Productive herpesvirus infection requires a profound, time-controlled remodeling of the viral transcriptome and proteome. To gain insights into the genomic architecture and gene expression control in Kaposi's sarcoma-associated herpesvirus (KSHV), we performed a systematic genome-wide survey of viral transcriptional and translational activity throughout the lytic cycle. Using mRNA-sequencing and ribosome profiling, we found that transcripts encoding lytic genes are promptly bound by ribosomes upon lytic reactivation, suggesting their regulation is mainly transcriptional. Our approach also uncovered new genomic features such as ribosome occupancy of viral non-coding RNAs, numerous upstream and small open reading frames (ORFs), and unusual strategies to expand the virus coding repertoire that include alternative splicing, dynamic viral mRNA editing, and the use of alternative translation initiation codons. Furthermore, we provide a refined and expanded annotation of transcription start sites, polyadenylation sites, splice junctions, and initiation/termination codons of known and new viral features in the KSHV genomic space which we have termed KSHV 2.0. Our results represent a comprehensive genome-scale image of gene regulation during lytic KSHV infection that substantially expands our understanding of the genomic architecture and coding capacity of the virus. Kaposi's sarcoma-associated herpesvirus (KSHV) is a cancer-causing agent in immunocompromised patients that establishes long-lasting infections in its hosts. Initially described in 1994 and extensively studied ever since, KSHV molecular biology is understood in broad outline, but many detailed questions are still to be resolved. After almost two decades, specific aspects pertaining to the organization of the KSHV genome as well as the fate of the viral transcripts during the productive stages of infection remain unexplored. Here we use a systematic genome-wide approach to investigate changes in gene and protein expression during the productive stage of infection known as the lytic cycle. We found that the viral genome has a large coding capacity, capable of generating at least 45% more products than initially anticipated by bioinformatic analyses alone, and that it uses multiple strategies to expand its coding capacity well beyond what is determined solely by the DNA sequence of its genome. We also provide an expanded and highly detailed annotation of known and new genomic features in KSHV. We have termed this new architectural and functional annotation KSHV 2.0. Our results indicate that viral genomes are more complex than anticipated, and that they are subject to tight mechanisms of regulation to ensure correct gene expression. Many of these ORFs had functional homologues in herpesvirus saimiri (HVS), a gamma-herpesvirus related to KSHV Kaposi's sarcoma-associated herpesvirus (KSHV) is a member of the gamma-herpesvirus family and the etiologic agent of Kaposi's sarcoma, primary effusion lymphoma (PEL), and multicentric Castleman's disease In an attempt to extend our current knowledge of the coding capacity of the KSHV genome during the productive stage of infection, we employed an unbiased functional genomics approach to study the transcription and translation profiles of lytic KSHV using mRNA-sequencing (mRNA-Seq), ribosome footprinting (Ribo-Seq), and genomic DNA sequencing (DNA-Seq). When combined, these methods provide a comprehensive, high-resolution view of gene regulation and expression dynamics By employing these techniques in parallel, we have generated a state-of-the-art annotation of the KSHV genome. Our approach confirms the presence and timing of expression of the majority of previously annotated ORFs, while revealing several novel and, in some cases, unexpected genomic features including ribosome protection of non-coding RNAs, new splice variants, and a plethora of upstream and small ORFs. In addition, we have confirmed and expanded the annotation of transcription start sites, polyadenylation sites, and initiation/termination codons of multiple known ORFs. Our analyses have also uncovered new instances of viral mRNA editing, strongly hinting at a new layer of viral gene regulation during reactivation. The wealth of information generated by integrating the data obtained from our combined methods has expanded our understanding of the viral genome architecture and dynamics, revealing a surprising coding capacity of KSHV that goes well beyond what was initially described based on its genome sequence alone.replication and transcriptional activator). The exogenous expression of RTA by Dox treatment in iSLK-219 cells is sufficient to induce the lytic reactivation of latent KSHV. Notably, latency in iSLK-219 cells is very strict with less than 0.1% of the cells showing lytic markers in the absence of induction The life cycle of KSHV can be separated in two very distinct stages: the dormant state known as latency and the productive state referred to as the lytic cycle To finely resolve the transcriptional profile and the ribosome occupancy of viral mRNAs, we induced iSLK-219 cells with Dox for 0, 8, 24, 48 and 72 hr . We evalmRNA-Seq and Ribo-Seq allowed us to perform an unabridged temporal analysis of viral gene expression coupled to a blueprint of the viral episome architecture, granting the opportunity to develop a revised version of the KSHV genome annotation, which we have designated KSHV 2.0 , 2 and 3Inspection of KSHV 2.0 reveals three prominent features: (1) coding and non-coding elements are densely packed in the episome, (2) multiple strategies are used to increase its polypeptide repertoire, including splicing, mRNA editing, and alternative start codon use, and (3) sORFs and uORFs populate many regions of the viral genome. The specific transcriptional and translational features of KSHV 2.0 are discussed in detail below.The transcriptional capacity of the KSHV genome has been traditionally studied using northern blotting and gene expression profiling with oligonucleotide microarrays Taking advantage of the peaks visible at the 5′ ends of transcripts in mRNA profiles , which aLike cellular messages, KSHV transcripts are protected by a 5′ 7-methylguanosine cap and a 3′ poly-adenylate (polyA) tail Interestingly, sequence analysis of a 60-nucleotide window centered on the predicted cleavage site for the polyadenylation machinery confirmed the presence of the canonical AAUAAA motif in 83% of mRNAs (35 out of 42), the alternative AUUAAA motif in 17% (7 out of 42), and the accompanying downstream GU rich element in all of these transcripts . SimilarBesides the densely packed coding regions and regulatory features we annotated, our mRNA-Seq data also show the massive accumulation of sequence reads that map outside of previously annotated coding regions, thus indicating highly permissive transcription of most of the viral genome late in the lytic cycle . Two of polyadenylated nuclear RNA). PAN is the most abundant viral transcript during the lytic cycle and is required for viral gene expression and virion production et al. indicates that the RSS provides an indirect measure of translation that allows the reliable differentiation between coding and non-coding transcripts It is noteworthy that this observation was not restricted to ALT and K1/11-AS. Surprisingly, our Ribo-Seq data also revealed the presence of ribosomes on the “non-coding” RNA PAN -ORFK4.2 . This an K1-11AS .Like other herpesviruses, KSHV makes widespread use of the cellular mRNA splicing machinery Notably, our data not only correctly annotated known splice junctions but revealed 7 novel ones, thus increasing the number of splicing events from 20 to 27 . One sucThe second splicing event in the ORF57 transcript results in the removal of a 571 nucleotide fragment encoding amino acids 266–455 within the second exon and leads to the loss of the canonical UAA stop codon of ORF57, resulting in the generation of a novel isoform, here named ORF57A, with a different C-terminus that contains 33 amino acids . Interesadenosine deaminase acting on RNA (ADAR), the enzyme implicated in A-to-I editing . These uORFs are present in the leader sequence of annotated ORFs and encode peptides of ∼100 aa or less An example of the regulatory capacity of uORFs in KSHV was recently documented by Kronstad and colleagues, who described the functions of two uORFs identified in our Ribo-Seq data as uORF35.1 and uORF35.2  [57]. uOThe phase switch from latency to the lytic cycle is a highly regulated process that requires the temporally controlled expression of genes. Our strategy of mapping transcripts and coding sequences across different stages of the lytic cycle in an RTA-regulatable expression system revealed a built-in timer for viral reactivation that relies on the use of specific TSS during the distinct stages of the viral life cycle. Fine temporal mapping of viral transcription include 4 latent messages, 13 early genes that are expressed starting at 8 hr, 19 genes expressed between 24 hr and 48 hr after reactivation and 38 genes at 48–72 hr following DNA replication . We tookAs to the lytic cycle, our data support that most gene expression during this phase is controlled through transcriptional regulation, as the vast majority of the newly synthesized mRNAs are protected by ribosomes without delay . A notabin silico prediction approaches, of the mechanisms employed by herpesviruses to widen the coding capacity of their genome through the use of diverse strategies including splicing, mRNA recoding, and alternative start codon usage. Furthermore, we demonstrate that the viral genome is not a conventional source of coding sequences as traditionally defined by in silico predictions and homology analyses, but a rather rich collection of diverse coding sequences that include numerous viral sORFs and uORFs. Some of those may exert translational modulation of other viral ORFs, thereby enriching and adding complexity to the viral gene regulatory profile. It is noteworthy that several dozen viral features uncovered by our group in this study were not documented earlier because of the limitations imposed by conventional methods employed to study gene expression.The wealth of information we generated by combining our DNA-Seq, mRNA-Seq and Ribo-Seq datasets allowed us to build a comprehensive, high-resolution map of the viral genome over the KSHV life cycle. Our approach showcases the great analytical power of next-generation sequencing technologies, as we were able to pan-genomically map coding, non-coding, and regulatory features of the KSHV episome in iSLK-219 cells. Most importantly, this study provides tangible evidence derived from experimental data, as opposed to Our results confirm the striking conservation of eukaryotic cis-regulatory elements in the KSHV genome, including TATA boxes, polyadenylation signals, and splice junctions. The conservation of both, the sequence and position of these features reflects the strict dependence of the virus on host factors and importantly, imposes a major constraint