We found that ngn-1 is required for axon outgrowth, nerve ring architecture, and neuronal cell fate specification. We also showed that ngn-1 may have roles in neuroblast migration and epithelial integrity during embryonic development. Using RNA sequencing and comparative transcriptome analysis, we identified eight transcription factors  whose transcription is activated, either directly or indirectly, by ngn-1. Our results show that ngn-1 has a role in transcribing known terminal regulators that establish and maintain cell fate of differentiated neural subtypes and confirms that ngn-1 functions as a proneural transcription factor in C. elegans neurogenesis.Proper nervous system development is required for an organism’s survival and function. Defects in neurogenesis have been linked to neurodevelopmental disorders such as schizophrenia and autism. Understanding the gene regulatory networks that orchestrate neural development, specifically cascades of proneural transcription factors, can better elucidate which genes are most important during early neurogenesis. Neurogenins are a family of deeply conserved factors shown to be both necessary and sufficient for the development of neural subtypes. However, the immediate downstream targets of neurogenin are not well characterized. The objective of this study was to further elucidate the role of  Defects during nervous system development are implicated in numerous neurological diseases that are polygenic in nature, such as autism spectrum disorder (ASD) and schizophrenia  transcription factors . These p“CANNTG” . This inXenopus embryos caused ectopic neurogenesis in ectodermal tissue  required for the development of pancreatic tissue and insulin-secreting cells . These aIn humans, neurogenin 1 is associated with multiple neurodevelopmental disorders. Two single nucleotide polymorphisms associated with schizophrenia fall within the regulatory region of the neurogenin 1 locus . ngn-1 also has roles in transcriptional repression, suppressing the transcription of almost 500 target genes, although its role here may be indirect and via non-cell autonomous mechanisms. These data establish ngn-1 as a key proneural gene in C. elegans nervous system development and demonstrate the value of comparative transcriptomics for identifying transcription factor down-stream targets.The association of neurogenins with multiple human neurological disorders demonstrates the need for greater understanding of neurogenin function during nervous system development. Neurogenins are highly conserved across phyla . The nemC. elegans strains were grown on nematode growth medium plates (NGM Lite) at 20° as described previously (dpy-5(e907) and mab-20(bx24), LGII vab-1(dx31), LGIII cnd-1(ju29), cnd-1(gk718) and unc-119(ed3), LGIV daf-18(ok480), ngn-1(ok2200) and efn-4(bx80), LGV him-5(e1490), LGX sax-3(ky123) and sdn-1(zh20). Integrated transgenes used in the course of this study were juIs76 [unc-25p::GFP + lin-15(+)], mgIs18 [ttx-3p::RFP], nIs394 [ngn-1::GFP + lin-15(+)], otIs33 [kal-1p::GFP], sIs14542 [hlh-34p::GFP + dpy-5(+)], and ujIs113 [pie-1::mCherry::Histone H2B + nhr-2p::mCherry::HIS-24 + unc-119(+)]. Extrachromosomal arrays used in this study were leEx1829 [unc-42p::GFP + unc-119(+)], quEx99 [sax-3(minigene) + odr-1p::RFP], and wwEx37 [ngn-1p::GFP + unc-119(+)]. The chromosomal rearrangement mIn1 [mIs14dpy-10(e128)] II was used to balance a lethal construct. All mutants were outcrossed at least four times except cnd-1(gk718) (2x), vab-1(dx31) (1x), daf-18(ok480) (2x), and sax-3(ky123) (3x). Double and triple mutant strains bearing reporter genes were constructed using standard genetic techniques. See Table S1 for details of strains generated during the course of this study including strain numbers and extra-chromosomal array construction.eviously . N2 (BrileEx1829 [unc-42p::GFP + unc-119(+)] and ngn-1(ok2200); leEx1829 [unc-42p::GFP + unc-119(+)] animals, the head regions of L1 larvae (collected within an hour of hatching) were captured as an image stack using a Zeiss LSM 700 confocal microscope at 40x magnification under identical settings, ensuring no detector saturation. Images were processed in Fiji using a Z-project - Sum Slices workflow, which rendered the summated stacks as 32-bit images. The head, from the nose tip to the posterior end of the pharynx was outlined using a segmented line and the region outside the line deleted using the Edit – Clear Outside function. Pixel values and counts were obtained using the Analyze - Histogram function  then processed in Microsoft Excel  and expressed as arbitrary fluorescence units.Well-fed worms grown under normal conditions were used to characterize expression patterns. Images were captured using either a Zeiss LSM 700 confocal microscope with Zen Black imaging software, a Zeiss AxioImager Z2 fluorescent microscope with Zen Blue imaging software, or an Olympus BX61 fluorescence microscope with CellSenseDimension software. Adult worms were staged for imaging by transferring 10-20 L4 hermaphrodites to a new plate and imaging those worms 24 hr later. Worms were mounted on 2% agarose pads, anesthetized with 3.5 μl of 10 mg/ml sodium azide solution, and then immobilized under an 18 × 18mm cover slip. Images were analyzed using Fiji software  z-stack images  were collected using a 63x magnification oil immersion objective on a Zeiss AxioImager Z2 microscope equipped with motorized z-axis stage. Data stacks were captured every 2 min for 300 cycles using Zen Blue software and analyzed using Fiji. The following developmental time points were scored: Ea/Ep cell ingression, gastrulation cleft opening, gastrulation cleft closure, ventral enclosure, comma stage, and elongation. Scores for mutant strains were compared to wild-type worms and statistical analysis was performed using the student’s kal-1p::GFP lineaging strain was constructed by crossing otIs33 [kal-1p::GFP] with a mCherry histone marker line, ujIs113 [pie-1::mCherry::Histone H2B + nhr-2p::mCherry::HIS-24 + unc-119(+)]. Embryo preparation and microscopy were performed according to previous studies (A  studies . 4D timengn-1(ok2200) mutants and were grown in liquid culture in M9 buffer supplemented with 1 mg/ml cholesterol, and 5 mls of 50% OP50 E. coli slurry as described previously  according to the vendor’s protocol and the aqueous phase separated by centrifugation in MaXtract High Density tubes . The aqueous phase was precipitated using an equal volume of 100% isopropanol plus 3 μl of 20 ng/ml glycogen, the RNA pellet washed once in isopropanol, once in 70% ethanol then resuspended in 50μl of diethylpyrocarbonate (DEPC) treated water. RNA concentration was estimated using a Thermo Scientific NanoDropTM Lite. Samples were then diluted to less than 10 ng/μl and analyzed on an Agilent 4200 Tapestation using High Sensitivity RNA ScreenTape. Only samples with an RNA integrity number (RIN) number of 8.9 or greater were sequenced. Four biological replicates of wild-type and ngn-1(ok2200) embryonic total RNA were sent to the University of Kansas Genome Sequencing Core for sequencing via an Illumina NextSeq 550 system .Wild-type and C. elegans reference genome (wbcel235/ce11) using Hisat2 ver.2.1.0 with Samtools 1.4. Aligned reads were assembled and transcripts quantified using Stringtie 1.3.3.2 via Samtools 1.6. Differential expression analysis was performed using DESeq2 ver.3.4.1 . Tissue Enrichment and Gene Ontology analyses were performed using a web-based implementation of the https://www.wormbase.org/tools/enrichment/tea/tea.cgi.RNA sequencing data analysis was performed using the Galaxy platform (ngn-1(ok2200) mutants. Figure S2 shows a representative kal-1p::GFP cell lineage. Figure S3 shows ngn-1; guidance cue double-mutant analysis of AIY neuron axon outgrowth. Table S1 shows strains generated in this study. Table S2 shows the inferred terminal cell fates identified in the kal-1p::GFP cell lineage. Tables S3 and S4 show the up-regulated and down-regulated lists of ngn-1 transcriptome hits identified in this work. Transcriptome files generated in this study are publicly available via the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143599). Supplemental material available at figshare: https://doi.org/10.25387/g3.12034392.Reagents generated in this study are available on request. Supplementary tables and figures are available via the GSA Figshare portal. Figure S1 shows the precocious embryo laying phenotype of ngn-1 in defining MI pharyngeal motorneuron cell fate, little is known about this deeply conserved gene in C. elegans nervous system development  mutants showed 45% embryonic lethality (ngn-1(ok2200) animals bearing a ngn-1::GFP translational transgene , indicating that the embryonic lethal phenotype is caused by the ngn-1(ok2200) allele and not a closely linked second-site mutation. ngn-1 mutant animals that survived to adulthood showed sluggish, uncoordinated movement, aberrant head movement, and precocious embryo laying . Overall, these observations suggest that ngn-1 is required for a broad array of neuromuscular functions.Beyond the role of mutation . In our ethality , Table 1ngn-1 in embryonic development, we scored whole broods of ngn-1 mutants for embryonic lethality and larvae that died before reaching adulthood .To further investigate the role of dulthood . In addingn-1 mutants were dying during embryonic development, we used 4-dimensional (4D) time-lapse microscopy to observe developing ngn-1(ok2200) embryos from the 2-4 cell stage through just prior to hatching  showed defects in gastrulation consistent with errors in neuroblast migration when compared to wild-type embryos (0/29 observed). Instead, the main arrest stage observed (25% of embryos) was due to a posterior ventral rupture during elongation (6/24 ngn-1(ok2200) embryos compared to 0/29 wild type). While only 33% of ngn-1(ok2200) embryos arrested during 4D imaging when compared to 46% when measured by whole brood analysis, this discrepancy is likely a result of the small sample size (n = 24) used in this assay. These data suggest that ngn-1 plays only a minor role in neuroblast migration but may have a non-cell autonomous role in posterior epithelial integrity.To determine when ngn-1 in development of the nervous system, ngn-1(ok2200) mutants were crossed with a kal-1p::GFP reporter gene, which labels a subset of neurons and glia in the head region  appears as a distinct narrowing of fluorescence around the isthmus of the pharynx  and 12.8 cell bodies (70%) were located in the isthmus region, with the remaining 5.7 (30%) clustered around the terminal bulb . However, many neurons were anteriorly displaced, with an average of 7.5 neurons (42%) now located in the corpus region (P < 0.001), 8.7 neurons in the isthmus  and only 1.7 neurons  in line with the terminal bulb. These data indicate the ngn-1 has roles in controlling both nerve ring architecture and neuronal cell body positioning.To further understand the role of nal bulb . ngn-1 mngn-1 function at the single cell level, we used a ttx-3p::GFP reporter gene to examine axon outgrowth in the AIY interneurons . In ngn-1 mutants, 98% of animals (49/50) show anteriorly displaced AIY cell bodies  showed defective dorsal axon extension, such that the AIYL and R axons fail to meet on the dorsal side  and 10.2 μm  compared with 28 μm  in wild type  showed such severe outgrowth defects that the axons remained