL mapping, may accelerate knowledge-based breeding. SuperSAGE, an improved version of the serial analysis of gene expression (SAGE) technique, generating genome-wide, high-quality transcription profiles from any eukaryote, has been employed in the present study. The method produces 26 bp long fragments (26 bp tags) from defined positions in cDNAs, providing sufficient sequence information to unambiguously characterize the mRNAs. Further, SuperSAGE tags may be immediately used to produce microarrays and probes for real-time-PCR, thereby overcoming the lack of genomic tools in non-model organisms.ResultsWe applied SuperSAGE to the analysis of gene expression in chickpea roots in response to drought. To this end, we sequenced 80,238 26 bp tags representing 17,493 unique transcripts (UniTags) from drought-stressed and non-stressed control roots. A total of 7,532 (43%) UniTags were more than 2.7-fold differentially expressed, and 880 (5.0%) were regulated more than 8-fold upon stress. Their large size enabled the unambiguous annotation of 3,858 (22%) UniTags to genes or proteins in public data bases and thus to stress-response processes. We designed a microarray carrying 3,000 of these 26 bp tags. The chip data confirmed 79% of the tag-based results, whereas RT-PCR confirmed the SuperSAGE data in all cases.ConclusionThis study represents the most comprehensive analysis of the drought-response transcriptome of chickpea available to date. It demonstrates that – inter alias – signal transduction, transcription regulation, osmolyte accumulation, and ROS scavenging undergo strong transcriptional remodelling in chickpea roots already 6 h after drought stress. Certain transcript isoforms characterizing these processes are potential targets for breeding for drought tolerance. We demonstrate that these can be easily accessed by micro-arrays and RT-PCR assays readily produced downstream of SuperSAGE. Our study proves that SuperSAGE owns potential for molecular breeding also in non-model crops.

The salt-responsive transcriptome of chickpea roots and nodules via deepSuperSAGE

BackgroundThe combination of high-throughput transcript profiling and next-generation sequencing technologies is a prerequisite for genome-wide comprehensive transcriptome analysis. Our recent innovation of deepSuperSAGE is based on an advanced SuperSAGE protocol and its combination with massively parallel pyrosequencing on Roches 454 sequencing platform. As a demonstration of the power of this combination, we have chosen the salt stress transcriptomes of roots and nodules of the third most important legume crop chickpea (Cicer arietinum L.). While our report is more technology-oriented, it nevertheless addresses a major world-wide problem for crops generally: high salinity. Together with low temperatures and water stress, high salinity is responsible for crop losses of millions of tons of various legume (and other) crops. Continuously deteriorating environmental conditions will combine with salinity stress to further compromise crop yields. As a good example for such stress-exposed crop plants, we started to characterize salt stress responses of chickpeas on the transcriptome level.ResultsWe used deepSuperSAGE to detect early global transcriptome changes in salt-stressed chickpea. The salt stress responses of 86,919 transcripts representing 17,918 unique 26 bp deepSuperSAGE tags (UniTags) from roots of the salt-tolerant variety INRAT-93 two hours after treatment with 25 mM NaCl were characterized. Additionally, the expression of 57,281 transcripts representing 13,115 UniTags was monitored in nodules of the same plants. From a total of 144,200 analyzed 26 bp tags in roots and nodules together, 21,401 unique transcripts were identified. Of these, only 363 and 106 specific transcripts, respectively, were commonly up- or down-regulated (>3.0-fold) under salt stress in both organs, witnessing a differential organ-specific response to stress.Profiting from recent pioneer works on massive cDNA sequencing in chickpea, more than 9,400 UniTags were able to be linked to UniProt entries. Additionally, gene ontology (GO) categories over-representation analysis enabled to filter out enriched biological processes among the differentially expressed UniTags. Subsequently, the gathered information was further cross-checked with stress-related pathways.From several filtered pathways, here we focus exemplarily on transcripts associated with the generation and scavenging of reactive oxygen species (ROS), as well as on transcripts involved in Na+ homeostasis. Although both processes are already very well characterized in other plants, the information generated in the present work is of high value. Information on expression profiles and sequence similarity for several hundreds of transcripts of potential interest is now available.ConclusionsThis report demonstrates, that the combination of the high-throughput transcriptome profiling technology SuperSAGE with one of the next-generation sequencing platforms allows deep insights into the first molecular reactions of a plant exposed to salinity. Cross validation with recent reports enriched the information about the salt stress dynamics of more than 9,000 chickpea ESTs, and enlarged their pool of alternative transcripts isoforms.As an example for the high resolution of the employed technology that we coin deepSuperSAGE, we demonstrate that ROS-scavenging and -generating pathways undergo strong global transcriptome changes in chickpea roots and nodules already 2 hours after onset of moderate salt stress (25 mM NaCl). Additionally, a set of more than 15 candidate transcripts are proposed to be potential components of the salt overly sensitive (SOS) pathway in chickpea.Newly identified transcript isoforms are potential targets for breeding novel cultivars with high salinity tolerance. We demonstrate that these targets can be integrated into breeding schemes by micro-arrays and RT-PCR assays downstream of the generation of 26 bp tags by SuperSAGE.

