Cetaceans  are a lineage of aquatic mammals from which some species became giants. Only recently, gigantism has been investigated from the molecular point of view. Studies focused mainly on coding regions, and no data on the influence of regulatory regions on gigantism in this group was available. Accordingly, we investigated the molecular evolution of non-coding regulatory regions of genes already described in the literature for association with size in mammals, focusing mainly on the promoter regions. For this, we used Ciiider and phyloP tools. Ciiider identifies significantly enriched transcription factor binding sites, and phyloP estimates the molecular evolution rate of the promoter.IGFBP7 and NCAPG promoters, in which repressive agents are present in small cetaceans and those that stimulate transcription, in giant cetaceans. In addition, we found evidence of acceleration in the IGF2, IGFBP2, IGFBP7, and ZFAT promoters.We found evidence of enrichment of transcription binding factors related to large body size, with distinct patterns between giant and non-giant cetaceans in the Our results indicate that regulatory regions may also influence cetaceans’ body size, providing candidate genes for future research to understand the molecular basis of the largest living animals.The online version contains supplementary material available at 10.1186/s12862-023-02171-5. Cetaceaensities , 5. Desp to 30 m , 7.Some ecological hypotheses have been proposed to explain the large body proportions in cetaceans, such as thermoregulation , a widerNon-coding sequences with regulatory functions  coordinate the spatial-temporal expression of genes . AlthougGHSR, IGF2, IGFBP2, IGFBP7, LCORL, NCAPG, PLAG1, and ZFAT, focusing on cetaceans. Our analyses were performed within a phylogenetic framework, where each promoter contained a consistent set of 52 species, including 39 from different orders of mammals and 13 species of cetaceans, of which eight were classified as giants with a minimum length of 10 m. The objective was to investigate differences in the enrichment of Transcription Factor Binding Sites (TFBS) between giant and non-giant cetaceans, as well as to identify potential evolutionary acceleration in these animals with large body sizes.Comparative genome-wide regulatory sequence approaches can provide insights into the evolutionary history of large body size in cetaceans. Specifically, our study focuses on species that are at least 10 m long and classified as giants. In Fig. To identify potential candidate genes that may contribute to gigantism in cetaceans, we examined the evolution of their regulatory regions. To this end, we generated phylogenetic trees for each gene selected in this study using a maximum likelihood approach. These trees were constructed to visually explore the evolutionary relationships among the promoter sequences of the species included in our dataset, focusing on potential convergence among giant species. Specifically, we generated phylogenetic trees for the promoter region (-1500 bp to + 500 bp from TSS) of each gene. The promoter regions were defined as the sequences upstream of the transcription start site, as this is where regulatory elements, such as transcription factor binding sites, are typically located. The correct grouping of mysticetes and odontocetes was observed in most of the phylogenetic trees, along with other groups of mammals, such as Artiodactyla, Carnivora, Primate, Cingulata, and Chiroptera  was grouped within the mysticete clade, and the mysticete minke whale  was grouped with the odontocetes . For example, in the EGF promoter, TCF7 and CDX1 were found to be spatially conserved across phylogeny at the − 500 bp position within the promoter region, as shown in Fig. To gain insights into the molecular evolution of non-coding regulatory regions of genes associated with body size in cetaceans, we employed a scanning approach using Ciiider to identify transcription factor binding sites (TFBS) for the nine promoters of interest across all species in our dataset. The identification of TFBS is crucial to understanding the regulatory mechanisms that control gene expression, and that may contribute to the evolution of morphological traits such as body size. By analyzing the presence and distribution of TFBS in these promoters, we aimed to identify potential regulatory modifications that may have contributed to gigantism in cetaceans and to gain a better understanding of the molecular basis of body size evolution in this group of mammals. The scanning approach performed in Ciider identified TFBS for all nine promoters in all species. The results of the enrichment analyses showed conservation patterns across different mammalian groups, suggesting that some transcription factors are evolutionarily conserved, as all mammals have the same patterns of transcription factor enrichment in the same approximate location of the promoter , a species of odontocete that can exceed 20 m in length. In contrast, all cetaceans not classified as giants showed the enrichment of the transcription factor FOXP3 in the − 800 bp position and the transcription factor ZBTB33 in the − 200 bp position.In the IGFBP7 gene, with giant mysticetes presenting a unique triple enrichment pattern at the − 1100 bp position with the transcription factor PAX2 shared only with the sperm whale.Similar patterns were also found in the Additionally, we used phyloP from the PHAST package to estimate the molecular evolution rate of the promoters and identify signals of evolutionary acceleration in specific branches. Specifically, we aimed to identify whether promoters of giant cetaceans underwent accelerated evolution compared to non-giants. To achieve this, phyloP calculated the conservation and acceleration scores in a partitioned tree through a set of named branches, the giant cetaceans, and all remaining species .Thus, the tests for conservation/acceleration occur in the set of named branches relative to the others. Positive scores indicate conservation and negative scores indicate acceleration. A substitution model, against which all subtrees were compared, was derived from the phyloFit program, from the same PHAST package. Our analysis revealed possible evidence of accelerated evolution in the promoters of gigantic cetaceans, as evidenced by negative scores in the IGF2, IGFBP2, IGFBP7, and ZFAT promoters did not yield relevant results for our research question.This study investigates the molecular evolution of regulatory regions of genes potentially linked to cetaceans’ gigantism, focusing on the promoter region. We found evidence of enrichment of transcription factors binding sites potentially related to large body size, with distinct patterns between giant and non-giant cetaceans in the Physeter catodon) odontocete to be grouped with the other mysticetes, excluding the minke whale, grouped with the other odontocetes. In this way, two clades of cetaceans are redeemed: one that contains only those classified as giants and the other with non-giant cetaceans. The use of the Bayesian approach also resulted in the formation of the same clades divided by size. The phylogenetic incongruence between the highly reliable species tree and the promoter tree is a common phenomenon across the Tree of Life, as different regions can have different evolutionary histories [Despite being non-coding regions, which are often known to be difficult to align and contain many neutrally evolving sites and potentially a few constrained ones, we obtained high-quality alignments from our promoters, with ultimately recovered phylogenetic trees consistent with known relationships among species, except for the NCAPG. In this case, the phylogenetic signal was strong enough for the sperm whale  has the transcription factors TEF and PBX1 in the region between − 1300 and − 1200 bp position like other