Drosophila melanogaster, male-specific splicing and translation of the Fruitless transcription factor (FruM) alters the presence, anatomy, and/or connectivity of >60 types of central brain neurons that interconnect to generate male-typical behaviors. While the indispensable function of FruM in sex-specific behavior has been understood for decades, the molecular mechanisms underlying its activity remain unknown. Here, we take a genome-wide, brain-wide approach to identifying regulatory elements whose activity depends on the presence of FruM. We identify 436 high-confidence genomic regions differentially accessible in male fruitless neurons, validate candidate regions as bona fide, differentially regulated enhancers, and describe the particular cell types in which these enhancers are active. We find that individual enhancers are not activated universally but are dedicated to specific fru+ cell types. Aside from fru itself, genes are not dedicated to or common across the fru circuit; rather, FruM appears to masculinize each cell type differently, by tweaking expression of the same effector genes used in other circuits. Finally, we find FruM motifs enriched among regulatory elements that are open in the female but closed in the male. Together, these results suggest that FruM acts cell-type-specifically to decommission regulatory elements in male fruitless neurons.In the fruit fly  Drosophila melanogaster is controlled by a well-defined neural circuit that is labeled by the male-specific transcription factor Fruitless (FruM). While FruM is known to change the number, anatomy and connectivity of neurons which comprise the circuit and has been suggested to repress the expression of a few gene targets, the mechanism of how FruM regulates genes across many different kinds of neurons is unknown. Using an approach to identify gene regulatory elements based on their chromatin accessibility states (ATAC-seq), we identified a large set of chromatin accessibility changes downstream of Fruitless. By examining the activity of these regulatory elements in vivo, we found that their activity was 1) sexually dimorphic and 2) specific to a single class of FruM neurons, suggesting that FruM acts on different chromatin targets in different neuron classes comprising the courtship circuit. Further, we found a known FruM-regulated enhancer of the FruM-repressed gene Lgr3 to have closed chromatin specifically in FruM neurons. Combined with an enrichment of FruM motifs in regions which are closed in FruM neurons, we present a mechanism where FruM directs the decommissioning of sex-shared regulatory elements to masculinize neurons in a cell-type specific manner.Courtship behavior in male  In many species, male and female brains generate distinct behavioral repertoires. The ability to compare behavior, brains, neurons, and gene expression across the sexes makes sexually dimorphic behaviors premier models for understanding structure-function relationships in neural circuits. In both vertebrates and invertebrates, master regulators of neuronal sex are induced downstream of the sex determination hierarchy and alter the composition of specific neurons and brain areas –4. CircuDrosophila melanogaster flies selectively perform courtship displays toward conspecific virgin females, extending and vibrating a single wing to sing a courtship song ..rox1 andfru+ versus fru- datasets. We found just 22 regions genome-wide whose accessibility was common to the two fru+ or two fru- datasets, consistent with a model where fruitless neurons are heterogeneous in the absence of FruM protein production . We also made use of this gene-scale ATAC coverage measurement to compare accessibility and RNA expression levels in the male fru+ condition; we found mild correlation . Using reference anatomies of fru neurons, we found that three swathes of the fru locus had many tiles labeling fru neurons: The P1 promoter, the intron upstream of the sexually dimorphic exon, and the second intron  the 1037 peaks specific to male fru+ versus male fru- and (2) the 861 peaks specific to male fru+ versus female fru+ , or absent in the male fru+ dataset and present in the other three (181 peaks)  Figs  and S4D.ks) Figs . We consM-dependent peaks were in or near 303 unique genes, which were particularly enriched for membrane proteins (122 genes), transcription factors (22) and immunoglobulin superfamily (IgSF) members (27)  . GO termers (27) . Of cour circuit .fru+ cell population, suggesting that FruM might induce different gene regulatory programs in different ontogenetic classes of fru+ neurons.We manually inspected a subset of the 436 candidate regulatory elements; they were clearly differentially accessible by both qualitative inspection and statistical thresholds, were reproducible between replicates, and were found in genes plausibly involved in the biological processes under study. However, these differential peaks were much smaller than the peaks we observed in promoters, which were of common height across samples Figs  and 1D. fru locus shown in M-dependent elements, and we selected the top seven ATAC-seq peaks specifically open in FruM cells for which reporter alleles were available  versus sex-shared (fru-) cells  cells .fru+ neurons, most of which also differed between male versus female fru- neurons  in gene expression between male and female  neurons . We inteM alters transcription of distinct genes across the dozens of classes of fruitless neurons, we would expect these differences to average out when the whole pool of fruitless neurons is analyzed en masse. In contrast, if FruM regulated the same effectors across all fruitless classes, we would expect to observe a strong transcriptional signature specific to male fru+ cells, containing FruM. We identified only seven transcripts that are uniquely active or inactive in cells containing FruM , from indirect targets (i.e. those regulated by direct FruM targets). To explore this using our ATAC data, we searched for Fru motifs across the 436 FruM-specific peaks. FruM has three DNA-binding domains, termed FruA, FruB, and FruC. The three isoforms are expressed in largely overlapping neuronal populations, with FruB and FruC expressed more broadly than FruA [B and FruC isoforms of FruM are each independently required in the male for courtship behavior and male aggression, while loss of FruA has little effect on courtship [C causes feminization of neuronal anatomy, while loss of FruA and FruB have little anatomic effect [To ask whether Fruhan FruA ,57,58. Tourtship ,31,57,58c effect ,31,58. Tc effect .A,B, and C motifs. In each peak, we quantified the strongest match to each of these motifs [M than in those specifically open in the presence of FruM  the regulatory elements to which it directly binds. Moreover, these results suggest that the 181 regions inaccessible in male fru+ cells are those most likely to be direct FruM targets, while regions specifically opened in male fru+ cells are likely to be downstream effectors of primary targets. Analysis of additional FruA motifs shown in We used FIMO to search our peak datasets for Frue motifs . We wereruM Figs . This pa of FruM . TogetheM and FruCOM share most of their protein coding regions and their DNA binding domains, and FruCOM can rescue loss of FruM in behavioral experiments [M-regulated sites overlapped with FruCOM-bound sites identified by ChIP from larvae through the modERN project (ENCODE ENCGM860XOW) [M versus open in the presence of FruM. We found strong enrichment of FruCOM binding centered over peaks closed in the presence of FruM, and weaker FruCOM binding enrichment at peaks opened in the presence of FruM  . We re-a of FruM . This anM cells function as enhancers in female fruitless neurons, we selected one such region, an intergenic peak located near cry, vib, and CG31475 . And while currently no high-resolution binding profiles exist for FruM, ChIP enrichment of FruCOM in whole third instar larvae correlates with accessible chromatin in third instar larval brains. This suggests Fru proteins bind nucleosome-displaced and not nucleosome-occupied regions. FruM may then use a “hit and run” mechanism–binding a locus, recruiting chromatin modifiers, and leaving when the region is no longer accessible. If so, ChIP-based methods may miss direct targets of FruM which can nevertheless be detected by looking at resultant chromatin changes with ATAC-seq.This decommissioning activity raises an important question of whether FruM on gene expression, this finding suggests that neuronal masculinization in the insect proceeds through cell-type-specific dismantling of female or sex-shared gene expression programs. Maleness is typically conceived as an addition to female programming, as exemplified by the striking male-specific courtship routines performed in birds and insects. However, FruM is not required for courtship actions per se, but rather for their regulation [fru-expressing neurons in the female control egg-laying, and ectopic FruM expression in female flies dismantles egg-laying behavior [Beyond a putative molecular mechanism for the action of Frugulation ,64. Morebehavior ,65. TogeM status alters the activity of distinct gene regulatory elements across distinct classes of fruitless neurons. Indeed, we find that each relevant genomic region is likely FruM-regulated in just one or a few fruitless