Aim2. We show that this unannotated AFE isoform of Aim2 is the predominant isoform expressed during inflammation and contains an iron-responsive element in its 5′UTR enabling mRNA translation to be regulated by iron levels. This work highlights the importance of examining alternative isoform changes and translational regulation in the innate immune response and uncovers novel regulatory mechanisms of Aim2.Determining the layers of gene regulation within the innate immune response is critical to our understanding of the cellular responses to infection and dysregulation in disease. We identified a conserved mechanism of gene regulation in human and mouse via changes in alternative first exon (AFE) usage following inflammation, resulting in changes to the isoforms produced. Of these AFE events, we identified 95 unannotated transcription start sites in mice using a de novo transcriptome generated by long-read native RNA-sequencing, one of which is in the cytosolic receptor for dsDNA and known inflammatory inducible gene,  Macrophages are critical cells in the innate immune system that combat infection by initiating acute inflammatory responses. Acute inflammation is tightly coordinated and begins with the detection of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs), which include toll-ike receptors (TLRs) . These iRecently, the process of alternative splicing has emerged as another key mechanism by which the immune system is regulated. Alternative splicing is a regulated process enabling a single gene to produce many isoforms, thus increasing the complexity of gene function and the proteome . Much ofWhile inflammation-induced alternative splicing in both human and mouse macrophages has been investigated on a genome-wide scale , to our Aim2. We show that this unannotated AFE isoform of Aim2 is the predominant isoform produced during inflammation and contains an iron-responsive element (IRE) in its 5′UTR, enabling mRNA translation to be controlled by iron levels. This work reveals that alternative transcript isoform usage plays a crucial role in shaping the transient nature of the inflammatory response. Isoform expression is an additional layer of regulation within the immune response and therefore a possible contributing factor to the development of auto-immune and inflammatory diseases. Understanding the exact isoforms of genes that are expressed during an inflammatory response will enable us to design better targets for therapeutic intervention of these diseases.Here we used both long- and short-read RNA-sequencing to uncover novel isoforms and classes of alternative splicing events following inflammation in human and murine macrophages. Interestingly the dominant conserved class of alternative isoform usage observed following inflammation is alternative first exon (AFE) usage, which involves alternative transcription start sites (TSS) usage coupled with alternative splicing. AFE events can have multilevel effects on protein diversity, regulating genes through alterations of the 5′UTR region, and directing the locality of proteins through alternative N-termini . We idenListeria or Salmonella, using our bioinformatic pipeline and with more stringent thresholds than the previous study, also revealed AFE events to be amongst the most significant prevalent alternative splicing events   and murine bone marrow-derived macrophages (BMDMs) with and without LPS treatment . We founI| ≥ 10) .Ncoa7 , localized to the cytosol. Aim2 is a dsDNA sensor that upon recognition induces the formation of an inflammasome complex releasing IL1β and IL18 from the cell as a defense mechanism to control infection (Aim2 (NM_001013779.2). Predominant isoforms  assembled from native RNA-sequencing with FLAIR identified the canonical isoform and five unannotated isoforms that use the inflammatory-activated promoter, revealing a new longer 5′UTR  and interferon response factor 3 (IRF3) . We founata sets .Aim2, we identified 106 individual TF motifs within the annotated promoter and 121 motifs within the unannotated promoter  was compared to the annotated 5′UTR (489 bp)  . The una(489 bp) , while e(489 bp) . To expl(489 bp) . We iden(489 bp) . Globallp)  bind to IREs located within the 5′UTR  and can block translation, while IREs in the 3′UTR  can promote translation . HoweverAim2 acts as a translational regulator endogenously, we performed polysome profiling followed by RT-qPCR on primary BMDMs in the presence and absence of LPS for 18 hr to determine the translational competency of the isoforms of Aim2  protein lysates was generated and Aim2 expression was measured by western blot , and Sgk1 .To overcome the current limitations of any transcriptome build, we used direct RNA nanopore technology on primary murine macrophages to build our own transcriptome de novo with the aim of identifying novel full-length transcriptional isoforms that are expressed with and without inflammation . The limAim2 is characterized as an interferon-inducible gene  in accordance with the guidelines.Human peripheral blood mononuclear cells (PBMCs) were enriched by density gradient centrifugation of peripheral blood from healthy human donors through a Ficoll-Paque PLUS  gradient. Monocytes were isolated from PBMC by negative selection using the EasySep Human Monocyte Isolation Kit (STEMCELL Technologies) according to the manufacturer’s instructions. To differentiate monocytes into macrophages, recombinant human M-CSF (50 ng/mL) was used in RPMI-1640 medium with 10% fetal bovine serum (FBS), 2 mM L-glutamine, 10 mM HEPES, 1 mM sodium pyruvate, 100 U/mL penicillin, and 100 µg/mL streptomycin. The culture medium that contained fresh recombinant human M-CSF was replaced every 2 days.Cells were cultured in DMEM with 10% FBS supplemented with penicillin/streptomycin or ciprofloxacin. Primary BMDMs were generated by cultivating erythrocyte-depleted bone marrow cells in the presence of 30% L929 supernatant, and the cells were used for experiments 6–9 days after differentiation. J2Cre virus  was used6 cells were seeded in 12-well format.Bone-derived macrophage cells were primed with 100 µM of FAC for 24 hr prior to TLR stimulation. BMDM cells were stimulated with TLR ligands for the indicated time points using the following concentrations: LPS 200 ng/mL (TLR4). For RNA and protein isolation, 1–2 × 10RT-PCR validation was completed using three biological replicates. KAPA HiFi HotStart ReadyMix PCR Kit (Kapa Biosystems) and the manufacturer’s suggested cycling protocol were used to complete the PCR reaction with the following primers:Mse_Denr_F1:ATCGCGATAAAGGCTCATTGMse_Denr_F2:GCTACCTGTCCTTTTCCCCAMse_Denr_R:AACTTGGCACTGTTCTTCGTMse_Arhgef7_F1:TGTTGTTCTGGGGTTTGTGAMse_Arhgef7_F2:CTGTGTGTTGCAGGTCTACCMse_Arhgef7_R:GTGTCACCAAGGAGCTGAGGMse_Ncoa7_F1:GTGGTGGAGAAGGAAGAGCTMse_Ncoa7_F2:TTCTATTGTGCCAGGCCTGAMse_Ncoa7_R:GCATGTTTTCCAGGAGTGCAMse_Ampd3_F1:CCCTACTGTAGATGAATCCCCTTAMse_Ampd3_F2:GCTGAGCTTTGTGTCTGTGTMse_Ampd3_R:GGGGACAGTAAACAGGGACAMse_Rcan1_F1:ACTGGAGCTTCATCGACTGCMse_Rcan1_F2:GACTGAGAGAGCGAGTCGTTMse_Rcan1_R:CATCGGCTGCAGATAAGGGGHu_NCOA7_F1:TGTTCAGTGGTCTCCCGATGTCTATGGHu_NCOA7_R:GGGCCGTAGGACAGGCAGCAHu_NCOA7_R2:AGCGTGGCTACAAGTAACTGTGGTGTHu_AMPD3_F1:TATGCAAAACAGAGACCTCCHu_AMPD3_R:CACTTCAGAGATGTTCAGCTHu_AMPD3_F2:CCTGCTTGGTTTTAGAGGATHu_RCAN1_F1:GACTGGAGCTTCATTGACTGHu_RCAN1_R:ATTCTGACTCGTTTGAAGCTHu_RCAN1_F2:TAGCGCTTTCACTGTAAGAABand intensities were measured for each band in each condition and sample using FIJI . The relTotal cellular RNA from BMDM cell lines or tissues was isolated using the Direct-zol RNA MiniPrep Kit (Zymo Research) according to the manufacturer’s instructions. RNA was quantified and controlled for purity with a nanodrop spectrometer (Thermo Fisher). For RT-qPCR, 500–1000 ng were reversely transcribed  followed by RT-PCR  using the cycling conditions as follows: 50°C for 2 min, 95°C for 2 min followed by 40 cycles of 95°C for 15 s, 60°C for 30 s, and 72°C for 45 s. The melting curve was graphically analyzed to control for nonspecific amplification reactions. Quantitative RT-PCR analysis was performed with the following primers:Mse_Aim2_F_Annotated: CCGCCATGCTTCCTTAACTAMse_Aim2_F_Unannotated: AGGCGGATGGTTTGAACTCTMse_Aim2_R_Exon2: TTGAAGCAACTTCCATCTGCMse_Aim2_CDS_F: AGTACCGGGAAATGCTGTTGMse_Aim2_CDS_R: GAGTGTGCTCCTGGCAATCTMse_Gapdh_F: CCAATGTGTCCGTCGTGGATCMse_Gapdh_R: GTTGAAGTCGCAGGAGACAACMse_Neat1_F: TTGGGACAGTGGACGTGTGGMse_Neat1_R: TCAAGTGCCAGCAGACAGCAThe GFP reporter plasmid was CMV-Zeo-t2A-GFP. Zeocin is flanked by NheI and AgeI. The sequence of the annotated and unannotated 5′UTR was used as defined by the UCSC RefSeq and our sequencing results to be. Using KAPA HiFi HotStart ReadyMix PCR Kit (Kapa Biosystems), the two 5′UTRs were amplified from cDNA.cDNA_F_Annotated:TTCCTGTCCTGTCTGCCGcDNA_F_Unannotated:TATATCTAAAATACCTCTGGTTGAGACCTCcDNA_R_5'UTR:CTCACAGTCCCAGGATCAGCThe 5′UTRs were then PCR amplified with primers containing restriction enzyme sites for AgeI and NheI.NheI_F_Annotated:GCTAGCTTCCTGTCCTGTCTGCCGGTNheI_F_Unannotated:GCTAGCTATATCTAAAATACCTCTGGTTGAGAGGTGGTAgeI_R_5'UTR:ACCGGTCTCACAGTCCCAGGATCAGCGGTPCR products, as well as the GFP plasmid, were then processed using AgeI and NheI restriction enzymes overnight. These samples were run on a 1% agarose gel. A gel extraction was completed for each band using the PCR clean-up Gel extraction kit (Machery-nagel). The PCR product was confirmed using Sanger Sequencing.CCCTGATGCTGGGGCAAAATAATTATAAATGCTAGTTTTCAGTTTC], 1 µL R primer (0.1 µg/µL) [Remove_IRE_R - GAAACTGAAAACTAGCATTTATAATTATTTTGCCCCAGCATCAGGG], 1 µL dNTP (10 nM), 1 µL Phu polymerase, and 40 µL dH2O. PCR program: 95℃ 1 min, 18 cycles of 95℃ 30 s, 55℃ 1 min, 72℃ 1 min, then end PCR with 72℃ 1 min and 4℃ hold. Add 0.5 µL of Dpn1 (NEB) to 25 µL PCR reaction. Incubate at 37℃ for 1 hr to digest parental DNA. Transform digested and undigested plasmid into DH5α competent cells. Pick ~10–15 colonies and start overnight cultures. Colony PCR plasmids using Dpn1_Colony_PCR_F - TTGGCTAGTCCTGGCCCTAT and Dpn1_Colony_PCR_R - GCTGGTTTAGTGAACCGTCAG to check for 20 bp deletion on a 3% agarose gel. Grow up colonies that have deletion, miniprep plasmids and send to Sequetech for sequence