Next-generation sequencing reveals novel differentially regulated mRNAs, lncRNAs, miRNAs, sdRNAs and a piRNA in pancreatic cancer

BackgroundPrevious studies identified microRNAs (miRNAs) and messenger RNAs with significantly different expression between normal pancreas and pancreatic cancer (PDAC) tissues. Due to technological limitations of microarrays and real-time PCR systems these studies focused on a fixed set of targets. Expression of other RNA classes such as long intergenic non-coding RNAs or sno-derived RNAs has rarely been examined in pancreatic cancer. Here, we analysed the coding and non-coding transcriptome of six PDAC and five control tissues using next-generation sequencing.ResultsBesides the confirmation of several deregulated mRNAs and miRNAs, miRNAs without previous implication in PDAC were detected: miR-802, miR-2114 or miR-561. SnoRNA-derived RNAs (e.g. sno-HBII-296B) and piR-017061, a piwi-interacting RNA, were found to be differentially expressed between PDAC and control tissues. In silico target analysis of miR-802 revealed potential binding sites in the 3′ UTR of TCF4, encoding a transcription factor that controls Wnt signalling genes. Overexpression of miR-802 in MiaPaCa pancreatic cancer cells reduced TCF4 protein levels. Using Massive Analysis of cDNA Ends (MACE) we identified differential expression of 43 lincRNAs, long intergenic non-coding RNAs, e.g. LINC00261 and LINC00152 as well as several natural antisense transcripts like HNF1A-AS1 and AFAP1-AS1. Differential expression was confirmed by qPCR on the mRNA/miRNA/lincRNA level and by immunohistochemistry on the protein level.ConclusionsHere, we report a novel lncRNA, sncRNA and mRNA signature of PDAC. In silico prediction of ncRNA targets allowed for assigning potential functions to differentially regulated RNAs.

omiRas: A Web server for differential expression analysis of miRNAs derived from small RNA-Seq data

SUMMARY Small RNA deep sequencing is widely used to characterize non-coding RNAs (ncRNAs) differentially expressed between two conditions, e.g. healthy and diseased individuals and to reveal insights into molecular mechanisms underlying condition-specific phenotypic traits. The ncRNAome is composed of a multitude of RNAs, such as transfer RNA, small nucleolar RNA and microRNA (miRNA), to name few. Here we present omiRas, a Web server for the annotation, comparison and visualization of interaction networks of ncRNAs derived from next-generation sequencing experiments of two different conditions. The Web tool allows the user to submit raw sequencing data and results are presented as: (i) static annotation results including length distribution, mapping statistics, alignments and quantification tables for each library as well as lists of differentially expressed ncRNAs between conditions and (ii) an interactive network visualization of user-selected miRNAs and their target genes based on the combination of several miRNA-mRNA interaction databases. AVAILABILITY AND IMPLEMENTATION The omiRas Web server is implemented in Python, PostgreSQL, R and can be accessed at: http://tools.genxpro.net/omiras/.