for the pathogen because of the need to compete with host factors for transcription and translation of virally-encoded products. To circumvent this competition, the virus causes the gradual but massive inhibition of host protein expression by increasing mRNA turnover. This mechanism, known as host shutoff, is orchestrated by the concerted action of the viral endonuclease SOX and the cellular exonuclease Xrn1 Another striking yet puzzling finding derived from our data is the protection of PAN by initiating and elongating ribosomes. This well-characterized and abundant viral RNA is expressed during the lytic cycle and has a predominant nuclear localization An interesting observation from our analyses is the detection of mRNA editing in two viral transcripts. This posttranscriptional mechanism of coding and non-coding RNA editing is conserved in eukaryotes. In humans extensive RNA editing has been reported, the majority of the events corresponding to A-to-I transitions mediated by the family of deaminases ADAR. Indeed, alterations in the activity of these enzymes are associated with disease Remarkably, our results strongly suggest that the peptide coding capacity of KSHV has been previously underestimated. This is illustrated in the overall high representation of sORFs and uORFs in the viral genome, often translated from near canonical start codons. The functional implications of such translation initiation events have been documented in several eukaryotes (yeast and metazoans) where translation initiation from near canonical codons under physiological conditions occurs more often than anticipated Another surprise revealed in our studies was the abundance of uORFs encoded in the 5′ ends of viral transcripts. Like sORFs, uORFs have been found in eukaryotes, where they serve as modulators of gene expression during cellular stresses in silico analyses. The striking conservation of the mechanisms employed by host and virus to regulate transcription, translation, and the diversity of the peptide repertoire, elevates KSHV as a valuable model system to explore the mechanistic underpinnings of the host-virus interactions in herpesviruses at large, as well as those of fundamental cellular processes, including the control of translation initiation in response to cellular stress.Taken together, our results illustrate the dynamics of gene regulation during the different stages of the KSHV life cycle, while they also reveal that the coding capacity of its genome goes well beyond what was anticipated by 2 atmosphere. iSLK-219 cells were grown in the presence of puromycin  to maintain selection for the viral episome. LEC-219 cells were maintained in EBM-2 (Lonza cc3156) media supplemented with the EGM2-MV kit (Lonza cc3203) in presence of 0.25 ug/ml puromycin to maintain selection for the viral episome. BCBL-1 cells were maintained in RPMI supplemented with 10% fetal bovine serum, L-glutamine , penicillin  and streptomycin  at 37°C under a 5% CO2 atmosphere.iSLK and iSLK-219 cells (kindly provided by JinJong Myoung) were maintained in DMEM supplemented with 10% fetal bovine serum, L-glutamine , penicillin  and streptomycin  at 37°C under a 5% CO5 cells/ml and 24 h after seeding (∼70–80% confluent) cells were induced with doxycycline . To prevent viral DNA replication in the cells collected at 24 hpi, we induced these cells with Dox in the presence of phosphonoformate (500 uM). All other time points were treated with Dox alone. At the indicated times after induction viral reactivation was evaluated by microscopy detection of the PAN-RFP reporter. To determine the timing of KSHV DNA replication, DNA was isolated at the indicated times following reactivation using the DNeasy blood and tissue kit following manufacturer guidelines (Qiagen cat 69581). The DNA (20 ng) was used for qPCR using primers for the LANA promoter  or GAPDH as a normalization control .To induce entry to the lytic cycle, iSLK-219 cells were seeded at 1–2.5×10Cells were washed and collected in RIPA buffer  at the indicated time points. Cell lysates were clarified and protein concentration was quantified using the Bio-Rad DC protein assay following manufacturer guidelines. 10 ug of protein per sample were fractionated by Bis-Tris PAGE (4–20% gels in MES SDS-running buffer) and transferred to nitrocellulose membranes. Immunoblots were processed, incubated with primary antibody overnight and developed using ECL reagent according to manufacturer recommendations.The following antibodies were purchased from commercial suppliers: K8.1 (Advanced biotechnologies cat. 13-213-100) Actin (Sigma-Aldrich cat. A2228), ADAR1 (Abcam cat. ab126745), vCyclin (SCBT cat. 19415) and K-bZIP (SCBT cat. F33P1). The LANA rabbit polyclonal antibody was raised against a synthetic peptide from the acidic domain of LANA . The Kaposin B and C antibody was raised against the DR1/DR2 regions of the protein . The ORF54 rabbit polyclonal antibody was raised against a synthetic peptide from the C-terminal region of the protein (aa 280–298 EDTNSVRKHTNEDNPVHEP) (Covance).Ribosome profiling was performed as previously described 7 cells using RNABee (AMSBIO cat. Cs-104B) following the manufacturer guidelines. Polyadenylated RNA was isolated from the total fraction using Oligotex mRNA kit (Qiagen cat. 70022). The resulting mRNA was partially fragmented by alkaline hydrolysis with sodium carbonate to ∼150 nt segments on average, and then fragments between 40–100 nt were isolated from gel. The laboratory of Jonathan Weissman has previously documented that this partial fragmentation results in the preferential accumulation of mRNA 5′ terminal fragments for most transcripts with a non-overlapping transcription start site Total RNA was extracted from 1×10Strand specific libraries were generated as in Ingolia et al., 2012, with the modifications described in Stern-Ginossar et al., 2012. Samples were sequenced on either the Illumina Genome Analyzer II or the HiSeq 2000 using the truseq sbs kit v3 50 cycles (Illumina cat. FC-401-3002). Sequence analysis was done as described in Stern-Ginossar et al., 2012. Briefly, linker and polyA sequences were removed from the 3′ end of the reads preceding the alignment. Sequencing reads were aligned with Bowtie2 The sequences that did not align to the viral and host genomes were analyzed with TopHat and HMMSplicer for splice junction discovery using default options While other strong and reliable bioinformatics tools, such as Cufflinks and Scripture, are available for transcript reconstruction, we were not able to use them for viral mRNAs annotation. These tools are optimized for the analysis of transcripts in genomes where transcriptional units are well spaced and well defined. In the case of the compact KSHV genome, most transcripts use common regulatory features, are overlapping, or are very close to each other. Furthermore, and as shown in our study and previous studies by our lab and others, virtually the entire viral genome is transcribed late during the lytic cycle The identification of translation initiation sites was done using a machine learning approach as previously described Stern-Ginossar et al., 2012. In total our approach successfully predicted the translation initiation site of 64% (56 of 87) of the previously annotated ORFs , 2 and 3Stretches of 5 or more consecutive adenosines (polyA), allowing one non-A base for every 5, were removed from the 3′ ends of mRNA sequencing reads before alignment. These reads were aligned to the viral genome and the 3′ end was determined as the last nucleotide before the start of the polyA stretch. To prevent false positives, polyA sequences were only used as evidence of 3′ termini when they mismatched the underlying reference genome sequence.5′ GGCAAAGACGACGAACTCAT 3′ Rev: 5′ GAGAAGAGACCACGCCTGACT 3′). The resulting products were separated in a 1.2% agarose gel and stained with ethidium bromide for visualization.For the validation of the second splicing event within the 3′ end of the ORF57 transcript we prepared cDNA from 1 ug of total RNA using the qScript cDNA-Supermix (Quanta cat. 95048-025) or SuperScript III First Strand Synthesis System for RT-PCR (Invitrogen cat. 18080-051) with a combination of oligo(dT) and random hexamers, following manufacturers recommendations. PCR was done using as a template 1% of the resulting cDNA. The following primers flanking the 5′ and 3′ ends of the second splice junction were used for 30 cycles of amplification For the validation of the 3′ end extension of ORF57, PCR was done as described above using the following primers  for 32 cycles following manufacturer guidelines. The following primers were used for amplification: RTA-74281: Fwd-T7 5′ taatacgactcactatagggagaCAGTCACAAGCACACAACCC 3′, Rev-T3 5′ aattaaccctcactaaagggagaTTCGGGTGATTAAGCAAAGG 3′; Probe B (25437–25929) : Fwd-T7 taatacgactcactatagggagaCCTTTGCTTAATCACCCGAA, Rev-T3 5′ aattaaccctcactaaagggagaGGTGACCGTACTGCCATACC 3′; Probe C (123441–122984): Fwd-T7 5′ taatacgactcactatagggCGCTAACAGGGGAAACGTTAACCTGC 3′, Rev-T3 5′ aattaaccctcactaaagggCTCATTGCCCGCCTCTATTA 3′Total RNA was extracted from cells using RNABee (AMSBIO cat. Cs-104B) following manufacturer instructions. Northern blotting was done for 10 ug of RNA, or 100 ng of mRNA per lane using the Ambion NorthernMax system (Invitrogen cat. AM1940). Gene and sense specific riboprobes were synthesized from PCR products using the Ambion MAXIscript T7-T3 Transcription Kit (Ambion cat. AM1326) according to manufacturer guidelines. The primers used for the PCR amplification of gene specific probes are: Probe A (24151–25437): Fwd-T7 http://johnsonlab.ucsf.edu/sj/mochiview-software/To open the file, import and activate the database. Restart the program and the new database will be available for viewing. The following files are included: GQ994935 sequence, KSHV2.0 location, mRNA_dox8h_minus, mRNA_dox8h_plus, mRNA_dox24h_minus, mRNA_dox24h_plus, mRNA_dox48h_minus, mRNA_dox48h_plus, mRNA_dox72h_minus, mRNA_dox72h_plus, fp_dox8h_minus, fp_dox8h_plus, fp_dox24h_minus, fp_dox24h_plus, fp_dox48h_minus, fp_dox48h_plus, fp_dox72h_minus, fp_dox72h_plus, fp_harr_dox48h_minus, fp_harr_dox48h_plus, fp_harr_dox72h_minus, fp_harr_dox72h_plusFigure S1Lytic reactivation of KSHV is induced by exogenous expression of RTA in iSLK-219 cells. (A) Immunoblot of latent (LANA) and late lytic (K8.1) products in iSLK-219 cells (B) Quantitative PCR of viral DNA, shows DNA replication starting at 48 hpi and increasing with time.(TIFF)Click here for additional data file.Figure S2Read length distribution and sequencing-coverage for mRNA-seq and Ribo-seq in iSLK-219. (A) Length of fragmented, size selected mRNA (40–100 nt) and monosome-protected footprints (∼30 nt) in lysates from cells in the lytic cycle (72 hr). (B) Number of reads for mRNA-seq and Ribo-seq of all samples included in this study. Note that the number of mRNA-seq and Ribo-seq reads that align to the viral cycle increase as the lytic cycle progresses.