verification.Set up PCR reaction with 1 µL 279 plasmid, with unannotated Aim2 5′UTR, 5 µL 10 × Phu buffer, 1 µL F primer (0.1 µg/µL) , . Parameters used for finding significantly differentially spliced events using compareSampleSets.py from JuncBASE are: --mt_correction BH --which_test t-test --thresh 10 --delta_thresh 5.0. Parameters used for finding significantly differentially spliced events using DRIMSeq are: min_samps_gene_expr = 6, min_samps_feature_expr = 3, min_gene_expr = 10, min_feature_expr = 0.The s v2.2.1  with parFor each experiment, a table with the union of significant events found using DRIMSeq and compareSampleSets.py was created. A list of genes with significant events was generated for each experiment using this table. BioVenn  and DrawFollowing differential splicing analysis with JuncBASE and DRIMSeq2, gene names of significant events  were curated by event type. Overlaps were determined between human and mouse JuncBASE and DRIMSeq2 events determined with and without the support of Nanopore-identified transcripts.Following differential splicing analysis with JuncBASE and DRIMSeq2, gene names of significant events  were curated by event type. Overlaps were determined between mouse JuncBASE and DRIMSeq2 events determined with and without the support of Nanopore-identified transcripts.Coordinates corresponding to mouse CAGE peaks (mm9.cage_peak_phase1and2combined_coord.bed) were downloaded from the FANTOM5 database . CoordinCoordinates corresponding to CAGE peaks identified as true TSS by TSS classifier (TSS_mouse.bed) were downloaded from the FANTOM 5 database. Coordinates were converted from mm9 to mm10 using liftOver. The proportion of true known and novel FLAIR-identified TSS sites was identified by determining TSSs that overlapped with the CAGE peaks. Bedtools (v 2.25.0) tool intersect was used to complete this analysis.https://github.com/ENCODE-DCC/atac-seq-pipeline) using the aligned reads as sequence input. Parameters that were used followed the basic JSON input file template, using an IDR threshold of 0.05. Peaks from both conditions were then merged using bedtools merge  from the aligned reads. The aligned input control reads were input as genomic background to account for noise in ChIP-seq experiments. The basic JSON input template file was used using an IDR threshold of 0.05. Differential peak analysis was done using the HOMER suite designed for ChIP-seq data (<0.25 and log2FC > 1.ChIP-seq fastq sequencing files for the NF-κB subunit p65 and interferon transcription factor Irf3 were downloaded from the GEO accession number GSE67357 published by seq data . Consens<0.05) AFE events were first filtered out from all results. For all significant results, if the inclusion exon had a Δ PSI >10, the inclusion exon coordinates from the JuncBASE table  were considered, and any inclusion exon with Δ PSI >10 was used. Redundant events with the same exon coordinates were then filtered out, leading to a final set of exon coordinates. The coordinates were then extended to include 10 kbp upstream of the exon. Overlap of differential chromatin accessibility and different TF binding was done using bedtools intersect  AFE events were first filtered out from all results. For all significant results, if the inclusion exon had a Δ PSI >10, the inclusion exon coordinates from the JuncBASE table were used as the coordinates for that splicing event. If the inclusion exon had a Δ PSI < –10, all other exons for that splicing event from the statistical testing (DRIMSeq or t-test) were considered, and any other exon with Δ PSI >10 was used  is there still transcriptional activation of one TSS and transcriptional silencing of the other? In other words, is there coordinated regulation of the two TSSs to ensure overall message abundance doesn't change, or does activation of one TSS inherently shut off the other ?3. The data suggesting that an IRE regulates translation of the induced 5'UTR is compelling, but more work should be done to confirm. Most importantly, the experiment in Figure 4J should be repeated with the deltaIRE version of the unannotated UTR. Also is the IRE regulation controlled upon LPS-stimulation, or just the presence of the IRE element? In other words, what is the distribution of the annotated and unannotated isoforms in the polysome in the absence of LPS (i.e. repeat 4P without LPS)? Can the authors comment on whether the level of iron or the activity of IRP1/2 change in LPS-stimulated cells?Reviewer #3:This manuscript by Robinson et al. presents an interesting and timely analysis of a wealth of transcriptome data upon immune stimulation. The unique combination of long-read Oxford Nanopore and short-read Illumina high-throughput sequencing across both human and mouse samples presents an opportunity many interesting inter-species immune response comparisons, as well as elucidation of full-length transcript information. This paper is well-written and has interesting validation and discussions regarding Aim2. My major concern is that the paper seems to narrow in on the characterization of Aim2 and class of RNA processing changes  quite quickly without really delving into the rest of the data and how they arrived there. Below are my major comments and suggestions:1. I would have liked the authors to provide more insight into how they honed-in on specifically talking about first exon changes, by discussing more of the other RNA processing changes they found. There is cursory mention in the text and figures of other alternative exon or splice site changes. Firstly, other studies (including those referenced by the authors) have found hundreds RNA processing changes genome-wide upon immune stimulation – especially of cassette exons, alternative splice sites, and last exon/3'UTR changes. However here, the authors only find tens of changes . Are they underpowered to identify changes and can they do any sort of analyses to show that they are sufficiently powered ?2. Similarly, I would also be interested in seeing an analysis indicating whether the 50 AFE events that overlap between the long-read and short-read sequencing analyses is a statistically significant overlap. Particularly, how many overlapping events would be expected given the difference in quantification power between the two methods? How many real AFE differences might the authors be missing because the long-read sequencing methods often do not have the power to identify them .conservation of splicing or RNA processing changes after immune activation, so I would love to see more analysis and discussion of this aspect genome-wide.3. Second, for the non-AFE changes that they did find, there is very little discussion about what those changes might represent. Specifically: (a) how many changes are validated with long-read data?, (b) is there any insight into specific domains being included/changed, especially using the long-read data?, (c) how many of these non-AFE changes overlap between species? and (d) which types of genes show higher overlap between species and what are their characteristics ? To my knowledge, this is the first study that is really designed to properly really look at the 4. The authors define significant splicing changes as those with a p-value <= 0.25 and |dPSI| >= 10. I'd like some more clarification on whether this is an adjusted p-value . Especially if this is adjusted, I find it surprising that the authors are choosing such a liberal statistical confidence level and that even with such a liberal threshold, they are only getting tens of significant events. I would like the authors to at least show these same trends across multiple p-value thresholds or with rank threshold analysis  to show biological trends.5. The authors introduce their long-read sequencing data by mentioning that they wanted to identify "additional splicing events that are not captured using short-read sequencing." They then go onto to only talk about novel first exon events identified with the long-read sequencing data. Did they identify any other non-AFE events in using the long-read that could then be quantified with the short read data? And second, how do they quantify confidence for novel AFE isoforms, when long-read data seems to have lots of issues with properly sequencing the terminal ends of transcripts ? They mention the use of ATAC-seq data to show putative promoter support, but mention at one point in their methods that ATAC regions within 10kb of AFEs are considered. These seems like it could be a rather large region to be sure that the ATAC peak is specific to a novel AFE – what is the average distance between AFEs? Finally, I would love to also see the incorporation of CAGE-seq data (or other 5'end data) to validate the specific AFEs sites – which I believe the FANTOM consortium has across many human and mouse tissues.Reviewer #4:In this manuscript, Robinson et al., identified alternative first exon (AFE) switching events conserved between mouse and human following macrophage inflammation. Using short and long-read sequencing, the authors identified a few unannotated transcription initiation sites (TSS) that are specific of an inflammatory response. Among those, they centered on an unannotated TSS in the Aim2 gene that drives expression of a novel isoform regulated by an iron-responsive element in its 5′UTR.eLife, I would expect further progress on global mechanisms and biological relevance of these AFE switching events, as well as evidence that the AFE are truly first exons/TSSs.While previous work had documented crucial AFE switching events in many other biological contexts, Robinson et al. presents here an interesting AFE switching event that can have potential implications for our understanding of the molecular regulation of the innate immune response. For publication in 1. Are the AFEs truly first exons/TSS? While both short-read and long-read sequencing detected changes in alternative splicing choices, neither of those are optimal methodologies to analyze first exons. Therefore, I suggest to use a more specialized method to identify (and quantify) more accurately the usage of first exons. Globally, cap analysis of gene expression (CAGE) would be ideal. For