Massive analysis of cDNA Ends (MACE) and miRNA expression profiling identifies proatherogenic pathways in chronic kidney disease

Epigenetic dysregulation contributes to the high cardiovascular disease burden in chronic kidney disease (CKD) patients. Although microRNAs (miRNAs) are central epigenetic regulators, which substantially affect the development and progression of cardiovascular disease (CVD), no data on miRNA dysregulation in CKD-associated CVD are available until now. We now performed high-throughput miRNA sequencing of peripheral blood mononuclear cells from ten clinically stable hemodialysis (HD) patients and ten healthy controls, which allowed us to identify 182 differentially expressed miRNAs (e.g., miR-21, miR-26b, miR-146b, miR-155). To test biological relevance, we aimed to connect miRNA dysregulation to differential gene expression. Genome-wide gene expression profiling by MACE (Massive Analysis of cDNA Ends) identified 80 genes to be differentially expressed between HD patients and controls, which could be linked to cardiovascular disease (e.g., KLF6, DUSP6, KLF4), to infection / immune disease (e.g., ZFP36, SOCS3, JUND), and to distinct proatherogenic pathways such as the Toll-like receptor signaling pathway (e.g., IL1B, MYD88, TICAM2), the MAPK signaling pathway (e.g., DUSP1, FOS, HSPA1A), and the chemokine signaling pathway (e.g., RHOA, PAK1, CXCL5). Formal interaction network analysis proved biological relevance of miRNA dysregulation, as 68 differentially expressed miRNAs could be connected to 47 reciprocally expressed target genes. Our study is the first comprehensive miRNA analysis in CKD that links dysregulated miRNA expression with differential expression of genes connected to inflammation and CVD. After recent animal data suggested that targeting miRNAs is beneficial in experimental CVD, our data may now spur further research in the field of CKD-associated human CVD.

SuperTAG Methylation-specific Digital Karyotyping Reveals Uremia-induced Epigenetic Dysregulation of Atherosclerosis-Related Genes

Background—Accelerated atherosclerosis is a hallmark of chronic kidney disease (CKD). Although the role of epigenetic dysregulation in atherosclerosis is increasingly appreciated, only a few studies focused on epigenetics in CKD-associated cardiovascular disease, virtually all of which assessed epigenetic dysregulation globally. We hypothesized that gene-specific epigenetic dysregulation in CKD exists, affecting genes pertinent to inflammation and atherosclerosis. Methods and Results—Ten clinically stable patients undergoing hemodialysis therapy and 10 healthy age- and sex-matched controls were recruited. Genome-wide analysis of DNA methylation was performed by SuperTAG methylation-specific digital karyotyping, in order to identify genes differentially methylated in CKD. Analysis of 27 043 436 tags revealed 4288 genomic loci with differential DNA methylation (P<10–10) between hemodialysis patients and control subjects. Annotation of UniTags to promoter databases allowed us to identify 52 candidate genes associated with cardiovascular disease and 97 candidate genes associated with immune/infection diseases. These candidate genes could be classified to distinct proatherogenic processes, including lipid metabolism and transport (eg, HMGCR, SREBF1, LRP5, EPHX2, and FDPS), cell proliferation and cell-cycle regulation (eg, MIK67, TP53, and ALOX12), angiogenesis (eg, ANGPT2, ADAMTS10, and FLT4), and inflammation (eg, TNFSF10, LY96, IFNGR1, HSPA1A, and IL12RB1). Conclusions—We provide a comprehensive analysis of genome-wide epigenetic alterations in CKD, identifying candidate genes associated with proatherogenic and inflammatory processes. These results may spur further research in the field of epigenetics in kidney disease and point to new therapeutic strategies in CKD-associated atherosclerotic disease.