below the pharynx, appearing as a tangle  animals carrying the ngn-1::GFP translational reporter gene showed complete rescue of cell body displacement and axon outgrowth defects, further confirming that the phenotypes observed were due to ngn-1 loss-of-function and not a closely-linked mutation  mutants  alone, suggesting that the ok2200 phenotype masks defects seen in each single mutant . Based on these data, it was unclear if ngn-1 has any role in the transcriptional regulation of these axon guidance cues.To further examine the genetic interactions between candidate axon guidance genes and ngn-1 regulatory targets was unsuccessful, we used RNAseq to identify transcript differences between wild type and ngn-1(ok2200) mutant embryos. This unbiased approach was employed to characterize components of the ngn-1 gene regulatory network downstream of ngn-1. To generate a comparative transcriptome of wild type and ngn-1(ok2200) null mutants (n = 4 replicates each), RNA was isolated from mixed-stage embryos, sequenced, and analyzed. This generated a list of 587 differentially expressed genes with a p-value ≤ 0.05 . We found that the majority of these genes  were upregulated in the ngn-1 mutant background, suggesting that ngn-1 acts primarily as a transcriptional repressor during embryogenesis (ngn-1 (as expected from the comparative transcriptome of a deletion mutant), ceh-17, and hlh-34 , but were outside the 40 most significant hits (ngn-1 controls the transcription of these genes directly or indirectly (via transcription of one or more intermediate factors or via non-cell autonomous mechanisms) is not known.RNA sequencing (RNAseq) is a powerful genetic tool to both identify and quantify gene expression .Our RNAseq assay generated an unbiased list of all l tissue . The SIAed genes . This rangn-1 having a role in activating gene transcription. Conversely, genes in the up-regulated dataset were mostly associated with cuticle and innate immune response terms. This suggests that ngn-1 may suppress transcription of target genes associated with the hypodermis including the innate immune response, possibly via non-cell autonomous mechanism(s).We corroborated our tissue enrichment analysis by performing a gene ontology (GO) term enrichment analysis on our up- and down-regulated target genes . Most ofngn-1 might be regulating transcription at the cellular level, we performed 4D-timelapse video analysis of ngn-1p::GFP expression in the early embryo  . unc-42 positively regulates transcription of multiple genes including nuclear receptor 2E fax-1, which is also significantly down-regulated in our transcriptome (P = 0.009), and the glutamate receptor glr-1 , suggesting that NGN-1 is required to activate transcription of this gene.The paired-like homeodomain transcription factor unc-42 transcriptome result, we crossed an unc-42p::GFP transgene  into the ngn-1(ok2200) mutant background and imaged L1  larva via confocal microscopy. Wild type worms had an average of 13 unc-42p::GFP expressing head neurons (unc-42p::GFP expression was imaged in the ngn-1(ok2200) background, GFP expression was significantly lower (P < 0.05) and restricted to less than 10 cells  mutants, hlh-34p::GFP expression was strongly suppressed, with only 5% of animals showing any GFP expression (P < 0.001). To confirm and validate the identity of the ngn-1(ok2200); hlh-34p::GFP strain, we crossed hermaphrodites with wild type males and scored F1 male progeny for the presence of GFP. All F1 males (anticipated genotype ngn-1(ok2200)/+; hlh-34p::GFP/+) were GFP-positive, confirming that our original strain was indeed homozygous at both the ngn-1(ok2200) and hlh-34p::GFP loci. We conclude that ngn-1 is almost exclusively required for hlh-34 transcription during development, but that there is a low-level, partial redundancy in the transcriptional activation of hlh-34 that can stochastically activate transcription in around 5% of animals.We performed a similar GFP reporter gene analysis on rneurons  – J. In ngn-1 in neural development and to identify downstream transcriptional targets using candidate gene and comparative transcriptomic approaches. Our data demonstrate roles for ngn-1 in embryonic epithelial integrity, fate specification, cell body positioning, and overall morphology of multiple neuron classes , making it a useful reporter for nerve ring structure  is not known. Work is ongoing to identify early acting GFP reporter genes that can be used to investigate NGN-1 transcriptional activation vs. repression at the single cell level during early embryogenesis.One of the surprising discoveries from our transcriptome study was NGN-1’s extensive role as a transcriptional repressor ( imaging . WhetherC. elegans using multiple techniques including genetic approaches (both forward and reverse), yeast-1 and 2-hybrid assays, chromatin immunoprecipitation, and protein binding microarrays , we were still able to identify over 500 targets whose transcript levels changed significantly. In particular, we identified hlh-34 as a ngn-1 target (hlh-34 is only expressed in a single pair of cells (the AVJL/R interneurons), we still had the statistical resolution to identify this ngn-1 target. Overall, this suggests that comparative transcriptomics offer a powerful general approach to identifying transcription factor targets during embryogenesis. Future work will refine our transcriptomics approach by taking this to a single cell or single embryo level to provide tighter analysis of either cell lineage or developmental timing.Transcription factors and gene regulatory networks have been investigated in 1 target . Althoug


D. pseudoobscura, but remains elevated in D. miranda, the species with the younger and larger Y chromosome. The repeat-rich Y of D. miranda still contains many actively transcribed genes, which compromise the formation of silencing heterochromatin. Elevated TE expression results in more de novo insertions of repeats in males compared to females. This lends support to the idea that the ‘toxic’ Y chromosome can create a mutational burden in males when genome-wide defense mechanisms are compromised, and suggests a previously unappreciated epigenetic conflict on evolving Y chromosomes between transcription of essential genes and silencing of selfish DNA.Large portions of eukaryotic genomes consist of transposable elements (TEs), and the establishment of transcription-repressing heterochromatin during early development safeguards genome integrity in Drosophila. Repeat-rich Y chromosomes can act as reservoirs for TEs (‘toxic’ Y effect), and incomplete epigenomic defenses during early development can lead to deleterious TE mobilization. Here, we contrast the dynamics of early TE activation in two Drosophila species with vastly different Y chromosomes of different ages. Zygotic TE expression is elevated in male embryos relative to females in both species, mostly due to expression of Y-linked TEs. Interestingly, male-biased TE expression diminishes across development in  Selfish DNA such as transposable elements cause intragenomic conflict. This study finds that on an evolutionarily young, gene-rich Drosophila Y chromosome, transposable elements evade heterochromatic suppression, leading to elevated transposition rates in males. Concordant with genome-wide activation of zygotic expression, the embryo also initiates silencing of genomic regions whose transcription would be harmful4. In particular, large fractions of eukaryotic genomes consist of repetitive DNA, including transposable elements (TEs)5, and transcriptional activation of TEs could result in their mobilization, causing insertional mutations and genomic instability6. Silencing of repeats is achieved in part through establishment of constitutive heterochromatin in all cells during early development at repetitive DNA at centromeres, telomeres, and along the repeat-rich Y chromosome8. TEs are in a constant evolutionary battle with their host genome to avoid silencing, and may have evolved mechanisms to mobilize early in embryogenesis before widespread heterochromatin is in place9. In many species, including Drosophila, the Y chromosome consists almost entirely of repetitive DNA, and male embryos may thus be especially challenged to silence their TEs10.In most animals, the zygotic genome is initially inactive and epigenetically “naive” Drosophila species  and show a similar repeat complement, with most TEs being shared between species12. Yet, the size of their Y chromosome differs dramatically: a fusion between an autosome and the ancestral Y created a neo-Y in D. miranda ~1.5 Ma . This drastic size expansion is driven almost entirely by the accumulation of TEs on the neo-Y14; for example, the most common TE on the neo-Y (the ISY element) is inserted roughly 22,000 times on the neo-Y (and occupies over 16 Mb of sequence), while it only shows ~1500 copies on the neo-X (<1 Mb of sequence)14. High-quality genome assemblies that contain large amounts of highly repetitive DNA, including pericentromeric regions and Y-linked sequences, exist for both species14, which allow us to study TE expression and heterochromatin formation at the molecular level, and the role of the Y chromosome.Here, we contrast TE activation and heterochromatin formation during early embryogenesis in two closely related ies Fig. . DrosophD. pseudoobscura and D. miranda, we combine gene expression and chromatin profiles in single embryos during early development. In Drosophila, zygotic transcription begins at the pre-blastoderm (stage 2) and gradually increases; by the end of stage 4 , widespread zygotic transcription is observed  transition, see Fig. 19. We analyzed transcriptomes of replicate single male and female embryos of D. pseudoobscura and D. miranda that have been developmentally staged from embryonic stage 2 through 12 ). Hierarchical clustering of the samples by their TE transcript abundance divides the embryos into two distinct groups that coincide with the MZ transition ; this resulted in 20 and 79 Y-enriched TEs in D. pseudoobscura and D. miranda, respectively , presumably in the introns and UTRs Fig. . In cont28  and D. melanogaster strain Oregon-R kept at 25 °C.We used 19 to analyze sex-specific repeat expression during embryogenesis. For read counts at genes, pair-end reads were mapped to the D. pseudoobscura12or D. miranda14 genomes using bwa mem (v0.7.15)35 on default settings, and sorted with samtools (v1.5)36. We then used featureCounts (v1.6.2) from the Subread package37 to determine the number of reads mapping to annotated genes of the two genomes. For counts at TEs, we mapped reads with bwa mem to the TE library specific to the D. pseudoobscura species subgroup generated by ref. 11. Reads mapping to each repeat entry were then tallied from the sam files. We also used bowtie2 (v2.3.4.1), which generated similar patterns in sex-biased expression, indicating that our results are robust to mapping strategies . We spiked DNA from D. pseudoobscura single embryos with DNA from D. melanogaster stage 7 (gastrulation) embryos so that each sample had 20% spike (D. melanogaster) DNA . We set aside 10% of each sample as input and incubated the remaining chromatin with Dynabeads Protein G (Invitrogen) for 2–6 h. The H3K9me3 antibody  was incubated for >3 h with Dynabeads Protein G to bind the antibody to the beads, before adding it to the chromatin (0.25 µl per embryo) for overnight