populations.Fruitless masculinizes dozens of classes of neurons in the central brain alone, and does so by altering cell genesis, apoptosis, arbor anatomy, connectivity, and neurophysiology ,21,32,33M action across distinct fruitless neurons could result from distinct availability of cofactors with different DNA-binding domains, or from differences in the pre-existing chromatin landscape that FruM encounters in each cell type. Fruitless is one of 40 members of the BTB/POZ family of transcription factors in Drosophila. The BTB/POZ domain is a dimerization domain, and family members have been shown to both homo- and hetero-dimerize. The presence of different FruM heterodimers across neuron types that bind different composite motifs is thus a feasible mechanism. Another interesting possibility is that the distinct factors that activate fruitless transcription in different populations of fruitless neurons could themselves cooperate with FruM in regulating cell-type-specific effectors.The specificity of FruM-bound sites genome-wide through ectopic expression of tagged isoforms that enzymatically label DNA. Each of these experiments is conceptually difficult to interpret because FruM is studied outside of the typical cellular context and transcriptional millieu. A DamID study [M-Dam expression is driven by leak from an uninduced UAS-driven construct, allowing expression in any cell at any time of development; a BirA/BLRP study [M expression in the S2 cell line, which is not neural [Two previous studies attempted to identify FruID study  analyzedRP study  induced t neural . Both stt neural  versus gM alters expression of distinct genes across fru subpopulations, and that these transcriptional differences are masked when the whole fru populations are analyzed by RNA-seq. However, a previous study identified 772 genes enriched in male fru+ neurons in adult by TRAP [fru versus 1.5-fold enrichment by TRAP). One possibility is that differences in TRAP were dominated by fru+ Kenyon cells, which we filtered out in our analysis  in a humidified incubator at 25C on a 12:12 light:dark cycle. Flies analyzed in all experiments were 2–7 day old adults who were housed in mixed-sex groups.Genotypes:Brains were dissected for up to 90 minutes in Schneider’s medium supplemented with 1% BSA and placed on ice. Optic lobes were removed during dissection. Brain dissections were interspersed such that both male and female brains were dissected throughout the dissection period. About 20 brains were obtained for each sex. After all dissections were completed, collagenase was added to a final concentration of 2mg/mL and samples were incubated at 37C for 20 minutes, without agitation.Samples were dissociated by trituration and spun down at 300g, 4C, for 5 minutes. Collagenase solution was removed and replaced with PBS+0.1% BSA, and cells were passed through a cell strainer cap and supplemented with 50ng/mL DAPI before being subjected to flow cytometry on an a FACS Aria II. Plasticware for cell dissociation and collection was pre-treated by rinsing with PBS+1% BSA to prevent cells from sticking to bare plastic.+. We collected 6,000–10,000 GFP+ cells for each fru+ sample and analyzed matched numbers of GFP-/fru- cells. For each replicate, we sorted male and female cells during the same session and performed transposition or RNA extraction in parallel. During sorting, we made two adjustments to protect the fly primary cells, which were very delicate—we disabled agitation of the sample tube, and sorted using the “large nozzle,” e.g. 100μm, i.e. using larger droplet size and lower pressure. For ATAC-seq, we sorted cells into PBS supplemented with 0.1% BSA. For RNA-seq, we sorted cells directly into Trizol-LS.During flow cytometry, dead and dying cells were excluded using DAPI signal, and forward scatter and side scatter measurements were used to gate single cells. Using our dissociation methods, 50–80% of singlets appeared viable (DAPI-low), and 2–5% of viable singlets were GFPBrains were dissected in external saline . For two-photon imaging, brains were then transferred fresh to 35mm imaging dishes and pinned to sylgard squares with tungsten wire. Imaging was performed on a Bruker Investigator using a 1.0 NA, 20x, water-dipping objective. Stacks were collected along the anterior-posterior axis with 1 micrometer spacing in Z and ~350nm axial pixel size.For immunostaining and confocal imaging, brains were dissected for up to twenty minutes before being transferred to 1% paraformaldehyde in PBS, on ice. All steps were performed in cell strainer baskets (caps of FACS tubes) in 24 well plates, with the brains in the baskets lifted from well to well to change solutions. Brains were fixed overnight at 4C in 1% PFA in PBS. On day 2, brains were washed 3x10’ in PBS supplemented with 0.1% triton-x-100 on a shaker at room temperature, blocked 1 hour in PBS, 0.1% triton, 4% Normal Goat Serum, and then incubated for at least two overnights in primary antibody solution, diluted in PBS, 0.1% triton, 4% Normal Goat Serum. Primary antibody was washed 3x10’ in PBS supplemented with 0.1% triton-x-100 on a shaker at room temperature, then brains were incubated in secondary antibodies for at least two overnights, diluted in PBS, 0.1% triton, 4% Normal Goat Serum. DAPI (1 microgram/mL) was included in secondary antibody mixes. Antibodies and concentrations can be found in the resources table.Brains were mounted in 1x PBS, 90% glycerol supplemented with propyl gallate in binder reinforcement stickers sandwiched between two coverslips. Samples were stored at 4C in the dark prior to imaging. The coverslip sandwiches were taped to slides, allowing us to perform confocal imaging on one side of the brain and then flip over the sandwich to allow a clear view of the other side of the brain. Scanning confocal stacks were collected along the anterior-posterior axis on a Leica SP8 with 1 micrometer spacing in Z and ~150nm axial pixel size.After FAC-sorting, nuclear isolation and Tn5 transposition were performed as in  with modThe quality of prepared libraries was verified with a Bioanalyzer 2100 using a high sensitivity DNA kit (Agilent). Libraries were quantified using KAPA qPCR assay (KAPA Biosystems), and multiplexed and sequenced on a NextSeq Illumina machine with 75 base pair, paired-end reads. Libraries were sequenced to a depth of 35–70 million reads.Adapters were trimmed using cutadapt and reads below 18 bases were discarded. Reads were aligned to dm6 with Bowtie2 with option–X 1000 to set the maximum fragment length for paired-end reads. About 70% of reads mapped uniquely to the fly genome. Aligned reads were processed with samtools to create a bam file. Picard MarkDuplicates was used to mark duplicates and samtools was used to generate a final bam file with de-duplicated reads passing a q30 quality filter. After de-duplication, we obtained 8.9–16.3 million unique reads per library.usegalaxy.org to analyze the data [The sequencing data were uploaded to the Galaxy web platform, and we used the public server at the data . Peaks wFor comparisons between neurons of the same sex status , DiffBind was run with default parameters using filtered bam files for each replicate and MACS2 called peaks for each replicate as input.In female flies, the X chromosomes accounted for 20–21% of all ATAC-seq reads, while only accounted for 14% of all male ATAC-seq reads across samples. For comparisons of chromatin accessibility between sexes, this difference in X:A DNA ratio introduced extremely large bias in regions called as differentially accessible. We initially identified 1374 differentially accessible regions between male fru- neurons and female fru- neurons, compared to the 95 regions reported in this paper. This is due to whole-genome rather than per-chromosome normalization performed by DiffBind and results in artificially high numbers of regions to be female-biased on X and male-biased on the autosomes.Therefore, we separated aligned reads from the X chromosome and autosomes using “Slice BAM by genomic regions”, selecting for chrX, or for chr2L, 2R, 3L, 3R, and 4. DiffBind was run using the same MACS2 called peaks, but with either X chromosome or autosome aligned reads. Separate lists of changed regions (FDR <0.05) were concatenated per comparative analysis.M-specific regions with annotated protein-coding genes were filtered for unique genes. List of genes were loaded into gProfiler with default settings. Annotation from combined MACS2 peak calls used as custom background list. FruM-specific gene lists were also loaded into DAVID for domain annotation, without a custom background.Tables of genomic regions were downloaded and processed in R as follows: Peaks were annotated using ChIPSeeker–nearest gene, +/- 50bp promoter and exported as tables. FruRNA Purification from TriZol samples using PicoPure columns ). We summarize the protocol as follows:Cells were sorted into Trizol-LS and stored at -80C until RNA isolation. For RNA extraction, we followed the standard Trizol-LS protocol until the aqueous phase was isolated. We then passed the aqueous phase over Arcturus Picopure columns, including a DNAse treatment on the column (Qiagen RNAse-free DNAse set #70254) using the following unpublished protocol with unknown authors: Trizol sampleProcess TriZol samples with 0.2 vol of chloroformSpin max speed for 5 minutes (start PicoPure