(TIF)Click here for additional data file.Figure S3Transcription of viral genes is highly permissive during the lytic cycle. mRNA-seq (red) and Ribo-seq (CHX blue) profiles for the entire KSHV genome at 8 and 72 hr following Dox induction. Note the change in the read density as the viral lytic cycle progresses. The Y axis (number of reads) was cut at 50 (plus strand) and −50 (minus strand) for ease of visualization.(TIFF)Click here for additional data file.Figure S4ALT and K1-11-AS are modestly bound by ribosomes. (A and B) mRNA-seq and Ribo-seq (CHX and Harringtonine) profiles for the lincRNAs K1/11-Antisense (A) and ALT (B) at 72 hr post lytic reactivation. Only the strand of RNA coding for the lincRNA is shown. Solid blue arrows represent transcripts, light blue arrow heads coding regions and red thick arrows represent the lincRNA.(TIFF)Click here for additional data file.Figure S5The highly abundant viral transcript PAN, codes for three putative small peptides. (A) Timecourse of ribosome accumulation on the putative peptides within PAN. Notice the difference in the scale of the number of reads (y axis). The double head arrow indicates that the PAN transcript continues after the region shown in this figure (B) Accumulation of releasing ribosomes at the stop codon of PAN1.1 (top panel) and K8.1 (bottom panel). Where indicated the mRNA-seq and Ribo-seq (CHX and no drug) profiles for PAN (8 hr) and K8.1 (48 hr) coding regions are shown. (C) mRNA-seq and Ribo-seq of the ORFK7 and putative PAN peptides at 72 hr post reactivation. The right panel is a zoom in of the start codon of ORFK7. Notice the difference in the number of reads on the y axis. (D and E) Ribosome Release Score (RRS) was calculated as RSS = [(footprint reads coding region/footprint reads 3′UTR)/(mRNA reads coding region/mRNA reads 3′UTR)] using the read values from the 48 and 72 hr timepoints. RRS above 14 have been previously calculated for coding transcripts by Guttman et al., thus strongly supporting the coding capacity of PAN (TIFF)Click here for additional data file.Figure S6Identification of a novel splice junction within the 3′ end of ORF57. (A) Presence of a novel splice site within the 3′ of ORF57. The ORF57 sequence from 81886 to 83490 (GQ994935) includes the previously annotated 5′ splice and the novel 3′ splice annotated in this study. The splice donor and acceptor sites are highlighted in italic bold. Underlined is the sequence of the 3′ and 5′ introns. (B) Splicing of the second intron of ORF57 was evaluated by end-point PCR on cDNA from latent and lytic BCBL-1 and iSLK-219 cells, using primers flanking the intron boundaries (black solid lines). (C) The presence of a 3′ extension of the ORF57 transcript was confirmed in lytic iSLK-219 and BCBL-1 cells by end-point PCR, using three combinations of primers  flanking the annotated polyA cleavage site for this transcript. 28S was used as an internal loading control.(TIF)Click here for additional data file.Figure S7Higher levels of Kaposin mRNA-editing correlate with an increase in ADAR during the lytic cycle. (A) PCR amplification followed by Sanger sequencing of the region of Kaposin cDNA where mRNA editing occurs. Highlighted by the red box is the edited position, nucleotide 117809. Note the gradual and dramatic change from A to G as the lytic cycle progresses. (B) Quantification of the results obtained in (A). (C) Changes in the levels of ADAR isoforms  during the lytic cycle. Actin: Loading control, KbZip: Viral reactivation control, NS: not specific. (D) Fold changes in the mRNA and ribosome occupancy levels for two cellular genes that escape the host shutoff response, interleukin 6 (IL6) and the Hypoxia inducible factor 1 (HIF1), one gene sensitive to host shutoff protein kinase, DNA-activated, catalytic polypeptide (PRKDC) and ADAR. The fold change was calculated as the Log2 of the ratio of rpkM at 48 hr over 0 hr, for the mRNA-seq (left panel) or Ribo-Seq (right panel) samples. The black and grey bars represent two independent biological replicates. Note that the changes in ADAR mRNA and ribosome occupancy are very similar to the host shutoff escapee HIF1.(TIFF)Click here for additional data file.Figure S8KSHV uses alternative start codons to increase peptide diversity. mRNA-Seq and Ribo-Seq  profiles for ORF K6 (A) and ORF62 at 72 h post reactivation (B). The blue arrowheads represent the annotated ORF and the red arrowheads represent the ORFs expressed from the alternative start codons. The dashed lines mark the multiple translation initiation start sites.(TIF)Click here for additional data file.Figure S9uORFs and sORFs populate the viral genome in KSHV. (A) Percentage of uORFs and sORFs starting from canonical and near cognate start codons. Other codons are ATT, ATC, ATA, TTG and ACG (see  ACG see . (B) The(TIFF)Click here for additional data file.Table S1Transcription start sites in KSHV. Annotations for the transcription start sites described in this study. The second column contains the sequence of 50 nt 5′ and 10 nt 3′ from the annotated TSS. The TATA box is in bold, italic font. The third column indicates the distance from the start of the TATA box to the TSS. The eight TATA-less genes are indicated as No TATA.(DOCX)Click here for additional data file.Table S2PolyA sites in KSHV. Polyadenylation sites were annotated in the KSHV genome using mRNA-seq. Cleavage sites were determined as the last nucleotide before a stretch of 5 or more consecutive adenosines. The sequence in column 7 corresponds to 30 nt flanking the polyA cleavage site. The AA/UUAA and GU sites are in italic, bold font and the cleavage site(s) are in bold font.(DOCX)Click here for additional data file.Table S3PAN represent a large percentage of the KSHV mRNA-seq. The PAN mRNA or ribosome footprint (FP) reads were calculated for the region between 28661 to 29690. The percentage of KSHV reads was calculated using the total number of viral reads for mRNA-seq or Ribo-seq for each timepoint. 1. The PAN RNA reads in this latent sample are likely coming from the 0.1% of spontaneous lytic cells or from minor cross-contamination during library preparation. 2. Technical replicates.(DOCX)Click here for additional data file.Table S4Splice junctions in the KSHV transcriptome. Splice junctions were identified and annotated using TopHat and HMMsplicer. The novel splice junctions identified in this study are in bold font. Donor and acceptor sites are in italic, bold font (column 6). Columns 7 and 8 contain the TopHat and HMM generated scores for two replicates at 48 hr (rep1 and rep2) and one at 72 hr post reactivation. These scores reflect the strength of the alignment (HMM splicer) and the number of reads that mapped to the region (TopHat and HMM splicer).(DOCX)Click here for additional data file.Table S5mRNA editing in KSHV. mRNA editing events were predicted in silico and were annotated by position and gene. We include in this table a DNA mutation identified in the GQ994935.1 genome in ORF63 at position 102377 (A-to-C change). Three editing events, Kaposin 117809, RTA 72841 and RTA 72795 have been confirmed by Sanger sequencing.(DOCX)Click here for additional data file.Table S6Alternative start codon usage and internal ORFs in KSHV. From our Ribo-seq data we identified five viral ORFs  with multiple in frame initiation codons where ribosomes accumulate. The size of the predicted protein products originating from the alternative start codons is indicated in the column labeled amino acids. In addition, five viral ORFs  contain internal initiation codons, in or out of frame with the primary ORF, where we detect initiating ribosomes. The size of the predicted products is indicated in the column labeled amino acids .(DOCX)Click here for additional data file.Table S7Upstream and small ORFs are widely distributed in KSHV. The translation boundaries for uORFs and sORFs were predicted by the SVM based on the Ribo-seq data from harringtonine treated cells at 72 h post reactivation. The ORF coordinates were then manually curated. The translation efficiency (TE) for the ORFs larger than 15 amino acids was determined as Footprint-rpkM/mRNA-rpkM. For the calculation we excluded the first 45 nucleotides from the start codon. .(DOCX)Click here for additional data file.File S1Summary of novel features annotated for KSHV. The novel genomic features annotated in our study include the ORFs derived from alternative start codons, internal, small and upstream ORFs, and polyadenylation sites. All coordinates are annotated based on the human herpesvirus 8 accession number GQ994935.1.(XLSX)Click here for additional data file.File S2Mochiview files for updated annotation and mRNA-seq/Ribo-seq profiles. Supplementary file 1 is a Mochiview file (ZIP)Click here for additional data file.


Gata4 had little impact on gross liver architecture and function, even under conditions of regenerative stress, and, despite the large number of GATA4 occupied genes, resulted in relatively few changes in gene expression. To address possible redundancy between GATA4 and GATA6, both factors were conditionally excised. Surprisingly, combined Gata4,6 loss did not exacerbate the phenotype resulting from Gata4 loss alone. This points to the presence of an unusually robust transcriptional network in adult hepatocytes that ensures the maintenance of liver function. GATA transcription factors and their Friend of Gata (FOG) cofactors control the development of diverse tissues. GATA4 and GATA6 are essential for the expansion of the embryonic liver bud, but their expression patterns and functions in the adult liver are unclear. We characterized the expression of GATA and FOG factors in whole mouse liver and purified hepatocytes. GATA4, GATA6, and FOG1 are the most prominently expressed family members in whole liver and hepatocytes. GATA4 chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) identified 4409 occupied sites, associated with genes enriched in ontologies related to liver function, including lipid and glucose metabolism. However, hepatocyte-specific excision of  GATA transcription factors typically function in concert with Friend of GATA (FOG) cofactors to regulate the formation of numerous cell types ,2 . The GATA factors play a key role in the development of the liver. The liver develops from the ventral foregut endoderm in close proximity to the cardiogenic mesoderm . Inductiin vivo. GATA6 expression has been reported in fetal and adult hepatocytes as well as adult cholangiocytes ,21,24. RHex [Studying liver-related functions of GATA4 and/or GATA6 in knockout mice has been hampered by early embryonic lethality unrelated to adult liver function -29. In tHex , a cytocHex , and hepHex . HoweverHex . GATA4 hHex . MoreoveHex . TogetheAdditional circumstantial evidence for GATA factor function in the adult liver derives from our studies of mice homozygous for FOG1 point mutations that disrupt the interaction between FOG1 and the chromatin remodeling complex NuRD. In addition to hematopoietic defects -39 emergGata4 or both Gata4 and Gata6 from hepatocytes yielded a surprisingly mild hepatic phenotype. This suggests the presence of an unusually robust transcriptional