incubation. The chromatin–antibody–bead complexes were washed first with low-salt buffer, then with high-salt buffer. DNA was eluted from the chromatin–antibody–bead complexes by shaking at 65 °C for 1–1.5 h. We extracted DNA from our ChIP samples and from our input using a phenol/chloroform/isoamyl alcohol mixture, then cleaned the DNA with Agencourt AmpureXP beads before library preparation. Libraries were prepared using the ThruPLEX DNA-seq kit (Rubicon) followed by two rounds of cleaning with AmpureXP beads. A total of 100 bp paired-end sequencing of our samples was performed at the Vincent J. Coates Genomic Sequencing Laboratory at UC Berkeley.We performed ChIP-seq using a protocol adapted from ref. D. miranda were downloaded from the SRA under BioProject PRJNA601450. Like the D. pseudoobscura libraries they were spiked with D. melanogaster embryos. To distinguish the spike-in reads, pair-end reads were aligned using bwa mem to a concatenated reference combining the D. melanogaster genome (r6.12), with either the D. pseudoobscura or D. miranda genomes. Since bwa mem by default only reports the best alignment, spike-in reads are then extracted as those mapping to the D. melanogaster contigs. We used bedtool’s genomeCoverageBed39 to obtain per base coverage of the sample, and spike-in reads after sorting with samtools sort. For library and mapping information see Supplementary Data ChIP-seq libraries for nome r6.1, with eiFor the genome-wide coverage H3K9me3 enrichment in both spike-in and actual samples, enrichment at 50 kb window is calculated as:For spike-in normalization, we first generated an enrichment “reference” by averaging the enrichment of all spike-ins. Since all spike-ins should have the same enrichment, systematic differences between spike-ins are predominantly the result of different antibody pulldown efficiencies during library preparation. We used a quantile normalization procedure, matching the distribution of the enrichment values to that of the reference. The extent of change for each enrichment value after the normalization is then applied to windows of the same enrichment value in the actual sample. In effect, if windows with enrichment of 1.5 in the spike-in is increased to 2 after quantile normalization, windows with enrichment of 1.5 in the actual sample will be increased to 2. For details on the procedure, see Supplementary Fig. 40 to infer overlaps and distances between windows with elevated H3K9me3, genes, and TEs. H3K9me3 enrichment around genes were inferred by extracting the per bp enrichment 5000 bp upstream and downstream of the TSS of the relevant genes; the median enrichment at each position is plotted. For TEs, enrichments were extracted 5000 bp upstream and downstream of the midpoint of the relevant TE annotation.We used the R package IRanges41 (v3.3) with the following command:We annotated the genomes of the two species and masked the repeats with the repeat library using RepeatMaskerRepeatMasker -norna -nolow -dir output.directory -gff -u -lib TE.library.fasta genome.faPair-end reads of inputs from the ChIP-seq experiments are mapped as single ends to their respective repeat-masked genomes and the repeat library. We first identified read pairs where one maps uniquely to the genome and the other maps only to the TE library, thus identifying pairs that flank insertion junctions. Reads of the same mapping orientation (forward or reverse strand mapping) that map <100 bp of each other in the genome are deemed to be capturing the same junction. Because fusion of DNA fragments during library preparation can generate chimeric reads, which can create such TE-to-unique junctions, we conservatively estimated insertions by further requiring that both the 5′ and 3′ junctions of the insertions must be identified. Thus, an insertion is only called if a forward mapping junction at the 5′ is followed by a reverse mapping junction <100 bp away at the 3′. Note, since TEs can insert in either direction, we do not stipulate any directionality to reads mapping within the TEs. For true de novo insertions in the embryos, we removed all insertions that are found within < 50 bp of any other insertion found across all samples.With each of the two species, we used the following ANOVA model in R:mentclass2pt{minimThe residuals are then used to compare the difference in the amount of insertions due to sex and developmental stage. We calculated the expected number of TE insertions per chromosome by assuming uniform insertion rates; we multiplied the number of observed insertions genome wide by the size of the chromosomes (in the genome assembly) proportional to the diploid genome of females and males.Further information on research design is available in the Supplementary InformationPeer ReviewReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1-4


Toxoplasma gondii is a common parasite of humans and animals, causing life-threatening disease in the immunocompromized, fetal abnormalities when contracted during gestation, and recurrent ocular lesions in some patients. Central to the prevalence and pathogenicity of this protozoan is its ability to adapt to a broad range of environments, and to differentiate between acute and chronic stages. These processes are underpinned by a major rewiring of gene expression, yet the mechanisms that regulate transcription in this parasite are only partially characterized. Deciphering these mechanisms requires a precise and comprehensive map of transcription start sites (TSSs); however, Toxoplasma TSSs have remained incompletely defined. To address this challenge, we used 5′-end RNA sequencing to genomically assess transcription initiation in both acute and chronic stages of Toxoplasma. Here, we report an in-depth analysis of transcription initiation at promoters, and provide empirically-defined TSSs for 7603 (91%) protein-coding genes, of which only 1840 concur with existing gene models. Comparing data from acute and chronic stages, we identified instances of stage-specific alternative TSSs that putatively generate mRNA isoforms with distinct 5′ termini. Analysis of the nucleotide content and nucleosome occupancy around TSSs allowed us to examine the determinants of TSS choice, and outline features of Toxoplasma promoter architecture. We also found pervasive divergent transcription at Toxoplasma promoters, clustered within the nucleosomes of highly-symmetrical phased arrays, underscoring chromatin contributions to transcription initiation. Corroborating previous observations, we asserted that Toxoplasma 5′ leaders are among the longest of any eukaryote studied thus far, displaying a median length of approximately 800 nucleotides. Further highlighting the utility of a precise TSS map, we pinpointed motifs associated with transcription initiation, including the binding sites of the master regulator of chronic-stage differentiation, BFD1, and a novel motif with a similar positional arrangement present at 44% of Toxoplasma promoters. This work provides a critical resource for functional genomics in Toxoplasma, and lays down a foundation to study the interactions between genomic sequences and the regulatory factors that control transcription in this parasite. A precise map of transcription start sites (TSSs) is indispensable for identifying promoters and other regulatory factors that mediate gene expression in an organism. Quantitative maps of transcription initiation have revealed the complex and dynamic nature of transcription initiation landscapes in a range of model eukaryotes, including yeast, flies, worms, zebrafish, and mammals e.g., . These sToxoplasma gondii, Plasmodium spp., and Cryptosporidium spp. These parasites have complex life cycles that typically involve several developmental stages underpinned by distinct transcriptional programs. This complexity is illustrated by Toxoplasma, which can infect and replicate within any nucleated avian or mammalian cell, establishing both acute and chronic stages in intermediate hosts and undergoing sexual recombination within the definitive feline hosts. Asexual replication in intermediate hosts, like humans, is characterized by rapidly-dividing tachyzoites (Tz), which convert into the slow-replicating bradyzoites (Bz) that establish chronic infections lasting for the life of the host  were largely generated via computational prediction of coding sequences (CDSs) on the basis of RNA-seq and pre-RNA-seq expression data. However, 5′ and 3′ untranslated regions (UTRs), including TSSs, have remained incompletely defined due to the lack of data specifically addressing transcript boundaries. While standard RNA-seq is a powerful approach for quantifying gene expression, identifying splice isoforms, and discovering novel transcripts, it fails to accurately capture the 5′ end of transcripts, where information on TSSs and 5′ UTRs  is contained. This is because RNA-seq coverage is seldomly uniform, and typically diminishes toward the 5′ end of transcripts, owing to biases introduced during common sample preparation procedures  correlate TSSs with sequence or chromatin elements that might influence gene expression, and to (ii) discern between and to accurately quantify distinct 5′ termini arising from alternative TSSs which can contribute to the functional or regulatory complexity of genes  and chronic stages (Bz). Using state-of-the-art approaches for systematically characterizing mRNA 5′ ends . Tz from the strains RH and ME49 were maintained at 5% CO2 in Dulbecco’s modified Eagle’s medium  supplemented with 3% or 10% heat-inactivated fetal bovine serum (IFS) and 10 µg/ml gentamicin (Thermo Fisher Scientific), referred to as standard medium. Bz from the strain ME49 were maintained at ambient CO2 in alkaline-stress medium, consisting of RPMI 1640 (Sigma), supplemented with 1% IFS and 10 µg/ml gentamicin, and buffered with 50 mM HEPES, adjusted to pH 8.1 with 10 N NaOH. HFFs that were destined for use in Bz experiments were maintained exclusively in DMEM supplemented with 10% IFS and 10 µg/ml gentamicin prior to infection.2 to allow for stage conversion. The medium was then aspirated and HFF monolayers rinsed with PBS. Following addition of 3 ml PBS, the host-cell monolayer was detached by scraping, followed by the mechanical release of parasites by serially passing once through 27- and twice through 30-gauge needles before filtering through a polycarbonate filter with a 3-µm pore size (Whatman). The efficiency of stage conversion was estimated at 98%, as assessed by immunofluorescence staining and microscopy at the time of the Bz harvest. Guinea-pig anti-CDPK1, diluted 1:1000, provided a general parasite stain, while fluorescein-labeled dolichos biflorus agglutinin , diluted 1:150, served as an early-Bz marker. DBA is a lectin that recognizes N-acetylgalactosamine on the Bz-specific cyst-wall protein CST1 .g, and washed once in PBS. Pellets were resuspended in TRIzol (Ambion) at about 1 ml per 5 × 106 parasites, followed by vortexing, and incubating at room temperature for 5 min. Homogenized samples were flash-frozen in liquid N2 and intermittently stored at -80°C. Upon complete thawing at room temperature, samples were loaded onto MaXtract High Density tubes (Qiagen), and total RNA was extracted as per manufacturer’s instructions. RNA pellets were resuspended in nuclease-free water and concentration was determined using the Qubit RNA HS Assay Kit (Thermo Fisher). Genomic DNA contamination was removed from total RNA extracts using the TURBO DNA-free Kit (Invitrogen), following the manufacturer’s protocol (routine DNase treatment). Digest of DNA contaminants was confirmed by PCR on extracts before and after this procedure.Parasite suspensions were centrifuged for 5 min at 1000 × RAMPAGE libraries were constructed from 5 µg of DNaseI-digested total RNA per biological replicate, largely following a previously published protocol  with thePaired-end sequencing was performed with 75-nt reads, and a 6-nt index read. In a pilot, RH replicate libraries were sequenced on a MiSeq (Illumina), which revealed an ~85% G-nucleotide bias at the first position of Read 1, complicating the use of a NextSeq platform (Illumina) for sequencing of the full set of six RAMPAGE libraries. Read mapping to the ME49 genome assembly (v.45) revealed that ~85% of reads started with one non-encoded G, and ~10% of reads started with two non-encoded Gs, which were introduced in the non-templated addition of C nucleotides during reverse transcription. We therefore chose to employ an initial five chemistry-only (dark) cycles to enable sequencing on a NextSeq. 