giant mysticetes. In contrast, the minke whale has more similarities with smaller odontocetes than its giant mysticetes relatives. Our results suggest that these regions have undergone different selective pressures and that some of the TFBS may have evolved more rapidly in certain lineages. These findings provide further evidence that the NCAPG promoter has experienced unique evolutionary processes that could contribute to the observed incongruence in the phylogenetic tree.The analyses implemented in the Ciiider program identified the transcription factors binding sites in promoters. Subsequently, the enrichment test revealed some patterns in the promoters of our dataset. First, the same transcription factors were found in the same approximate position in different mammalian lineages, demonstrating evolutionary conservation, and this is the case for the ter Fig. . RegulatTEF (Thyrotroph embryonic factor) is a protein that belongs to the proline- and acidic amino acid-rich (PAR) bZIP family and is expressed initially in the embryonic anterior pituitary, whereas in adults, it is involved in controlling the cell cycle and the death of hematopoietic cells , 28. TheNCAPG.Together, both TEF and PBX1 factors are related to general growth processes, such as the control of cell proliferation or directly linked to embryonic growth like PBX1, highlighting the biological meaning of their enrichment pattern only in giant cetaceans, mainly when this enrichment occurs in the promoter of a gene strongly associated with increased body size, such as NCAPG (Non-SMC Condensin I Complex Subunit G) gene was previously associated with increased body size and weight gain in horses, donkeys, pigs, humans, and chickens [NCAPG is associated with many essential features such as birth weight, wither height, feeding efficiency, and pubertal growth [NCAPG gene has evidence of positive selection in giant cetaceans [The chickens –41. In bl growth –44. In petaceans . Our proIGFBP7 promoter also showed a specific triple pattern transcription factor only shared by giant cetaceans: the PAX2 (Paired Box Gene 2), which is critical during the embryonic development of systems such as the central nervous system , kidney, eye, ear, and urogenital tract [IGFBP7 gene, a member of the IGFBP superfamily responsible for the viability of insulin-like growth factors (IGFs) that are molecules involved in promoting cell growth and division [The al tract , 47. PAXal tract . Furtheral tract , 50. Thidivision . This gedivision –54. One Tursiops truncatus, Orcinus orca, Lipotes vexillifer, Phocoena sinus, and Balaenoptera acutorostrata scammoni. In the enrichment analyses performed in Ciiider, only these cetacean species share the transcription factors FOXP3 and ZBTB33 in the NCAPG promoter. The first, FOXP3 (Forkhead box protein P3), is a transcription factor belonging to the forkhead box protein family and may act as a transcriptional activator or repressor [The cetaceans not classified as giants in this work comprise epressor . It is aepressor –58.The ZBTB33 , exhibits bimodal DNA recognition and acts as a transcriptional repressor and activator depending on the sequence context and cellular phenotype . As a reIGF2, IGFBP2, IGFBP7, and ZFAT promoters. The first three  are a group of genes that work together to promote growth. The insulin-like growth factors (IGFs), such as IGF2, are important in somatic growth and cell proliferation and responsible for fetal and post-natal growth [IGFBPs), a group that serves as transport proteins for insulin-like growth factors, regulating the bioavailability and function of IGFs [IGFBP7 coding sequence was also associated with positive selection in investigating gigantism in cetaceans [NCAPG and IGFBP7 showing conservation specifically in cetacean groups. Notably, IGFBP7, which also underwent evolutionary acceleration as detected by phyloP, may be associated with multiple gene functions, including body growth and tumor suppression.We found evidence of accelerated evolution in l growth . This ac of IGFs . For thietaceans . Likewisetaceans –68. TherIGF2BP1 has been associated with larger body sizes in chickens [STAT3 promoter has been shown to contribute to larger body size traits in cattle [IGF1 protein levels by long non-coding RNA has been implicated in the size of large dogs [Recent studies in other lineages have also highlighted the importance of regulatory regions in controlling body size. For instance, a deletion in the promoter region of chickens , and varn cattle . Additiorge dogs . These fAlthough with some limitations, such as the number of genes used, our study provides the first steps toward what other works can reach, especially those related to experimental validation. It is far from the definitive answer to a complex question. Still, this start could be useful in future research, indicating which genes are possibly related to gigantism in cetaceans and that this phenomenon must be understood in an integrated way.IGFBP7 and the NCAPG promoters. In NCAPG, observing the presence of repressive transcription factors only in cetaceans of small body-size was also possible. Furthermore, evolutionarily acceleration was detected in the promoters of the IGF2, IGFBP2, IGFBP7, and ZFAT genes. In conclusion, our study provides evidence of the evolution of cetacean gigantism from a regulatory approach.We investigated the promoter regions of genes possibly associated with increased body size in giant cetaceans. In summary, we found evolutionary conservation and evidence of differential transcription factors enrichment, with distinct patterns between giants and non-giants cetaceans for EGF , GHSR (Growth Hormone Secretagogue Receptor), IGF2 (Insulin-Like Growth Factor 2), IGFBP2 (Insulin-Like Growth Factor Binding Protein 2), and IGFBP7 (Insulin-Like Growth Factor Binding Protein 7) are part of the growth hormone/insulin-like growth factor (GH-IGF) axis, which plays a central role in regulating growth in vertebrates [LCORL (Ligand Dependent Nuclear Receptor Corepressor Like), NCAPG (Non-SMC Condensin I Complex Subunit G), PLAG1 (Pleomorphic Adenoma Gene 1), and ZFAT (Zinc Finger And AT-Hook Domain Containing) are associated with the body enlargement of species such as cows, pigs, sheep, and goats, which are artiodactyls, evolutionarily close to cetaceans [The promoters of nine genes were chosen because they have been described in the scientific literature as associated with changes in body size. The tebrates , 73. Theetaceans , 75. TheTursiops truncatus, Orcinus orca, Lipotes vexillifer, Physeter catodon, and Phocoena sinus), and eight mysticetes . The sequences for Eubalaena australis and Eubalaena glacialis were retrieved from genomes available on the public platform DNA Zoo. All other cetacean sequences were retrieved from GenBank, and the Additional file 1: Supplementary Table For cetaceans, we used sequences from 13 species, five odontocetes , sperm whale (Physeter catodon), gray whale (Eschrichtius robustus), humpback whale (Megaptera novaeangliae), fin whale , South Atlantic right whale , North Atlantic right whale , and Pacific right whale .Following Lambert et al. 2010 , gigantiBos taurus, Capra hircus, Bison bison, Odocoileus virginianus, Ovis aries, Sus scrofa, Camelus dromedarius, Camelus ferus, Camelus bactrianus), Carnivora , Primate , Cingulata (Dasypus novemcinctus), and Chiroptera . Thus, there were the same 52 species in each promoter studied.In addition to cetaceans, we included 39 other species to represent the major mammalian groups, such as the order Artiodactyla  package to estimate the molecular evolution rate of the promoter and detect signals of evolutionary acceleration in specific branches , 87. FirBelow is the link to the electronic supplementary material.EGF gene. Numbers under nodes represent bootstrap support; Figure GHSR gene. Numbers