validation of specific AFE changes, the qPCR technique has a few issues. First, it does not have nucleotide resolution, so the authors should not refer to TSSs if they used this technique for validation. Second, many downstream first exons are also used as internal exons in other isoforms. There is not a direct technology to analyze specifically first exons/TSSs here. Also, RNA-sequencing technologies, depending on their depth, can definitely miss specific isoforms. Considering a low coverage in 5'end of genes in RNA-seq analysis, this is particularly important for first exons. A qPCR would only analyze the well-known TSSs. Thus, 5'RACE or a similar technology should be perform to assess the relative usage of AFE specifically.2. Global mechanism. The authors assumed that the mechanism of AFE switching is generated by transcription initiation and looked for transcription factors binding and chromatin structure modifications in promoters. However, they did not ruled out the possibility that the global switching effect is a post-transcriptional regulation, such as differential mRNA stability. A transcription initiation measurement  is necessary to demonstrate that the changes in AFE usage are co-transcriptional. In addition, in terms of their ATAC-Seq analysis, the chromatin structure changes in promoters can be cause or consequence of transcription initiation. Thus, it should not be listed as one mechanism driving the expression of AFE events (line 145). Also, to demonstrate a mechanism based on transcription factor binding more than 2 transcription factors should be considered. In any case, the expression patterns of the transcription factors considered are not clear. As a minor note, the bioinformatic analysis of the two promoters regions driving the isoforms of Aim2 (line 156) is not explained in the method section.3. Biological relevance. Could the authors evaluate whether the translation regulation of Aim2 based on its AFE switching is a more generalized phenomenon? Are there any global gene regulation changes triggered by the other genes with significant changes is AFE usage?eLife. Your revised article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.Congratulations, we are pleased to inform you that your article, "Inflammation Drives Alternative First Exon usage to Regulate Immune Genes including a Novel Iron Regulated Isoform of Aim2", has been accepted for publication in Reviewer #1:I appreciate the authors' efforts to address the issues of global mechanism and biological relevance, as well as other points raised. This new version of the manuscript is significantly stronger and it represents an important contribution to the literature. However, I still have concerns about the evidence the authors present to demonstrate that the AFE are "true" first exons/TSSs.In my previous report, I suggested the authors use CAGE instead of RNA-seq (either short or long-reads) and 5'RACE instead of qPCR, both specific techniques to analyze first exons/TSSs. In this version, the authors used CAGE data from the FANTOM project to classify their first exon calls as "true" TSS. They found that only 45% of the novel TSS identified overlap with CAGE peaks. In my opinion, this raise the possibility that the other 55% are not "true" TSSs. Further, before publication, I would expect 5'RACE or any other direct method to validate that specific TSS are "true" TSS, at least for Denr, Arhgef7 and Aim2 which they validated using RT-qPCR.Reviewer #2:1. While the authors have now included a brief discussion about whether AFEs are regulated by transcription, splicing, or both in their discussion, I still think the authors should tone down the mechanistic implications of the language throughout the rest of the text, perhaps use the term "alternative RNA processing changes" instead of "alternative splicing changes"?2. The authors mention that they implemented a new parameter in their FLAIR analysis to incorporate a bed file of ATAC annotations to calibrate the identification of novel AFEs. Do the authors plan to release this new parameter as an update to their existing software? I think this would be of great use to the community that analyzes long-read RNA data.Reviewer #3:The authors have fully addressed all of the concerns raised by adding additional data. This enhances the significance of the work and strengthens the conclusions. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #2:Our understanding of the transcriptomic impact of innate immune signaling remains incomplete. Here Robinson et al., use both long and short read RNA sequencing to gain further insight into LPS-induced changes to mRNA isoform expression in human and mouse macrophages. Their studies report the novel observation that the most common change in isoform expression is alternative use of the first exon. Such changes are indicative of transcriptional regulation, and is thus consistent with the known impact of innate immune signaling on activation of multiple transcription factors. Despite some minor concerns with details of the study, this is a well-executed and important study that will be of interest and importance to many studying innate immunity, as well as those interested in gene regulation.1. In some ways this is minor, but the authors should be careful to not describe alternative first exon use as alternative splicing. While a novel splice junction is created, mechanistically this is driven by changing transcriptional regulation, and then splicing occurs in the only pattern available to that TSS. In general this is described appropriately in the manuscript, but at a few points there is confusing terminology.Many thanks for this discussion point. There are a couple of nuances that we would like to address and that we hope clarifies our use of language throughout this manuscript. We now clearly define that AFEs involve alternative TSS usage coupled with alternative splicing and thus changed the terminology to “Alternative Isoform Usage”, where more appropriate. Not all of the AFE events are only changed due to transcriptional regulation. In the literature, we do see evidence of alternative splicing coupled with a new TSS and upstream exon. A recent study by Fiszbein et al. challenges the notion that promoter choice is first, but rather the splicing of internal exons can impact promoter choice (PMID: 31787377). This study shows that internal exons can dictate which first exon is spliced in the mature RNA molecule. We hope that using “Alternative Isoform Usage” strikes a balance that is clearer in this version of the manuscript.2. An interesting and somewhat surprising point in the manuscript is that 50% of the AFE events don't show an overall change in gene expression. For Aim2, which does change, the authors show that the AFE change is due to activated use of the unannotated TSS in LPS-stimulated cells. For those genes for which AFE use doesn't correlate with a change in gene expression  is there still transcriptional activation of one TSS and transcriptional silencing of the other? In other words, is there coordinated regulation of the two TSSs to ensure overall message abundance doesn't change, or does activation of one TSS inherently shut off the other ?Thank you for your interest in the other AFE genes. We have now included further characterization information of inclusion and exclusion isoforms of Ncoa7, Rcan1, Ampd3, Denr and Arhgef7 in supplemental figure 11. We show that of the 5 candidates, only Denr is significantly differentially expressed, with a log2 fold change more than 2 or -2. While Ncoa7, Rcan1, Ampd3 and Arhgef7 are not significantly differentially expressed with a log2FC more than 2. However, more importantly, these normalized counts show that all genes are expressed with or without LPS stimulation. Furthermore, looking at figure 1 (G-H) it is clear that Arhgef7 exclusion TSS is shut off by LPS, while both the Denr isoforms, at low amounts, are expressed with or without LPS.Supplemental figure 11G shows that Ncoa7 inclusion TSS isoform is solely expressed with LPS, 11H shows that Rcan1 exclusion TSS closes with LPS, and 11I shows Ampd3 has both isoforms expressed with and without LPS.Now to take a step further to determine the potential mechanism of how the promoters for these AFE are regulated, we have utilized publicly available tracks tracks including ATACseq (GSE74191), PU.1 ChIPseq (GSE109965), p65 ChIPseq (GSE67343) and IRF3 ChIP seq (GSE67343). In supplemental figure 11F for Ncoa7, we cannot conclude a mechanism driving from inflammatory TFs since there are no p65 or IRF3 binding driving the inclusion isoform and the PU.1, myeloid specific transcription factor is most enriched to the exclusion isoform. Interestingly, The ATAC peak for the inclusion isoform for Ncoa7 is more open for the LPS BMDM sample, which could conclude that the promoter is opened by LPS. Rcan1, SupFig11H, shows ATAC opening by inflammation for the inclusion isoform, this promoter also shows enrichment of IRF3 for the inclusion isoform promoter. SupFig11I for Ampd3, it is clear that the inclusion isoform is driven by p65 and IRF3 binding, however the promoter of the exclusion is less clear since the ATACseq shows the promoter to be closed. Arhgef7, SupFig11J, shows a clear strong promoter for the novel inclusion isoform. This is shown by the strong PU.1 binding, ATACseq reads, as well as IRF3 and p65 binding. The exclusion isoform is open as shown by ATACseq reads and has PU.1 binding, but much less in comparison to the inclusion isoform. Finally, SupFig11K for Denr, shows a convoluted promoter region. These two isoforms TSSs are about 250bp away from one another, therefore the ATACseq reads, p65 and IRF3 binding are not distinct between promoters, but to give confidence to the promoter regions it is clear that there are unique promoters by examining the PU.1 binding in BMDMs +/-LPS. In conclusion the mechanisms governing TSS choice and isoform usage are complex and gene and context specific. 