5′-tag positions were then shifted 5′ by 4 nt, which should match the position of the 5′-most nucleotide for ~85% of the original mRNA molecules, assuming all six libraries were structured similarly. This is expected to result in a minor loss of positional resolution. Analyzing the nucleotide composition around predicted TSSs revealed the canonical pyrimidine-purine dinucleotide at the -1 and 0 positions, which further validates the positional correction.Low-quality reads, and reads aligning to rRNA were filtered using TagDust2 v.2.33  and a cu-t 4 -dust 97 -fe 3 -1 R:NFiltered reads were mapped onto the ME49 genome assembly (v.45) using STAR v.2.7.1a  with the–runMode alignReads –sjdbFileChrStartEnd [ … ] –alignIntronMax 6000 –alignSJoverhangMin 8 –alignSJDBoverhangMin 1 –readMatesLengthsIn NotEqual –alignMatesGapMax 1000 –outFilterMultimapNmax 1 –outFilterMismatchNoverLmax 0.04 –outSAMprimaryFlag AllBestScore –clip5pNbases 0 15 –outSAMtype BAM SortedByCoordinate –outSAMorder PairedPCR duplicates were flagged within the BAM file using STAR v.2.7.1a with the following settings:–inputBAMfile [ … ] –bamRemoveDuplicatesType UniqueIdentical –runMode inputAlignmentsFromBAM –bamRemoveDuplicatesMate2basesN 15Flagged duplicates were then removed with SAMtools v.1.10  using:samtools view -b -F 0x400BedGraph files of 5′ tags were then generated from BAM files using STAR v.2.7.1a:–inputBAMfile [ … ] –runMode inputAlignmentsFromBAM –outWigType bedGraph read1_5p –outWigNorm NonePositions of 5′ tags in the BedGraph files were shifted 5′ by 4 nt, and absolute values were normalized by dividing counts by the total number of counts per sample, and multiplying by 1000000 to generate counts per million (CPM).http://www.R-project.org) unless otherwise noted.Analyses were performed in Python or R, and plots generated in R (Strand-specific 5′-tag-count-to-gene–association windows were defined from -3500 nt to +200 nt relative to the start codons of the 8322 protein-coding genes (ME49 v.45). Upstream window sizes were shortened by the stop codon of an upstream gene on the same strand, or by contig boundaries.This analysis was restricted to (i) genes with unshortened gene-association windows, and (ii) genes with cumulative 5′-tag counts above the 1st percentile of the respective dataset, arriving at the sample sizes indicated in the graphs. The 5′-tag count at each position within individual gene-association windows was divided by the cumulative 5′-tag count for the respective window. Density values from biological replicates were then averaged for each position, followed by averaging densities for each position across all gene-association windows to generate 5′-tag counts as percent of cumulative 5′-tag counts. A scatter plot with a trendline and 95% confidence interval was generated in R using the ggplot2 package with the geom_smooth argument set to: method = “gam”, formula = y ~ s. The empirical cumulative density plot was also generated with these values.TSSs were identified separately for each biological replicate as the position with the highest 5′-tag count within a gene-association window. Replicate TSSs were flagged as reproducible if they were within 40 nt, or as non-reproducible if they were farther than 40 nt apart. For reproducible TSSs, the rounded geometric center between the two TSSs was used for plotting and subsequent analyses, and are also available in a table compilation Data S1.n = 1017) was defined as “inactive” genes in Tz for the analyses pertaining to the nucleosome occupancy around inactive Bz TSSs.This comparison was restricted to TSSs that were reproducible at the nucleotide level between biological replicates of ME49 Bz and Tz. TSSs between both stages were then compared by their distance to annotated start codons, whereas TSSs farther than 40 nt apart were flagged as putative stage-specific alternative TSSs. Genes for which a TSS was only identified in one stage were flagged as unique. The set of unique Bz genes , followed by reproducible to the nucleotide position between ME49 Tz replicates (category II), reproducible to the nucleotide position between ME49 Bz replicates (category III), reproducible within 40 nt between ME49 Tz replicates (category IV), reproducible within 40 nt between ME49 Bz replicates (category V), and reproducible at the nucleotide position between two ME49 replicates (category VI). For categories IV and V, the geometric center between TSSs from biological replicates was used to define the position of the TSS prediction. Predictions are available in a table compilation Data S1 Sequence logos were generated using the WebLogo v.3.7.5 command line tool .2 scale) was determined using the DESeq2 R package v.1.21.16  or absence (Tz) of Shield-1 as described and analyzed previously  and a histogram (binwidth = 20 bp). A local minimum in antisense 5′-tag density demarcating the upstream border of the antisense cluster on the opposite pole of the NDR relative to the sense TSS was determined at -293 bp as described above. Diverging TSSs separated by 293 bp or less were considered to be bidirectionally-paired.2-fold change of 2, padj ≤ 0.001; n = 556), TSSs were selected from nucleotide-matching Bz replicates as determined above, and (ii) for genes downregulated in Bz or unregulated (n = 5412), TSSs were selected from nucleotide-matching Tz replicates as determined above. Motif analysis using MEME v.5.1.1 . Toxoplasma ME49 Bz and Tz 5′ leaders were derived from the midpoint of TSSs that replicated within 40 nt between biological replicates as described above. 5′-leader lengths were then calculated from the distance between TSSs and start codons—ignoring the possibility for introns that may occur within the defined 5′-untranslated regions. For plotting and calculating the sample sizes  and median lengths , all 5′ leaders shorter than two nucleotides were discarded. For defining TSSs and 5′ leaders in P. falciparum 3D7, we retrieved previously published CAGE data from the Gene Expression Omnibus data repository . We proToxoplasma (TgME49 v.45) and Homo sapiens (GRCh38). This resulted in the following sample sizes for Toxoplasma vs. Homo sapiens: introns (40103 vs. 1716073), exons (49023 vs. 1716073), and intergenic spaces (7646 vs. 33985).A previously published python script [gtfstats.py ] was useThis analysis was performed on unspliced full-length 5′-leader predictions as defined above. We defined uAUGs as any AUG triplet upstream of the start codon of the CDS, and uORFs as any open reading frame with a minimal length of nine nucleotides, whose initiating codon lies within the 5′ leader. In the event of multiple initiation sites for a given stop codon, we selected the most distal in‐frame uAUG as the uORF start. Out-of-frame overlapping uORFs (category B) were only detected down to the first codon that overlapped with the main ORF. Expected values were determined from reshuffled datasets, and are the average of 15 different simulations. These reshuffled datasets were generated using the Python module ushuffle , which rToxoplasma, and to generate a genome-wide map of transcription initiation at single-nucleotide resolution. To capture strain- and life-cycle–dependent differences in transcription initiation, we constructed libraries from total RNA extracts of two canonical strains: the type I RH strain commonly used in cell culture and the type II ME49 strain frequently used in animal studies (We adapted the RAMPAGE protocol  to systevia a template switching oligo (TSO), and for Read 2 toward the 3′ end of the mRNA transcript via a randomly-priming reverse-transcription (RT) primer . RAMPAGE employs two approaches to enrich for 5′-intact mRNA based on the presence of the 5′-m7G cap structure (cap): by (1) introducing the 5′-sequencing adapter via template switching, which preferentially occurs at the cap . Upon library amplification and paired-end sequencing, reads from both replicates of all three samples were mapped to the ME49 genome assembly , and reads originating from ribosomal RNA (rRNA) contamination were removed .The RAMPAGE protocol preserves the mRNA polarity in the resulting cDNA library by the directed introduction of Illumina-sequencing adapters for Read 1 at the 5′ end  the cap , and by SAG1 gene, and found that 5′ tags indeed peaked at the previously identified major TSS . Windows extended 200 nt into annotated open reading frames (ORFs) to enable the capture of 5′ tags for genes with erroneously-annotated start codons. The upstream distance of 3500 nt was chosen to maximize the number of genes for which the full distribution of 5′ tags could be captured, while minimizing the risk of erroneously capturing pervasive transcription from neighboring genes. As a point of reference, we used annotations from the Toxoplasma genome database (ToxoDB.org), and determined that the upstream window size of 3500 nt captures nearly all (98.7%) current TSS annotations . We found that 5′-tag density is normally-distributed across gene-association windows, with the mode of the distribution at ~700 nt upstream of the CDS and 50% of tags collected within ~900 nt proximal to the CDS . These values were highly-reproducible between the two biological replicates of each sample  were positively correlated  tail or the 5′-cap structure in RNA-seq or RAMPAGE, respectively. Indeed, we found that histone variants lacking poly(A) tails were underrepresented by RNA-seq, while those with poly(A) tails (via oligo(dT) priming (n = 144), RAMPAGE (n = 224), or either technique  tails  were qua priming  and 5′-tIn summary, our definition of gene-association windows provides a robust approach for the association of 5′-tag counts with gene models by demonstrating (i) the ORF-proximal accumulation of 5′-tag counts within gene-association windows, (ii) the reproducibility of cumulative 5′-tag counts, and (iii) the correlation of cumulative 5′-tag counts with conventional RNA-seq–derived transcript counts.When assigning TSSs, we chose not to assess the spread of 5′ tags at individual promoters, and instead defined the TSS as the nucleotide position with the maximal 5′-tag count within a gene-association window S3A. An Toxoplasma genes , and thereby accumulated 7603 TSSs successively from six categories that may