network that stably maintains hepatocyte-specific gene expression patterns even in the absence of two GATA factors.We find that GATA4, GATA6, and FOG1 are expressed in whole mouse liver and purified primary hepatocytes. Using chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq), and conditional excision we characterize in-depth the GATA4-controlled transcriptional program in the adult liver. We found that GATA4 occupies over 4000 sites in the genome. The ontologies of sites associated with these genes relate to liver specific functions. Conditional excision of Fog1 ki/ki mice were previously described [Fog1 ki/ki mice were maintained on a mixed C57BL/6xSv129 background. Gata4 fl/fl (stock# 008194) mice were purchased from Jackson Laboratories. Gata4,6 fl/fl mice were a generous gift from Stephen Duncan . Genotyping primers are listed in escribed . Wildtyp2 and 40 µg/ml Liberase (Roche), were pumped into the IVC using a variable flow mini pump (Fisher Scientific). Liver tissue was dissociated in hepatocyte wash medium (GIBCO) and filtered through a 100 µm membrane. To isolate the hepatocyte fraction, the single cell suspensions were mixed 1:1 with Percoll Plus solution  and spun at 120 x g. To isolate “all cells”  the single cell suspension was spun in hepatocyte wash medium alone at 120 x g. ACK lysing buffer (Lonza) was used to lyse potentially contaminating erythrocytes. Cells were either directly placed in Trizol to proceed with RT-qPCR or fixed in formaldehyde to proceed with ChIP. Mice were anesthetized with inhaled isoflurane (Baxter). The abdominal cavity was opened and inferior vena cava (IVC) was exposed. An angiocath was inserted into the IVC and HBSS with 1 mM EGTA, followed by HBSS with 5 mM CaCl5-1x106 cells from all liver cells or hepatocytes. Reverse transcription reactions were performed using Superscript II (Invitrogen). cDNA was quantified by Power Sybr Green qPCR. Data were normalized to beta actin and GAPDH, with no major differences. Data in the figures show expression relative to actin. Relative expression was calculated as 2CT actin – CT primer. RT-PCR primers are listed in RNA was isolated using TRIzol reagent (Invitrogen) from 0.1 mg whole liver or 0.5x10ChIP on whole liver, all cells and hepatocytes was performed as described previously . “Whole 6 hepatocytes, all liver cells, 293T cells and derivatives, or JC4 erythroid cells and from 40-100 mg of whole liver or heart. 100 μg of protein for whole liver, hepatocytes or all liver cells and 1-25 μg of protein for controls were run on a 10% SDS-PAGE gel followed by transfer to a nitrocellulose membrane. Antibodies used for western blotting are anti-GATA4 C-20 (Santa Cruz), anti-FOG1 M-20 (Santa Cruz) and anti-beta actin-peroxidase (Sigma). Nuclear extracts were made from 5-10x10Gata4 fl/fl mice were intravenously injected with 1.5x1011 virus particles of either AAV8-Tbg-Cre (AV-8-PV1091) or AAV8-Tbg-GFP (AV-8-PV0146), purchased from Penn Vector Core. Two weeks post injection, hepatocytes were isolated and RT-qPCR, ChIP or western was performed.RNA purification was carried out using Trizol reagent (Invitrogen) according to manufacturer’s recommendations. RNA quality was determined using the Bioanalyzer (Agilent). Only samples with RNA integrity number (RIN) >7.5 were used for further studies. Equal amounts (100ng) of total RNA were amplified with Epicentre TargetAmp™- Nano Labeling Kit for Illumina® Expression BeadChip and hybridized to the MouseWG6v2 mouse whole genome bead arrays. Illumina GenomeStudio software was used to export expression levels and detect p-values for each probe of each sample. Arrays were quantile-normalized and filtered to remove non-informative probes . A list of significant genes differentially expressed between two classes of samples was determined by using SAM method  with falChIP was performed using anti-GATA4 C-20 (Santa Cruz) on two biological replicates, each consisting of whole liver from two mice (age 2.5 to 3.5 months). For sequencing libraries, the input (liver) and ChIP DNA fragments were repaired to generate blunt ends, with a single adenine added to each end. Illumina genomic adaptors were ligated to both ends of the fragments, ligation products were amplified by 18 cycles of PCR, and the PCR products between 250 and 450 bp were gel purified according to standard Illumina protocols. The quantity and quality of each library was evaluated by qPCR and Bioanalyzer . Cluster generation and sequencing chemistry were performed using Illumina-supplied kits as appropriate. The ChIP DNA library was sequenced in single-read mode on the Illumina HiSeq 2000 platform.47.9 million (M) (replicate 1) and 47.6M (replicate 2) GATA4 ChIP-seq, and 60.5M (replicate 1) and 53.1M (replicate 2) INPUT 48-bp sequencing reads were aligned to the canonical reference mouse genome  using bowtie v.0.12.7  on GalaxMACS v1.3  implemenhttps://sites.google.com/site/anshulkundaje/projects/idr), which is based on the reproducibility method developed by Qunhua Li and Peter Bickel [Peaks (“occupied sites”) were called using a modified version of the SPP peak caller  and MACSr Bickel . BrieflySPP peaks (4811 regions) were intersected with the MACS 2.0.10 peaks (5867 regions) using BedTools v2.16.1 , and defBuild 37 (mm9) mouse reference gene coordinates were obtained from the table "RefGene" on the UCSC table browser (http://genome.ucsc.edu). The transcription start site (TSS), transcription termination site (TTS) and exons coordinates were extracted from the table to define the following custom intervals in BED format: (1) 5 kb upstream of TSS to the end of first exon, (2) first intron, (3) rest of gene, and (4) last exon to 5 kb downstream of TTS. Any peak outside the range TSS-5kb, TTS+5kb was deemed intergenic. An additional set of custom intervals substituting 10 kb in the above definitions was also used, as indicated in figure and text. The 4409 “common” peaks were intersected with the custom intervals using BedTools v2.16.1  to generWGATAR motif. The distribution of the strength of the underlying signal (SPP's tag enrichment score) of peaks with and without the WGATAR motif was used to generate boxplots.Motif discovery and enrichment was conducted with the motif discovery tool suit MEME ChIP v.4.9.0 , availabHeart GATA4 occupied segments genomic coordinates were obtained from He et al, 2011. The inthttp://bejerano.stanford.edu/great/public/html/. Association rule settings were to single nearest gene with an extension of no greater than 50 kb from transcriptional start site, including curated regulatory domains.GREAT analysis  was perfData were analyzed and peaks assigned using two windows spanning either 5 kb or 10 kb upstream of the transcription start site (TSS) and downstream of the transcription termination site (TTS). Peaks outside these ranges were defined as intergenic. Mouse ENCODE’s eight week old mouse liver H3K4me1 and H3K27ac histone marks peaks were obtained from the UCSC genome browser (http://genome.ucsc.edu), and intersected with the set of intergenic peaks with BedTools v2.16.1 . The ChIP-seq and microarray datasets have been deposited in a superseries in GEO Database under the accession number GSE49132. The hematoxylin-eosin (HE) stains were performed using 5 µm thick sections from formalin-fixed, paraffin-embedded tissue blocks. Specimens were fixed in formalin (Fisher) immediately after harvesting and followed by gradient dehydration with 50%, 70%, 95%, and 100% ethanol. Tissue were then processed in xylene (Fisher) and embedded in paraplast tissue embedding medium (Fisher). Slides were prepared using Microm HM550. Paraffin sections were deparaffinized in xylene followed by rehydration with 100%, 95%, 70% ethanol and then Millipore water before the staining. Harris hematoxylin (Fisher) was used for nuclei staining. Excess hematoxylin was removed by dipping slides in acid alcohol . Slides were then placed in running warm water until the nuclei turned blue. Eosin  was used to stain for cytoplasm. Slides were later mounted using permount  after clearing with xylene. Digital images of slides were taken on Axioskop 2 microscope (Zeiss).Blood was obtained from the heart and allowed to coagulate at room temperature for one hour, then spun at 10,000 rpm for 10 min. Serum was removed to a fresh tube and analyzed with a Vitros 350 Chemistry Analyzer (Johnson & Johnson). A mouse was anesthetized by inhaled isofluorane. An incision was made down the midline of the abdomen and the left and middle lobes of mouse liver were exposed. The falciform ligament was cut down to the superior vena cava and the membrane linking the caudate and left lobes was also cut. A 1-0 suture was placed at the bottom of the left and middle lobes. The ends of the suture were tied tightly and the lobes were cut. After determining that no bleeding occurred, the abdominal incision was closed with a 4-0 suture in one layer.We examined the expression of GATA and FOG mRNAs in whole adult mouse livers by quantitative RT-PCR. GATA4 and GATA6 are the only significantly expressed members of their family  with levin vivo is unknown. We performed chromatin immunoprecipitation (ChIP) with two independent anti-GATA4 antibodies followed by qPCR to assess GATA4 binding to presumed GATA4 target genes in whole liver preparations. Primer pairs were designed to span two sequences each at the albumin enhancer and factor X promoter upstream region, according to previously described GATA4 target sites [WGATAR sequence was included at each of these genes  followed by high throughput sequencing (ChIP-seq) in whole adult mouse liver. Genomic DNA not subjected to immunoprecipitation was sequenced as input control. Using two independent peak calling methods and irreproducible discovery rate (IDR) analysis , we idened sites . This peed sites . In addiAR motif . TogetheTo further explore the functional significance of liver GATA4 OS, we used the Genome Regions Enrichment of Annotations Tool (GREAT)  to assigGata4 alleles (fl/fl) were injected via tail veins with adeno-associated virus expressing Cre recombinase (AAV-Cre) or, as control, GFP (AAV-GFP) from the hepatocyte-specific thyroxine-binding globulin (Tbg) promoter [Gata4  analysis identified 716 genes with a false discovery rate (FDR) less than 25%, without using fold-change cutoffs. Of these genes, 71 were upregulated and 32 were downregulated by at least 1.5 fold  that were validated by RT-qPCR  of all microarray genes with an FDR <25% or an FDR <26% and either fold change >1 or >1.2. We used these relaxed FDR and fold-change thresholds because IPA analysis requires a larger number of genes in order to organize pathways that are statistically significant. The four pathways common to all these analyses include fatty acid beta oxidation, stereate biosynthesis I, hepatic cholestasis, and bile acid biosynthesis . We next examined the expression of hepatocyte-specific genes strongly occupied by GATA4 and possibly also by GATA6 in purified hepatocytes. Endothelial and macrophage genes were depleted in both control