3. The data suggesting that an IRE regulates translation of the induced 5'UTR is compelling, but more work should be done to confirm. Most importantly, the experiment in Figure 4J should be repeated with the deltaIRE version of the unannotated UTR.Thank you for the suggestion of this important experiment. We have generated a ΔIRE BMDM cell line data in supplemental figure 15, which supports our findings and strengthens our new mechanism of posttranscriptional regulation of Aim2. To generate a ΔIRE we used our lab’s previously published CRISPR/Cas9 immortalized BMDM cell line (PMID: 29051223) and gRNAs were chosen based off of the CRISPR/Cas9 target tracks on the genome browser .After careful selection of 3 gRNAs, we electroporated them in our BMDM Cas9 cell line, and through immediate cellular dilution we generated a KO IRE cell line. Performing LPS time course experiments, similar to that of Fig4R-S, we determine that the Aim2 protein is regulated by this IRE motif. Our WT BMDM Cas9 cell line shows a loss of protein at the 48 hr LPS time point while our ΔIRE BMDM Cas9 cell line shows a significant increase in Aim2 protein at this time point. With these novel cell lines and data, we are confident in our proposed IRE mechanism of regulatingAim2. .Also is the IRE regulation controlled upon LPS-stimulation, or just the presence of the IRE element? In other words, what is the distribution of the annotated and unannotated isoforms in the polysome in the absence of LPS (i.e. repeat 4P without LPS)?The post-transcriptional regulation is completely controlled by the IRE hairpin present in the novel isoform of Aim2. Figure 2D-H, clearly shows that the annotated Aim2 isoform expression is not regulated by inflammation, while the IRE containing unannotated isoform is highly upregulated during inflammation. Additionally, we have generated a new cell line that lacks the IRE hairpin, genomically, and the Aim2 protein is no longer regulated by LPS, shown in supplemental figure 15. Finally, in figure 4Q, we redid the polysome profiling experiment to include unstimulated macrophages. In these data, we see that the annotated transcript is still enriched in the high polysome fraction and the unannotated transcript is enriched in the low polysome fraction, and these data are not dependent on LPS. (Manuscript Lines 257-266)Can the authors comment on whether the level of iron or the activity of IRP1/2 change in LPS-stimulated cells?We have now added the expression of IRP1 and IRP2, found in supplemental figure 14, showing that they are not significantly differentially expressed by LPS in primary BMDMs. Therefore, the amount of IRP1 or IRP2 would likely not be affected by LPS. (Manuscript Lines 265-268).Reviewer #3:This manuscript by Robinson et al. presents an interesting and timely analysis of a wealth of transcriptome data upon immune stimulation. The unique combination of long-read Oxford Nanopore and short-read Illumina high-throughput sequencing across both human and mouse samples presents an opportunity many interesting inter-species immune response comparisons, as well as elucidation of full-length transcript information. This paper is well-written and has interesting validation and discussions regarding Aim2. My major concern is that the paper seems to narrow in on the characterization of Aim2 and class of RNA processing changes  quite quickly without really delving into the rest of the data and how they arrived there. Below are my major comments and suggestions:1. I would have liked the authors to provide more insight into how they honed-in on specifically talking about first exon changes, by discussing more of the other RNA processing changes they found. There is cursory mention in the text and figures of other alternative exon or splice site changes. Firstly, other studies (including those referenced by the authors) have found hundreds RNA processing changes genome-wide upon immune stimulation – especially of cassette exons, alternative splice sites, and last exon/3'UTR changes. However here, the authors only find tens of changes . Are they underpowered to identify changes and can they do any sort of analyses to show that they are sufficiently powered ?Our lab was interested in the global alternative splicing profiles of primary macrophages during inflammation. To determine this, we utilized both primary human and mouse macrophages and analyzed them using both JuncBASE and DRIMSeq to determine the significant differential splicing events. It was clear that in both species Alternative First Exon (AFE) changes are the most prevalent event-type of the significant events. To ensure that our AFE signature is inflammatory specific, we reanalyzed the datasets generated by Pai et al. using methods mirroring those we used for analyzing our own data. Following this, in supplemental figure 4 A-D we see that the results show a higher prevalence of AFE events using either adjusted p-value thresholds of.05 or.25 (PMID: 27690314). On the other hand, in the Pai publication, their figure 2A and C shows the AFE exons are prevalent amongst the significant events in both experimental conditions, even more prevalent than cassette exons (PMID: 27690314). (Manuscript Lines 110-114).While AFE events are not the most prevalent amongst all event types in their analysis, this may be due to other factors such as difference in methodologies applied. We focused on alternative isoform changes that are coupled with alternative splice site usage; therefore, we did not examine TandemUTR events. To define alternative splicing events, we used JuncBASE which has features that allows for defining alternative first exons with higher confidence (PMID: 26294686). For statistical testing, we used a t-test through the JuncBASE package or DRIMSeq, which applies a Dirichlet multinomial framework. On the other hand, Pai et al., evaluated changes in RNA processing events with MISO, which uses Bayesian inference in order to detect differentially expressed isoforms. MISO identifies differentially expressed isoforms based on annotation, while JuncBASE allows for the detection of unannotated splicing changes. In addition, significance was defined differently between both studies. In our method, significance was generally defined as AS events with a corrected p-value <= 0.25 and |ΔPSI| >= 10%. In Pai et al., significance is designated to events following infection were defined as those with at least at least 10% of individuals having a Bayes Factor (BF) > = 5 and the | mean ΔPSI| ≥ 5%. The smaller threshold of | mean ΔPSI| ≥ 5% attributes to the increase in the number of splicing changes reported in Pai et al. compared to those reported in our study.2. Similarly, I would also be interested in seeing an analysis indicating whether the 50 AFE events that overlap between the long-read and short-read sequencing analyses is a statistically significant overlap. Particularly, how many overlapping events would be expected given the difference in quantification power between the two methods?To address this comment, we have revised the manuscript  to more clearly indicate that an alternative splicing identification and quantification analysis was not completed using the long-read sequencing data. The long-read sequencing data was only used to assemble high confidence isoforms to supplement the existing gencode transcript annotation to more accurately identify alternative splicing events in the Mouse data with JuncBASE and DRIMSeq. Figure supplement 5 indicates overlap of significant alternative splicing events found using either the gencode annotation file or our ‘Gencode + de novo’ (Manuscript Lines 115-117).How many real AFE differences might the authors be missing because the long-read sequencing methods often do not have the power to identify them .For our primary BMDM direct RNA sequencing, we had an average read depth of 1 million reads per condition (+/- LPS). In a previous study of direct RNA sequencing, we found that increased sequencing depth will result in additional isoforms detected; therefore, we expect to see additional unannotated AFE with increased sequencing depth (PMID: 31740818). We have made sure to include this point in our Discussion. .3. Second, for the non-AFE changes that they did find, there is very little discussion about what those changes might represent. Specifically: (a) how many changes are validated with long-read data?To address this point, we have now generated venn diagrams for all other splicing events . The venn diagrams show the overlap of differential alternative splicing events from short-read RNA-seq data using either the publicly available gencode.gtf file or our combined gencode and de novo.gtf file generated from our direct RNA nanopore sequencing data. .(b) Is there any insight into specific domains being included/changed, especially using the long-read data?Looking deeper in the motifs of the mouse AFE genes, in SFigure 12 , we see that overall there are more motifs in LPS (inclusion) first exons in comparison to the CTL (exclusion) first exons. Musashi binding element, MBE, was the most prevalent motif identified in the first exons. Because AFE events were the predominant type of alternative isoform usage, we focused on motif analysis for AFE events.