reflect the confidence of the prediction  TSSs of ME49 Bz and ME49 Tz.Alternative TSSs increase protein and regulatory diversity by giving rise to transcripts encoding protein isoforms with alternative N termini, or transcripts with distinct 5′ leaders that can entail differential translational regulation . A recenal cycle . In Arab stimuli . We wond). To increase specificity, we only considered TSSs that replicated at the exact nucleotide position in both replicates from a given stage. Comparing the resulting TSSs from ME49 Bz (6127) to Tz (5868), we identified 5110 genes for which TSS predictions exist in both stages . Identification of TSSs unique to ME49 Bz (1017) or Tz (758) correlated with their stage-dependent expression as measured by RNA-seq. Of the 5110 genes with predictions in both stages, 4428 genes had matching TSSs in both life-cycle stages, whereas 682 genes appeared to have alternative TSSs that were used in a stage-dependent manner  upstream of a phased nucleosomal array. Generating a genome-wide map of transcription initiation in nt plots S6A. We xoplasma . On a br). To relate this observation to data on nucleosome positioning, we reanalyzed publicly available MNase-sequencing (MNase-seq) data generated from Tz of the Toxoplasma Pru strain , which is in contrast to genome-wide studies in yeast and in vitro studies, where GC content is a strong predictor for nucleosome positioning   . In yeas–300 bp) , whereas–300 bp) . In Toxoeam edge .Toxoplasma promoters. Studies in yeast and metazoans showed that nucleosome phasing and transcriptional activity are correlated and co-dependent ( unique set), we found that phasing, while reduced, was still detectable, and that the NDR was nearly as prominent as NDRs at randomly-sampled active promoters . The maintenance of nucleosome phasing, and the presence of a constitutive NDR in particular, could facilitate inducible activation by rendering Bz-specific promoters accessible for the binding of transcription factors during the Tz stage.Stage-specific data on transcription initiation also enabled us to compare nucleosome positioning at active and inactive ependent . Accordiependent . HoweverToxoplasma promoters, we projected averaged 5′-tag densities of both sense and antisense strands onto nucleosome density within ±1000-bp windows around ME49 Tz TSSs   the erroneous association of antisense transcription from highly-active upstream promoters of neighbouring genes, or (ii) by the pairing with spurious hypothetical gene models that appear to have been annotated on the basis of antisense transcription from the same promoter  protein-coding genes that diverge from a common promoter. Bidirectional transcription of protein-coding genes is common in yeast, metazoans, and lciparum , and frelciparum . To detein yeast . We reaslciparum , we foun or less . As expeToxoplasma promoters, that initiates within discrete clusters, localized deep into nucleosomes. This is consistent with a model described for yeast, in which the transcriptional machinery assembles in bidirectional configuration at internucleosomal spaces, and transcription initiation is facilitated by the respective +1 nucleosome   —and the GCATGCP) . CollectToxoplasma, P. falciparum, and Neospora caninum  P. falciparum 3D7 as defined strictly analogously, using publicly-available 5′-end RNA-seq data (Toxoplasma (792–837 nt) and P. falciparum (431 nt) were substantially longer than those of the other analyzed eukaryotes (114–220 nt) (Toxoplasma relative to other eukaryotes such as humans (median lengths of 792–837 vs. 220 nt) contrasts with its otherwise compact genome reflected in the reduced median lengths of introns (468 vs. 1747 bp) and intergenic sequences (1198 vs. 15396 bp) . 5′ leaders with 1–6 uAUGs or 1–5 uORFs were slightly less prevalent than expected; however, there was no consistent deviation between observed and expected occurrences of these features, suggesting reduced selection against them in Toxoplasma  was largely generated via computational CDS prediction on the basis of RNA-seq and pre-RNA-seq expression data. However, untranslated regions, including TSSs, have remained incompletely defined due to the lack of integration with specific data on transcript boundaries, which has complicated investigations into the molecular mechanisms of transcriptional control. A pioneering study from 2010 used oligo-capping   , which mcleosome , whereascleosome . In Toxorevisiae , and simToxoplasma promoters, we found that transcription initiates bidirectionally within discrete clusters that co-localize with the nucleosomes of a highly-symmetric phased nucleosomal array. Such patterns of pervasive divergent transcription at eukaryotic promoters are common  frequency, suggesting that Toxoplasma-specific nucleosome positioning factors may override intrinsically unfavorable DNA sequences.Nucleosome positioning in itioning , and NDRitioning . By contToxoplasma stage conversion  these elements, and instead initiate translation at the downstream mAUG . BedGraph files are also accessible through the genome browser at ToxoDB.org.The datasets presented in this study are accessible through online repositories. Raw RAMPAGE sequencing data, and processed BedGraph files of strand-specific 5′-collapsed read coverage have been deposited in NCBI's Gene Expression Omnibus  and are BM and SL conceptualized and designed the study. BM conducted experiments and analyzed the data. BW supplied the conventional RNA-seq datasets and provided key technical advice. BM wrote the first draft of the manuscript. HL and SL acquired funding. SL supervised the study. All authors contributed to the article and approved the submitted version.This work was supported by a Boehringer Ingelheim Fonds PhD fellowship to BM and funds from the National Institutes of Health to SL (1R01AI144369) and HL (U19AI110819).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.


Kip2 is a member of the Cip/Kip family of CKIs and frequently inactivated by genomic mutations associated with human overgrowth disorders. There is increasing evidence for p57 to control cellular processes in addition to cell cycle and CDK regulation including transcription, apoptosis, migration or development. In order to obtain molecular insights to unknown functions of p57, we performed a protein interaction screen. We identified the transcription regulator four-and-a-half LIM-only protein 2 (FHL2) as a novel p57-binding protein. Co-immunoprecipitation and reporter gene assays were used to elucidate the physiological and functional relevance of p57/FHL2 interaction. We found in cancer cells that endogenous p57 and FHL2 are in a complex. We observed a substantial induction of established FHL2-regulated gene promoters by p57 in reporter gene experiments and detected strong induction of the intrinsic transactivation activity of FHL2. Treatment of cells with histone deacetylase (HDAC) inhibitors and binding of exogenous FHL2 to HDACs indicated repression of FHL2 transcription activity by HDACs. In the presence of the HDAC inhibitor sodium butyrate activation of FHL2 by p57 is abrogated suggesting that p57 shares a common pathway with HDAC inhibitors. p57 competes with HDACs for FHL2 binding which might partly explain the mechanism of FHL2 activation by p57. These results suggest a novel function of p57 in transcription regulation.The eukaryotic cell cycle is negatively regulated by cyclin-dependent kinase inhibitors (CKIs). p57 Using a conserved N-terminal cyclin/CDK binding motif, the three family members p21Cip1/Waf1 (p21), p27Kip1 (p27) and p57Kip2 (p57) regulate the activity of cyclin/CDK complexes. In their unmodified form, binding inhibits the kinase activity of the associated cyclin/CDK complex. Cip/Kip proteins were reported as candidate tumor suppressor genes2. In contrast to mice deficient for p21 or p27, p57 knock-out mice show severe developmental defects. The observed prenatal lethality resembles many features of Beckwith-Wiedemann syndrome (BWS), a genetic disorder characterised by developmental defects and predisposition to the development of tumors in humans4. Interestingly, alterations of the p57 coding gene CDKN1C have been frequently observed in BWS patients and genetic and epigenetic alterations impairing p57 expression or function are the most frequent cause of BWS7. However, some BWS patients carry mutations outside the cyclin/CDK binding domain and mouse knock-in studies revealed a CDK-independent contribution of p57 in BWS8. Therefore, not all of the observed phenotypes can be attributed to the ability of p57 to bind and to inhibit cyclin/CDK complexes9. Some phenotypes of p57-deficient mice were even enhanced when a cyclin/CDK binding deficient mutant (p57CK−) was expressed in mice, indicating additional dominant effects of the p57CK− mutant by so far unknown mechanisms8. Several recent publications highlighted a role of the closely related p27 protein as a transcription regulator which can be CDK-dependent and CDK-independent13. p57 has also been reported to directly and indirectly regulate transcription; it binds and inactivates CDK7 and CDK9 and interacts with the transcription factor E2F1 thereby repressing E2F1 regulated genes14. In the proposed model p57 is recruited to promoter sites by E2F1 where it can bind CDK7 or CDK9 and inhibit the phosphorylation of RNA Polymerase II C-terminal repeat domain (CTD)14. Transcriptional regulation by p57 was also described to play a role in myogenesis and neurogenesis16. p57 stabilises the transcription factor myoD by direct binding or by inhibiting CDK2 and thereby promoting myogenesis in a cell culture model17. In addition, p57 was reported to repress neuronal differentiation after mitogen withdrawal and suggested to play a role as a context-dependent repressor of neurogenic transcription factors like Mash1, NeuroD and Nex/Math216.Cell proliferation is negatively regulated by proteins of the Cip/Kip-familyIn order to gain more insight into novel functions of p57, we aimed to identify novel p57 binding partners. Therefore, we performed a yeast two-hybrid screen and obtained the protein FHL2 as a novel p57-interactor.18. Although FHL2 does not directly bind to DNA, it modulates the activity of several transcription factors20. FHL2 was first described to bind to the hormone-activated androgen receptor (AR) which increases the activity of AR-dependent reporter genes21. FHL2 is expressed in the cytoplasm and the nucleus. Interestingly, in several cancer types high levels of nuclear FHL2 correlate with disease progression towards a malignant state. This indicates that FHL2 dependent transcription contributes to cancer development and progression23.FHL2 is a multifunctional LIM domain only protein which binds cellular proteins via its LIM domains and thereby regulates various cellular processesHere we report that p57 strongly activates FHL2 transactivation function and induces the activity of known FHL2-regulated promoters. We provide experimental evidence supporting the hypothesis that FHL2 is repressed by HDACs and p57 relieves this repression by competing with HDACs for FHL2-binding. FHL2 and p57 might regulate transcription as components of chromatin remodeling complexes.Detailed descriptions of novel plasmid constructs, including cloning strategies and sequences of oligonucleotides used are presented in Supplementary information.24. The histone deacetylase inhibitors sodium butyrate (NaBu) and Trichostatin A (TSA) were both purchased from Sigma-Aldrich, St. Louis, MO, USA and used from 1 and 0.66 M stock solutions, dissolved in water (NaBu) or DMSO (TSA). 