column conditioning)Take aqueous layerAdd equal volume of 70% ethanol (RNAse-free)PicoPure column conditioningCondition PicoPure column for 5 minutesSpin out at max speed for 1 minute.Bind RNA to picoPure columnLoad up to 300 uL of the supernatant/70% ethanol mix at a timeSpin at 100g x 2 minutes for each loadAfter last load, spin 16,000 x 30 secondsDiscard flowthroughWash with 100 uL of Wash Buffer 1 (WB1) and spin 8,000g x 1 minuteDNAse treatmentFor each sample, combine 35 uL of RDD buffer with 5uL DNAse I stock solution (previously resuspended per Qiagen protocolAdd 40 uL of DNAse mix onto membrane.Incubate at RT x 15 minutesAdd 40 uL of Wash Buffer 1 (WB1) and spin 8,000g x 15 secondsWash with 100 uL of Wash Buffer 2 (WB2) at 8,000g x 1 minuteEmpty flow-throughWash again with with 100uL of Wash Buffer 2 (WB2) at 16,000g x 2 minutesCheck to make sure no wash buffer remains.ElutionTransfer to a new 0.5 mL tube (in kit)Add 11 uL of 42C pre-warmed elution buffer to membraneWait 1 minuteSpin at 1,000g x 1 minute to distribute bufferElute at 16,000g x 1 minuteConsider doing a second elution to capture as much flowthrough as possible.Store at -80CRNA quantification and quality assessment were performed on an RNA TapeStation at the UMich Genomics Core. We typically obtain 0.1–0.3pg of RNA per cell, depending on cell type and developmental stage. Note that insect 28S rRNA is processed to a size similar to 18S rRNA, thus “RNA Integrity Number” or similar that are calculated by these machines will not reflect the true RNA quality.RNA libraries were prepared by the UMich Genomics Core with the following protocol:Samples were subjected to quality control on an RNA TapeStation. Total starting RNA was around 1 ng per sample. Library preparation was performed using the NEBNext Single Cell/Low Input RNA Library Prep Kit for Illumina, with amplification cycles calibrated to the amount of total RNA. Unstranded, poly-A selected libraries were sequenced on an Illumina NovaSeq using 150bp paired end reads to a depth of ~30 million reads per library.All processing steps were done through the Galaxy web platform. Reads were trimmed with Trim Galore! with automatic adapter detection and aligned to dm6 using HiSAT2 in paired end mode. Uniquely aligning reads above MAPQ 30 were selected using JSON and MarkDuplicates, retaining 25.4–39.6 million reads per library. FastQC and MultiQC were used to visualize quality metrics across samples. Coverage tracks were generated using deeptools “bamCoverage”.Exon reads were counted and aggregated at a gene-level using featureCounts. TPM values were calculated using StringTie and averaged between replicates. Differential expression was determined using DESeq2 using an p.adj threshold of >0.05 for reporting significance.lncRNA:roX2 binding to chromatin using CHART was lifted over from dm3 into dm6 using UCSC LiftOver. Lifted-over bed files were sorted using bedtools “sortBed” and relative distance of lncRNA:roX2 tethering sites was determined using bedtools “RelDist” for the X chromosomes only.Genomic locations of Histone chromatin immunoprecipitation from adult neurons  was downRatio files of FruA, FruB, and FruC DamID enrichment over dam-only control were downloaded in GFF format from GEO (GSE52247). Regions were resized to remove 5bp on each flank, thus removing overlapping regions of microarray probes and rebased to bed/bedGraph compatible format. Genomic regions were then lifted over from dm3 into dm6 using RLiftOver. Tables were exported as bedGraph files, uploaded to the Galaxy web platform and converted from bedGraph to bigWigs using “Wig/BedGraph-to-bigWig” using default parameters.fru+ and male fru+ were converted to FASTA using bedtools MakeFasta. FASTA sequences were run through the web version of FIMO using Fru motifs identified by SELEX [M-specific were flagged. Tables were converted to cumulative frequencies and plotted.All ATAC-seq genomic regions from DiffBind comparison (FDR > = 1) between female by SELEX  with no S1 Figfru+ (GFP+) neurons collected. Example plot corresponds to male neurons from replicate 2 of RNA-seq experiments. B. Heatmap of Spearman correlations of uniquely aligned, deduplicated reads from ATAC-seq libraries. C. PCA analysis of uniquely aligned, deduplicated reads from ATAC-seq libraries. PC1 reflects read depth. D. Fragment length of ATAC-seq libraries. E. Heatmap of Spearman correlations of uniquely aligned, deduplicated reads from RNA-seq libraries. F. PCA analysis of uniquely aligned, deduplicated reads from RNA-seq libraries. G. UCSC genome browser screenshots of ATAC-seq and RNA-seq signal across neural (nSyb), glial (repo), and neuroblast (dpn) specific genes.A. Example FACS plot showing gates for (TIF)Click here for additional data file.S2 Figdsx locus. Blue signal tracks represent INTACT histone ChIP data for R57C01(Nsyb)-labeled neurons in the adult head [sxl and tra loci. E. Zoom in of highlighted regions in D, corresponding to sexually dimorphic splicing events. Displayed are RNA-seq coverage tracks and 20 aligned reads in male fru+ neurons and female fru+ neurons. Reads correspond to replicate 1 data. For both genes, male mRNA contains only non-functional isoforms, while female mRNA contains both protein-coding and non-functional isoforms.A. Flowchart of computational pipeline used to identify differentially accessible regions between male and female samples using ATAC-seq. B. Barplots displaying number of differentially accessible regions using DiffBind FDR <0.05 as a cutoff. Corrected regions are produced by the pipeline in (A), while uncorrected regions are produced without splitting of the X chromosome and autosomes before running DiffBind. Colors correspond to ult head . D. UCSC(TIF)Click here for additional data file.S3 Figfru+ and fru- neurons in both sexes. B. Of 22 regions differentially accessible between fru+ and fru- neurons, 19 correspond to protein-coding genes (labeled on the X axis). The log2(Fold Change) in accessibility of these genes relative in female neurons is plotted. C. Schematic of calculating chromatin accessibility across a gene locus. D. Barplot of log2(Fold Change) of genes with gene-scale differential coverage between male fru+ neurons and male fru- neurons. ey is a Kenyon cell marker. E. Scatterplot of whole-gene chromatin accessibility versus gene expression level in the male fru+ dataset.A. UpSet plot showing the intersection of regions which are differentially accessible (DiffBind FDR <0.05) between (TIF)Click here for additional data file.S4 Figfru+ neurons. Points at the top border in the volcano plot and along the bottom border in the MA plot have been thresholded such that they are visually comparable to plots in B. B. Volcano and MA plots of regions with differential accessibility (DiffBind < 0.05) between male fru- and fru+ neurons. C. Binary heatmap of regions called differentially accessible in 1 or more comparisons. D. Scatterplot of log2 fold changes in accessibility compared to male fru+ neurons. Figure shows analysis in M-specific regions, separated by regions which are selectively open or closed in male fru+ neurons.A. Volcano and MA plots of regions with differential accessibility (DiffBind < 0.05) between female and male (TIF)Click here for additional data file.S5 Figfru expression. Brain-wide speckle signal is autofluorescence. D. In-silico enhancer bashing of enhancer reporter element 91A09  and female brains. Arrows point to cells with sex-specific expression  or dimorphic labeling intensity (92B12). C. UCSC genome browser screenshot of region covering enhancer reporter 84D12 and 2-photon stack of adult male and female brains. Labeling is mutually exclusive with nt 91A09 . UCSC ge(TIF)Click here for additional data file.S6 Figfru status. C. Gene ontology analysis of genes enriched in both fru+ datasets  over fru- datasets. Enrichment is over a custom background of genes with expression > 10 TPM across the four datasets. D. Expression of 303 genes near FruM-closed or FruM-open ATAC-seq peaks in male versus female fru+ cells. P-value by Welch two-sample t-test.A. MA plots of differential RNA expression analysis between four datasets. B. Clustered heatmap of TPM values of genes with differential expression in one or more comparisons. Differential expression is dominated by (TIF)Click here for additional data file.S7 FigA motifs. B. Cumulative frequency plots for SELEX and SANGER Fru (FruA) motifs (FlyFactorSurvey) across FruM-open, FruM-closed and unchanged regions. C. Volcano plots showing motif strengths across FruM-specific regions. D. Binary heat map of overlap of FruM-specific regions with previously reported FruM targets in S2 cells [A. SELEX and SANGER FruS2 cells . E. Expa(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableTables display peak location and statistical comparisons, annotation of peak location, and data on nearest gene.(XLSX)Click here for additional data file.S3 Tablehttps://implegacy.brainbase.at/bbweb/ using our knowledge of neural anatomy and reference images from [Annotations were performed by downloading stacks from ges from ,3.(XLSX)Click here for additional data file.S4 TableStatistical results of each comparison are shown in separate sheets. Tables display peak location and statistical comparisons, annotation of peak location, and data on nearest gene.(XLSX)Click here for additional data file.