(c) How many of these non-AFE changes overlap between species?We generated 8 new venn diagrams to identify the overlap between mouse and human alternative splicing events during acute inflammation . Interestingly, we see that alternative first exons have the most conservation of all other splicing events. (Manuscript Lines 115-117)(d) Which types of genes show higher overlap between species and what are their characteristics ? To my knowledge, this is the first study that is really designed to properly really look at the conservation of splicing or RNA processing changes after immune activation, so I would love to see more analysis and discussion of this aspect genome-wide.We appreciate this inquiry, and hope to take a deeper look into this on our next project. The scope of this study was primarily focused on alternative first exons since they are the most prevalent event type across species after inflammatory activation. Our first step to take a global approach in assessing the exclusion and inclusion of AFE was to look at length. It is known that longer 5’UTRs lead to a decrease in translational efficiency of a transcript (PMID: 11897027); therefore we looked at the length of all inclusion and exclusion exons . There is no significant difference between the length of inclusion and exclusion exons. However, it is important to see that the average ΔAFE was 37, meaning there is a slight shift in a longer 5’UTR in the inclusion AFEs. Furthermore, we took a deeper approach to investigate the possible motifs in the mouse inclusion (LPS) and exclusion (CTL) exons  by using RegRNA2.0. This is a web server that takes an unbiased approach to scan for 44 potential murine motifs. We show that there are more motifs identified in inclusion (LPS) exons, and the most prominent motif is the musashi binding element (MBE). .4. The authors define significant splicing changes as those with a p-value <= 0.25 and |dPSI| >= 10. I'd like some more clarification on whether this is an adjusted p-value . Especially if this is adjusted, I find it surprising that the authors are choosing such a liberal statistical confidence level and that even with such a liberal threshold, they are only getting tens of significant events. I would like the authors to at least show these same trends across multiple p-value thresholds or with rank threshold analysis  to show biological trends.mt_correction BH. The adjusted p-values from DRIMSeq are also adjusted using Benjamini and Hochberg (BH) by default . A 0.05 significance threshold is somewhat of an arbitrary significance threshold that has been designated in the field and an adjusted p-value < 0.25 has been used in a variety of other studies in genomics  We felt that a less stringent threshold of 0.25 would be appropriate to use in this study as the identification of alternative splicing events following LPS stimulation began as an exploratory analysis and the implementation of a 0.05 adjusted p-value threshold yielded few results. As requested by the reviewer we performed a rank threshold analysis of the top 5%, 10%, 15%, 20%, and 25% based on uncorrected p-values and they show that alternative first exon events remains amongst the most prevalent significant events . (Manuscript Lines 107-114).Adjusted p-values from JuncBASE are adjusted using Benjamini and Hochberg (BH) by implementing the option --(5) The authors introduce their long-read sequencing data by mentioning that they wanted to identify "additional splicing events that are not captured using short-read sequencing." They then go onto to only talk about novel first exon events identified with the long-read sequencing data. Did they identify any other non-AFE events in using the long-read that could then be quantified with the short read data?Thank you for asking this question. This is a very critical point, and we hope our corrections make this point clearer throughout the manuscript. With our study we first did a differential splicing analysis, using the publicly available gencode gtf file. We took this a step further, by then performing long-read direct RNA sequencing to generate a new macrophage specific gtf file using FLAIR. We then reanalyzed our short-read RNAseq data with our de novo transcriptome. From this new transcriptome file, we did find other differential splicing events that were novel to macrophages, as well as events that were identified in the short read sequencing that were supported by our new gft file. This data can be found in both Figure 1C, as well as in the venn diagrams of supplemental figure 7.And second, how do they quantify confidence for novel AFE isoforms, when long-read data seems to have lots of issues with properly sequencing the terminal ends of transcripts ? They mention the use of ATAC-seq data to show putative promoter support, but mention at one point in their methods that ATAC regions within 10kb of AFEs are considered.FLAIR collapse, the software tool used in order to assemble high confidence isoforms from nanopore reads, is designed to handle truncated reads . In addition, we implemented an optional parameter of FLAIR collapse (-p) which instructs the tool to use an input file of coordinates (a BED file) corresponding to promoter regions to aid with distinguishing isoform start sites. When implementing this parameter, we only used promoter regions that were also supported by ATAC-seq data. The ATAC regions within 10kb of AFEs are considered for a separate analysis looking into chromatin accessibility. Methods found in the manuscript lines 786-793.These seems like it could be a rather large region to be sure that the ATAC peak is specific to a novel AFE – what is the average distance between AFEs? Finally, I would love to also see the incorporation of CAGE-seq data (or other 5'end data) to validate the specific AFEs sites – which I believe the FANTOM consortium has across many human and mouse tissues.We appreciate the inquiry. To address this comment, we elected to use theFANTOM5 CAGE data and complete two analyses. In both analyses, “known” TSS are those that overlap TSS in the Gencode M18 annotation and “novel” TSS are those that do not. In the first analysis, we plotted the distribution of CAGE scores of the CAGE peaks that overlap known and novel TSS. The similarity in the distribution between the known and novel CAGE score distribution indicates to us that the novel TSS identified using Nanopore sequencing coupled with the bioinformatics tool, FLAIR, are truly TSS. A second analysis to further support that claim was completed in order to observe the proportion of known and novel TSS that overlap CAGE data corresponding to “true” TSS as classified with machine learning methods (PMID: 21596820). 45% of the novel and ~62% of known TSS overlap with CAGE peaks corresponding to “true” TSS, respectively. Because FANTOM5 CAGE data is an aggregation of data collected from multiple studies using different cell-types and experimental conditions, we expect that many of the non-overlapping CAGE peaks classified as “true” TSS are a result of these experimental differences. .Reviewer #4:In this manuscript, Robinson et al., identified alternative first exon (AFE) switching events conserved between mouse and human following macrophage inflammation. Using short and long-read sequencing, the authors identified a few unannotated transcription initiation sites (TSS) that are specific of an inflammatory response. Among those, they centered on an unannotated TSS in the Aim2 gene that drives expression of a novel isoform regulated by an iron-responsive element in its 5′UTR.While previous work had documented crucial AFE switching events in many other biological contexts, Robinson et al. presents here an interesting AFE switching event that can have potential implications for our understanding of the molecular regulation of the innate immune response. For publication in eLife, I would expect further progress on global mechanisms and biological relevance of these AFE switching events, as well as evidence that the AFE are truly first exons/TSSs.1. Are the AFEs truly first exons/TSS? While both short-read and long-read sequencing detected changes in alternative splicing choices, neither of those are optimal methodologies to analyze first exons. Therefore, I suggest to use a more specialized method to identify (and quantify) more accurately the usage of first exons. Globally, cap analysis of gene expression (CAGE) would be ideal. For validation of specific AFE changes, the qPCR technique has a few issues. First, it does not have nucleotide resolution, so the authors should not refer to TSSs if they used this technique for validation.We appreciate the inquiry about the confidence of our isoforms that have novel TSS. To address this comment, we elected to use the FANTOM5 CAGE data and complete two analyses. In both analyses, “known” TSS are those that overlap TSS in the Gencode M18 annotation and “novel” TSS are those that do not. In the first analysis, we plotted the distribution of CAGE scores of the CAGE peaks that overlap known and novel TSS. The similarity in the distribution between the known and novel CAGE score distribution indicates to us that the novel TSS identified using Nanopore sequencing coupled with the bioinformatics tool, FLAIR, are truly TSS. A second analysis to further support that claim was completed in order to observe the proportion of known and novel TSS that overlap CAGE data corresponding to “true” TSS as classified with machine learning methods (PMID: 21596820). 45% of the novel and ~62% of known TSS overlap with CAGE peaks corresponding to “true” TSS, respectively. Because FANTOM5 CAGE data is an aggregation of data collected from multiple studies using different cell-types and experimental conditions, we expect that many of the non-overlapping CAGE peaks classified as “true” TSS are a result of these experimental differences. .Second, many downstream first exons are also used as internal exons in other isoforms. There is not a direct technology to analyze specifically first exons/TSSs here. Also, RNA-sequencing technologies, depending on their depth, can definitely miss specific isoforms. Considering a low coverage in 5'end of genes in RNA-seq analysis, this is particularly important for first exons. A qPCR would only analyze the well-known TSSs. Thus, 5'RACE or a similar technology should be perform to assess the relative usage of AFE specifically.While it is true that there are limitations of these RNA sequencing technologies to determine the true first nucleotide, we still are able to determine nearly all splice junctions depending on read depth. Our illumina sequencing run was very deep, around 30million per library. Our nanopore sequencing libraries were much lower, at 1million reads per library, but they were used to generate a new transcriptome using FLAIR. The limitations of nanopore, as discovered by Mulroney et al. show that direct-RNA long read nanopore sequencing is unable to detect the first ~11nt of a transcript (DOI: 10.1101/2020.11.18.389049). Then we re-analyzed our illumina RNA-sequencing libraries using our new transcriptome. Thus the exact transcriptional start site is not critical in the definition of alternative first exon. We also used orthogonal ATAC-seq data to