under nodes represent bootstrap support; Figure IGF2 gene. Numbers under nodes represent bootstrap support; Figure IGFBP2 gene. Numbers under nodes represent bootstrap support; Figure IGFBP7 gene. Numbers under nodes represent bootstrap support; Figure LCORL gene. Numbers under nodes represent bootstrap support; Figure PLAG1 gene. Numbers under nodes represent bootstrap support; Figure ZFAT gene. Numbers under nodes represent bootstrap support; Figure GHSR promoter implemented in the Ciiider program. The result shows transcription factors in bars. Mammals are cetaceans, artiodactyls, carnivores, primates, bats, and cingulates; Figure IGF2 promoter implemented in the Ciiider program. The result shows transcription factors in bars. Mammals are cetaceans, artiodactyls, carnivores, primates, bats, and cingulates; Figure IGFBP2 promoter implemented in the Ciiider program. The result shows transcription factors in bars. Mammals are cetaceans, artiodactyls, carnivores, primates, bats, and cingulates; Figure LCORL promoter implemented in the Ciiider program. The result shows transcription factors in bars. Mammals are cetaceans, artiodactyls, carnivores, primates, bats, and cingulates; Figure PLAG1 promoter implemented in the Ciiider program. The result shows transcription factors in bars. Mammals are cetaceans, artiodactyls, carnivores, primates, bats, and cingulates; Figure ZFAT promoter implemented in the Ciiider program. The result shows transcription factors in bars. Mammals are cetaceans, artiodactyls, carnivores, primates, bats, and cingulates.Supplementary Material 1: Additional file 1: Table


For decades, plants have been considered as organisms of choice to study chromatin dynamics and epigenetic modifications, such as DNA methylation and post-translational modifications of histone proteins. Plants can display very complex genomes, varying tremendously in size from one species to another, and displaying very sophisticated epigenetic pathways involved in various chromatin-related processes regulating gene expression, repressing DNA repeats such as transposable elements (TEs) or controlling genome stability. All these processes play crucial roles to ensure proper plant development and appropriate responses to an ever-changing environment.Plant Epigenetics and Chromatin Dynamics - EPIPLANT 2021-2022’ was set in the frame of the two later EPIPLANT meetings held in 2021 and 2022, which gathered scientists studying molecular and evolutionary aspects of plant epigenetics and chromatin dynamics. This Research Topic compiles one review article, one mini-review article and five original research articles describing various aspects of chromatin-related processes.The Research Topic ‘Jeynes-Cupper and Catoni give an exhaustive summary of long-distance signaling of RNA molecules, such as small RNA (sRNAs) that are involved in post-transcriptional gene silencing (PTGS) by cleavage or translational inhibition of RNA targets, as well as in transcriptional gene silencing (TGS) through the deposition of epigenetic modifications in a sequence-specific manner. This aspect of long-distance RNA movement is put into the context of plant grafting, given the importance of this horticultural technique for crop production and improvement.In their review, Velay et al. review the phenomenon of chromatin barrier that restrains heterochromatin spreading to insulate euchromatin regions from transcriptional repression. They discuss the importance of active DNA demethylation at TE boundaries to maintain the proper expression of adjacent genes. They also review the function of Arabidopsis topoisomerase VI (Topo VI) in maintaining the proper expression of genes located in euchromatin islands that are surrounded by repressive heterochromatic blocks. They finally consider the potential connections between the chromatin barrier, DNA looping and insulator elements involved in the three-dimensional organization of genomes [like the formation of topological-associated domains (TAD)] or regulation of enhancer-promoter interaction in plants.Meschichi et al. describe the ANCHOR system, also known as ParB/ParS, which is an elegant fluorescent protein (FP)-based DNA-labeling tool allowing real-time monitoring of a single locus subnuclear localization in planta. The ANCHOR system, which is derived from bacteria, can be used to determine the subnuclear localization and dynamics of a single locus in various plant tissues, with different ploidy levels, or at particular stages of the cell cycle. Using a combination of FPs, the ANCHOR system can ultimately allow the detection of several loci in the same nucleus, which will be of major interest for further understanding of chromatin dynamics at single locus resolution.In their original research article, Chlamydomonas reinhardtii, the MUT9p kinase phosphorylates histone H3 at threonine 3 to fulfill transgene and TE silencing. The Arabidopsis genome encodes four MUT9p-like kinase homologs that have been named MLKs. In their research article, Huang et al. investigate the genetic interaction between the Arabidopsis mlk mutants. They show that MLKs act redundantly to regulate flowering time through the phosphorylation of H2A at serine 95 (H2AS95) and, as previously shown for MLK4, MLK3 interacts with Circadian Clock Associated 1 (CCA1), which is a master regulator of the circadian clock in plants.In in situ hybridization (immuno-FISH) coupled to ultrastructural super-resolution microscopy, Karimi-Ashtiyani et al. describe in their research article the epigenetic determinism of the hybrid wheat-rye centromere of a reconstructed chromosome 1B. They found that the CENH3 histone variant, a marker of centromere activity, is only partially incorporated at the 1BL/1RS centric translocation. Furthermore, the authors noticed that only the centromeric regions deriving from rye did incorporate CENH3 of wheat into the 1BL/1RS hybrid.Using immuno-labelling with fluorescent Niu et al. investigate the mechanisms by which the DDR complex regulates the transcription and transposition of the ATCOPIA78/ONSEN TE family, which is reactivated upon heat treatment. The authors also confirm that ONSEN transcription and transposition can be uncoupled in several epigenetic mutant combinations, with the drd1 mutant showing the strongest transposition phenotype in comparison to dms3 and rdm1 mutants.RNA-directed DNA methylation (RdDM) is an important epigenetic pathway involving specialized enzymes that collectively repress TEs. DEFECTIVE IN RNA-DIRECTED DNA METHYLATION (DRD1), DEFECTIVE IN MERISTEM SILENCING 3 (DMS3) and RNA-DIRECTED DNA METHYLATION 1 (RDM1) form the DDR complex, which plays a pivotal role in the RdDM pathway. In their research article, Nugroho et al. characterize the function of the CLF homolog in Brassica rapa, which is called BrCLF. The authors show that as in Arabidopsis, BrCLF is important for the regulation of developmental processes such as plant organ development and floral transition. They also uncover that BrCLF plays a role in stress-related metabolic pathways.In Arabidopsis, CURLY LEAF (CLF) is a histone methyl-transferase involved in the deposition of H3K27me3 to repress developmentally-regulated genes. In the last research article of this Research Topic, In conclusion, this Research Topic covers a broad range of chromatin-related processes including chromatin dynamics, small RNA-mediated regulation of gene expression, subnuclear organization of chromatin, TE biology and post-translational modification of histones. Considering the importance of epigenetic pathways involved in plant response to challenging environments, it illustrates the pertinence of studying these molecular processes in the context of climate breakdown.GM: Writing – original draft. MM: Writing – review & editing. CC: Writing – review & editing. CV: Writing – review & editing.