293 and 293 T cells were transfected by calcium phosphate precipitation25, HeLa cells by Lipofectamine 2000 (Thermo Fisher Scientific). Cells were lysed in Laemmli buffer26 or IP-buffer  using an ultrasonic homogeniser 27.The human embryonic kidney (HEK) cell lines 293 and 293 T, the human cervix carcinoma cell line HeLa and the colon carcinoma cell line HRT-18  were cultured in DMEM  supplemented with 10% FBS (PAA) plus 100 U/ml penicillin, 100 µg/ml streptomycin  according to ATCC guidelines. Cells were treated with 1 nM of the synthetic androgen R1881 (Organon) as described28. In order to avoid cytoplasm-contaminated nuclear fractions and to minimize loss of nuclear proteins during the procedure minor modifications of the protocol were applied. All steps were done at 4 °C. Briefly, 10 ×15 cm cell culture plates of subconfluent HRT-18 cells were rinsed twice with PBS and carefully scraped in 10 ml PBS per plate and collected into 50 ml Falcon tubes. After centrifugation (300 g for 5 minutes), cell pellets were washed again with 10 ml PBS and transferred to a 15 ml Falcon tube. After another centrifugation step supernatant was carefully removed and cell pellet volume judged. Cells were resuspended in 3x pellet volume digitonin buffer ) and immediately centrifuged at 1000 g for 3 min. Before, a small aliquot was tested for trypanblue exclusion and only when more than 90% of cells were “blue” fractionation was carried on. Supernatant was collected as cytoplasmic fraction. The nuclear pellet was washed once with 5 pellet volumes 50 mM Tris pH 7.5, 150 mM NaCl and extracted in 3 pellet volumes IP-buffer ) using an ultrasonic homogeniser . The nuclear extract was cleared by centrifugation at 25000 g for 30 min. and the supernatant saved as nuclear fraction. Similar volumes  of cytoplasmic and nuclear fractions were used in immunoprecipitation experiments.Crude cytoplasmic and nuclear fractions from HRT-18 cells for subsequent use in immunoprecipitation experiments were obtained by using digitonin as a detergentS. cerevisiae PJ69-4A strain to obtain the “bait” strain PJ69-4A-p57. The “Human HeLa MATCHMAKER cDNA library”  was used to transform PJ69-4A-p57 with “prey” expressing cDNAs in the vector pACT2. Isolated pACT2 plasmids were verified by retransformation. Finally, the plasmids from 19 clones were recovered and sequenced. Eight plasmids contained the coding sequence of FHL2 in frame with the Gal4 transactivation domain. Specific interaction with FHL2 was tested by transforming a PJ69-4A strain containing stably integrated pACT2-FHL2 plasmid together with pAS2-1-p57 or pAS2-1-p21 or pAS2-1-p27 or pAS2-1-RanGAP(tail).The “MATCHMAKER Two-hybrid System” (Clontech) was applied with minor modifications. The plasmid pAS2-1-p57 was stably integrated into the For protein expression and IP experiments cells were harvested 24 to 30 hours after transfection and stored as cell pellets at −80 °C. Levels of the individual proteins in extracts were determined by Western blotting and if necessary adjusted before IP. Antibodies for IP were covalently coupled to protein A-agarose . For HA- and FLAG-IPs antibody-coupled beads were incubated with cellular extracts for 4 hours and for IP of p57 over night at 4 °C under continous rotation. After incubation beads were washed five times with IP-buffer and proteins eluted from beads by incubation in Laemmli-buffer without DTT at 40 °C for 10 minutes and subjected to Western blotting. Primary antibodies were detected with horseradish peroxidase-coupled secondary antibodies and enhanced chemoluminescence (ECL).29, for FLAG-epitope tagged proteins: rabbit polyclonal ANTI-FLAG and mouse monoclonal ANTI-FLAG M2  and HA-epitope tagged proteins: mouse monoclonal anti-HA . For subcellular fractionation rabbit polyclonal Lamin A/C: 10298-1-AP (Proteintech) and mouse monoclonal GAPDH: sc-47724  were used.Following antibodies were used for detection or IP of p57 and FHL2: rabbit polyclonal p57 (C-20): sc-1040, mouse monoclonal p57 (KP39): sc-56341, mouse monoclonal FHL-2 (F4B2-B11): sc-52667 , for detection of PSTAIR-motif containing CDKs: mouse monoclonal anti-PSTAIRE−9 M) for 24 hours. Cyclin D1- and MMP1-promoter experiments were performed with HeLa cells in 12-well format. Typically, 1.5 µg plasmid DNA was composed of 0.3 µg reporter plasmids cyclin D1-Luc or MMP1-Luc or pGL3-Basic together with 1.2 µg pDEST-3xHA-p57 and 10 ng Ubi-RLuc. Gal4-reporter experiments were performed with 293FR-cells in 6-well format. Typically, a total of 5 µg DNA was composed of 2 µg Gal-fusion expressing plasmids pSG424-FHL2 or pSG424-FHL2-LIM1/2-2, 3 µg pDEST-3xHA-p57 or 3 µg pDEST-3xHA-p27 and 20 ng UbiRluc. In RhoA experiments 1.5 µg pDEST-3xHA-p57 were co-transfected with 1.5 µg pEF-neo or pEF-RhoAV14 or pEF-RhoAN17. pDEST-Control vector was used as the non-expressing reference. Cells were lysed and luciferase activities determined using the Dual-Luciferase Assay system  and a Lumat LB9501 luminometer (Berthold). For quantification firefly luciferase counts were divided by Renilla luciferase counts and expressed relative to control transfected as “fold activation”.The cell line 293FR was generated by transfecting 293 cells with pFR-Luc together with a puromycin-resistance conferring plasmid. Single colonies were isolated under puromycin selection (1 µg/mL) and tested for activation by pFA-c-fos . From several positive clones one was selected for low basal and considerable Gal-c-Fos induced reporter activity. MMTV-promoter experiments were performed with 293 cells in 24-well format. Typically, 250 ng MMTV-Luc, 12.5 ng pSG5-hAR, 40 ng pDEST-3xHA-p57 and 2 ng Ubi-RLuc were adjusted to 2 µg plasmid DNA with pUC18 and transfected. Four hours after transfection cells were treated with R1881 -width vs FL2 (PI)-area dot blots, GFP-positive (transfected) cells were gated in FL2 (PI) vs FL1 (GFP) dot blots. Cell cycle distribution was analysed in FHL2 (PI) histograms with a FACScan flow cytometer (BD Bioscience) and quantified with FlowJo software (Tree Star). The cell cycle distribution from control  transfected cells is not considerably different to untransfected (GFP-negative) cells.Cell cycle phase distributions of transfected cells were done as described earlier31. After 30 hours cells were fixed using 4% paraformaldehyde and permeabilised with 0.1% Triton X-100 . After blocking with 0.5% gelatin , samples were incubated with the following primary antibodies: rabbit polyclonal p57 ((C20) sc-1040, Santa Cruz) and monoclonal mouse anti-FLAG . After washing, cells were incubated with fluorescence labeled antibodies 488 and 555 . Cells were mounted in a DAPI containing mounting medium . Analysis was performed on a Leica DMi8 inverted widefield microscope.For immunofluorescence HeLa cells were seeded on glass cover slips and grown in 12-well plates. Cells were transfected with FLAG-FHL2 and/or HA-p57-wt encoding plasmids using PEI (polyethylenimine) as transfection reagentFor fluorescence microscopy HeLa cells were grown in chambered coverslips . Cells were transfected with Cherry-FHL2 and/or YFP-p57-wt or p57-Nt/p57-Nt-NLS1 encoding plasmids using PEI or Polyfect as transfection reagent. YFP (yellow fluorescent protein) was excited at 510 nm and mCherry at 550 nm. Analysis was performed on a Leica DMi8 inverted widefield microscope.32. Briefly, 293 T cells were transfected with pCruz-OctA-FHL2 and pCruz-OctA-p57 and harvested 40 hours later. Extracts were loaded onto a prepacked Superose 6 column  in 150 mM NaCl, 50 mM Tris (pH 7.5), 1 mM PMSF, and 1 mM DTT. Size exclusion chromatography was performed by collecting fractions (0.5 ml) at a flow rate of 0.6 ml/min at 4 °C by isocratic elution over 1.5 column volumes using an FPLC/HPLC ÄKTA Purifier . Fractions were analysed using SDS-PAGE and Western blot detection. Molecular weight marker kit MWGF200  was used for molecular mass determination.SEC of protein complexes was basically done as described beforeStatistical significance was evaluated by the parametric Student’s unpaired two-tailed t test using GraphPad Prism version 8.1. Values of p < 0.05 were considered significant and p < 0.01 highly significant. The data are presented as mean ± standard deviation (SD).In order to identify novel p57 interacting proteins we performed a yeast two-hybrid screen with p57 fused to the DNA binding domain (DBD) of Gal4 as a bait. The yeast strain PJ64-4A-p57 was transformed with a prey library of HeLa cDNAs fused to the Gal4 transactivation domain. Prey plasmids were isolated from 42 colonies, sequenced and interactions verified by retransformation of the plasmids into PJ64-4A-p57. Of these 42 clones, 8 contained the ORF of the transcriptional coactivator four-and-a-half-LIM only protein 2 (FHL2) in frame with the Gal4 DBD. In addition, three clones encoding the known p57 interacting proteins cyclin D1 (two clones) and cyclin D3 (one clone) confirmed specificity of the assay.(tail)). Yeast colonies grew primarily in the presence of p57, indicating a specific interaction of p57 with FHL2  or to the unrelated protein RanGAP  and HRT-18 (colon carcinoma) cells. Endogenous FHL2 coprecipitated with endogenous p57 from both cell lines Fig. , indicatFHL2 consists of four full and one half LIM-domains. To determine the structural requirements for p57 binding, we generated a series of deletion mutants of FLAG-tagged FHL2 Fig. . These mFunctional and structural domains of p57 have been characterised within the N- and the C-terminal part of the protein Fig. . To obtaThese data suggest that FHL2 binds to the N-terminal part of p57 which harbors domains which are essential and sufficient for cyclin/CDK binding and CDK inhibition. Therefore, FHL2 might compete with CDKs and cyclins for p57 binding and thereby interfere with the CDK inhibitory function of p57.33. To characterise the interaction of FHL2 and p57, we investigated if exogenous co-expression of FHL2 and p57 might alter their subcellular distribution. The localisation of FHL2 and p57 in HeLa cells was determined by fluorescence microscopy in fixed as well as in living cells. As expected, p57 predominantly localises to the nucleus and to a small fraction in the cytoplasm -34 and the β-catenin controlled cyclin D1-luciferase reporter genes38 by p57. We found that p57 substantially induced all three FHL2-regulated reporter genes  - dependent firefly luciferase reporter construct pFR-Luc (293FR) were transfected with plasmids encoding the fusion protein Gal-FHL2 alone or in combination with p57 or p27. Strikingly, we observed a strong activation of Gal-FHL2 activity by p57  transactivation activityp57 Fig. , indicatp57 Fig. . Since tp57 Fig. , we testInterestingly, neither the N-terminal nor the C-terminal p57 domain could activate Gal-FHL2 Fig. . This su39. We investigated whether Gal-FHL2 activation might be the consequence of p57-induced RhoA signaling. We transfected 293FR cells with Gal-FHL2 and p57 in presence or absence of a plasmid coding for a dominant negative allele of RhoA (RhoA-N17) and determined Gal-FHL2 activity in luciferase reporter assays. Whereas the activation of Gal-FHL2 by a constitutive active mutant of RhoA (RhoA-V14) was strongly reduced by co-expressing