and excised hepatocytes, indicating proper enrichment of the hepatocyte fraction . UnexpecGata4,6 double excised hepatocytes and control hepatocytes (n=6) using Illumina BeadChip. SAM analysis identified 137 genes that met an FDR threshold of less than 25%, without any fold change cutoffs. At FDR<25% with a fold change greater than 1.5, there are 3 upregulated and 48 downregulated genes  of all microarray genes with an FDR <25% or an FDR <30% and fold change >1.2. Common ontologies to these analyses include farnesoid X receptor (FXR) and retinoid X receptor (RXR) function and detoxification related processes . Nevertheless, it remained possible that this occupancy pattern was produced by GATA4 in non-hepatocytes. To address whether the GATA4 OS reflect a bone fide hepatocytic occupancy profile, and to establish that the anti-GATA4 antibodies are specific, we performed ChIP-qPCR in all cells and hepatocytes following hepatocyte specific GATA4 excision. In both samples, GATA4 was depleted from all targets examined, validating that GATA4 occupies its targets in a hepatocyte specific manner. Comparison of GATA4 OS data sets with those obtained from the heart  revealedGata4 have outwardly normal livers with ostensibly normal histology, and exhibit levels of serum proteins similar to control mice. Nevertheless, using gene profiling we detected subtle changes in gene expression upon hepatocyte Gata4 deletion, with 103 genes differentially expressed by at least 1.5 fold at an FDR threshold less than 25%. However, the number of differentially expressed genes after Gata4 excision was only a small fraction of the total number of genes occupied by GATA4. This has also been observed in a cardiomyocyte cell line in which Gata4 depletion affected only a small portion of GATA4 occupied genes [To examine the function of GATA4 in the adult liver, we excised it specifically in hepatocytes using cre recombinase driven by a hepatocyte specific promoter in a viral vector with hepatocyte tropism. Mice lacking ed genes . Dependied genes . It is ped genes . Of courGata4 or Gata6 does not perturb organ formation, whereas double knockout of Gata4 and Gata6 leads to pancreatic agenesis and death shortly after birth [Gata4 or Gata6 deletion is compatible with relatively normal heart development but compound heterozygote animals die in utero due to a spectrum of cardiovascular deficiencies [A possible reason for the mild hepatic phenotype in the absence of GATA4 is potential redundancy with GATA6. In the mouse pancreas, conditional deletion of either er birth . In the ciencies .Gata4 and Gata6 excision in hepatocytes. Surprisingly, despite depletion of both GATA4 and GATA6 mRNAs and GATA4 protein and target gene occupancy, we detected no difference in weight or serum protein levels in these mice. Although there were some variations between Gata4 and Gata4,6 depleted cells, it is fair to conclude that the additional loss of Gata6 did not augment gene expression changes found in the single Gata4 knockout.To address the possibility that GATA6 is compensating for the absence of GATA4, we performed double Gata4,6 excised hepatocytes is that there are disproportionately more downregulated than upregulated genes, which held true at all FDR thresholds examined with fold change >1.5 or 2 fold. Interestingly, shRNA knockdown of GATA4 in a cardiac cell line yielded a similar pattern of disproportionate number of downregulated genes (approximately 3 fold more than upregulated), indicating a more prominent role of GATA4 at activating genes [One interesting finding from the transcriptome of the ng genes . Cyp7b1, an enzyme in the alternative pathway of bile acid synthesis [Although GATA4 has previously been shown to activate certain liver-related genes in hepatoma cell lines -33, our ynthesis  and epidynthesis . GATA4 rSoat2), encoding an enzyme which mediates esterification of cholesterol [Cish), involved in the negative regulation of signal transducer and activator of transcription (STAT) signaling which regulates a wide variety of metabolic and inflammatory functions in the liver [GATA4 also activates genes that play roles in the liver, including sterol-O-acyltransferase 2  only mildly affects erythroid cell function, but strongly exacerbates anemia and hemolysis to the point of lethality upon oxidative stress [Pxr knockout mice develop more severe liver damage than wild type controls [Gata4 depletion and found little difference in their regenerative potential when compared to controls. It will be important to examine whether Gata4,6 doubly excised mice display defects when challenged with partial hepatectomy.It appears that in the homeostatic adult liver, both GATA4 and GATA6 are relatively dispensable for normal function. However, GATA4 and GATA6 function might only revealed during stress. For example, deletion of transcription factor forkhead box o3  Table showing the fraction and frequency of wild type (WT), Fog1 heterozygous (ki/+) and Fog1 homozygous (ki/ki) mice with macroscopic liver tumors, *p<0.05 by Fisher’s exact test. (B) Image of livers from WT and Fog1ki/ki mice. Note the multifocal tumors present in the ki/ki liver. (C) H&E staining of a Fog1ki/ki tumor section at 20X magnification identifies it as hepatocellular carcinoma. Tumor cells contain nuclear polymorphisms with hyperchromasia and chromatin clumping (a), prominent nucleoli (b), and mitotic Figures (c). Scale bar represents 100 μm.(TIF)Click here for additional data file.Figure S2Validation of GATA4 OS. (A) ChIP-qPCR of GATA4 OS with a range of IDR significance levels. (B) Genome browser screenshot showing prominent GATA4 ChIP-seq peak at factor X promoter (denoted by red bar). Factor X gene is marked with blue box. Y axis shows read counts for GATA4 ChIP-seq (upper track) and input DNA (lower track). (TIF)Click here for additional data file.Figure S3Patterns of GATA4 occupied sites. (A) Distribution of GATA4 occupied sites among gene elements with extended window, including 10 kb upstream of TSS to end of first exon  and last exon to 10 kb downstream of TTS . (B) Distribution of GATA4 occupied sites in regular 5 kb window  of unique to heart ChIP-seq dataset, excluding heart/liver common occupied sites. Intersection of combined H3K4me1 and H3K27ac occupied sites (enhancer marks) and intergenic GATA4 occupied sites outside of 5 kb (C) or 10 kb (D) windows.(TIF)Click here for additional data file.Figure S4Non-canonical GATA motifs. Fraction and percentage of peaks containing non-canonical GATA motifs WGATA or (A/T)GATA and GATAR or GATA(A/G) as identified by MEME motif analysis.(TIF)Click here for additional data file.Figure S5Gata4.Phenotype of mice with hepatocyte-specific deletion of  (A) Fractional total body weight changes at one and two weeks after injection with AAV-GFP (circle) and AAV-Cre (triangle) (n=5-12). (B) Serum blood urea nitrogen (BUN) and albumin, (C) cholesterol and triglycerides, (D) alanine aminotransferase (ALT) and aspartate aminotransferase (AST), (E) alkaline phosphatase  and total bilirubin in AAV-GFP and AAV-Cre injected mice (n=3-4). Reference ranges: BUN 18-29 mg/dL, albumin 2.5-4.8 g/dL, cholesterol 36-96 mg/dL, triglycerides 55-144 mg/dL, ALT 28-132 U/L, AST 59-347 U/L, alkaline phosphatase 52-209 U/L, total bilirubin 0.1-0.9 mg/dL. Hematoxylin and eosin (H&E) staining of liver section from AAV-GFP injected mice at 10X (F) and 20X (G) magnification and AAV-Cre injected mice at 10X (H) and 20X (I) magnification show intact tissue architecture, no fibrosis and no inflammation. Arrows point to central veins and arrowheads indicate portal tracts. Scale bars represent 100 μm.(TIF)Click here for additional data file.Figure S6Liver weights after partial hepatectomy. The liver weight to body weight ratio in Gata4fl/fl mice injected with AAV-Cre or AAV-GFP at 3 days after partial hepatectomy (phx) or sham surgery (sham). (TIF)Click here for additional data file.Figure S7Gata4fl/fl mice.Enrichment of hepatocytes following purification from AAV-Cre and AAV-GFP injected  Relative mRNA levels as measured by RT-qPCR of endothelial cell markers CD31 and VE-cadherin, and Kupffer cell marker CD68 from all liver cells  or hepatocytes (hep) (n=5-7) of AAV-GFP (black bar) and AAV-Cre (striped bar) injected mice. Student’s unpaired t test was not significant when comparing all liver cells or hepatocytes from AAV-GFP and AAV-Cre injected mice.(TIF)Click here for additional data file.Figure S8Gata4 and Gata6.Phenotype of mice with hepatocyte specific deletion of A) Fractional total body weight changes at one or two weeks after injection with AAV-GFP (circle) and AAV-Cre (triangle) (n=6-9). Student’s unpaired t test was not significant comparing weights for two groups. (B) Serum blood urea nitrogen (BUN) and albumin, (C) cholesterol and triglycerides, (D) alanine aminotransferase (ALT) and aspartate aminotransferase (AST), (E) alkaline phosphatase  and total bilirubin in AAV-GFP and AAV-Cre injected mice (n=2). Student’s unpaired t test was not significant comparing any of the serum values between two groups. Reference ranges: BUN 18-29 mg/dL, albumin 2.5-4.8 g/dL, cholesterol 36-96 mg/dL, triglycerides 55-144 mg/dL, ALT 28-132 U/L, AST 59-347 U/L, alkaline phosphatase 52-209 U/L, total bilirubin 0.1-0.9 mg/dL. Note that albumin in Cre samples in panel B and total bilirubin in panel E have no error bars because the values were the same for both replicates. Hematoxylin and eosin (H&E) staining of liver section from AAV-GFP injected mice at 10X (F) and 20X (G) magnification and AAV-Cre injected mice at 10X (H) and 20X (I) magnification show intact tissue architecture, no fibrosis, and no inflammation. Arrows point to central veins and arrowheads indicate portal tracts. Scale bars represent 100 μm.(TIF)Click here for additional data file.Table S1Genotyping primer sequences.Sequences for forward and reverse genotyping primers for each mouse strain.(PDF)Click here for additional data file.Table S2RT-qPCR primer sequences.Sequences for forward and reverse RT-qPCR primers.(PDF)Click here for additional data file.Table S3ChIP primer sequences.Sequences for forward and reverse ChIP-qPCR primers. (PDF)Click here for additional data file.Table S4Gata4 excision that contain at least one GATA4 OS.Genes up-regulated upon  “Start” and “end” denote genomic coordinates of GATA4 OS.  represents upregulated). Note that some genes have more than one GATA4 OS. (PDF)Click here for additional data file.Table S5Gata4 excision that contain at least one GATA4 OS.Genes down-regulated upon  “Start” and “end” denote genomic coordinates of GATA4 OS.  represents downregulated). Note that some genes have more than one GATA4 OS. (PDF)Click here for additional data file.Table S6Gata4,6 double excision that contain at least one GATA4 OS.Differentially expressed genes upon  “Start” and “end” denote genomic coordinates of GATA4 OS.  or negative sign (-) represent upregulated or downregulated, respectively). Note that some genes have more than one GATA4 OS. (PDF)Click here for additional data file.