The complex biology of AHR activation in cancer and beyond. The aryl hydrocarbon receptor (AHR) signaling pathway is a complex regulatory network that plays a critical role in various biological processes, including cellular metabolism, development, and immune responses. The complexity of AHR signaling arises from multiple factors, including the diverse ligands that activate the receptor, the expression level of AHR itself, and its interaction with the AHR nuclear translocator (ARNT). Additionally, the AHR crosstalks with the AHR repressor (AHRR) or other transcription factors and signaling pathways and it can also mediate non-genomic effects. Finally, posttranslational modifications of the AHR and its interaction partners, epigenetic regulation of AHR and its target genes, as well as AHR-mediated induction of enzymes that degrade AHR-activating ligands may contribute to the context-specificity of AHR activation. Understanding the complexity of AHR signaling is crucial for deciphering its physiological and pathological roles and developing therapeutic strategies targeting this pathway. Ongoing research continues to unravel the intricacies of AHR signaling, shedding light on the regulatory mechanisms controlling its diverse functions. By modulating both tumor cell intrinsic malignant properties as well as anti-tumor immunity, AHR can promote the progression of tumors. However, depending on the tumor type and stage of tumor development, it can also exert tumor suppression .Fig. 1ScThe AHR is a member of the basic helix-loop-helix (bHLH), and periodic circadian protein (PER) – AHR nuclear translocator (ARNT) – single minded protein (SIM) [PAS] superfamily of transcription factors that sense and respond to a plethora of different signals In its inactive state the AHR resides in the cytoplasm and forms a complex with two 90 kDa heat shock proteins (HSP90), the AHR-interacting protein , the co-chaperone prostaglandin E synthase 3  and the protein kinase SRC proto-oncogene, non-receptor tyrosine kinase (SRC) p-dioxin (TCDD) The AHR was originally discovered to mediate the toxic effects of environmental pollutants such as 2,3,7,8-tetrachlorodibenzo-In the past decades, the physiological role of the AHR in regulating important biological functions including angiogenesis in vivo investigation of AHR functions. Various clinically approved drugs including omeprazole, 4-hydroxytamoxifen, flutamide, leflunomide, and nimodipine, activate the AHR without increasing cancer risk The AHR is a key regulator of tumor progression by modulating both tumor cell intrinsic malignant properties as well as anti-tumor immunity B. Many t2While it was initially believed that only planar molecules with hydrophobic structures activate the AHR, it is now recognized that a plethora of structurally diverse environmental and endogenous ligands can activate this receptor b]carbazole (FICZ) and TCDD exerted opposing effects on CD4+ T cell differentiation Cyp1a1), AHR target gene, induction and observed the same dynamics and responses for TCDD and FICZ Binding and activation of the AHR by diverse compounds results in the induction of diverse genes and mediates a variety of effects. One example for this phenomenon is that the high affinity AHR agonists 6-formylindolopyrene (BaP) results in predominant formation of AHR-ARNT complexes. ARNT2 expression in cells with reduced ARNT protein results only in minimal induction of CYP1A1 protein. However, the expression of ARNT2 reduces TCDD-mediated induction of endogenous CYP1A1 protein by 30%, even though AHR-ARNT2 complexes could not be detected in nuclear extracts. Taken together, ARNT2 has the ability to dimerize with the liganded AHR in vitro but it may rather inhibit than enhance canonical AHR activation In mice, the ARNT paralog ARNT2, which is ∼ 80% identical to ARNT in the N-terminal bHLH and PAS regions, but is more divergent in the C terminus, is primarily expressed in the kidney, central nervous system, and retinal epithelium ARNT itself is present in a long and a short splice isoform, ARNT isoform 1 and ARNT isoform 3, respectively 5After transcriptional activation, the AHR is exported from the nucleus to the cytoplasm where it is subject to ubiquitin-dependent proteasomal degradation Of note, AHR degradation is also regulated by the AHR target gene TCDD-inducible poly (ADP-ribose) polymerase (TIPARP), which ADP-ribosylates AHR, hence increasing its susceptibility to degradation 6https://gtexportal.org/home). In addition to AHR, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and specificity protein (SP) transcription factors SP1 and SP3 may regulate AHRR expression, possibly contributing to the context-specificity of AHR activation The AHR repressor (AHRR) is induced by binding of the AHR to an XRE in its promoter sequence and exerts AHR inhibition + myeloid cells, T cells, type 3 innate lymphoid cells and intraepithelial lymphocytes, suggesting that AHRR may restrain excessive AHR activation in immune cells of barrier organs. In contrast, AHRR is barely expressed in liver and undetectable in intestinal epithelial cells. In LPS-induced septic shock, Ahrr knockout confers enhanced resistance, which is opposite to the high LPS sensitivity of Ahr-deficient mice. On the other hand, Ahrr knockout exacerbates colitis similar to Ahr deficiency Using an AHRR reporter and knockout mouse strain, Brandstätter and colleagues found that AHRR expression in naïve mice is very cell type-specific While AHRR DNA hypomethylation is associated with increased lung cancer risk, AHRR is silenced by DNA hypermethylation in multiple human malignancies including breast, cervix, colon, lung, ovary and stomach, suggesting that it may exert tumor suppressive properties in these tumor entities 7The non-canonical interaction of the AHR with other transcription factors and co-regulators as well as its crosstalk with other signaling pathways also contribute to the context-specificity of AHR activation The AHR binds to many different transcription factors such as KLF transcription factor 6 (Krueppel-like factor 6) Furthermore, HIF1α and HIF2α also bind to ARNT and compete with ligand-bound AHR for the interaction with ARNT, hence reducing AHR activity AHR crosstalk with other transcription factor and signaling pathways has extensively been reviewed and we therefore direct our readers to excellent reviews that have comprehensively covered the landscape of AHR regulators and co-regulators The direction of the transcriptional response, i.e. repression or activation, is not only determined by the transcription factors themselves, but also by their recruitment of co-activators or co-repressors. AHR has been described to interact with several of these co-regulators, e.g. nuclear receptor coactivators 2 and 3 (NCOA2/3), E1A binding protein P300 (EP300), or protein methyltransferase 4 (PRMT4), which has recently been extensively reviewed by Gargaro and colleagues 8Emerging research has uncovered non-genomic effects of AHR that extend beyond its role in gene regulation .In the cytoplasm, AHR interacts with the non-receptor tyrosine kinase, SRC Moreover, cytoplasmic AHR also interacts with STAT1. Activation of JAK-STAT signaling leads to release of phosphorylated STAT1 to the cytoplasm, where it can form a heterodimer with AHR that is