provide additional confidence that the first exon was at a putative promoter.2. Global mechanism. The authors assumed that the mechanism of AFE switching is generated by transcription initiation and looked for transcription factors binding and chromatin structure modifications in promoters. However, they did not ruled out the possibility that the global switching effect is a post-transcriptional regulation, such as differential mRNA stability. A transcription initiation measurement  is necessary to demonstrate that the changes in AFE usage are co-transcriptional.Thank you for this astute point. While we did not assess differential mRNA stability of the identified isoforms, we can confirm many of the events by using our ATAC-seq and ChIP-seq analysis. For instance, by examining the p65 and IRF3 transcription factors, we were able to show that 37 of the promoters are regulated by either p65 or IRF3. Additionally, 26 promoters of the AFE genes undergo chromatic accessibility changes during LPS stimulation . While 47 of the 95 AFE events are unaccounted for, this can be explained by our global assessment of promoters of the AFE events , showing that many of the transcription factors enriched are regulated by metabolism and other biological processes . Assessing additional mechanisms that may be driving these AFE events we fell are outside the scope of this current study.In addition, in terms of their ATAC-Seq analysis, the chromatin structure changes in promoters can be cause or consequence of transcription initiation. Thus, it should not be listed as one mechanism driving the expression of AFE events (line 145). Also, to demonstrate a mechanism based on transcription factor binding more than 2 transcription factors should be considered. In any case, the expression patterns of the transcription factors considered are not clear.Thank you for this astute point, we have changed the text to address this concern. We investigated transcription factor binding as another potential contributing factor to the changes in AFE usage. The transcription factors we chose, p65 and IRF3, are two of the key inflammatory transcription factors driving thousands of immune response genes (described line 4346). Additionally, there are not many ChIP-seq datasets available for transcription factors of interest in primary BMDMs. The datasets chosen are for an inflammatory time course, 0-120min, with 120min chosen for analysis. This is the time point in which the highest binding of IRF3 or p65 is seen. While additional examination of other TFs could provide more mechanistic understanding we feel those studies are outside the scope of this paper. However, as an alternative approach to analyzing more ChIP-seq data from primary BMDMs, we performed a HOMER analysis of all AFE promoters . We found that the majority of TFs are regulated by metabolism .As a minor note, the bioinformatic analysis of the two promoters regions driving the isoforms of Aim2 (line 156) is not explained in the method section.Thank you for this point, we have edited our methods to include the promoter analysis on Aim2 (Manuscript Lines 892-899).3. Biological relevance. Could the authors evaluate whether the translation regulation of Aim2 based on its AFE switching is a more generalized phenomenon?Drosophila studies, but in the context of inflammation in mammalian cells is a novel finding . For our results within this manuscript, we can definitively say that the AFE of Aim2 downregulates the protein expression through an IRE motif by our GFP expression experiments, polysome profiling experiments, western blot analysis of primary BMDMs and our Cas9 IRE KO experiments in immortalized BMDMs . (Line 374-389).The general phenomenon of AFE usage has been seen previously in a handful of plant or Furthermore, we took a deeper approach to investigate the possible motifs in the mouse inclusion (LPS) and exclusion (CTL) exons  by using RegRNA2.0. This is a web server that takes an unbiased approach to scan for 44 potential murine motifs. We show that there are more motifs identified in inclusion (LPS) exons, and the most prominent motif is the musashi binding element (MBE).  IRE motifs were not found in any other AFE event.Are there any global gene regulation changes triggered by the other genes with significant changes is AFE usage?After performing a global assessment of all inclusion and exclusion AFEs, using RegRNA2.0, we found that the inclusion AFE events had more motifs than the exclusion exons . This could mean that there are additional post-transcriptional regulations affecting the translation of the genes during inflammation. It was beyond the scope of the paper to do another global sequencing experiment to assess gene regulation of the isoforms, such as polysome profiling or ribosome profiling. While our study does not look at the global translational implication of all AFE events, it does unearth a novel mechanism of an AFE event in the Aim2 protein coding gene. Aim2 was originally identified in a seminal 1997 study (PMID: 9242382) as an interferon inducible gene. From our work we now know that it is this alternative isoform that is inducible and it leads to a decrease in protein expression most likely as a way to control the pathway and return it to homeostasis.


Open systems can only exist by self-organization as pulsing structures exchanging matter and energy with the outer world. This review is an attempt to reveal the organizational principles of the heterochromatin supra-intra-chromosomal network in terms of nonlinear thermodynamics. The accessibility of the linear information of the genetic code is regulated by constitutive heterochromatin (CHR) creating the positional information in a system of coordinates. These features include scale-free splitting-fusing of CHR with the boundary constraints of the nucleolus and nuclear envelope. The analysis of both the literature and our own data suggests a radial-concentric network as the main structural organization principle of CHR regulating transcriptional pulsing. The dynamic CHR network is likely created together with nucleolus-associated chromatin domains, while the alveoli of this network, including springy splicing speckles, are the pulsing transcription hubs. CHR contributes to this regulation due to the silencing position variegation effect, stickiness, and flexible rigidity determined by the positioning of nucleosomes. The whole system acts in concert with the elastic nuclear actomyosin network which also emerges by self-organization during the transcriptional pulsing process. We hypothesize that the the transcriptional pulsing, in turn, adjusts its frequency/amplitudes specified by topologically associating domains to the replication timing code that determines epigenetic differentiation memory. In 1944, Erwin Schrödinger (one of the founders of quantum mechanics and Physics Nobel Prize winner of 1933) published his famous lecture “What is life?”  and claiIn general, as posed by Ilya Prigogine, any living dissipative system undergoing exchange of matter and energy with the environment is a subject of structured attenuated oscillations ,7,8. ThuAlthough the dominating view in the last decade was that the A and B compartments behave as relatively independent self-organized crowded fluids, some recent data still point to the leading role of heterochromatin in genome maintenance, highlighting the attractions between heterochromatic regions as being central to phase separation of the active and inactive chromatin domains ,21. ReceThe three main properties of CHR—the position variegation effect, stickiness ,33, and In our recent publication , we wereThe mesoscopic approach to the description of complex systems is guided by unveiling the presence of “organization principles”. Its main peculiarity is the independence from the detailed knowledge of the basic bricks of the system and the emergence of relevant features from the correlation structure of the studied phenomenon, where “all the entities can be considered as networks of interacting parts” . Here, wIn this article, we will review the state of several relevant aspects of cell nucleus research and hypotheses for a “heterochromatin code” from the perspective of Schrödinger’s aperiodic crystal and a general concept of positional information as it was formulated for cellular morphogenesis by Leo Wolpert ,49. AccoRoughly, we discriminate two main complementary-opposite components of the supra-chromosomal functional network: The “knots”, i.e., pericentromere-associated domains (PADs) formed by densely packed centromere aggregates, and the transcription hubs, i.e., transcription factories including splicing speckles for synthesis and processing of mRNA, which converge the loops of transcriptionally active chromatin domains ,50,51. IIn order to reveal the supra-chromosomal network knotted by CHR, image analysis of in situ microscopic cell nuclei was applied: (1) By labeling it with epigenetic histone mark H3K9me3 alone or with CENPA , without and after suppression of RNA synthesis; (2) by affinity staining for CHR by absorption dye toluidine blue (using bright-field microscopy); (3) by mathematical skeletonization of denser nuclear areas and overlay of the most dense CHR clusters in cell nuclei with specifically stained DNA in cell imprints, studied in the course of developmental acceleration and cessation of transcription; (4) by extracting RNA from cell nuclei with nucleic acid specifically stained by acridine orange (AO), without and after suppression of RNA synthesis; (5) by electron microscopy of cell nuclei treated with heparin, short bleomycin, or DNAse I—cutting, solubilizing, and extracting the active chromatin on isolated whole nuclear mounts treated on EM supports and in situ for ultra-thin sections in cell cultures.p < 0.05). The chromocenters, when discriminated by our approach, either as the most optically dense DNA-specifically stained granules (p < 0.01) , the networks confined by arrays of ~1.12 µm-thick knotty chromatin strands were composed of smaller and larger alveoli (~2 µm on average). The larger ones generally embraced the apparent nucleoli and had a rosette-like