To define the set of active SNAPc-dependent promoters in human cells, we have localized genome-wide four SNAPc subunits, GTF2B (TFIIB), BRF2, pol II, and pol III. Among some seventy loci occupied by SNAPc and other factors, including pol II snRNA genes, pol III genes with type 3 promoters, and a few un-annotated loci, most are primarily occupied by either pol II and GTF2B, or pol III and BRF2. A notable exception is the RPPH1 gene, which is occupied by significant amounts of both polymerases. We show that the large majority of SNAPc-dependent promoters recruit POU2F1 and/or ZNF143 on their enhancer region, and a subset also recruits GABP, a factor newly implicated in SNAPc-dependent transcription. These activators associate with pol II and III promoters in G1 slightly before the polymerase, and ZNF143 is required for efficient transcription initiation complex assembly. The results characterize a set of genes with unique properties and establish that polymerase specificity is not absolute in vivo.SNAP c-dependent promoters are unique among cellular promoters in being very similar to each other, even though some of them recruit RNA polymerase II and others RNA polymerase III. We have examined all SNAPc-bound promoters present in the human genome. We find a surprisingly small number of them, some 70 promoters. Among these, the large majority is bound by either RNA polymerase II or RNA polymerase III, as expected, but one gene hitherto considered an RNA polymerase III gene is also occupied by significant levels of RNA polymerase II. Both RNA polymerase II and RNA polymerase III SNAPc-dependent promoters use a largely overlapping set of a few transcription activators, including GABP, a novel factor implicated in snRNA gene transcription.SNAP Basal transcription from pol II snRNA promoters requires, in addition, TBP, TFIIA, GTF2B (TFIIB), TFIIF, and TFIIE, and from pol III type 3 promoters TBP, BDP1, and a specialized GTF2B-related factor known as BRF2 c and thus stabilizing the transcription initiation complex on the DNA  that enhances transcription and a proximal sequence element (PSE) required for basal transcription. In pol II snRNA promoters, the PSE is the sole essential core promoter element whereas in type 3 pol III promoters, there is in addition a TATA box, which determines RNA pol III specificity c-recruiting promoters differ in the way transcription terminates. In pol III genes, there are runs of T residues at various distances downstream of the RNA-coding sequence, which direct transcription termination  to localize four of the five SNAPc subunits, GTF2B, BRF2, and a subunit of each pol II and pol III. These studies define a set of SNAPc-dependent transcription units and show that although most loci are primarily bound by one or the other polymerase, the RPPH1 (RNase P RNA) gene is occupied by both enzymes. Pol II is detectable up to 1.2 kb downstream of the end of the RNA-coding regions of pol II snRNA genes, thus defining a broad region of transcription termination. Localization of POU2F1 and ZNF143 shows widespread usage of these activators by PSE-containing promoters, and we find that several of these promoters also bind the activator GABP Although model snRNA promoters have been extensively studied, it is unclear how broadly SNAPc subunit, SNAPC1 (SNAP43), and SNAPC5 (SNAP19) in IMR90Tert cells. To localize SNAPC2 (SNAP45), we used an IMR90Tert cell line expressing both biotin ligase and SNAPC2 tagged with the biotin acceptor domain for chromatin affinity purification (ChAP)-seq (see We performed ChIP-seq with antibodies against SNAPC4 (SNAPC190), the largest SNAP-seq see . We alsoc subunits and either BRF2 and pol III, or GTF2B and pol II, as described in Methods. We obtained loci encompassing all known type 3 pol III genes as well as most annotated pol II snRNA genes. In addition, we obtained a few novel loci occupied by SNAPc and pol II. RNU1 and one RNU2 snRNA genes (in red in the first column). It also shows, in grey, RNU2 genes that are still in the “chr17_random” file of the human assembly and were thus not in the reference genome used for tag alignment.Most of the human pol II snRNA and type 3 pol III genes are repeated and/or have given rise to large amounts of related sequences within the genome. We therefore aligned tags as described before SNORD13 gene shown in c subunits– and they are labeled in yellow in the first column of RNU1 gene (U1-12). Such peaks probably result from attribution of tags with multiple genomic matches to an incorrect genomic location and are thus likely to be artifacts. Consistent with this possibility, U1-11, U1-12, U1-like-8, U3-2, U3-2b, U3-4, and U3-3, all labeled in orange in c subunits scores with either 0% or, in the cases of U3-4, less than 15%, unique tags. We consider these loci unlikely to be occupied by pol II in vivo. In contrast, the POLR2B peak on the RNU2 snRNA gene on chromosome (chr) 11, even though interrupted about 500 base pairs downstream of the snRNA coding region, is constituted mostly of unique tags, as are the GTF2B and SNAPc subunit peaks. This gene is likely, therefore, to be indeed occupied by pol II and other factors, and is labeled in striped yellow in the first column , involved in the maturation of pre-ribosomal RNA, as well as snRNA-derived sequences. The relationship of these loci with previously described snRNAs and snoRNA genes is described in the Results section of c subunits, as well as GTF2B and POLR2B, peaks constituted by at least 20% of unique tags and, therefore, likely to correspond to new actively transcribed regions. These are labeled Unknown-1 to 7 , the other factors were located within the 5′ flanking region, with GTF2B and BRF2 close to, or overlapping, the TSS. Although peaks were sometimes constituted of too few tags to allow an unambiguous determination of the peak summit location , and RNU1 (U1-9) and RNU1 (U1-13) genes), as well as for the non-annotated genes. For the RPPH1 gene, the best match to a 3′ box was located within the RNA coding sequence, from −73 to −61 relative to the end of the RNA coding sequence  for ZNF143 and, as ZNF143 was found to bind often together with GABP , for the α subunit of GABP (GABPA). The scores for all genes are listed in snRNA promoters are characterized by an enhancer element (DSE) typically containing an octamer motif and a ZNF143 binding site (Z-motif), which in some specific genes has been shown to recruit, respectively, the POU domain protein POU2F1 and the zinc finger protein ZNF143 , a large majority had a ZNF143peak (81%), and about half had a GABPA peak (45%). Interestingly, some genes had specific combinations of activators; for example the RNU5 and U5-like genes as well as most pol III genes had peaks for both POU2F1 and ZNF143 but not for GABPA. In contrast RNU6ATAC, SNORD13, and RNU3 genes had POU2F1 and GABPA peaks but no ZNF143 peak. Only few genes had only one activator  suggesting that most snRNA genes require some combination of the three activators tested for efficient transcription. Indeed, altogether 23 genes had peaks for all three factors and 23 had peaks for both ZNF143 and POU2F1 but not GABPA. Thus, the very large majority (79%) of SNAPc-binding genes bound both POU2F1 and ZNF143. The scores for the various activators were surprisingly correlated (see RNU4ATAC and U1-like-5) with the three factors present, and two examples (Unknown-6 and tRNAU1) with only POU2F1 and ZNF143. In all cases, the factors bound upstream of the PSE with GABP, when present, generally binding the furthest upstream.Of the genes clearly occupied by basal factors, all displayed peaks for at least one activator with three exceptions, ated see , perhapsU1-like-10, and GABP for U5E-like, U4-1, and unknown-7 genes) where no convincing motif could be identified. We then aligned all occupied motifs  to generate the LOGOs shown in c-recruiting genes.We analyzed 5′ flanking sequences for motifs and identified POU2F1  was apparent 3 h after release and peaked after 6 h, consistent with the accumulation of U6 RNA earlier in G1 than U1 and U2 RNA.Transcription of NU2 loci . This waRNU1 and RNU6 promoters, becoming clearly detectable at 3 h and reaching a maximum at 6 h for RNU1 and 4 h for RNU6. In contrast, ZNF143 was undetectable on the RNU2 promoters. POU2F became detectable at 3 h on the RNU1, RNU2, and RNU6 promoters and then remained at a more or less constant level. GABP was detected only on the RNU1 promoters and was recruited early, starting 2 h after the release and reaching a maximum at 5 h. Thus, activators were recruited on the promoters expected from the ChIP-seq data above, with kinetics slightly faster than the polymerase. Among activators, GABP was recruited the earliest, followed by concomitant recruitment of ZNF143 and POU2F1.We then examined promoter occupancy by transcription activators . ZNF143 c, GTF2B, and BRF2, we monitored occupancy by these factors at mitosis (1 h after release) and in mid-G1 (7 h after release). On the pol II RNU1 snRNA promoter, we observed enrichment of GTF2B and SNAPc subunits, as well as the pol II subunit POLR2B, the activators ZNF143, POU2F1, and GABP, and H3 acetylated on lysine 18 (H3K18Ac) at mid-G1 compared to mitosis , suggesting that the transcript synthesized by pol II is highly unstable, at least under the conditions tested. It is conceivable that the ratio of RPPH1 genes transcribed by pol II and pol III, as well as the ratio of stable pol II and pol III RNA products, change in different cell types or under different conditions. The observation that a gene can be transcribed by two different polymerase in vivo thus raises the possibility of an added layer of complexity in the regulation of gene expression. It is not clear why the U6-2 and RPPH1 promoters are capable of recruiting significant levels of pol II. The RPPH1 promoter has a short TATA box, but the U6-7 and U6-8 promoters have the same TATA box and are not promiscuous. An intriguing possibility is that the presence of a 3′ box at a correct distance downstream of the TSS, together with a weak TATA box, allow pol II recruitment.A peculiarity of SNAPc subunits we tested are remarkably consistent with what is known about the architecture and DNA binding of SNAPc. SNAPC4, the largest SNAPc subunit and the backbone of the complex, binds directly to the PSE through Myb repeats located in the N-terminal half of the protein The locations of the occupancy peaks for the four SNAPRNU1 and RNU2 genes as well as all other pol II snRNA genes. This suggests that transcription termination downstream of snRNA gene 3′ boxes does not occur at a precise location but rather over a broad 1.2 Kb region, and is triggered by passage of the polymerase through the processing signal, reminiscent of transcription termination downstream of the poly A signal, in this case in a region of several Kbs The 3′ end of pol II snRNAs is generated by processing at a sequence called the 3′ box c-dependent promoters has been shown to depend on a DSE and on the binding of POU2F1 and ZNF143  by direct association U6-1 promoter U6-1 is weakly POLR3D-occupied compared to other human RNU6 genes c-dependent promoters are targeted very early in G1 by as yet unidentified factors that lead to histone modifications, in particular H3K18 acetylation. It will be interesting to determine how this modification combines with the H3K4me3 mark observed on pol III promoters, including type 3 pol III promoters Activation of several SNAPF143 see . Our ChIChIPs were performed as described The sequence tags obtained after ultra-high throughput sequencing were mapped onto the UCSC genome version Hg18, corresponding to NCBI 36.2, as before www.rajajothi.com/sissrs/) c subunit antibodies gave relatively weak signals. We therefore divided the genome into 200 nucleotide bins, counted tags obtained for each of the four SNAPc subunits analyzed, and retained only bins displaying an enrichment for at least two of the SNAPc subunits. Bins were considered positive only if the tag number in bin reached at least the minimum tag count determined by sissrs for enriched regions with a 0.001 false discovery rate as the one used in sissrs set at the default parameters. We then considered genomic