thought to repress formation and nuclear translocation of STAT dimers CYP1B1 and AHRR mRNA, ubiquitination and phosphorylation was ∼ 5-fold more regulated in response to BaP than to FICZ Also non-transcriptional effects of AHR activation show ligand dependency as alterations in multiple post-transcriptional modifications (PTMs), which were dependent on the administered AHR ligand, were detected by mass spectrometry two hours after stimulation of monocyte-derived macrophages with BaP or FICZ in the presence or absence of lipopolysaccharide (LPS) Moreover, AHR mediates non-transcriptional actions through the assembly of a CUL4B-based ubiquitin ligase complex. Through this mechanism AHR promotes the ligand-dependent proteasomal degradation of specific substrates including ESR1, AR as well as peroxisome proliferator-activated receptor gamma (PPARG) Furthermore, AHR has been implicated in the regulation of intracellular calcium levels and calcium-dependent signaling pathways. Pyrene, for instance, was reported to act as an antagonist of AHR transcriptomic responses, but it induced an AHR-dependent increase in calcium within minutes after exposure Taken together the diverse non-genomic effects of AHR may also contribute to the context-specificity of AHR activation.9PTMs are also known to regulate a variety of proteins that help the cell adapt to environmental and metabolic changes, and thus can lead to context-specific responses Indications that PTMs may be involved in ligand binding, nuclear import, DNA binding and degradation of the AHR have been accumulating for decades. With the development of high-throughput OMICs technologies, several additional PTMs have been discovered in recent years. An overview of PTMs of AHR identified by high-throughput methods and other low-throughput studies is shown in 9.1Cell- and context-specific expression and activity of kinases and phosphatases alter the phosphorylation status of transcription factors within the cell, thus regulating nuclear translocation, protein–protein interaction, and DNA binding Modulation of ligand binding by phosphatases is established for various nuclear receptors Since AHR localization is a critical step in the induction of target genes, PTMs regulating AHR shuttling could also explain the cell type specificity of the AHR. For other shuttling proteins, phosphorylation and dephosphorylation have previously been shown to regulate their intracellular distribution CYP1A1 expression Phosphorylation of AHR is also essential for its DNA binding and transcriptional activity. Early studies showed that phosphatases and kinase inhibitors suppressed DNA complex formation and TCDD-induced A growing body of evidence indicates the involvement of PKC in AHR phosphorylation. Some studies suggested PKC to regulate DNA binding and transcriptional activity of AHR CYP1A1 induction, XRE reporter gene activity and DNA binding of the AHR in human keratinocytes In addition to serine phosphorylation, phosphorylation on tyrosine residues is important in regulating AHR activity Several mechanisms have been proposed for how phosphorylations may regulate DNA binding. While phosphorylation of AHR seems to be important for DNA binding, phosphorylation of ARNT tends to affect the AHR-ARNT interaction in some cells Taken together, AHR translocation and DNA binding are regulated by phosphorylation of AHR and its interaction partners. Expression and activation of the involved kinases may vary between different cell types and in response to diverse stimuli and hence may contribute to the complexity of AHR activation.9.2SUMOylation is the covalent attachment of the small ubiquitin-related modifier SUMO (a polypeptide) to a lysine residue (K) in the sequence ψKXE, where ψ is a large hydrophobic amino acid, X is any amino acid and E is glutamate The first study on AHR and SUMOylation by Xing et al. showed that SUMOylation at K63 and K510 stabilized the protein by inhibiting its ubiquitination in MCF-7 cells vice versa. Taken together, the SUMOylation status of AHR, ARNT and AHRR may contribute to the context-specificity of AHR activity.In addition to AHR, also ARNT and AHRR are regulated by SUMO-1 Although the PTMs of AHR are not fully understood, there is increasing evidence that PTMs are involved in the regulation of AHR translocation, DNA binding and transcriptional activity. Some kinases, such as PKC and tyrosine kinases, are thought to be involved in AHR phosphorylation, but the involvement of other kinases remains nebulous. Furthermore, AHRR and ARNT PTMs have also been shown to affect AHR transcriptional activity. Context- and cell type-specific differences in the expression of PTM-mediating enzymes and substrates as well as cell type-specific effects of PTMs, may lead to differences in AHR activation and various context-specific biological responses. We refer to 10Each cell in the body has a unique repertoire of functions required for a particular tissue type or, more generally, in a specific context. These functions are enabled by specific gene expression programs, which are driven by accessibility of cell-specific regulatory regions in the chromatin DNA methylation is a central epigenetic mark that occurs predominantly at positions in the DNA where a cytosine is followed by a guanine, at so called CpG sites. Methylation contributes to the silencing of gene expression directly by interfering with the binding of transcription factors, or indirectly by recruiting repressive methyl-binding proteins that recruit transcriptional co-repressors such as HDACs, leading to gene silencing Together these epigenetic modifications alter chromatin accessibility, thereby enhancing or reducing the transcriptional potential of the underlying DNA sequence and they determine and maintain gene expression programs that underlie cell fate In the context of exposure to environmental chemicals, epigenetic silencing of either AHR itself or of its downstream targets involved in phase I metabolism or in anti-oxidative stress response, might lead to insufficient elimination of harmful chemicals or alterations in redox-balance and thereby contribute to disease development 10.1AHR promoter has been described as a first regulatory level of AHR transcription. The proximal AHR promoter contains several binding motifs for transcriptional activators including SP1 binding sequences AHR transcriptional silencing AHR by hypermethylation has also been described for different cancer cell lines including the osteosarcoma cell lines U2OS and 143B, the diffuse large B-cell lymphoma cell lines U-2932 R2 and OCI-LY19, the chronic myeloid leukemia cell line K562 and the ALL cell lines Jurkat and REH AHR promoter demethylation AHR promoter methylation with HK2 expression, which was associated with worse overall patient survival Epigenetic modification of the AHR transcription is not only important in different cancers but has also been described for autoimmune disease. In rheumatoid arthritis, e.g. it has recently been shown that methylation of the CpG islands in the AHR promoter was significantly increased compared to controls Epigenetic regulation of AHR transcription. To date, the involvement of histone acetylation in expression of the AHR has been demonstrated in several cell lines. Constitutive expression of AHR was induced in murine