RhoA-N17, we found no considerable effect on p57 activation of Gal-FHL2 by blocking RhoA signaling  relocalised Gal-FHL2 from cytoplasmic focal adhesion sites to the nucleus, leading to the activation of a Gal4-dependent reporter geneing Fig. . Interes37. However, the association of FHL2 with corepressors has not been reported previously. Promoters can be silenced by chromatin remodeling factors, which often act in concert with additional proteins as multimeric complexes. c-Jun for instance was reported to be transcriptionally inactive although bound to its sites at promoters of regulated genes42. Initial evidence for the existence of such repressor molecules came from experiments where the effect of c-Jun expression in the Gal-c-Jun system was tested. It was shown that wildtype c-Jun, which cannot bind to the Gal4-sites in the Gal-reporter gene, increased Gal-c-Jun transactivation. From these experiments it was concluded that putative repressors are sequestered by c-Jun leading to a derepression of Gal-c-Jun40. We speculated that also Gal-FHL2 activity might be regulated by an unknown repressor. In a similar approach we investigated the effect of wildtype FHL2 expression on Gal-FHL2 transactivation. To rule out increased transactivation by dimerization of Gal-FHL2 with co-expressed FHL2, we included an FHL2 mutant consisting of the first half and two full LIM-domains . This mutant does not to bind to the full-length FHL2 protein44. Interestingly, Gal-FHL2 activity was induced by increasing amounts of co-expressed wildtype FHL2 . To elucidate whether FHL2 coactivator activity is repressed by HDACs, we treated 293FR-cells with increasing amounts of the two HDACis sodium butyrate (NaBu) or Trichostatin A (TSA) and determined FHL2 coactivator activities. We found that both drugs induced Gal-FHL2 activity in a dose-dependent manner Fig. , indicatin vivo. We wondered if FHL2 activation by p57 might be the consequence of interference with HDAC function. We analysed 293 cells, transfected with the Gal-dependent reporter FR-LUC and determined Gal-FHL2 activation by p57 in the presence or absence of the HDACi NaBu. Strikingly, we found rather an inhibition than a further activation of Gal-FHL2 by p57 in the presence of NaBu  as a novel p57 interacting protein. Endogenous p57/FHL2 complexes were detected in the cervix carcinoma cell line HeLa and the colon carcinoma cell line HRT-18 which may indicate a general functional role of this interaction, not restricted to specific tissues or tumor cell types.21. However, in following studies FHL2 turned out as a multifunctional protein with binding partners involved in numerous cellular processes20. Despite of its role as a transcription regulator, FHL2 most likely does not directly bind to chromatin. As a coactivator or corepressor of transcription factors, FHL2 contains an intrinsic transactivation or transrepression activity45. Transcription factors like AP-1, β-catenin or the androgen receptor43 have been shown to be coactivated by FHL2. The molecular mechanisms of activation, however, is not understood in detail.FHL2 was initially identified as a binding partner and transcription coactivator of the human androgen receptorin vivo, these results describe a so far unprecedented regulatory property of p57 in transcription.p57 activated expression of AP-1, β-catenin and androgen receptor dependent reporter genes, all established promoters of FHL2-regulated genes and strongly enhanced the intrinsic (autonomous) transactivation activity of Gal4-FHL2. Although the latter might not fully reflect p57 action at natural promoter sites 39. Interestingly, in previous reports another LIM-domain containing protein, the LIM-kinase-1 (LIMK-1), a downstream effector of Rho-signaling, was shown to bind p5747. Although the LIM-domain amino acid sequences between FHL2 and LIMK-1 are not similar {Kadrmas et al., 2004}, the secondary structure of both proteins might enable binding to p57. Interestingly, LIMK-1 binding to mouse p57 requires amino acids 93 to 260 (104–228 in human), a central region of p57 devoid of cyclin/CDK-binding and inhibition. We found a minimal FHL2-binding sequence in human p57 from amino acid 1 to 125 (1–115 in mouse) which contains the cyclin/CDK-binding domain. It would be interesting to examine whether the overlapping sequence of human p57 from 104 to 125 (or 93–114 in mouse) contains a docking site for LIM-domain containing proteins.When we investigated the possible mechanism of FHL2-activation by p57, we could rule out an indirect activation of FHL2 by Rho signaling. Activation of the RhoA pathway was shown previously under similar experimental conditions to relocalise FHL2 from the cytoplasm to the nucleus and to induce Gal-FHL2 activity up to 10-fold39 and third and most importantly, blocking the RhoA pathway by a dominant negative RhoA-mutant did not reduce the activation of Gal-FHL2 by p57. Also, a constitutively active RhoA-mutant together with p57 synergistically activated Gal-FHL2, which is an expected result in case of two independent activation pathways affecting the same effector molecule48.Based on several observations, an involvement of RhoA signaling in p57 dependent FHL2 activation is unlikely. First, we did not see changes in localization of FHL2 upon p57 co-expression in fluorescence microscopy, second, LIMK-1 expression did not activate Gal-FHL242. Binding and repression of c-Jun by HDAC3 was shown to be relieved upon titration of HDAC3 and c-Jun kinase (JNK)-dependent phosphorylation of c-Jun40. Several lines of experimental evidence support the idea that FHL2, at least at some promoters, might be repressed by a similar mechanism. First, Gal-FHL2 was activated by increasing amounts of wildtype FHL2, indicating the presence of a titratable repressor like HDAC3 for c-Jun. Second, we could detect binding of FHL2 to histone deacetylase 1 and 3 and, most strikingly, treatment of cells with the two histone deacetylase inhibitors sodium butyrate (NaBu) and Trichostatin A (TSA) strongly induced FHL2 activity.Transcription activators can be inactive even when bound to their promoter or enhancer sites. For example, the AP-1 transcription factor c-Jun is repressed by multiprotein-complexes containing histone deacetylases (HDACs)18. Interestingly, it has already been reported that FHL2 interacts with and regulates components of chromatin remodeling complexes. By building a trimeric complex with the transcription factor β-catenin and the GNAT-family histone acetylase CBP/p300, FHL2 stimulates histone acetylation of β-catenin leading to activated transcription of Wnt-dependent genes like cyclin D137. In addition, FHL2 was also shown to function as a corepressor of the transcription factor FOXO149 by stimulating FOXO1 deacetylation by Sirtuin1 (SIRT1), a class III histone deacetylase. We show for the first time, that FHL2 binds to the transcriptional repressors HDAC1 and 3, however, given the high similarity of class I HDACs, more studies have to be conducted to determine the whole repertoire of histone deacetylases which may bind FHL2.The presence of multiple LIM-domains in FHL2 hints towards a scaffolding function of FHL2 in multimeric complexesin vivo.The molecular weight of chromatin remodeling complexes ranges from several hundred kD to more than two MDa. Our size exclusion chromatography experiments revealed the presence of ectopically expressed FHL2 in size fractions higher than 600 kD, indicating that FHL2 might be a component of such high-molecular-weight complexes. It will be an exciting challenge to determine endogenous multiprotein complexes which contain FHL2 and HDACs and to explore their functional role 8. Binding of p57 to HDACs might also explain the repression of neurogenic transcription factors like Mash1, NeuroD and Nex/Math2 in neuronal differentiation . Interestingly, the same authors demonstrated transcriptional repression by a Gal4-DBD-p57 fusion protein in a Gal4-dependent reporter system. It is tempting to speculate that also p57 might be a regulatory component of multiprotein complexes composed of FHL2, HDACs and other chromatin remodeling factors. It will be exciting to determine the components of large p57 and FHL2 containing protein complexes and to reveal their physiological roles, including pathophysiological processes like tumor initiation and promotion.Our data are consistent with one potential mechanistic model, where Gal-FHL2 activity is silenced by histone deacetylases. The strong activation of FHL2-coactivator function by p57 might involve its interference with HDAC-activity. We also observed that p57 could not further stimulate activation of Gal-FHL2 in the presence of the histone deacetylase inhibitor sodium butyrate. Rather, activation by sodium butyrate was reduced in the presence of p57. This result is consistent with the model that p57 is bound to FHL2 and thereby hinders HDACs from binding and repressing FHL2-dependent transcription. We could demonstrate reduced binding of HDAC1 and 3 to FHL2 in the presence of increasing amounts of p57 in co-immunoprecipitation experiments, indicating a competition of p57 and HDACs for binding to FHL2. However, the induction of Gal-FHL2 activity by p57 is much stronger compared to that obtained by titration with FHL2. This substantial difference might be the result of p57 binding to Gal-FHL2. p57 binding to FHL2 might shield from HDAC rebinding or might attract positive transcriptional regulators like histone acetylases. Based on the FHL2/p57-binding experiments, one could speculate that the N-terminus of p57 binds to FHL2 and the C-terminus of p57 might serve as a docking site for transcriptional activators. Such a model is supported by the potential need for full-length p57 to activate FHL2 and its association with high molecular weight complexes. Interestingly, recombinant p57 was also recently found to bind to the class II histone deacetylase HDAC7 in a protein array15. Intriguingly, also for FHL2 a role in promoting myogenesis has been reported43. Ectopic expression of FHL2 induced differentiation of the mouse myoblast cell line C2C12. It is reasonable to speculate that increasing levels of p57 in C2C12 upon differentiation15 might induce the transcription activity of FHL2 and thereby contribute to differentiation. Whether this shared role of p57 and FHL2 in myogenesis depends on the interaction of both proteins and if p57 acts as a positive regulator of FHL2 transcription activity in the regulation of myogenic genes is an important question and needs to be investigated in the future.A role for p57 in transcription control has been uncovered earlier. p57 can inhibit the phosphorylation of the myogenic transcription factor MyoD, which leads to a stabilization of MyoD and activation of gene-promoters involved in myogenesisKip2 in addition to cell cycle control. It will be challenging and exciting to examine the contribution of p57 to transcription programs. This might be also relevant for a better understanding of cancerogenesis and tumorigenesis. It is tempting to speculate that p57, due to its interaction with transcription factors like FHL2 or as a component of other larger transcription complexes might modulate the activity of a broader spectra of transcription factors.The finding that p57 regulates the transcriptional activity of FHL2 and FHL2-regulated genes clearly represents a novel function of p57Supplementary Information.