APADB: a database for alternative polyadenylation and microRNA regulation events

Alternative polyadenylation (APA) is a widespread mechanism that contributes to the sophisticated dynamics of gene regulation. Approximately 50% of all protein-coding human genes harbor multiple polyadenylation (PA) sites; their selective and combinatorial use gives rise to transcript variants with differing length of their 3′ untranslated region (3′UTR). Shortened variants escape UTR-mediated regulation by microRNAs (miRNAs), especially in cancer, where global 3′UTR shortening accelerates disease progression, dedifferentiation and proliferation. Here we present APADB, a database of vertebrate PA sites determined by 3′ end sequencing, using massive analysis of complementary DNA ends. APADB provides (A)PA sites for coding and non-coding transcripts of human, mouse and chicken genes. For human and mouse, several tissue types, including different cancer specimens, are available. APADB records the loss of predicted miRNA binding sites and visualizes next-generation sequencing reads that support each PA site in a genome browser. The database tables can either be browsed according to organism and tissue or alternatively searched for a gene of interest. APADB is the largest database of APA in human, chicken and mouse. The stored information provides experimental evidence for thousands of PA sites and APA events. APADB combines 3′ end sequencing data with prediction algorithms of miRNA binding sites, allowing to further improve prediction algorithms. Current databases lack correct information about 3′UTR lengths, especially for chicken, and APADB provides necessary information to close this gap. Database URL: http://tools.genxpro.net/apadb/

SuperTAG Methylation-Specific Digital Karyotyping (SMSDK) Reveals Uremia Induced Epigenetic Dysregulation of Atherosclerosis-Related Genes

Background—Accelerated atherosclerosis is a hallmark of chronic kidney disease (CKD). Although the role of epigenetic dysregulation in atherosclerosis is increasingly appreciated, only a few studies focused on epigenetics in CKD-associated cardiovascular disease, virtually all of which assessed epigenetic dysregulation globally. We hypothesized that gene-specific epigenetic dysregulation in CKD exists, affecting genes pertinent to inflammation and atherosclerosis. Methods and Results—Ten clinically stable patients undergoing hemodialysis therapy and 10 healthy age- and sex-matched controls were recruited. Genome-wide analysis of DNA methylation was performed by SuperTAG methylation-specific digital karyotyping, in order to identify genes differentially methylated in CKD. Analysis of 27 043 436 tags revealed 4288 genomic loci with differential DNA methylation (P<10–10) between hemodialysis patients and control subjects. Annotation of UniTags to promoter databases allowed us to identify 52 candidate genes associated with cardiovascular disease and 97 candidate genes associated with immune/infection diseases. These candidate genes could be classified to distinct proatherogenic processes, including lipid metabolism and transport (eg, HMGCR, SREBF1, LRP5, EPHX2, and FDPS), cell proliferation and cell-cycle regulation (eg, MIK67, TP53, and ALOX12), angiogenesis (eg, ANGPT2, ADAMTS10, and FLT4), and inflammation (eg, TNFSF10, LY96, IFNGR1, HSPA1A, and IL12RB1). Conclusions—We provide a comprehensive analysis of genome-wide epigenetic alterations in CKD, identifying candidate genes associated with proatherogenic and inflammatory processes. These results may spur further research in the field of epigenetics in kidney disease and point to new therapeutic strategies in CKD-associated atherosclerotic disease.

slan-defined subsets of CD16-positive monocytes: impact of granulomatous inflammation and M-CSF receptor mutation

Human monocytes are subdivided into classical, intermediate, and nonclassical subsets, but there is no unequivocal strategy to dissect the latter 2 cell types. We show herein that the cell surface marker 6-sulfo LacNAc (slan) can define slan-positive CD14(+)CD16(++) nonclassical monocytes and slan-negative CD14(++)CD16(+) intermediate monocytes. Gene expression profiling confirms that slan-negative intermediate monocytes show highest expression levels of major histocompatibility complex class II genes, whereas a differential ubiquitin signature is a novel feature of the slan approach. In unsupervised hierarchical clustering, the slan-positive nonclassical monocytes cluster with monocytes and are clearly distinct from CD1c(+) dendritic cells. In clinical studies, we show a selective increase of the slan-negative intermediate monocytes to >100 cells per microliter in patients with sarcoidosis and a fivefold depletion of the slan-positive monocytes in patients with hereditary diffuse leukoencephalopathy with axonal spheroids (HDLS), which is caused by macrophage colony-stimulating factor (M-CSF) receptor mutations. These data demonstrate that the slan-based definition of CD16-positive monocyte subsets is informative in molecular studies and in clinical settings.