Hepa-1 hepatoma cells by the HDAC inhibitors butyrate and trichostatin A (TSA) Histone modifications also play a role in the regulation of AHR expression related to decreased histone 3 lysine 27 trimethylation (H3K27me3), which is generally associated with gene silencing and the maintenance of a repressive chromatin state Conversely, in long-term estrogen-exposed MCF-7 cells increased AHR also has been reported as a potential target of miR-375 based on studies in human HepG2 cells Deregulation of micro-RNAs (miRNAs) as epigenetic and post-transcriptional regulators add another level of control to AHR expression and its downstream regulatory functions. Several miRNAs have been implicated in the control of AHR expression. MiR-124 inhibits AHR expression by directly targeting the 3′ untranslated region of AHR Taken together, these studies support a significant involvement of epigenetic mechanisms involved in the regulation of AHR expression that are highly dependent on the cell type.10.210.2.1AHR consensus sequence of the Cyp1a1 enhancer inhibited AHR binding to this site CYP1A1 but also of CYP1B1 in several human cell lines More than three decades ago it was shown in nuclear extracts of murine hepatoma (Hepa1c1c7) cells that methylation in the CYP1A1 and CYP1B1 that are known to show tissue and cell type-dependent expression Cy1a1 expression by TCDD has been related to AHR-dependent demethylation at the Cyp1a1 promoter CYP1A1 and CYP1B1Several human CYP-encoding genes have AHR consensus sequences in their 5′-regulatory regions, including CYP1B1 by the AHR agonist b-naphthoflavone (bNF), CYP1B1 inducibility in HuH7 hepatoma cells was not affected by DAC pretreatment. This differential response was related to methylation at two CpG sites in XREs bound by AHR upstream of the CYP1B1 promoter in HepG2 cells that was not observed in HuH7 cells CYP1A1 in cancerous LNCaP cells but not in noncancerous PWR-1E and RWPE-1 prostate cell lines. In line, only LNCaP cells showed high methylation levels in the CYP1A1 promoter, while their noncancerous counterparts did not. Also in human prostate cancer samples, the CYP1A1 enhancer was methylated in 11 out of 30 samples, while no methylation in the CYP1A1 promoter was detected in healthy control tissue Nrf2 (aka Nlfe2l2), another established AHR target gene and transcription factor that is known to crosstalk with AHR NRF2 promoter was hypermethylated and DAC treatment restored Nrf2 transcription NRF2 overexpression as well as repression have been associated with carcinogenesis and tumor aggressiveness in different cancer types Even in cells derived from the same tissue type, distinct DNA methylations in regulatory regions of CYP-encoding genes may occur. While pretreatment of HepG2 hepatoma cells with DAC increased expression of AHRR is an important regulator of AHR and an AHR target gene AHRR have been described in a multitude of studies investigating the effects of smoking and also particulate matter exposure based on DNA from blood samples AHRR associated to current or past smoking in adults, but it was also consistently shown in cord blood as a consequence of maternal smoking/environmental tobacco smoke exposure during pregnancy CYP1A1 promoter CYP1A1 was hypomethylated in current smokers, while in the cancerous tissue the same region was hypermethylated The AHR repressor AHRR- and CYP1A1-related CpGs, the opposing methylation differences suggest different underlying mechanisms that remain incompletely understood.Although it is tempting to speculate that AHR activation by ligands present in tobacco smoke contributes to the methylation differences of BRCA1 in MCF7 breast cancer cells Epigenetic modifications not only affect classical AHR target genes. Also for several tumor suppressors AHR-mediated promoter hypermethylation has been described as a mechanism for transcriptional repression. TCDD enhanced promoter methylation of the tumor suppressors p16 (INK4a) and p53, thereby reducing their transcription These findings, support the notion that unmethylated AHR consensus sequences are a prerequisite for AHR-DNA binding. However, it might not be sufficient as also other transcriptional coactivators such as p300 or transcription factors might be necessary and need to be expressed for a sufficient induction of AHR target genes Also other epigenetic modifications particularly histone modifications add an additional layer to AHR cell type-specific responses.10.2.2Cyp1a1 promoter contributing to its silencing CYP1A1 in human breast MCF-7 or cervical cancer Hela cells The role of histone modifications in modulating AHR target genes has also mainly focused on the P450 enzyme coding gene family. Studies showed constitutive binding of HDAC1 to the CYP1A1 promoter. Ovensen et al. found that different xenobiotic ligands of the AHR induced similar histone modification patterns, including an increase in H3K4me3 and H4K16Ac in the Cyp1a1 promoter of Hepa-1 cells Cyp1a1 promoter with a concomitant decrease in the repressive marker, H4K20me3 Moreover, AHR agonist activation was described to alter histone modifications in the CYP1A1 and CYP1B1 promoters CYP1A1 promoter of HepG2 cells, the promoter region of CYP1B1 showed less activated histone marks compared to MCF-7 cells, both before and after TCDD treatment, consistent with a lack of inducibility of CYP1B1 in HepG2 cells CYP1A1 regulatory region, TCDD did not induce CYP1A1 expression.In MCF7 cells, TCDD led to an induction of H3K4me3, H4Ac, H3K9Ac, H3K14Ac in the Cyp1a1/CYP1A1 in the murine YAMC and human Caco-2 colon carcinoma cell lines, respectively. While CYP1B1, AHRR and TIPARP were enhanced by TCDD in Caco-2 cells, they were only marginally induced in YAMC cells CYP1A1 promoter and enhanced H3K47Ac and H3K9Ac there CYP1B1CYP1A2 expression in Hep3B, MCF-7 and in Hela cells The HDAC inhibitors butyrate, panobinostat and vorinostat enhanced TCDD-mediated induction of Altogether, these studies reveal the importance of histone modifications in the expression of AHR target genes and underline the cell type specificity effects related to histone modifications.10.2.3AHR regulates several non-coding RNAs that may affect AHR-mediated downstream signaling and function in different species SOX4 has been attributed to induction of miR-335 and the miR-213/132 cluster via activation of AHR. Different AHR-activating ligands including TCDD, 6-methyl-1,3,8-trichlorodibenzofuran (MCDF), gallic acid (GA) and Flavipin induced miR-335 and miR-213/132 thereby decreasing SOX4 expression SOX4 expression confirming AHR interaction with these miRNAs Sex-determining region Y-related (SRY) high-mobility group box 4 (SOX4) is known to promote tumorigenesis and is highly expressed in different tumor types The role of AHR in the differentiation of T cell subpopulations such as IL-17-expressing T helper cells (Th17) and regulatory T cells (Treg) and its significance for autoimmune disease has been established Il17-targeting miR-495 and miR-1192 while decreasing miRNA-31, miRNA-219 and miRNA-490 that target Foxp3 only in AHR+/+ wild type mice. The opposing effect on this miRNA profile was observed when using FICZ. In line, I3C and DIM attenuated Th17 generation and promoted Treg while the opposite was observed for FICZ Further miRNAs have been implicated in AHR-mediated