There is a life-long relationship between rhinovirus (RV) infection and the development and clinical manifestations of asthma. In this study we demonstrate that cultured primary bronchial epithelial cells from adults with asthma (n = 9) show different transcriptional and chromatin responses to RV infection compared to those without asthma (n = 9). Both the number and magnitude of transcriptional and chromatin responses to RV were muted in cells from asthma cases compared to controls. Pathway analysis of the transcriptionally responsive genes revealed enrichments of apoptotic pathways in controls but inflammatory pathways in asthma cases. Using promoter capture Hi-C we tethered regions of RV-responsive chromatin to RV-responsive genes and showed enrichment of these regions and genes at asthma GWAS loci. Taken together, our studies indicate a delayed or prolonged inflammatory state in cells from asthma cases and highlight genes that may contribute to genetic risk for asthma. Britney Helling et al. report that cultured bronchial cells from adults with asthma show different gene expression and chromatin accessibility patterns when exposed to rhinovirus than do cells from individuals without asthma. Their data suggest that rhinovirus infection leads to a delayed or elongated activation of inflammatory genes in individuals with asthma compared to those without asthma. Although RV often causes minor upper respiratory tract illnesses, RV infections can be more frequent and severe in people with respiratory diseases, such as asthma, chronic obstructive pulmonary disease, cystic fibrosis, and idiopathic pulmonary fibrosis7. In particular, RV infection in individuals with asthma is associated with greater lower airway inflammation and decreased lung function compared to individuals without asthma8. Moreover, RV-associated wheezing illness in the first 3 years of life is associated with increased risk of asthma later in childhood11, suggesting that impaired response to RV may be an early manifestation of asthma in many children. The durable relationship between RV infection and asthma may be the consequence of shared genetic and/or environmental risk factors or of defective remodeling of the airway epithelium in response to RV that predisposes to the subsequent development of asthma12.Human rhinovirus (RV) is a nonenveloped, single-stranded RNA virus that is responsible for more than half of occurrences of the “common-cold”16. It has previously been shown that bronchial epithelial cells (BECs) from asthma cases and non-asthma controls have different transcriptional and cytokine responses to RV in cell culture21. However, the mechanisms underlying these differences remain unclear. We hypothesized that modifications of the chromatin landscape in BECs from individuals with asthma are associated with altered transcriptional responses to RV. To address this hypothesis, we performed a multi-omic study of gene expression (by RNA-seq) and chromatin accessibility (by Assay for Transposase-Accessible Chromatin with high-throughput sequencing [ATAC-seq]) in the presence of either rhinovirus strain RV-A16 or vehicle in cultured human BECs, and of chromatin interactions (by promoter capture [pc]Hi-C) in ex vivo human BECs. Our study revealed profound differences in transcriptional and epigenetic responses to RV between cells from individuals with and without asthma. Using pcHi-C, we were able to predict the target genes of RV-responsive chromatin changes and identify correlated transcriptional and chromatin responses. These RV-responsive genes and correlated RV-responsive chromatin-gene pairs were also enriched for genes within asthma genome-wide association study (GWAS) loci22. Our study provides a first-time view of the relationship between transcriptomics, chromatin accessibility, GWAS and three-dimensional chromatin landscape in BECs exposed to an important viral trigger of asthma and asthma exacerbations. These data suggest a prolonged inflammatory response to RV in asthma cases and revealed potential gene targets and pathways involved in the aberrant BEC responses to RV in asthma.Airway epithelial cells are the primary site of RV infection and replication, and release cytokines that orchestrate downstream responsesWe characterized the transcriptional and chromatin landscape in human primary BECs from 18 adult lung donors, nine with and nine without asthma. Basal BECs were grown in submerged monolayer culture, triplicates of each were treated with either rhinovirus (RV) type A16 or vehicle control; cells were harvested for RNA-seq and ATAC-seq studies. Ex vivo BECs from eight additional donors (four asthma cases and four controls) were collected and processed for pcHi-C. Donor characteristics are summarized in Table p < 10–5). Surprisingly, only 1929 genes showed significant responses to RV in both cases and controls at an FDR < 0.05 . Moreover, among the 1929 genes that were RV-responsive in both groups, the controls had overall larger responses (paired Wilcoxon sign rank test p < 10–5), with median absolute fold-change among the 1929 genes of 1.5 in controls and 1.4 in cases   and one gene increasing in cases and decreasing in controls (ZMIZ1) in response to RV. Examples of different patterns of RV response in cases and controls are presented in Supplementary Fig. To investigate BEC responses to a virus associated with worsening asthma and exacerbations, we assessed transcriptional responses to RV in cases and in controls. These analyses revealed significantly more RV-responsive genes in the controls  Fig. . Among tp values ≤0.05 as significantly enriched23. Four pathways were enriched in both the cases and controls , six additional pathways were enriched in the controls but not in the cases  and one additional pathway was enriched in the cases but not in the controls (necroptosis)  terms among the RV-responsive genes using iPathwayGuide (Advaita), considering pathways with Bonferroni adjusted is) Fig. . Using Gis) Fig. . In contis) Fig. .Fig. 2Pap < 10–5; Fig. p = 0.013). The striking reduction of chromatin response to RV in BECs from asthma cases compared to controls suggests either a loss of response in cells from asthma cases or a fixed state in the cases in which the chromatin is primed to be RV-responsive.Based on the large proportion of genes that responded to RV only in the cases or only in the controls, we expected to observe differences in chromatin responses to RV between cases and controls. The larger number of genes that responded to RV in the controls, and their enrichment in more diverse pathways and different biological processes, further suggested that chromatin changes would be more dynamic in the controls. Indeed, we identified 2458 RV-responsive regions of open chromatin in cells from the controls, but only 238 RV-responsive regions of open chromatin in cells from the cases at an FDR < 0.05  that overlap with changes in chromatin accessibility. Regions with increased accessibility (n = 153 in cases and 1757 in controls) and decreased accessibility (n = 85 in cases and 701 in controls) were assessed separately to allow predictions of directionality. Fig. Next, we considered the sequences within the areas of RV-responsive chromatin and tested for enrichment of transcription factor binding sites using HOMER motif analysisIRF1 and SP1) or controls  also showed increased expression after treatment with RV. This is consistent with the prediction that genes regulated by these transcription factors would have enhanced expression after exposure to RV. In contrast, in areas with decreased accessibility (more closed chromatin) in the controls, only 10 of 20 corresponding transcription factors show decreased expression . This implies that for half of the transcription factors at regions with decreased accessibility, there is both decreased accessibility of binding sites and decreased availability of transcription factors; for the other half there is increased availability of the corresponding transcription factor despite decreased accessibility of binding sites. Many of the transcription factors with enriched binding motifs at regions with RV-responsive chromatin have been implicated in studies of lung function , immune function , and asthma .We next explored the direction of effect for each RV-responsive transcription factors with enriched binding motifs. In-line with expectations, the majority of the transcription factors in areas with increased accessibility (more open chromatin) after RV exposure in either cases . Overall, we detected 601,845 chromatin-promoter interactions    of RV-responsive chromatin that were correlated with the expression of RV-responsive target genes , the chromatin (n = 119), or both (n = 63) were not RV-responsive in the cases. The 119 pairs in which genes were RV-responsive in the cases but the ATAC-seq peaks that were connected by pcHi-C loops were not RV-responsive are examples of regions in which chromatin accessibility is responsive to RV only in the controls, despite their target genes being RV-responsive in both cases and controls, and potential examples of priming. Of the 119 areas of RV-responsive chromatin in the controls but not in the cases at q < 0.05, 75 showed some evidence of RV response in the same direction in cases and controls (the log fold changes corresponding to response were both positive or both negative in the cases and controls); 41 of the 75 chromatin responses were nominally significant at an unadjusted p values <0.05 in the cases. The remaining 44 areas of open chromatin had RV responses in the opposite direction in cases and controls, 24 of these opposite responses were also nominally significant in the cases. We consider the remaining 54 of the 119 areas of open chromatin without evidence of an RV response (unadjusted p > 0.05) that were connected to an RV-responsive gene as suggestive of chromatin that is primed (or poised) in the cases, allowing the gene to respond upon treatment. Thus, at least 45.4% of the 119 RV-responsive gene-chromatin pairs determined by pcHi-C in cells from controls that were not paired in cells from cases because of lack of chromatin response, show evidence of priming in the cases.To determine whether chromatin is primed for response to RV in the cases, we next evaluated the 182 RV-responsive gene-chromatin pairs determined by pcHi-C in cells from controls that were not also paired in cells from cases because either the gene  in cases and controls. SLC25A25 shows increased expression in response to RV in both cases and controls, while the interacting ATAC-seq peak (chr9 130911117–130911919) shows decreased accessibility after exposure to RV only in the controls. In both the RV and vehicle treated cells from the cases, chromatin accessibility is not responsive to RV and is similar to the RV-treated cells from controls, suggesting that the chromatin in cases is already primed. For the EPHX1 gene, RV treatment results in decreased expression in both cases and controls, but the interacting ATAC-seq peak (chr1 225997611–225998251) is RV-responsive only in the controls. This area of open chromatin is more accessible in cells from controls with RV treatment compared to vehicle treatment and similar to both the RV