substructure  f.Using antibody labelling against heterochromatin methylation sites H3K9me3, fluorescence imaging a can be The networks visualized by five different labelling methods in different cell types and species  showed similar sizes of the alveoli in all samples . This inIn MCF7 cells, we observed a positive correlation between H3K9me3-stained PADs and centromere numbers a,b alongRaymond Noble and colleagues, discussing the complexity of biological systems, recently posed: “Biological relativity requires circular causality but not a symmetry of causation: so, where, what, and when are the boundaries?” . AnotherIn conventional eukaryotic interphase cells, heterochromatin segregates towards the perinucleolar and inner nuclear envelope surfaces ,65,66,67In this system, presented in The radial movement vector of the dynamic, small chromocenters and the network, first creating and expanding its small alveoli and then flopping and contracting them towards the perinucleolar boundary during activation and irreversible cessation of transcription, correspondingly decreasing and increasing DNA concentration in them, is seen well in the graphs on Similar contraction of CHR around/inside nucleoli was also observed at the initial suppression of rRNA synthesis by actinomycin D (AcD) ,75; thisThus, the concentric constraints of the nucleolus and nuclear envelope form the radial gradient of the heterochromatin network mobility vectors which coincide with the generally radial position of chromosome territories in interphase cell nuclei  known siThe radial vector represents the shortest path of network signal transduction for its main function—linking DNA transcription to RNA translation between the cell nucleus and cytoplasm in oscillating cycles, as studied in a model system . The comThe clusters of constitutive heterochromatin are composed of multiple reiterated satellite DNA sequences. They may serve as “capacitors” for the induction of locally different electric potentials for chromatin and DNA ,84,85,86The precipitation of the inactive domain forming heterochromatin network is in fact a phase transition. This can be appreciated, e.g., from experiments using the SV-40 transformed hamster cell fibroblast cell line 4/21 presented in Interestingly, the precipitation of the heterochromatin network using cell imprints on microscopic slides with results presented in Therefore, the same result achieved by air-drying and by localization microscopy and mathematical operations on the recorded data set of the freshly fixed cells corroborates that heterochromatin networks with certain topology and determinants in nucleus organization are predestined by the “physics of this compartment”. How are the CHR network determinants set in embryonic development? p = 0.012) [Drosophila cells that attachment of TADs to the nuclear lamina makes them more condensed but decreases overall chromatin density in the nucleus by stretching interphase chromosomes.In mouse embryogenesis, lamina-associated domains (LADs) are settled in the zygote prior to topologically associating domains (TADs) . The lat= 0.012) . Similar= 0.012)  showed oAll in all, we can conclude that the intra-supra-chromosomal heterochromatin network seems to be both physically and developmentally preprogrammed and possesses two nuclear determinants: the nucleolus and nuclear lamina. Its general shape can be also revealed by perturbations depending in particular on the viscosity, charge, and humidity of both heterochromatin and active domains and DNA strand breaks. It changes function according to the laws of self-organization of viscous fluids ,101 and Experiments with MCF7 cells inducing suppression of rRNA and mRNA synthesis at a smaller and larger concentration in the time course of actinomycin (AcD) treatment, and after alpha-amanitin suppression of mRNA and 5S RNA synthesis, were performed. To evaluate rRNA synthesis, the cells were stained by immunofluorescence for the cofactor of Pol I—RPA and fibrillarin involved in the processing of rRNA. They were also labelled for spliceosomes (speckles) by Sc35  and for The radial-concentric order of the nuclear components also includes the concentric ring of the nuclear envelope (NE) schematized as a dashed circle in To further analyze this aspect, we shall touch on two points: the mechanobiology of transcription and transcriptional pulsing itself. EM studies of speckles  examined in very active cancer cells revealed their direct link to both perinucleolar and perinuclear heterochromatin a, which An actomyosin-based cytoskeleton using ATP energy plays a role in heterochromatin segregation and the organization of specific heterochromatin compartments: β-actin deficient cells exhibit changes in the spatial organization of H3K9Me3/HP1α—positive heterochromatin . At the The involvement of fibrillar nuclear actin (F-actin) in the activation and process of transcription with all three RNA polymerases is well-established ,116,117.Actin is part of pre-initiation complexes and is necessary for transcription by RNA polymerase II; it is involved both in the initiation and elongation of transcription, acting through ribonucleoprotein interactions and the chromatin remodeling, but also interacting with pure DNA ,122. Nucstatu nascendi. This network is constrained by the flexibly stiff heterochromatin attached to two nuclear boundaries, and similar circular-radial boundaries also determine a similar organization principle of the isotropic cytoplasmic F-actin network, as exemplified in The involvement in RNA transcription of actomyosin activity of nuclear speckles, their phase separation, their anchoring to the perinucleolar and perinuclear heterochromatin, as well as their spring-like morphology, visualized by EM a,b, is aThe results of our AcD experiments together with the literature data analyzed above are deduced in the schematics in Balbiani was the first who discovered vacuoles and their discharge from the nucleolus in active cells and called the nucleolus the “heart of the cell” . NucleolIn this connection, the question arises whether the emergence of function in transcription alveoli may be associated with nucleolus-associated domains (NADs). rDNAs are the most abundant genes, and the stability of the rDNA cluster was considered a key element of genome maintenance .NADs are defined as genomic regions enriched at the nucleolar periphery in interphase cells . In addiIn any case, the available molecular and morphological data indicate that most NADs are associated with PADs, and this link can be hypothesized to play a role in the formation of functional alveoli and scale-free transcriptional pulsing of their mutual network coordinating rRNA and mRNA synthesis in the radially directed relay. Therefore, we should shortly review some data on transcriptional pulsing.Transcriptional bursting, also known as transcriptional pulsing, is a fundamental property of genes, which have been observed in diverse organisms, from bacteria to mammals . The burThe sense of radial-circular nuclear organization for transcriptional pulsing is not only in the structural conveyor coordinating the production of pre-ribosomes with the synthesis and maturation of mRNA but also in the adaptation of this process to the needs of the cell as an open system. It means feedback from sensing the environment and cell community which, in case of life threat , can become synchronised. In other words—it is an element of explorative adaptation requiring the system to act as a whole. As described above, the cytoskeleton is part of this pulsing-sensing feedback where the assembly and interactions between DNA and F-actin and phase separation functions are also a subject of the self-organizing forces . But theWe can suggest that the tissue-specific code is established by genome-wide stable replication timing . It can be mathematically determined using segmentation of the cell nucleus image in a radial-concentric matrix around a central origin, e.g., a center of the nucleolus. Inside this matrix, the “geographic” map locating the coordinates of constitutive heterochromatin clusters can be defined (schematically shown in Guided by a mesoscopic approach to complex systems, we attempted to unveil the presence of “organization principles” from the correlation structure of the studied phenomenon, where “all the entities can be considered as networks of interacting parts” . Thus, w


CDX2 expression, possibly, via alteration of the methylation status of CDX2 gene. Here, we further investigated the effects of exogenous follistatin supplementation on developmental competence and CDX2 methylation in bovine somatic cell nuclear transfer (SCNT) embryos. SCNT embryos were cultured with or without follistatin for 72h, then transferred into follistatin free media until d7 when blastocysts were collected and subjected to CDX2 gene expression and DNA methylation analysis for CDX2 regulatory regions by bisulfite sequencing. Follistatin supplementation significantly increased both blastocyst development as well as blastocyst CDX2 mRNA expression on d7. Three different CpG rich fragments within the CDX2 regulatory elements; proximal promoter  and intron 1  were identified and selected for bisulfite sequencing analysis. This analysis showed that follistatin treatment induced differential methylation (DM) at specific CpG sites within the analyzed fragments. Follistatin treatment elicited hypomethylation at six CpG sites at positions -1374, -279, -163, -23, +122 and +3558 and hypermethylation at two CpG sites at positions -243 and +20 in promoter region and first intron of CDX2 gene. Motif analysis using MatInspector revealed that differentially methylated CpG sites are putative binding sites for key transcription factors (TFs) known to regulate Cdx2 expression in mouse embryos and embryonic stem cells including OCT1, AP2F, KLF and P53, or TFs that have indirect link to CDX2 regulation including HAND and NRSF. Collectively, results of the present study together with our previous findings in IVF embryos support the hypothesis that alteration of CDX2 methylation is one of the epigenetic mechanisms by which follistatin may