differentiation of Th17 and Treg cells in a ligand-dependent fashion. Indole-3-carbinol (I3C) and 3,3′-diindolylmethane (DIM) increased Taken together, these studies corroborate a role of AHR-mediated miRNAs expression that can differ in a ligand- and cell type-dependent fashion.10.3AHR might function as an epigenetic regulator by altering the chromatin structure through its direct interaction with transcriptional co-activators or repressors and by mediating histone- or DNA-modifying enzymes.Several reports exist that show AHR involvement in regulation of chromatin accessibility and remodeling. AHR can interact with transcriptional cofactors such as the mediator complex As indicated above, AHR may also function as an epigenetic regulator by modulating expression of histone- and DNA-modifying enzymes.HDAC8 has been reported as an AHR target as it was induced by TCDD and by overexpression of AHR in hepatoma cell lines. Also in hepatocellular carcinoma patients, HDAC8 expression significantly correlated with AHR transcription and protein levels. As HDAC8 inhibition had an anti-proliferative effect in hepatoma cells, it has been suggested that xenobiotics can alter the epigenetic dynamics by stimulating the AHR-HDAC8 axis and thereby contributing to hepatocarcinogenesis AHR was also shown to directly promote histone modification. Ligand activation led to binding of an AHR-KLF6 complex to enhancer regions in the DNA TET2 promoter contains an AHR-binding site, which the receptor binds after its activation with Kyn. TET2 induction by AHR ligand activation promotes demethylation at the promoter of the 5′-nucleotidase NT5E, which encodes the membrane-bound CD73 protein that converts AMP to the anti-inflammatory adenosine. In SLE, downregulation of AHR was associated with increased methylation levels at the NT5E gene promoter, suggesting a contribution to reduced adenosine production in Treg and likely B cells of SLE patients Ten-eleven translocation enzymes (TETs) are dioxygenases that catalyze the oxidation of methylated cytosines in the DNA to promote demethylation. Chen et al. demonstrated that the In summary, AHR may affect histones modifications through transcriptional regulation of HDACs, by recruitment of histone modifiers and can promote DNA demethylation by inducing TET2 expression. Although studies showing direct involvement of the AHR in the regulation of the expression of epigenetic modifiers are sparse, reports so far suggest that this avenue of AHR activation should further be explored.11−/− mouse lung tissue induced a 4-fold increase in AHR reporter activity compared to extracts from wild type lungs, suggesting that in the absence of the Ahr less AHR ligands are degraded in vivo in mice, constitutive expression of Cyp1a1 diminished the levels of endogenous AHR ligands resulting in an almost AHR-deficient state, which was rescued by increasing the intake of AHR ligands Activation of the AHR induces CYP enzymes which in turn modify AHR ligands, leading to their metabolic clearance 2O2), the photochemical crosslinker trioxalen, the antifungal agent ketoconazole, the DNA intercalator ellipticine, the dietary supplement diosmin as well as the protein synthesis inhibitor cycloheximide activate the AHR by inhibiting the degradation of trace amounts of FICZ contained in cell culture medium This feedback mechanism implies that the levels and the activity of the CYP enzymes can affect AHR activity. Indeed, Wincent and colleagues demonstrated that multiple factors that inhibit CYP activity including UVB irradiation, hydrogen peroxide ; A method to multiplex tryptophan and its metabolites ; A transcriptional signature to determine AHR activity ; Interleukin-4-induced gene 1 (IL4I1) as a biomarker ; Interleukin-4-induced gene 1 (IL4I1) and its metabolites as biomarkers for cancer .


Their formation in cells is still under debate. We performed CUT&Tag sequencing using the anti-iM antibody iMab and showed that iMs form within the human genome in live cells. We mapped iMs in two human cell lines and recovered C-rich sequences that were confirmed to fold into iMs in vitro. We found that iMs in cells are mainly present at actively transcribing gene promoters, in open chromatin regions, they overlap with R-loops, and their abundance and distribution are specific to each cell type. iMs with both long and short C-tracts were recovered, further extending the relevance of iMs. By simultaneously mapping G-quadruplexes (G4s), which form at guanine-rich regions, and comparing the results with iMs, we proved that the two structures can form in independent regions; however, when both iMs and G4s are present in the same genomic tract, their formation is enhanced. iMs and G4s were mainly found at genes with low and high transcription rates, respectively. Our findings support the in vivo formation of iM structures and provide new insights into their interplay with G4s as new regulatory elements in the human genome.i-Motifs (iMs) are four-stranded DNA structures that form at cytosine (C)-rich sequences in acidic conditions   G4s. G4s+ hee Figure  2). Due. Due+ hee Figure .Initial biophysical and molecular biology approaches have characterized G4s as key players in pivotal biological processes, such as transcription, replication, epigenetic modifications, genome instability and recombination ,4. The din vitro analyses revealed the presence of iM-forming regions at promoters, UTRs and introns (1 phase)  . In rice1 phase) . More re1 phase) . Both th1 phase)  and CUT&1 phase) , allowinTo answer the still open question of where iMs form in the human genome, we performed CUT&Tag with the anti-iM iMab antibody in two human cell lines of embryonic and tumor origin. The analysis was performed in parallel with the anti-G4 BG4 antibody to assess differences in iM/G4 formation and localization within the genome. We demonstrated that iMab, used in the CUT&Tag technique, consistently recognizes iM-forming sequences; these are highly enriched at gene promoters, close to the transcription start site (TSS), but are also detected in non-open chromatin regions. Genes with folded iM at their promoters display transcription rates in the lower expression range, in contrast to G4s. The two different cell lines show specific iM enrichment, confirming that iMs, like G4s , are cel2, 95% humidity, 37°C). Desalted oligonucleotides were purchased from Sigma-Aldrich  and all sequences are reported in 93T449 (WDLPS) cell line (ATCC #CRL-3043) was cultured in RPMI 1640 medium  supplemented with 10% heat-inactivated fetal bovine serum (FBS)  and 1× Penicillin–Streptomycin . Human embryonic kidney (HEK293T) cell line (ATCC #CRL-3216) was cultured in DMEM medium  supplemented with 10% FBS and 1× Penicillin–Streptomycin. Cells were grown under standard conditions  and the bacteria suspension was incubated 10 min on ice. 4.5 ml of 1:5 TES buffer with 2 mM MgCl2 and benzonase were added before incubation for 30 min at 4°C. Bacterial cells were pelleted at 8000 g for 20 min and the supernatant was incubated on HisPur–Ni NTA  columns for 30 min at 4°C for BG4 purification. Wash buffer  was used to perform two washes and BG4 antibody was finally eluted using 5 ml elution buffer . Eluted fractions containing BG4 were pooled together and dialysed in PBS + 20% glycerol overnight at 