and vehicle treated cells from cases, again suggesting that the chromatin is primed for a response to RV in cells from the cases.Figure 22. GWAS loci were defined by LD using FUMA43, as described in Pividori et al.22. Overall, 741 coding genes were annotated to the 61 loci, of which 294 genes were expressed in BECs in our study .To explore the possibility that RV-responsive genes may contribute to asthma genetic risk, we explored the overlap of the locations of RV-responsive genes and 61 GWAS loci for childhood onset or adult onset asthmap = 0.12). Finally, we repeated this for the ATAC-seq regions, randomly selecting 170 regions in each of 100 permutations. A mean of 1.57 (SD = 1.1) regions fell within GWAS loci in the permuted data set compared to eight observed regions in our study, a greater than 5-fold enrichment (permutation p < 0.01).Next, we asked whether the set of genes or chromatin regions participating in RV-responsive gene-chromatin pairs Fig.  were enrPSMB8 and PRKCQ) , four genes out of nine RV-response genes at two 5q31.1 loci , and 15 HLA region genes (out of 39 RV-responsive genes in this region). Two smaller networks of two genes each included three additional HLA regions genes  in addition to one gene at the 13q32.3 locus (GPR183). The fourth network was comprised of six out of 25 RV-responsive genes at the Epidermal Differentiation Complex (EDC) on chromosome 1q21.3 , the second most statistically significant childhood onset asthma locus22  . Importantly, innate immune memory has been shown to be reversible in some cases48, suggesting that an understanding of innate immune memory in BECs may lead to novel therapeutic targets for RV infections and its sequelae in individuals with asthma, or potentially to reducing the subsequent risk for asthma in young children.Although innate immune memory was initially described in innate immune cells, other cell types, including epithelial stem cells, have been shown more recently to demonstrate responses consistent with innate immune memory22, as well as the most significant shared locus between childhood and adult onset asthma at the HLA region, which accounts for 27.6% of all RV-responsive genes within asthma-GWAS loci. Other notable asthma-GWAS loci overlapping with RV-responsive genes are at chromosomes 2q12.1 , 11q13.5 (LRRC32), 16p12.1 (IL4R), 16q12.1 (NOD2), and 18q21.32  . It is possible that the observed differences in transcriptional and epigenetic responses to RV between cases and controls is a consequence of delayed, more rapid, or prolonged responses in the cases. That is, the response in cases at 24-hours may more closely resemble a later or earlier post-infection timepoint response in controls. In fact, the enrichment in inflammatory GO terms in cases and enrichment for apoptotic GO terms in controls suggest a delayed or prolonged response to RV in asthma cases. Future time course studies of response to RV in asthma cases and controls can directly address this question. Second, our sample size of nine cases and nine controls was underpowered to look directly for genetic effects on RV response. Nonetheless, we see an enrichment of RV-responsive genes within asthma-GWAS loci, including those that were targets of RV-responsive chromatin accessibility sites. Third, although we use pcHi-C to wire RV-responsive ATAC-seq peaks to their RV-responsive gene targets, the pcHi-C was performed in cells from different individuals as those used in the cell culture studies and in ex vivo cells compared to cultured cells in this study. Differences between the donors with respect to sex ratio or other features, or differences in ex vivo vs. in vitro cells may have resulted in our underestimating the number of interactions present in the cultured and RV-treated cells. Lastly, our study focuses solely on the basal cell subtype of BECs. Because basal cells express the RV-A16 receptor (ICAM1), we chose to focus on this population of epithelial cells in this study. Future assessment of bronchial epithelial response to RV in a differentiated cell model will add further to our understanding of the effects of RV infection in other BEC types.50. Together with the enrichment of asthma-GWAS loci among RV-responsive epigenetic sites, these results suggest potentially novel therapeutic targets that could ameliorate the downstream consequences of RV infection or possibly prevent the inception of asthma in at-risk individuals.To our knowledge, this study provides the first description of pcHi-C in BECs, and of ATAC-seq in BECs from individuals with asthma. Utilizing these unique datasets, we were able to link potential functional changes in the epigenome to target genes and show correlations between chromatin accessibility and gene expression, and RV-responsive genes to asthma-GWAS loci. The data provided in this study are important not only to asthma but could also inform studies of other diseases of the lung epithelium. Finally, the results of our studies suggest that epigenetic priming and trained immunity may underlie the differential responses to RV infection in asthma cases and controls, and that these differences may reflect a prolonged inflammatory phase in BECs from individuals with asthma. Both priming and prolonged inflammation have previously been reported in asthma cases in response to RV-A1651. Medical information, including sex, age, whether the donor had asthma, and cause of death were collected on all donors and group data are summarized in Table Lungs from 18 donors, not used for transplantation, were collected from the Gift of Hope, Organ and Tissue Donor Network, transferred to the University of Chicago, and processed within 48 h of harvest. BECs were isolated from the main bronchus as previously described52 until 80–90% confluency before being transferred to 12-well collagen coated transwell membranes. Cell culture experiments were conducted in basal cells, a subset of BECs that express ICAM1, the main receptor for RV-A. After reaching confluency (3–5 days), cells were treated with either 10.2 × 106 plaque forming units of RV-A16 in 5.1 ul of PBS or 5.1 ul of PBS (vehicle control). The RV was propagated and purified at the University of Wisconsin, Madison as previously described53. At harvest, cells were disassociated from the culture surface using Accutase (Sigma–Aldrich) and quantified using a hemocytometer and trypan blue to differentiate live and dead cells. All cultures had greater than 90% viability at the time of the harvest. After quantification, 5000 cells from each culture well were immediately processed for ATAC-seq and the remaining cells were resuspended in RLT buffer and stored at 4 °C for less than four hours prior to RNA and DNA extraction using the Qiagen AllPrep Mini Kit (Qiagen 80204).BECs from the 18 donors were cultured on collagen coated flasks in F-media54. Read counts were adjusted to counts per million and normalized using TMM normalization55. PCA was conducted in R (version 3.6.2) using prcomp to select covariates to adjust for in the model; library prep technician, cDNA concentration, cDNA prep date, library concentration, and culture plate orientation were significant and adjusted for in the model. Voom was used to adjust for significant technical covariates56. Each sample was studied in triplicate , which was statistically accounted for using duplicateCorrelation in the Limma R package version 3.5 (https://bioconductor.org/packages/release/bioc/html/limma.html). Genes significantly associated with RV treatment or asthma were identified using Limma linear mixed modeling in R, while including sex as a covariate and blocking by sample to pair RV and vehicle treated samples from the same individual. An FDR-adjusted p value of 0.05 was used as a significance threshold, using the method of Benjamini and Hochberg57.RNA quality and concentration were assessed using an Agilent 2100 bioanalyzer. RIN scores were 7.8 or better for all samples, with a mean RIN of 9.6. RNA was converted to cDNA using the SMART-Seq v4 Ultra Low Input RNA kit for sequencing (Takara Bio cat. 634898). Standard library preparation was completed using the Illumina Nextera XT DNA Library Preparation Kit and library quality and concentration were assessed using an Agilent 2100 bioanalyzer. Indexed samples were pooled and sequenced on the Illumina Hi-Seq 4000 with 100 base-pair, paired-end sequencing to a minimum of 10 million mapped reads per sample and a mean of 14 million reads per sample. RNA-seq reads were mapped to genome assembly GRCh37(hg19) reference sequence using STAR (version 2.6.1)58 with the following modifications: the transposition reaction was incubated on a heated shaker at 37 °C for 30 min at 1000 rpm and the Qiagen MinElute PCR Purification kit was used to clean up the transposition reaction. Samples were indexed, pooled, and sequenced on the Illumina Hi-Seq 4000 with 50 bp single-end reads. Sample pools were resequenced until each sample had a minimum of 50 million mapped reads. Of the 108 samples sequenced, two samples from different donors failed QC. Two RV-treated samples from asthmatics were excluded ; one was an outlier in several PCs including in PC1 and one did not have sufficient DNA concentrations for library prep . Sequencing data that passed QC was mapped onto hg19 using bowtie2 version 2.3.2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml). ATAC-seq peak calls were determined using MACS2 (version 2.1.0)59. Using bedtools (version 2.26.0), peak calls from all samples were pooled across all samples and overlapping or duplicate peaks were consolidated into a single peak that ranged from the 3′ most base to the 5′ most base in all overlapping peaks to create a peak-library or collection of all of the regions of accessible chromatin  (raw and processed data available in GEO). Next, the number of reads aligned to each peak were determined for each sample and normalized to counts per million peak-library mapped reads. The fraction of reads in peaks or FRiP score was 7.2% across samples with 17.7% of peaks at Roadmap predicted transcriptional start sites60 and 70% of peaks overlapping with ENCODE transcription factor binding sites61. PCA was conducted in R using prcomp to identify potential confounding of biological and technical covariates; number of additional PCR cycles and cell culture plate location were significant and adjusted for in the model. Each sample was part of a biological triplicate . Replicate samples were adjusted in analyses using duplicateCorrelation and RV and Vehicle treated samples were paired using the blocking option as implemented in Limma. Peaks that were associated with asthma or RV infection in cases or controls were identified using Limma with a threshold of significance set at an FDR-adjusted p value of 0.05, using the method of Benjamini and Hochberg57.For each sample, 5000 cells were processed immediately using the Nextera DNA Library Prep Kit (Illumina FC-121-1030) after collection using a standard ATAC-seq protocol23. Multiple testing correction for pathways was addressed within the iPathwayGuide program by Bonferroni correction. To determine enrichment for pathways and GO terms in subset gene lists, specifically the RV-responsive genes within GWAS loci, were assessed