Breast cancer consists of multiple distinct tumor subtypes, and results from epigenetic and genetic aberrations that give rise to distinct transcriptional profiles. Despite previous efforts to understand transcriptional deregulation through transcription factor networks, the transcriptional mechanisms leading to subtypes of the disease remain poorly understood.We used a sophisticated computational search of thousands of expression datasets to define extended signatures of distinct breast cancer subtypes. Using ENCODE ChIP-seq data of surrogate cell lines and motif analysis we observed that these subtypes are determined by a distinct repertoire of lineage-specific transcription factors. Furthermore, specific pattern and abundance of copy number and DNA methylation changes at these TFs and targets, compared to other genes and to normal cells were observed. Overall, distinct transcriptional profiles are linked to genetic and epigenetic alterations at lineage-specific transcriptional regulators in breast cancer subtypes.https://bitbucket.org/qzhu/breast.cancer.tf/.The analysis code and data are deposited at Bioinformatics online. Tumorigenesis in breast cancer is thought to be the result of a combination of somatic genetic events including copy number alterations (CNA), point mutations and epigenetic alterations such as DNA methylation (DNAme). In contrast to normal tissue development, in tumors somatic mutations accumulate at various points of the differentiation process, making normal cells acquire properties of stem cells that turn them into cancer cells. Despite the extensive generation of molecular data, how these mutations and aberrations specifically affect transcription factors and their targets is not well understood.Breast cancer is a heterogeneous disease comprised of several molecular subtypes: luminal A, luminal B, Her2, basal-like and normal-like . Of thesTo study the regulatory effect on transcription, various TF regulatory networks have been constructed from ChIP-seq and ChIP-ChIP data, including a general TF network created by the ENCODE project  datasets to infer regulators and targets underlying breast cancer subtypes. We used 152 breast cancer gene expression datasets adding to a total of 10280 samples, including TCGA , METABRIextended subtype-specific gene signatures (ESG)   based ones (ESG) . The ESGes (ESG) .ChIP-seq data processing: We used the collection of ChIP-seq datasets processed by the ENCODE Analysis Working Group. To derive a gene score representing the amount of binding per gene, we first divide the peak signal score (the number of tags) by the 75-percentile peak score of the whole experiment, multiplied by base score 500. Next the peaks’ chromosomal locations are aligned to all gene regions ±50 kb TSS using BEDOPS  is the set of peaks in the vicinity of g, p(f) is the peak score of f, n(f) is the number of genes f overlaps.Cell line prioritization for subtypes: we used GORILLA  processing: TCGA array-based DNAme dataset was downloaded from ICGC in nucleotide-resolution methylation frequencies . We excluded blood-derived normal samples, and metastatic samples from the TCGA list. We used primary tumor samples and also obtained a separate normal breast tissue sample set from TCGA for comparison. To start, nucleotide-resolution beta values per patient sample was summed to gene-level values. Then, to derive subtype-specific hyper- and hypo-methylated genes, for every gene, we compared the distribution of methylation frequency of subtype patient group and that of the healthy group consisting of normal breast tissue samples, by performing 2-sample t-tests with unequal variance with respect to the normal sample group. Genes with P < 0.01 significance are deemed hyper or hypo-methylated respectively.Copy number alteration (CNA) processing: For CNA , original data consists of copy number for various chromosomal segments detected per patient. Each copy number ranges from -1 to +1  which is relative to the normal copy number. To derive a gene-based copy number, let cna be the copy number of segment f; len be the number of genes contained in f; GF(g) be the set of gained fragments containing gene g; LF(g) be the set of lost fragments containing g. We calculate:t-test to obtain genes significantly associated with CNA gains and CNA losses for each tumor subtype.Then we performed 1-sample Associations between TFs and deregulations: The rank-based enrichment test GORILLA , H1 embryonic stem cells, mesenchymal stem cells (MSC), A549 and MCF10a-Er-Src cells. Accession IDs were SRR7225842, ENCSR000ELW, ENCSR731QLJ, ENCSR7940FW, ENCSR385SZQ and ENCSR000ENV. ENCODE-provided data were already in narrowPeaks. For MDA-MB231, we processed the data from raw reads by performing alignment  and peak calling  ChIP-seq of A549, MCF10a-Er-Src and H1-hESC. (2) DNase-seq of H1, MSC and HMEC. In (1) experiments of each line form groups that we used to perform a group-based 2-sample t-test . Groups were epithelial (A549 and MCF10a-Er-Src) and stem (H1-hESC). This determines whether each gene is significantly assigned to epithelial or stem lineage. In (2) we used log fold-change between cell lines as each cell line is just 1 experiment. Hierarchical clustering was performed, and gene clusters that are specific to each epithelial, stem and mesenchymal were chosen. MSC was used to assign the mesenchymal lineage.https://bitbucket.org/qzhu/breast.cancer.tf/.The analysis scripts and data are deposited in Bitbucket We inferred regulatory TFs of the subtype-specific extended signature genes (ESGs) from a combination of sources: (i) ChIP-seq experiments and (ii) motif and coexpression evidence. ChIP-seq measures experimental binding while motif, coexpression evidence are designed to expand the scope of inferable TFs from ENCODE by a search of additional motifs that may be overrepresented in the regulatory regions of ESGs (Section 2). Additionally, TFs pointed to by the motifs must be coexpressed with the subtype genes to be reported as regulators in this study. We first performed a global ChIP-seq prioritization as per subtype’s ESGs .ChIP-seq based evidence. The unbiased search of ChIP-seq experiments, where the sum of binding intensities at the regulatory regions of the subtype-specific ESGs were significantly higher than binding at random genes, identified the three well-known luminal A-associated TFs: Esr1, Gata3 and Foxa1  were 7, 9 and 16 respectively] (P < 1 × 10−5) come from MCF7 . This returned 16 TFs (d 16 TFs  that inc10P < 40), Gr, Fosl2, Tcf12, Atf3  and Tead4, Chd1, Jund, Rbbp5, Ctbp2 and several others   , suggest0P > 10)  were ideIntriguingly, two cell lines, A549 and H1-hESC, were identified as associated to the basal subtype in addition to the conventional MCF10a-Er-Src . This diIn the next step, we connected the above inferred TFs to the subtype’s ESGs based on the evidence for binding. In doing so, we created a directed TF-TF binding network  through Luminal A suggests a simple network that is dominated by estrogen response, where the majority of regulators target ESR1 and FOXA1. In the basal subtype, however, downstream genes (red nodes) can be seen distinctly partitioned into groups according to unique partners of upstream factors (blue nodes) . These dTo investigate whether sites indicated by ChIP-seq peaks of A549 and H1-hESC TFs are active in the basal subtype, we gather data for open-chromatin regions in a true basal cell line MDA-MB-231. We compared the peaks of Tead4, Max, Hdac2 ChIP-seq in H1-hESC  with the ATAC-seq of MDA-MB-231. Pooled peaks of these TFs represent 45% of all open-chromatin peaks in MDA-MB-231 . For A54P = 0.0351), c-Met oncogene , FOXQ1 (P = 0.0008) which are EMT hallmarks and regulators. We examined a public Zeb1 ChIP-seq sample (GSM2360622) to ask how basal ESGs are targeted by Zeb1, which represses E-cadherin and is a key inducer of EMT. Indeed, basal ESGs are significantly enriched in Zeb1 targets (P = 7.67 × 10−29). We hypothesize that the basal subtype contains mixed cell states and populations marked by the distinct expression of lineage-specific genes for multiple lineages. To this end, we decompose basal ESGs  into those belonging to one, or the other cell lineage, or both lineages  (P < 0.05), and embryonic stem lineage (P < 0.05) respectively  . We focuectively . 91 geneectively . Second,ectively . Overlapectively . These gectively .To experimentally verify that these identified TFs indeed regulate ESGs, we obtained public transcriptomic datasets from TF knockout and siRNA conditions for 4 TFs from basal and luminal A regulator lists: BCL11A, CTBP2, REST, and, FOXA1. For each of the luminal A and basal subtypes we found that a greater proportion of subtype-specific genes and subtype-specific TFs are perturbed (i.e. fold-change > 1.5) upon TF knockdown or knockout, when compared to a random set of genes of matched size . OverallDevelopment of cancer involves dysregulation at multiple levels, including changes to the DNA, such as somatic CNA and aberrant promoter DNAme. We therefore assessed whether in the different breast cancer subtypes the sets of cancer subtype-specific TFs described here, were more often subjected to genetic (CNA) and/or epigenetic (DNAme) aberrations than random sets of TFs. To address this, we used data from 700 breast tumors with subtype-specific DNAme, and CNA data from TCGA and METABRIC. We summarized aberrations on a gene-level to facilitate comparisons with the ESGs, and tested the relevance of each type of subtype-specific aberration  on the subtype-specific regulators.et al. in et al. in et al. and Fleischer et al. are external cohorts provided in addition to TCGA). In the basal subtype, basal TFs and their targeted genes are dysregulated by CNA , but not by DNAme  could be used to derive a list of TFs connected in gene regulatory networks in the basal subtype, delineating TFs and their targets and show that they are related to the EMT process and stemness property. The specific TFs and regulatory targets in the basal subtype are first reported here. We have been able to divide these genes as belonging to one of distinct lineages  through our network, and summarized TF binding and open chromatin regions.–, PR– and Her2 low, same as in basal-like breast cancer. Overall, the similarity to these two cell lines shows that there are at least two subpopulations in the basal tumors which are associated with embryonic stemness, epithelial and mesenchymal transitional process. Thus, using ENCODE ChIP-seq samples from a diverse panel of cell lines enabled us to postulate unique tumor cell types located within the heterogeneous basal subtype.Applying the combined ChIP-seq/SEEK method also enabled to decompose the heterogeneity among the basal extended signature genes. These genes, otherwise indistinguishable due to their coexpression, have been thanks to the TF binding analysis decomposed into genes belonging to epithelial and to stem cell lineages by the use of A549 and H1-hESC ChIP-seq experiments. A549 and H1-hESC cell lines resemble cell populations in the basal subtype. Of note, H1-hESC is an embryonic cell line. The embryonic stem cell population may be related to the fetal mammary stem cells reported prior , which abtz709_Supplementary_DataClick here for additional data file.