regulates CDX2 expression in preimplantation bovine embryos.Caudal Type Homeobox 2 (CDX2) is a key regulator of trophectoderm formation and maintenance in preimplantation embryos. We previously demonstrated that supplementation of exogenous follistatin, during in vitro culture of bovine IVF embryos, upregulates The online version contains supplementary material available at 10.1186/s12958-021-00829-7. Somatic cell nuclear transfer (SCNT) is a powerful tool used to propagate valuable livestock species and to generate genetically modified animals that can be used in biomedical research and therapeutic cloning , 2. DespCDX2 expression and trophectoderm (TE) cell number on day 7 of development. Most recently, we demonstrated that follistatin supplementation during the initial 72h of in vitro embryo culture (IVC1) upregulated CDX2 expression in IVF blastocysts that was tightly associated with DNA methylation at specific CpG sites within the regulatory elements of CDX2 gene including the proximal promoter and intron 1. These CpG sites contain putative binding sites for several key transcription factors implicated in regulating Cdx2 expression in mouse embryos and trophoblast stem cells [CDX2 expression in bovine blastocysts is associated with improved pregnancy rates after embryo transfer [CDX2 expression in bovine SCNT embryos and to investigate whether these effects are linked to changes in DNA methylation as we previously reported in IVF embryos [CDX2 regulatory regions. Results show that follistatin treatment induces changes in DNA methylation at specific CpG sites within CDX2 regulatory regions in SCNT blastocysts, these changes are associated with upregulation of CDX2 expression.A previous study demonstrated that exogenous follistatin supplementation during in vitro embryo culture increased blastocyst development and trophectoderm (TE) cell number in bovine SCNT embryos . Follistem cells . Importatransfer . Therefo embryos . To addrAll reagents were purchased from Sigma Aldrich  unless mentioned otherwise.Bovine cumulus oocytes complexes (COCs) were retrieved from ovaries collected at local slaughterhouse in the state of Michigan. Oocyte selection and in vitro maturation (IVM) were carried out as we previously described , 12. SomCDX2 gene expression analysis. Figure The activated SCNT embryos were cultured in KSOM media with or without the maximal stimulatory dose of follistatin, 10 ng/ml  for 72h . Then, 8n = 15 blastocysts/group, n = 6 replicates) were used for isolation of genomic DNA and total RNA using Allprep DNA/RNA micro kit . The extracted total RNA was used for cDNA synthesis using iScript cDNA synthesis kit  according to the manufacturer’s protocol. Quantification of the relative transcript abundance of CDX2 gene was done by the 2−ΔΔCt method as described elsewhere [CDX2 transcript abundance was normalized relative to the abundance of bovine ribosomal protein S18 (RPS18) as an endogenous control and expressed as fold change relative to the control group. Primer sequences and GenBank accession numbers were previously reported [Total RNA isolation, cDNA synthesis and qRT-PCR were performed as previously described . Brieflylsewhere . CDX2 trreported .CDX2 gene  contains multiple CpG rich islands upstream and downstream of the transcription start site  (genomic Position 32083604). Given the CDX2 regulatory elements include the promoter region and intron 1 [CDX2 gene structure and position of selected fragments. Bisulfite treatment, DNA amplification, cloning and Sanger sequencing were performed following our previously reported protocols [Analysis of CpG content by MethPrimer software  revealedintron 1 , 22, thrrotocols . Brieflyrotocols .CDX2 reference sequence using BioEdit Sequence Alignment Editor . Aligned sequences with sequence identity≥ 90 % and conversion rate ≥ 95 were analyzed by Bisulfite Sequencing DNA Methylation Analysis (BISMA) Software [Raw bisulfite sequencing data were aligned to the Software . IndividSoftware , 25. MetSoftware .CDX2 gene. A vigilant literature search was carried out to uncover any role for these transcription factors in regulating CDX2 expression.Motif analysis was performed by MatInspector (Genomatix) to identify putative transcription factors that bind to the differentially methylated CpG sites within the regulatory regions of CDX2 gene expression data. Arcsine transformation was applied to the percentage data before analysis. Data are presented as untransformed mean ± standard error of mean (SEM), P < 0.05. For DNA methylation data, after initial analysis by BISMA, individual CpG sites with ≥ 20 methylation in either control or follistatin treated groups were analyzed by Fisher’s exact test to determine the differentially methylated CpG sites.Student T test was used to analyze embryo development and P<0.05; Fig. CDX2 was significantly increased in d7 blastocysts derived from follistatin treated group, compared to untreated controls  during the initial 72h of in vitro embryo culture (IVC) triggered a significant increase in the proportion of SCNT embryos that reached the blastocyst stage at d7 (31.34 ± 0.97% Vs 20.04%± 0.85%) for follistatin and control groups, respectively , during the first 72h of IVC modified the methylation status of regulatory regions in the software   and included 27 CpG sites Fig. A. The ovP<0.05; Fig. For fragment P2, the target sequence was 431 bp in length (-126 to +305) and contained 41 CpG sites  is putative binding site for Octamer-binding transcription factor 1 (OCT1) and Heart and neural crest derivatives expressed protein (HAND). In fragment P2, the hypomethylated CpG sites (n=4) contained putative binding sites for OCT1, Activating Enhancer-Binding Protein 2 family (AP2F), and p53 protein whereas the hypermethylated CpG sites (n=2) contained binding sites for Neuron-restrictive silencer factor (NRSF), a negative regulator of gene transcription. In fragment I of intron 1, there was a single hypomethylated CpG site in follistatin treated SCNT embryos which included a putative binding site for Krüppel-like transcription factor (KLF). Table CDX2 promoter region and intron 1. Here, we focused only on TFs that are known to regulate CDX2 expression in mouse embryos , and TFs that may have an indirect link to CDX2 regulation . Collectively, these results indicate that follistatin treatment induces changes in DNA methylation at specific CpG sites within proximal promoter and intron 1 of CDX2 in bovine SCNT blastocysts. These DM CpG sites contain binding motifs for key transcription factors implicated in CDX2 regulation.Transcription factor motif analysis combined with a literature search revealed several binding motifs in CDX2 gene expression in bovine SCNT embryos. Previously, we demonstrated that follistatin treatment of bovine IVF embryos augments CDX2 expression, at least in part, by modulation of the methylation status of CDX2 gene. Follistatin induced hypomethylation of several CpG sites within the proximal promoter and intron 1 of CDX2 gene in IVF blastocysts. These sites contain putative binding motifs for transcription factors implicated in regulation of CDX2 expression in mouse embryos and trophoblast cells [CDX2 expression may be conserved in SCNT embryos. To address this, we analyzed the methylation status of CDX2 regulatory elements, proximal promoter, and intron 1 following the same procedures we utilized in IVF embryos [CDX2 is generally hypomethylated which is in line with a previous study by Zhang and colleagues [CXD2 is hypomethylated in bovine SCNT blastocysts.A growing body of evidence supports an embryotrophic role of follistatin in IVF embryos , 27, 28.st cells . Therefo embryos . Bisulfilleagues  who repoCDX2 expression including OCT transcription factors, AP2 family, and the KLF family. In Frag P1, follistatin treatment triggered hypomethylation of one CpG site -1374 in SCNT embryos compared to hypomethylation of 2 sites −1384 and−1283 and hypermethylation of one CpG site −1501 in IVF embryos. In Frag P2, Five DM CpG sites  are conserved in SCNT and IVF embryos in addition to one DM CpG site in each group, -250 in IVF and 122 in SCNT embryos. In intron region (I) follistatin reduced methylation at one CpG site in SCNT embryos (3558) compared to five hypomethylated sites  in IVF embryos.Similar to our previous study in IVF embryos , follistCDX2 expression via modulation of DNA methylation. Interestingly, follistatin treatment reduced methylation at putative binding sites for Heart and neural crest derivatives expressed protein (HAND) in fragment P1 and P53 protein in fragment P2. Although, to the best of our knowledge, there are no direct links between these transcription factors and CDX2 regulation in embryos, it has been shown that HAND1 is essential for trophoblast differentiation in mice [Cdx2 expression in mouse embryonic stem cells [In the present study, Motif analysis revealed two putative binding sites for OCT1 were found in fragment P1 (-1374) and P2 (-279), one binding site for AP2 family is in fragment P2 -163) and one binding site for KLFS in fragment I (+3358). Follistatin also increased methylation at a specific CpG (20) site in fragment P2 that contained a putative binding site for the transcriptional co-repressor, neuron-restrictive silencing factor (NRSF). Notably, these results are consistent with our previous findings in IVF embryos [3 and one in mice , while aem cells .CDX2 expression in blastocysts. The positive effects of follistatin on CDX2 transcription are likely linked to alterations in DNA methylation within key regulatory regions of the CDX2 gene. Collectively, these data provide additional insights into how follistatin treatment augments CDX2 expression in bovine preimplantation embryos. Further epigenetic studies are required to elucidate the role of follistatin in bovine SCNT embryos.Results of the present study demonstrate that follistatin treatment during the first 72h of IVC enhances the developmental competence of bovine SCNT embryos and upregulates Additional file 1: Supplemental Table 1. Transcription factor motif analysis of methylated CpG sites effected by follistatin treatment in IVF and SCNT embryo