through ToppGene instead of iPathwayGuide due to the input criteria needed for the latter.Advaita iPathwayGuide (version v1906) was used to determine enriched pathways, and biological gene-ontology (GO) terms for both RV-responsive genes and asthma-associated genes in the vehicle treated and RV-treated samples , we identified transcription factor binding motifs that were enriched within the asthma-associated or RV-responsive DACs, using all of the areas of open chromatin as the background and a Benjamini–Hochberg FDR < 0.0538. In short, HiCUP version 0.5.9 was used to map pcHi-C reads to the genome and remove technical artifacts. To obtain promoter interaction maps, CHiCAGO version 1.6.0 was performed on the pooled set of all reads from each sample and significance was defined as a CHiCAGO score >5, as recommended62. NAA38 was removed from the analysis because of ambiguity on chromosomal location of the gene, which resulted in NAA38 interactions being more distant than any other interactions.BECs were harvested from eight additional lungs not used for lung transplant through the Gift of Hope, Organ and Tissue Donor Network. The lungs were collected from four asthmatic and four non-asthmatic donors and processed within 24 h of harvest. The clinical data for these eight individuals are summarized in Table For these analyses, the mean was calculated for biological replicates for all protein coding genes and regions of open chromatin, and correlations between chromatin accessibility and gene expression was assessed by Spearman’s rank-order correlation and using Benjamini–Hochberg FDR < 0.05 to adjust for multiple testing. Both cases and controls were used to assess correlation to increase sample size and for correlations that are observed in both cases and controls, the adjusted p value is slightly different based on number of tests conducted.p < 0.05 was considered statistically significant. For iPathwayGuide GO term enrichment, enrichment was calculated by iPathwayGuide and a Bonferroni adjusted p < 0.05 was considered statistically significant. Permutation analyses were conducted in R for GWAS enrichment and a finding of fewer than five of 100 permutations reaching the observed number were considered statistically significant. To assess correlation between chromatin accessibility and gene expression in pcHi-C pairs, Spearman’s rank-order correlations were conducted and Benhamini-Hochberg FDR < 0.05 was considered statistically significant. For HOMER motif enrichment, a Benjamini–Hochberg FDR < 0.05 was considered statistically significant.Triplicates of each culture condition were conducted in each BEC line. The R package limma was used for all linear regression modeling for both RNA-seq and ATAC-seq analyses and an FDR-adjusted Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting Summary


Hence, cytopenias, especially thrombocytopenia observed in some patients treated with imetelstat, are not mediated by off-target interactions with TLRs.Imetelstat sodium  is a first-in-class telomerase inhibitor that has demonstrated activity in patients with myeloproliferative neoplasms (MPNs). Treatment with imetelstat has been associated with thrombocytopenia and other hematologic adverse effects that were manageable and reversible. Toll-like receptors (TLRs) are proteins that recognize pathogen-associated molecular patterns and stimulate innate immune and pro-apoptotic responses. Because imetelstat is an oligonucleotide, and some oligonucleotides can activate TLRs, we conducted an in vitro study to rule out the possibility of imetelstat-associated thrombocytopenia by off-target effects through activation of TLRs. We used HEK293 cell lines stably co-expressing a human TLR gene and an NFκB-inducible reporter to investigate whether imetelstat can activate TLR signaling. We treated the cells with imetelstat or control oligonucleotides for 20 h, and used absorbance of the culture media to calculate the reporter activity. Treatment with imetelstat within or beyond the clinically relevant concentrations had no stimulatory effect on TLR2, TLR3, TLR4, TLR5, TLR7, or TLR9. This result was not surprising since the structure of imetelstat does not meet the reported minimal structural requirements for TLR9 activation. Furthermore, imetelstat treatment of the MPN cell line HEL did not impact the expression of TLR signaling pathway target genes that are commonly induced by activation of different TLRs, whereas it significantly reduced its target gene  There an = 18) who had not responded to or had unacceptable side effects with prior treatment, 83% reported anemia ), 83% reported neutropenia ), and 50% reported thrombocytopenia of any grade  [n = 33), 45% (n = 15) reported thrombocytopenia , 39% (n = 13) reported anemia (grade 3 in 30% (n = 10)), and 27% (n = 9) reported neutropenia ; all of these events were considered to be related to treatment [Treatment with imetelstat has been associated with thrombocytopenia, neutropenia, and reduced hemoglobin levels ,2. In a Given that imetelstat is an oligonucleotide, and some oligonucleotides can activate TLRs, it has been speculated that the thrombocytopenia observed in patients with myeloproliferative neoplasms treated with imetelstat may be mediated by off-target effects through the activation of TLRs such as TLR9 . It has We investigated whether imetelstat activates TLRs and, by extension, whether the thrombocytopenia observed in some patients treated with imetelstat may be due to the off-target stimulation of these receptors. Using an NFκB-inducible secreted embryonic alkaline phosphatase (SEAP) reporter assay for TLR activity we assayed imetelstat at clinically relevant or higher concentrations , as well as control oligonucleotides (mismatched or non-complementary sequences) at the same concentrations . We alsop = 0.26), although a dose-dependent increase of SEAP reporter activity was observed. Compared to the untreated control, 30 µM and 60 µM of imetelstat had 1.9-fold (p = 0.019) and 2.6-fold (p = 0.009) higher reporter activity, respectively; however, both concentrations were higher than the maximal concentration of imetelstat achieved clinically with the optimal dose. In contrast, compared to the positive control treatment, different concentrations of imetelstat treatment yielded significantly lower SEAP reporter activity (p = 2 × 10−9), 4.3-fold (p = 1 × 10−6), and 3.2-fold (p = 2.8 × 10−5) less reporter activity than the positive control, respectively. It is not clear why imetelstat at concentrations higher than clinical exposure showed stimulation of TLR8. It has been reported that TLR8 can recognize G-rich single-stranded RNA oligonucleotides from HIV-1 [Treatment with various concentrations of imetelstat even at concentrations higher than clinically relevant showed that SEAP reporter activity resulted in no significant difference compared to untreated cell controls, indicating no stimulatory effect on TLR2, TLR3, TLR4, TLR5, TLR7, or TLR9; neither mismatch nor sense oligonucleotides stimulated the TLRs in this study . For TLRactivity C. Imetelom HIV-1 , but imeCCR7, IFNB1, IL6, IL12A, IL12B, CD83, CD86, CD40, NFAT5, COX20, NOS2, CCL5, and TNF) that are reported to be commonly induced by the activation of different TLRs upon the recognition of specific ligands [CCR7, CCL5, and IL12B) with p-value < 0.05, none of them showed dose-dependent changes. The lack of induction in TLR signaling pathway target genes further supports that imetelstat did not activate TLRs. In contrast, imetelstat induced significant dose- and time-dependent decreases in the expression of its target gene hTERT (human telomerase reverse transcriptase), indicating that the mechanism of action is via on-target activity  cell line HEL was treated with different clinically relevant concentrations of imetelstat for 6 and 24 h. Gene expression profiling was performed using the Affymetrix GeneChip™. We evaluated the expression of a set of genes  ,2, it haIn conclusion, because imetelstat did not activate TLRs and had no impact on the expression of TLR signaling pathway target genes, the cytopenias, including anemia, neutropenia, and thrombocytopenia, observed in some patients treated with imetelstat are not mediated by off-target interactions with TLRs. These findings are supported by the structural differences between imetelstat and the minimal requirements to activate TLR9 , and by t-test was performed to compare each treatment group to its corresponding untreated control and positive control for each TLR study.Seven HEK293 cell lines, each stably co-expressing a human TLR gene  and an NFκB-inducible SEAP reporter gene (HEK-Blue™ TLR cells) and TLR ligands as controls  were used to test whether experimental agents see  activate® TIB-180™) was cultured in RPMI-1640 with 10% FBS. Cells were grown to 70% confluence, then treated with 1 µM, 5 µM, or 10 µM imetelstat for 6 or 24 h in triplicate. Untreated cells served as controls. Total RNA was purified using the AllPrep Mini Kit  on the QiaCube sample processor, per kit instructions. Transcriptome profiling was performed using the Affymetrix GeneChip™ Human Gene 2.0 ST Array , per the manufacturer’s recommendation. The gene expression profiles were compared between imetelstat-treated cells and untreated controls.Human erythroleukemia cell line HEL 92.1.7 (ATCC


During the development of mouse embryonic stem cells (ESC) to neuronal committed cells (NCC), coordinated changes in the expression of 2851 genes take place, mediated by the nuclear form of FGFR1. In this paper, widespread differences are demonstrated in the ESC and NCC inter- and intra-chromosomal interactions, chromatin looping, the formation of CTCF- and nFGFR1-linked Topologically Associating Domains (TADs) on a genome-wide scale and in exemplary HoxA-D loci. The analysis centered on HoxA cluster shows that blocking FGFR1 disrupts the loop formation. FGFR1 binding and genome locales are predictive of the genome interactions; likewise, chromatin interactions along with nFGFR1 binding are predictive of the genome function and correlate with genome regulatory attributes and gene expression. This study advances a topologically integrated genome archipelago model that undergoes structural transformations through the formation of nFGFR1-associated TADs. The makeover of the TAD islands serves to recruit distinct ontogenic programs during the development of the ESC to NCC. The ontogenic process begins with the pluripotent Embryonic Stem Cells (ESCs) of the blastocyst giving rise to all cell types in the body . This deStudies in our laboratory have shown that during retinoic acid (RA) induced in vitro formation of immature postmitotic neurons  from mouse ESCs, a total of 2851 genes  change their activities , the majRecently, protein-mediated DNA-DNA interactions between distant chromosome regions, 100’s or 1000’s of kilobases (kb) apart and even between DNAs of different chromosomes ,6,7 haveA significant portion of DNA-DNA interactions and consequent chromatin looping events are executed by chromatin architectural protein complexes containing the CCCTC-binding factor CTCF . CTCF biMutations of the FGFR1 gene interferes with gastrulation, as well as with the development of the neural