regions containing POLR2B and GTF2B, or POLR3D and BRF2, sissrs peaks as well as a bin positive for two SNAPc subunits within 100 nucleotides of the polymerase sissrs peak. We obtained 157 and 58 loci for the POLR2B and POLR3D lists, respectively, which were all visually inspected. We eliminated peaks in regions of high background, with shapes never found in known snRNA genes , or with identical shape and location in all samples. The most convincingly occupied loci are listed in c subunits. Scores were calculated as described in Peaks were detected with sissrs  correction and kept the bins with an adjusted P-value under 0.005 that were located within a 100 nucleotides of either a RPB2 and TF2B positive region, or a RPC4 and BRF2 positive region (as defined by sissrs). We then applied a second filter to keep only the bins containing at least two (of the four mapped) SNAPc subunits. This gave us a total of 275 bins, which contained all the genes listed in For the SNAPRPPH1-dependent transcription in vivo, 1.2×106 HeLa cells were transiently transfected (48 hours) with pU6/Hae/RA.2 RPPH1 promoter, or the RPPH1 promoter harboring a mutation in the TATA box (TTATAA changed to TCGAGA), as well as the RPPH1 3′ flanking region. To specifically inhibit POLR2B transcription, the cells were treated with 50 µg/ml of α-amanitin  for two or six hours before harvesting.To measure Clonal cell lines expressing U1 or U6-promoter-directed unstable RNA were established by transfection of HeLa cells with plasmid derivatives of pU6/RA.2+U6end-Dsred ATAAGCTGTGGTACCATCTTCCAGCTG region of the ZNF143 gene. HeLa cells were seeded at 2×106 cells per 10 cm plate the day before transfection. Thirty µl of INTERFERin transfection reagent (Polyplus) was added to 1 ml of DMEM serum-free medium containing 60 nM of siRNA duplex, incubated for 15 minutes, and added to the 10 cm plate containing 10 ml of medium. As negative control, we used a siRNA directed against the firefly luciferase st transfection, the cells were synchronized as described above.RNA was extracted from HeLa cells with TRIzol reagent (Invitrogen) according to the manufacturer's protocol and analyzed by RNase T1 protection as before (see Methods section of http://www.ncbi.nlm.nih.gov/geo) under accession number GSE38303.The data can be accessed at NCBI Gene expression Omnibus  gene showing the RPB2 peaks obtained when excluding (upper panel) or including (lower panel) tags aligning with mismatches (as selected by the ELAND software) onto the reference genome. (B). As in (A), but for the RNU1 (U1-12) genomic region.Interrupted peaks and SNPs. (A). UCSC genome browser view of the (TIF)Click here for additional data file.Figure S2RNU2 genes in chr17_random).Spearman correlations of scores for genes occupied by pol II, pol III, GTF2B, and BRF2. The scores obtained for the indicated factors refer to all genes listed in (TIF)Click here for additional data file.Figure S3RNU2 genes in chr17_random).Spearman correlations of scores for genes occupied by all SNAPc subunits tested . The scores obtained for the indicated factors refer to all genes listed in (TIF)Click here for additional data file.Figure S4RNU6 genes are numbered as in RPPH1 sequence contains many SNPs.Alignment of pol III PSEs and TATA boxes. The 5′ flanking sequence of the indicated pol III genes is displayed up to position -1. The PSE and TATA box regions are indicated with a thick line, with the PSE and TATA box as defined in (DOC)Click here for additional data file.Figure S5RPPH1 gene is also displayed. The PSE region is indicated by the thick line with the PSE as defined in U3-1 and U3-3; U3-2, U3-2b, and U3-4; U1-2, U1-3, and U1-4; U1-5 and U1-6; U1-11 and U1-12.Alignment of pol II PSEs. The 5′ flanking sequence of the indicated pol II genes is displayed up to position -1. The (DOC)Click here for additional data file.Figure S61–4 AANAA/G N AGA, see RPPH1 gene, the best match to a 3′ box was found inside the RNA coding sequence.Alignment of 3′ boxes. Sequences resembling 3′ boxes (consensus sequence GTTT N(DOC)Click here for additional data file.Figure S7RNU2 genes in chr17_random).Spearman correlations of scores for genes occupied by ZNF143, POU2F1, and GABP. The scores obtained for the indicated factors refer to all genes listed in (TIF)Click here for additional data file.Figure S8RNU2 genes in chr17_random) as well as the RPPH1 gene, the different motifs found in the promoter region  are represented by colored boxes as indicated. The direction of the motifs  is indicated with an arrow. The motifs that appeared not occupied, either because there was no corresponding ChIP-Seq occupancy peak or because they were not closest to the occupancy peak summit, are shown crossed-out (black crosses). In some cases, as for example in the divergent octamers in U1-like-5, two motifs appeared as likely to be occupied. The promoters are aligned relative to the PSEs and ranked by the POLR2B scores. (B) As in (A), but for the pol III genes in T/A)G (C/G)(G/C)NN(C/G)(C/T)(C/A)(C/G)(G/C)CG(G/C)(G/A)G) of unknown function found in nearly all type 3 pol III genes. The genes are aligned relative to the TATA box and ranked by the POLR3D scores.Schematic representation of promoter regions. (A) For each pol II gene in (TIF)Click here for additional data file.Figure S9RNY4, U1-9, U1-13, and unknown-3, which are not occupied. In unknown-6, the second octamer closer to the TSS is the best centered under the peak summit even though it is a less good octamer than the one further upstream. The numbers refer to the first and last position of the sequences shown relative to the +1 TSS position. The genes not shown in the list  have no matches (with up to two mismatches) to the octamer up to 400 bp upstream of the TSS. All octamer sequences present in the alignment are shown as boxes in U1-like-1/-5/-6/-7/-8/-9 promoter regions have two overlapping octamers of similar quality located in each case near the POU2F1 peak summit; one or both of these motifs may be occupied.Alignment of octamer sequences. Sequences similar to the POU2F1 binding site (octamer) located within peaks of POU2F1 occupancy in the 5′ flanking regions of the indicated genes, except for the octamers in (PDF)Click here for additional data file.Figure S10RMRP gene and the more upstream Z-motif in the RPPH1 gene, which are not under a ZNF143 peak. The numbers refer to the first and last position of the sequences shown relative to the +1 TSS position. The sequences in bold were identified by a MAST de novo search of motifs present under ZNF143 peaks. All Z-motifs present in the alignment are shown as boxes in Alignment of Z-motifs. Sequences similar to ZNF143 binding sites (Z-motif) located within peaks of ZNF143 occupancy in the 5′ flanking regions of the indicated genes, except for the Z-motif in the (PDF)Click here for additional data file.Figure S11RNU2 genes in chr17_random) identified by MAST U1-like genes, two GABP sites of similar quality were identified under the GABP peak.Alignment of GA-motifs. Sequences similar to GABP binding sites (GA-motif) present within 400 bp upstream of the TSSs of the genes in (PDF)Click here for additional data file.Table S1c subunits and either POLR2B and GTF2B, or POLR3D and BRF2 and all UCSC annotated snRNA gene (whether or not occupied by factors) together with the occupancy scores for POLR2B, GTF2B, POLR3D, BRF2, SNAPC5, SNAPC1, SNAPC4, SNAPC2, ZNF143, POU2F1, and GABPA.List of genomic loci occupied by at least two SNAP(XLS)Click here for additional data file.Table S2Summary of the occupancy scores for ZNF143, POU2F1, and GABPA for the same genomic loci as in (XLS)Click here for additional data file.Table S3Number of tags with unique and multiple matches mapped onto the genome for each ChIP_Seq experiment.(PDF)Click here for additional data file.Text S1Results section describing the relationship between loci listed in (DOC)Click here for additional data file.


The differentiation of megakaryocytes, the platelet progenitors, from hematopoietic stem cells is a well-known process that can be mimicked in culture. However, the efficient formation of platelets in culture remains a challenge. Platelet formation is a complicated process including megakaryocyte maturation, platelet assembly and platelet shedding. We hypothesize that a better understanding of the transcriptional regulation of this process will allow us to influence it such that sufficient numbers of platelets can be produced for clinical applications. After an introduction to gene regulation and platelet formation, this review summarizes the current knowledge of the regulation of platelet formation by the transcription factors EVI1, GATA1, FLI1, NFE2, RUNX1, SRF and its co-factor MKL1, and TAL1. Also covered is how some platelet disorders including myeloproliferative neoplasms, result from disturbances of the transcriptional regulation. These disorders give us invaluable insights into the crucial role these transcription factors play in platelet formation. Finally, there is discussion of how a better understanding of these processes will be needed to allow for efficient production of platelets  They monitor blood vessels for damage. Upon injury they initiate blood clotting and contribute to vessel wall repair. On the flip side of the coin platelets cause thrombosis when too many and/or hyperactive platelets cause vessel occlusion, leading to heart attacks or strokes. Each day about 10The hematopoietic system and its lineage decisions have been characterized in some detail. Transcription factors are key regulators of these decisions. Over the last decade a lot of progress has been made in identifying the factors involved in the differentiation of the bipotent megakaryocyte-erythroid progenitor (MEP) from hematopoietic stem cells (HSCs) in the bone marrow (BM). Also, factors involved in the lineage bifurcation into committed early erythroid or megakaryocytic cells have been identified, making it possible to model MK/erythroid differentiation Each cell of the body contains the same genetic information. Cell function and identity are defined by the genes that each cell expresses. Expression of actively transcribed genes is generally initiated by binding of ubiquitously expressed general transcription factors to the TATA sequence in the promoters of these genes. This leads to the recruitment of the transcription machinery consisting of other regulatory co-factors and RNA polymerase Complexes of different transcription factors can bind a single DNA element, inducing transciptional activation or repression cis-regulatory modules (CRMs). CRMs can be located several hundreds of kilobases (kb) or even more away from the promoter and still function as enhancers/repressors The specificity for spatio-temporal regulation of gene expression is acquired by combinatorial binding of multiple transcription factors The main driver of MK differentiation is the binding of thrombopoietin (TPO) to its receptor MPL Tpo or Mpl knock-out mice, probably driven by the interaction between the MK progenitor and the BM endothelial niche Besides TPO, other cytokines, chemokines and extracellular matrix proteins influence megakaryopoiesis Maturation of MKs is accompanied by successive rounds of DNA replication without cytokinesis (endomitosis), resulting in large polyploid cells with a lobulated nucleus. Concurrently, the MK matures into a cell that contains all the machinery necessary for platelet function. Secretion granules appear in the cytosol, which, in addition to many constituents found in other cells, also contain platelet-specific proteins that promote coagulation and vessel repair. MK maturation is also accompanied by the expression of cell surface receptors that allow platelet adhesion and aggregation.These cytoplasmic and membrane constituents ultimately need to be packaged into the small anucleate platelets before they are shed into the circulation. A widely accepted model for platelet assembly and shedding is the proplatelet model. BM MKs develop multiple branched extensions that protrude into the marrow sinusoids, where their terminal buds are released into the circulation in vivo in mice Release of MK fragments with proplatelet characteristics into