plate, neural crest, central nervous system, and somites by affecting the expression of diverse groups of genes . These oNuclear accumulation of newly synthesized FGFR1 is enabled by a unique transmembrane domain and is mediated by Fibroblast Growth Factor 2 (FGF2) and FR1-EGFP ,17,21.Nuclear FGFR1 accumulation is both essential and sufficient in promoting neuronal development. Transfection with tyrosine kinase deleted dominant-negative FGFR1(SP-/NLS/TK-), targeted specifically to cell nucleus with the replacement of its signal peptide domain with the FGF2 nuclear localization signal, blocks neuronal differentiation of human and mouse stem cells in vivo and in vitro induced by cAMP, BMP4 or RA. On the other hand, constitutively active nuclear FGFR1(SP-/NLS) is capable of inducing neuronal differentiation of cultured stem cells, in vivo brain stem cells, and neuroblastoma cancer cells ,23. BothNuclear FGFR1 interacts with the common transcription coregulator CBP and binds to thousands of conserved loci of the mouse and human genome ,4,23. OvThese current studies aimed to identify general structural features of the chromatin, their remodeling, and relation to genome functional programming during ESC to NCC development. We inquired on the roles of nFGFR1 and its partner CTCF in TADs delineation as a mechanisms for coordinated transcriptional regulation on a genome wide scale and in exemplary Hox loci. The results reveal distinct structural roles for CTCF and nFGFR1 in developmental chromatin dynamics and functional programming.With the advancement of 3C methods ,7 that rTowards these goals, mouse ESC and NCC genomes were processed together so that genome attributes could be compared for correlated activities. We used Hi-C and HiChIP data  generateTo assess the quality of the ESC and NCC interactomes, we analyzed valid interaction reads against randomized shuffled interaction anchors and a control that predicts expected interactions based on read density . The Hi-A circular diagram shows genome-wide and chromosome 6 overviews of Hi-C data sets along with RNA levels (RNA-seq FPKM), and nFGFR1 binding (ChIP-seq) data generated in our earlier study . The diaFrom the outside to the inside, the tracks on To investigate general chromatin structural differences between ESCs and NCCs, we compared interaction scores between the conditions in 1 mb by 1 mb interaction bins . The intTo investigate overall interaction changes from ESC to NCC, network comparisons were completed by applying Cytoscape Network Analyzer tool  on the tp = 1.69 × 10−20, DD p = 5.05 × 10−20. NCC-intra higher-CC p = 5.97 × 10−5, DD p = 3.48 × 10−13. ESC-inter higher-DD p = 1.15 × 10−52. NCC-inter higher DD p = 2.32 × 10−31  was completed on ESC and NCC 1 kb binned genomes A,B. In bIn ESC, the chromatin interaction anchors correlate strongly with the promoters and CpG Islands as well as with nFGFR1 binding locations indicating that the interaction anchors occur at or close to these locations A. In NCCTo investigate the predictability of genome attributes in determining RNA FPKM levels, a machine learning approach was used to analyze the combined 1 kb binned genome datasets. Individual and grouped sets of attributes were used to determine different ranges of RNA FPKM using a 2-window range (ranges in kb) neural network prediction model. Two RNA output categories  are highly predictive of RNA expression levels, even at smaller window ranges (1/1 kb windows give 95% predictability). The individual attributes that are not as strongly predictive in short window sizes, gain increased predictability with larger window sizes (200/240 kb windows give >75% for FGFR1 binding and >85% for promoters) C–F. The TADs are regions of the genome which interact over the span of thousands to millions of base pairs and bring together distal genomic loci into proximal three-dimensional space . TADs ocExemplary TADs that are remodeled in NCC are indicated by (*). Consistent with TAD remodeling, directionality indexes (+) also changed in relative strength and in some locations in direction (shown in The TADs identified in both ESCs and NCCs varied in size greatly (120 kb to 4 mb). Hence, 200 TADs of the same size, 480 kb (a common TAD size),  were staThe analysis of nFGFR1 binding showed that nFGFR1 binds consistently stronger in NCCs than in ESCs. In NCC nFGFR1 binds more strongly at the borders of TADs coinciding with the stronger border interaction anchor strength, however, in ESC nFGFR1 binding is less strongly bound at the borders, but more concentrated inside the TADs. C,D.Investigations into the feature locations relative to TAD size showed that exons E, enhancThe inherited blueprint of the genome is realized through the expression of its multi-gene programs, which during ESC to NCC differentiation involves an upregulation of 1477 and downregulation of 1384 mRNA genes and >100 (up and downregulated) noncoding RNAs . To inveInvestigations into the TADs which overlap the Diffgene midpoints (NCC+ and NCC− DiffTADs) showed that the majority of both the NCC+ and NCC− Diffgene midpoints are contained within TADs B. The geTo inquire whether such remodeling may serve to recruit distinct ontological programs, we analyzed the potential enrichment of different Gene Ontology (GO) categories +/−1 mb from the top NCC upregulated and downregulated Diffgene aligned midpoints. The results show that proliferative and general metabolic categories are overrepresented by the ESC genes that were downregulated in NCC , but not by genes that were upregulated in NCC D. In conSeveral recent studies have found that the binding affinities of proteins involved in chromatin structure and gene regulation  can be upregulated at the borders of TADs ,7,30. ToDifferential gene expression and interaction anchor strength together show that the changes in the interaction anchor strength and gene expression occur in parallel during ESC to NCC differentiation G. nFGFR1We used HiChIP  to identSimilar as with the global HiC-determined interaction anchor strength, nFGFR1 and CTCF associated interaction anchors are stronger on the borders of TADs, than within TADs . Delta cAnalysis by differential looping within +/−500 kb from DiffTAD borders, using delta change ESC-NCC and NCC-ESC, further reveals the remodeling of global (HiC), nFGFR1-containing (nFGFR1 HiChIP) and CTCF containing (CTCF HiChIP) loops during ESC to NCC development J–L. Hi-CSeveral proteins are known to be associated with TAD formation including CTCF, cohesin, WAPL, and PDS5 . A role The results show a vast assortment of protein binding motifs overrepresented in DiffTAD borders, some of which differ between NCC− and NCC+. Multiple motifs are overrepresented at all DiffTAD borders  on chromosome 11 including the location of the HoxB cluster C,D. HoxAContact map analyses of the individual HoxA/B/C/D gene clusters are shown on The experiments described above have indicated a role for nFGFR1 in the formation of the genome interactome. To further test our hypothesis, next we focused on the activation of the HoxA genes which govern the formation of different CNS regions and body parts and are activated during ESC to NCC neuronal transition . The HoxTwo main HoxA cluster regions delineated by strong CTCF binding at the loci 48.7 and 56.0 were identified-an upstream region containing genes HoxA1–A5 (0–48.7 kb) and a downstream region containing genes HoxA6-A13 (48.7–111.1 kb) I–L. In EIn summary, in ESC, HoxA1 engages in the interactions with all downstream, HoxA2—HoxA13, gene regions. At the midpoint of the HoxA cluster there is prominent CTCF binding (48.6–56.0) which separates differentially regulated upstream and downstream HoxA regions. During RA induced neuronal differentiation, the central CTCF binding increases, the HoxA1 locus maintains interactions only with the upstream HoxA cluster loci whereas the downstream HoxA1 loops are disassembled. Accompanying these structural changes, the upstream HoxA1–A5 region increases its binding with nFGFR1, while in the downstream region the nFGFR1 binding is reduced. As shown previously  * 100ChIP-qPCR measurements were calculated as Percent 10% Input:Percent Input = 100 * 2⌃(10% Input CT − IP sample CT)RT-qPCR measurement were calculated as Relative Expression:6Relative Expression = [100 * 2⌃(−1 * cDNA CT)] * 10Double immunostaining was performed according to our established protocol ,23 usingt-test). Z-Score Statistics: Two-Tailed Probability from the Central Area was used in assays to identify genomic locations (bins) within the ranges specified  in which the attribute score being analyzed falls outside the normal distribution of the scores for the entire range. Paired t-tests were used to compare binned interaction scores between ESC and NCC conditions using the t-test function available in the R-stats library, and where specified, the values were corrected using the Bonferroni method for multiple comparisons to adjust p values based on the number of tests performed. Two-Way ANOVA Tukey Method was completed to compare ESC and NCC values for RNA, TAD coverage, and interaction directionality analyses. Each test was completed on 5–1 kb values of ESC versus 5–1 kb values for NCC using the ANOVA and Tukey Method functions available in R-stats. Two-Way ANOVA with Fisher’s LSD Test was used to determine significance for the qPCR results for the HoxA cluster ESC versus NCC and PD inhibition analyses using the tools available in GraphPad Prism software.Statistical approaches were used throughout this study to analyze bins versus other bins in histograms (Z-Score Statistics), ESC histograms versus NCC histograms at the same bins (2-Way ANOVA Tukey), and interaction looping differences between ESC and NCC (Paired


AC2/C2 gene that plays a central role in the viral replication-cycle through suppression of host defenses and transcriptional regulation of the late viral genes. The AC2/C2 proteins encoded by mono- and bipartite geminiviruses and the curtovirus C2 can be considered virulence factors, and are known to interact with both basal and inducible systems. This review highlights the role of AC2/C2 in affecting the jasmonic acid and salicylic acid (JA and SA) pathways, the ubiquitin/proteasome system (UPS), and RNA silencing pathways. In addition to suppressing host defenses, AC2/C2 play a critical role in regulating expression of the coat protein during the viral life cycle. It is important that the timing of CP expression is regulated to ensure that ssDNA is converted to dsDNA early during an infection and is sequestered late in the infection. How AC2 interacts with host transcription factors to regulate CP expression is discussed along with how computational approaches can help identify critical host networks targeted by geminivirus AC2 proteins. Thus, the role of AC2/C2 in the viral life-cycle is to prevent the host from mounting an efficient defense response to geminivirus infection and to ensure maximal amplification and encapsidation of the viral genome.Geminiviruses are a significant group of emergent plant DNA viruses causing devastating diseases in food crops worldwide, including the Southern United States, Central America and the Caribbean. Crop failure due to geminivirus-related disease can be as high as 100%. Improved global transportation has enhanced the spread of geminiviruses and their vectors, supporting the emergence of new, more virulent recombinant strains. With limited coding capacity, geminiviruses encode multifunctional proteins, including the  Geminiviridae is a family of single-stranded DNA (ssDNA) viruses that infect agricultural crops in tropical and sub-tropical regions worldwide, and are responsible for billions of dollars in annual losses contributing to famine and loss of life  having 1–4 species each (The  of life . The Int of life . The gen studied . By conties each . Geminivies each . ConsideAfrican cassava mosaic virus (ACMV), Bean golden mosaic virus (BGMV), Potato yellow mosaic virus (PYMV), and Tomato golden mosaic virus (TGMV), the AC4 gene is not critical for virus infection  ∼300 bp in size, a portion of which, called the Common Region (CR), is shared by DNA-A and DNA-B in bipartite begomoviruses. Transcription occurs bidirectionally from promoters within the IR, which also contains the origin of replication. In bipartite begomoviruses, the virion sense strand of DNA-A encodes the capsid protein . In monopartite begomoviruses, the virion sense strand encodes CP/V1 that also acts as a movement factor, as well as an additional movement protein (MP/V2). In all begomoviruses, the complementary strand encodes the replication protein , a transcriptional activator protein and pathogenicity factor , a replication enhancer protein , and AC4/C4, which also appears to function as a pathogenicity determinant. In some begomoviruses, including nfection . DNA-B onfection . In the nfection . Howevernfection . The curnfection .Geminiviruses exhibit a strategy that is common among DNA viruses, where gene expression is separated into early and late phases. Genes that are expressed early, meaning prior to DNA synthesis, typically encode proteins necessary for replication of the viral genome and/or to modulate the host cell environment. After viral DNA replication, the late genes encoding structural proteins needed to package DNA and form virions are expressed. DNA tumor viruses, including polyomaviruses such as SV40 and papilloma viruses, rely on cellular polymerases that are typically expressed during S-phase of the cell cycle. Thus, they have evolved mechanisms to subvert many of the cellular checkpoints that control cell cycle. Geminiviruses have also evolved to produce a protein, Rep, that interferes with cell cycle controls to drive infected cells from quiescence into S-phase in order to promote virus replication  is ∼15 kDa in size and is conserved among all Begomoviruses. Using the protein of TGMV as a representative, full-length AC2 comprises 129 amino acids with a basic N terminal region (amino acids 13 to 28), a nuclear localization signal , and a C terminal acidic region (amino acids 101 to 129) containing a transcriptional activation domain  (The  to 129) .The central region of AC2 contains a series of conserved cysteine and histidine residues (amino acids 33 to 56) and a zinc finger-like domain  . AC2 hasBeet curly top virus (BCTV) and Beet curly top virus-SpCT (BCTV-SpCT), exhibits very little sequence similarity to the AC2 protein, apart from the central region containing the conserved cysteine and histidine residues within the zinc finger-like motif. The limited sequence and structural similarity correlate with the observation that the C2 protein in curtoviruses appears to lack the ability to activate transcription  pathways, the ubiquitin/proteasome system (UPS), and RNA silencing pathways. The AC2/C2 proteins encoded by mono- and bipartite geminiviruses and curtovirus C2 have been recognized as pathogenicity determinants based on their capacity to cause damage in a host. Inactivating mutations in the pression , and curpression . This supression . In manypression , the firpression . ETI typpression . This impression . This deEffector-triggered immunity against a plant pathogen is a localized resistance reaction that typically involves a hypersensitive response (HR), characterized by localized cell death that often leads to arrest of the pathogen . HR is aCotton leaf curl Kokhran virus (CLCuKoV), Papaya leaf curl virus (PaLCuV), and Tomato leaf curl Java virus (TLCJV), is able to induce HR in Nicotiana benthamiana and N. tabacum (Tomato leaf curl New Delhi virus (ToLCNDV) and Bean dwarf mosaic virus (BDMV), it appears as though NSP is the inducer of HR, and the AC2 protein counteracts the HR in an NLS and ZFD-dependent manner (Tomato yellow leaf curl-Sardinia virus (TYLCSV) elicits a strong HR response in N. benthamiana, N. tabacum, and A. thaliana, suggesting that C2 may represent the viral effector (Tomato yellow leaf curl virus (TYLCV) does not induce HR (avr) factors, resulting in development of an HR response that could inhibit viral spread. However, in some cases at least, the virus has developed countermeasures, and the AC2/C2 protein appears to have evolved to avoid or inhibit the HR response, allowing for systemic spread of the virus , in a proteasome-dependent manner (Cabbage leaf curl virus (CaLCuV), where COI1-induced genes are reduced, and transcripts encoding components of the ubiquitin-proteasome pathway, including 32 genes specifying 11 core and 13 regulatory subunits of the 26S proteasome complex, were elevated  C2 protein, which interacts with and inhibits UPS-mediated degradation of S-adenosyl-methionine decarboxylase 1 (SAMDC1) (S-adenosyl-methionine (SAM) are modulated in part through the decarboxylase activity of SAMDC1, which therefore affects host DNA methylation status  . Levels n status . Loss ofectivity . Whetherectivity .N gene–mediated resistance response against Tobacco mosaic virus (TMV) requires a functional RAR1/SGT1  complex  (Pseudomonas syringae 2 (RPS2)-mediated ETI  . Treatmend APR3) . Thus, tated ETI . This isIt is important to note that while the UPS appears to be a high value target for geminivirus AC2/C2 proteins, it seems unlikely that a single host protein is impacted. It is possible that geminiviruses need to enhance degradation of some host proteins and inhibit the degradation of others. Thus, a more likely scenario is that AC2/C2 target multiple proteins of the UPS system, similar to AC2/C2 mechanisms that have evolved to interfere with different components of the RNA silencing pathway .In addition to what may be described as classical defense mechanisms, plants are capable of altering their metabolic systems in times of environmental or parasitic stress. In this regard, geminiviruses have been shown to target and inactivate two proteins important for plant cellular metabolism, sucrose non-fermenting-related kinase 1 (SnRK1) and adenosine kinase (ADK) . SpecifiN. benthamiana plants with reduced SnRK1 activity due to expression of an antisense SnRK1 transgene display enhanced susceptibility to geminivirus infection similar to that observed upon expression of TGMV AC2 and BCTV C2 transgenes . Consistent with this, sequence analysis revealed that the AC2/C2 proteins of some New World begomoviruses , nearly all Old World begomoviruses examined , and all curtoviruses , lack a consensus SnRK1 phosphorylation site. However, the AC2 proteins of some New World begomoviruses  do in fact contain a SnRK1 consensus motif. Thus, the available evidence suggests that the SnRK1:AC2/C2 interaction is in flux: in some systems, SnRK1 may phosphorylate AC2 and reduce virus accumulation, while in most cases the AC2/C2 proteins are likely not phosphorylated and instead may inhibit SnRK1-mediated antiviral defense. That AC2/C2 may be under selection to avoid SnRK1 phosphorylation further highlights the importance of this interaction to viral pathogenesis.Interestingly, it has been reported that Arabidopsis SnRK1 can phosphorylate CaLCuV AC2 protein nfection . By contactivity . More readk mutant Arabidopsis plants and in N. benthamiana following transient silencing of ADK expression or treatment with a pharmacological inhibitor of ADK. Similar expression changes were observed following over-expression of begomovirus AC2 and curtovirus C2. It should be noted that over-expression may not reflect the same conditions observed in a host during an actual viral infection with respect to transcript abundance, protein abundance, and/or timing of expression. However, observations that geminivirus infection increased expression of cytokinin responsive genes, and that exogenous application of cytokinin increased susceptibility to infection  and transcriptional gene silencing (TGS) . In planN. benthamiana plants (WEL1) and regulator of gene silencing calmodulin-like protein (rgs-CaM)  transgene in a plants . AC2 wasrgs-CaM) . A seconrgs-CaM) , 2005. Irgs-CaM) . BecauseTranscriptional gene silencing was first implicated as a defense against geminiviruses with the observation that viral replication in transfected protoplasts is greatly reduced when the inoculum DNA is methylated . Later sCotton leaf curl Multan virus (CLCuMuV), suppresses TGS and PTGS by interacting with SAM synthetase  is a suppressor of PTGS and has been shown to interact with rgs-CaM from Nicotiana tabacum (Nt-rgsCaM), and to increase the levels of Nt-rgsCaM (Turnip mosaic virus (TuMV) and Cucumber mosaic virus (CMV) 2b protein , and ACMV AC2 (N. benthamiana (Nbrgs-CaM), resulting in suppression of RNA silencing through repression of RNA dependent RNA polymerase 6 (RDR6) expression , then it is expected that AC2 could also have widespread impact on the host transcriptome. Several large-scale microarray studies have been conducted to survey changes in the host transcriptome induced by the AC2 protein or its homologs (Mungbean yellow mosaic virus-Vigna (MYMV), Affymetrix GeneChips (ATH1), and transient expression assays with Arabidopsis protoplasts, 55 genes were found to be up-regulated >2-fold by MYMV AC2  by which AC2/C2 interact to disable the UPS are not understood, nor is it clear that we have discovered all of the defense-related pathways that are targeted by these viral proteins. As an example, plant hormones have significant roles in plant–pathogen interactions  that mediates repression in phloem cells is unknown. Fourth, the mechanism by which geminiviruses switch from early to late gene expression requires further study. For example, are there different populations of dsDNA templates for replication and transcription, or is a single population used for both? Lastly, how are the different functions of AC2 regulated? Consider that TGMV AC2 protein can interact with ADK, SnRK1, rgsCaM, and PPD2. We know that AC2 is found in both phosphorylated and non-phosphorylated forms in plant cells (It is clear that the AC2/C2 proteins encoded by begomoviruses are multifunctional and have critical roles in both suppression of host immune responses and in regulation of the ractions , and thent cells , and thant cells . So, areGS: writing and editing entire manuscript, figures, and submission. JG: writing, draft of AC2 and HR, UPS, sulfur and transcription section. ER: writing, draft of metabolism, silencing section, and figures. LL and JR: writing, drafts computational section. DB: writing and editing the entire manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.