the circulation has been observed As the process of platelet formation is still debated, so is the trigger for this process. Although the role of TPO in MK maturation is undisputed, TPO seems dispensable for platelet formation and maybe even inhibits this process in vivo platelet formation Location of the MK and interaction with its environment seems to be crucial for platelet formation. It is known that the relocation of immature MKs from the osteoblastic niche to the endothelial niche driven by chemotactic agents is essential for maturation and platelet production In addition to this direct MK-to-endothelial cell contact, extracellular matrix proteins also influence proplatelet formation. Type I collagen inhibits proplatelet formation through integrin αIIβI in vivo and in culture 2N or 4N MKs form proplatelets Surprisingly, proplatelet formation is not strictly within the remit of the higher ploidy MKs. Both et al. It has been hypothesized that polyploidization is required to meet the MK's vast need for protein synthesis and cell growth. This is supported by a study by Raslova In keeping with the idea that platelet shedding is not necessarily coupled with ploidy, genes regulating platelet production, such as NFE2 , are not involved in the regulation of polyploidization Specific transcription factors implicated in late MK differentiation and abnormalities causing disease are summarized in Fig. Late MK differentiation involves endomitosis, cytoplasmic maturation and proplatelet formation. Depicted are the transcription factors (black) shown to play a role in these processes and, if identified, their downstream targets (grey) performing this role. Some of these also affect platelet function. The arrow indicates in which stages the transcription factors have been implicated .Gata1 knock-out MKs, but does not rescue expression of late MK genes or proplatelet formation GP1BB promoter proximal region causes a form of Bernard-Soulier syndrome GATA1 (GATA-binding factor 1) and GATA2 are zinc-finger transcription factors. Both bind to a common co-factor ZFPM1 . GATA1 is highly expressed in erythroid cells, mast cells and MKs. Although GATA2 plays a role in the maintenance of early multipotent stages of hematopoiesis, it is also expressed in MKs and there are indications that GATA2 may have an overlapping function with GATA1 GATA1 are causative for a number of human disorders, further illustrating its central role in megakaryopoiesis GATA1 gene, which universally leads to the expression of a truncated form called GATA1s. GATA1s overlaps in its function with GATA1 in that it allows for commitment to the MK lineage, but does not allow full maturation of the MK. This leads to marked expansion of MK progenitors. It is believed that this is the mechanism that leads to a pre-leukemic transient myeloproliferative disorder (TMD) present in 4–10% of newborns with DS. TMD is characterized by an expansion of immature Mks, but subsequently often undergoes spontaneous remission. The development of DS-AMKL requires additional hits to the trisomy 21 and GATA1s mutations. For example, over-expression of ETS family transcription factors such as ERG , in which germline heterozygote RUNX1 mutations lead not only to thrombocytopenia, but also to impaired platelet function. In addition, FDP/AML patients also carry a high risk of development of myelodysplasia and leukemia. Platelets from these patients have abnormal expression of, amongst others, the TPO receptor RUNX1 (AML1) is a member of the RUNT family of transcription factors and together with its cofactor CBFB  represents the most common mutational target in human acute leukemia. RUNX1's essential role in HSCs is illustrated by the fact that emergence of all definitive hemopoiesis out of the aorto-gonadal-mesonephros during embryogenesis is prevented in mice that completely lack RUNX1. Conditional knock-out studies also show a fundamental role for RUNX1 in megakaryopoiesis, with a marked decrease in polyploidization and cytoplasmic development of MKs, similar to what is observed for Friend leukemia virus integration 1 (FLI1) and another member of the family of ETS transcription factors, GABPA , seem to act in tandem in MKs. As MK maturation progresses, the ratio of FLI1/GABPA increases. In keeping with this observation, GABPA regulates expression of early MK genes (including GPIIB and the TPO receptor MPL) whilst FLI1 binds to both early and late  MK genes Conditional knock-out studies of TAL1  in the hemopoietic lineage have shown a specific decrease of red cell and MK production t translocation found in MK leukemias. SRF deficiency leads to a drop in platelet count accompanied by an increase in the BM content of MK progenitors, similar to that observed in NFE2 and FLI1-deficient animals Serum response factor (SRF) is a MADS-box transcription factor regulating growth factor-inducible genes and genes controlling cytoskeleton structures involved in cell spreading, adhesion and migration. Its role in megakaryopoiesis was identified when its co-factor MKL1 (megakaryoblastic leukemia [translocation] 1) was found to be involved in the MECOM locus on chromosome 3, encoding EVI1 (ecotropic virus integration site 1), is implicated in 4–6% of all AML cases. Interestingly, the so-called 3q21q26 syndrome leukemias present with particular dysmorphic MKs and an elevated platelet count. This is thought to be caused by inhibition of CDK2 (cyclin A dependent kinase inhibitor 2) expression mediated by the abnormal expression of EVI1 The EVI1 is expressed in hematopoietic progenitor cells, MKs and platelets. Ectopic expression in UT-7/GM cells changes these cells into polynuclear large cells that express PF4 RBM8A (RNA binding motif protein 8A) gene can underlie the thrombocytopenia with absent radii (TAR) syndrome RBM8A. The SNP increases binding of EVI1 and leads to a reduction of transcription of RBM8A and the encoded protein. TAR patients have low numbers of MKs in the BM that seem to have a maturation defect Additional evidence for the role of EVI1 in megakaryopoiesis is supported by a recent study showing that a mutation, creating an EVI1 binding site, discovered by exome sequencing in the promoter of the The discovery by different groups in 2005 of a JAK2V617F mutation present in 50% of patients with essential thrombocythemia (ET), 60% of patients with myelofibrosis (MF) and over 90% of patients with polycythemia vera (PV) emphasized the importance of the JAK2 signaling pathway in MK growth and the production of platelets. The main downstream effectors of JAK2 are the STATs and the MAPK and the PI3K pathway, all of which ultimately regulate transcription.There is evidence that not only MK growth, but also the last steps of maturation are altered in the above-mentioned myeloproliferative neoplasms (MPNs). One study showed that culture-derived MKs from patients with ET had an increased ability to form proplatelets and that the number of proplatelet-forming MKs in culture correlated with the platelet count in the patient from whom the MKs were derived Jak2 mutations, significantly higher levels of activated STAT3 were found in the BM as the complex of the three transcription factors binds to the Stat3 promoter, inducing its expression.What is not clear at this stage is through which of the transcription factors JAK2V617F influences the maturing MKs. STAT3 analysis of a small group of ET patients showed that in about half of the patients, there was a STAT3 hyperactivation, but interestingly this did not correlate with the presence of the JAK2 mutation. In support of the central role played by STAT3 in MPNs, forced expression of RUNX1/MDS1/EVI1 in the BM of mice leads to an ET phenotype with an elevated number of dysplastic platelets with anisocytosis, degranulation and giant size It is well known that the MAPKs play a central role in megakaryopoiesis Finally, JAK2V617F also activates the PI3K pathway, which can in turn exert a transcriptional effect through mammalian target of rapamycin (mTOR). There is ample evidence for a role of mTOR in megakaryopoiesis in vitro in response to TPO, but are unusually large and have a disorganized demarcation membrane, and granules are small and sparse, indicating a late maturation block NFE2 is expressed in hematopoietic progenitor cells as well as in the myeloid, erythroid and MK lineages in vivo platelet production. However, transplantation of NFE2 over-expressing cells only accelerated platelet production and did not lead to an increase in the maximum level or total number of platelets detected in recipient blood When expressed ectopically in BM cells, NFE2 also influences earlier stages of MK differentiation and allegedly enhances in vitro platelet formation.The apparent role of NFE2 in proplatelet formation might be explained through the function of some of its direct transcriptional targets, such as TUBB1 Although it is clear that the transcription factors described above play a role in MK differentiation, the level of complexity regarding how they regulate gene expression is way beyond their individual function. Several of the transcription factors discussed above have been shown to interact with each other to regulate megakaryopoiesis, possibly in a linear hierarchy. For example, GATA1, GATA2 and TAL1 have been shown to regulate NFE2 The advent of new generation sequencing and techniques such as chromatin immunoprecipitation (ChIP) allow us to identify where transcription factors bind in the genome. As transcription factor binding tends to cluster on sites that are critical for gene regulation, looking at multiple transcription factors simultaneously greatly improves the ability to infer biological relevance from a binding event. In a previously published study, we looked at simultaneous binding of GATA1, GATA2, RUNX1, FLI1 and TAL1 in primary human MKs Undoubtedly, microRNAs, epigenetic mechanisms and post-translational phenomena play an additional key role in the eventual expression of a given protein besides transcription factors. Detailed discussion of this additional layer of protein expression regulation in the context of the MK falls outside the remit of this review. It is, however, interesting to point out that the level of transcription factors involved in late MK differentiation and platelet formation, such as GATA1, FOG1, FLI1, TAL1, RUNX1 and NFE2, does not increase with polyploidization, whereas transcripts of their target genes are up-regulated The study of the difference between fetal and adult megakaryopoiesis and platelet formation may uncover other gene regulatory mechanisms to add to our current knowledge. Fetal platelets are extremely large, with a diameter 1.6 times larger than adult platelets, and contain a large amount of RNA. This may be due to differences in gene expression regulation caused by differential expression of key transcription factors (such as GATA1) Ultimately, the knowledge of how transcriptional regulation affects megakaryopoiesis and platelet formation will be invaluable and relevant to clinical care. TAR and GATA binding-related Bernard-Soulier syndrome may be only two of a series of inherited disorders where the causative mutation lies not in a coding, but in a regulatory region of the genome. Understanding how these genetic variants can lead to disease will further inform our knowledge of how MK maturation is controlled at the transcriptional level. The same reasoning applies to the understanding of how genetic variations affect gene transcription in healthy individuals. A recent GWAS study identified 68 loci associated with platelet count and volume in vitro for human use. This includes platelets for transfusion, as it would potentially have major advantages over donor-derived products in terms of safety and blood group matching. The most promising cell source in this aspect is human induced pluripotent stem cells (iPSCs). Protocols to derive MKs from iPSCs have been published, but these clearly fail on two fronts. Firstly, the number of MKs produced per seeded stem cell is very low  will remain a dream.Finally, there is currently a dedicated drive in the biomedical community to try to produce cells The authors state that they have no conflict of interest.