4°C. BG4 was then concentrated using Amicon 3K  and aliquots were stored at –20°C.BG4 antibody preparation was optimized starting from previously published protocols ,27,28. TBiotinylated oligonucleotides  were reset al.  at 37°C for 1 h. To stop tagmentation and digest proteins, 15 mM EDTA, 0.1% SDS and 500 mg/ml proteinase K were added and further incubated at 63°C for another 1 h. Supernatants containing DNA fragments were purified by phenol-chloroform extraction using phase-lock tubes  and libraries were amplified using NEBNExt HiFi 2× PCR master mix with uniquely barcoded i5 and i7 primers , were used to analyze all data (http://www.bioinformatics.babraham.ac.uk/projects/fastqc), reads were aligned to the human reference genome (GRCh38) using Bowtie2 (v2.4.2) (www.graphpad.com). Fraction of reads in peaks (FRiP) was calculated using deepTools module CountReadsPerBin by sampling the genome into 10000 positions of size 1 base (https://github.com/lh3/seqtk) to cover varying sequencing depth, to obtain downsampled bam files. The downsampled files were processed as above up to SEACR peak calling, then the mean read number derived from the three biological replicates, falling within the called peaks were estimated. Library complexity was calculated with the preseq program   with the(v2.4.2)  with bin(v2.4.2)  in strin(v2.4.2) . Annotat(v2.4.2)  and Venn(v2.4.2) . G4-iM s(v2.4.2) . For GVI(v2.4.2)  visualiz(v2.4.2)  BigwigCoe 1 base . Saturatnome GRCh using Boreported . For botreported .https://www.genome.ucsc.edu/cgi-bin/hgTables/ and used to display reads occupancy at the TSS. Bin of 25 bp were used to average the score over the region length. Bigwig files used to compute z-score were library size scaled. When biological replicates were not plotted individually, a mean bigwig file computed by means of WiggleTools was employed (All heatmap plots were generated using deepTools suite . Refseq employed .https://meme-suite.org/meme/tools/meme-chip). For G4/iM prediction, FASTA files were used as input for Quadparser tool analysis. Quadparser script was obtained from https://github.com/dariober/ as indicated previously (DNA sequence of high confidence peaks was extracted with ‘write2FASTA’ function of R package ‘ChIPpeakAnno’  and G4/ieviously  and appl2 × dmol−1 (molar ellipticity). CD spectra were performed in duplicate and plotted with R.Oligonucleotides  were dilTotal RNA was extracted from HEK293T (3 million cells per sample) using the GeneJET RNA Purification Kit  according to the manufacturer's instructions. RNA concentration and quality were checked using the Qubit (Thermo Fisher Scientific) and the TapeStation System (Agilent), respectively, before and after library construction. RNA-seq libraries were generated using Quant Seq 3′ mRNA-seq Library Prep kit (Lexogen). Sequencing was performed on NextSeq500 ILLUMINA instrument to produce at least 35 million reads (75bp SE) per sample. The experiment was performed in three independent biological replicates. Sequencing data were uploaded to the Galaxy web platform and the public server at usegalaxy.org, together with R (version 3.4), were used to analyze all data. RNA-seq data for the WDLPS cell line were downloaded from GEO (accession number: GSE145543) . Bioinfohttps://reactome.org/.Reactome  pathway P-value < 0.0001)  , and the 0.0001)  11,48).,48.P-valKRAS, MSMO1, PIM1 and KIT promoter regions, previously reported to fold into iMs in vitro . These represent up to 65% of all peaks, which is in line with previously reported G4-CUT&Tag data . In the o Figure   consisting of i) C-tracts with 2 or more Cs and ii) short (0–7) or long 0–12) loops. We defined stringency levels based on C-tract length and number of repeats  Figure . A fifth2 loops. C-tracts ,52. For C-tracts , which ain vitro ,49. We nin vitro . These dThe data collected so far prove that iMs form in live cells, along with G4s. Because of the different distribution of iMs and G4s in HEK293T and WDLPS cells, we wondered whether they might form in the same genomic regions. To this end, for each cell line we identified shared peaks, i.e. common peaks among iM- and G4-CUT&Tag analyses. In HEK293T cells, we counted 17155 shared peaks, while the remaining unique iM- and G4-peaks were moderately fewer Figure . In WDLPTo better investigate this issue, we classified iM- and G4-peaks (both unique and shared with the counterpart) into bottom, middle and top SEACR signal intensity ranges, according to quartile division  . For theOur data indicate that both iMs and G4s are predominantly present in regulatory genomic regions, suggesting that both structures may be involved in transcription regulation. To investigate this further, we integrated CUT&Tag data with RNA-seq analysis to correlate secondary structure formation with gene expression. We found that genes containing both iMs and G4s in the promoter region within 3 kb from the TSS produced the highest number of transcripts in both cell lines Figure , B. We oPrevious studies have shown that G4 formation depends more on chromatin accessibility and remodeling, rather than on transcriptional activity. Subsequently, folded G4s in open chromatin regions were shown to contribute to the recruitment of various transcription factors, thereby increasing transcript levels .For iMs, the relationship with transcription and open chromatin has not been investigated. Therefore, we combined iM- and G4-CUT&Tag data in the HEK293T cells with available public ATAC-seq data in the same cell line. Correlation analysis revealed high Pearson's correlation values for both G4- and iM-CUT&Tag samples Figure . InterseNext, we investigated the overlap of iMs with DNA-RNA hybrids (R-loop), whose relationship with G4s has been previously reported ,55,56. WHere, we investigated and compared the iM- and G4-landscape in two live cell lines. To this end, we optimized and successfully performed CUT&Tag sequencing using iMab, the available anti-iM antibody  provides evidence that both iMs and G4s are associated with the regulation of different pathways other than transcription, expanding their biological relevance. The Balasubramanian group has previously shown that transcription itself is not a primary determinant of G4 folding at promoters : ChIP-seHow iMs/G4s form in condensed chromatin would need further mechanistic studies; the chromatin condensation level relies on epigenetics modifications  and in tHowever, the predominant presence of both structures in regulatory regions, such as promoters, may indicate a specific association with active transcription. Indeed, we found that approximately half of the iM/G4-peaks overlapped with R-loops. As R-loop formation implies an unfolded nature of the DNA, they have been related to G4s, which in turn have been shown to influence R-loop formation and stability, and consequently transcription -67. Consin vivo. Here, iMab-immunoprecipitated genomic regions were remarkably enriched in C-rich sequences that also fold into iMs in vitro, as demonstrated by CD analysis, with a clear pH-dependency. Encouragingly, several regions that had been previously reported to fold into iMs in vitro with different transitional pH (pHT), such as KRAS (pHT 6.2–6.9) (MSMO1 (pHT 6.7) (PIM1 (pHT 7) (KIT (pHT 5.8–6)  , MSMO1 (pHT 6.7) , PIM1 (p (pHT 7)  and KIT T 5.8–6) , were alT 5.8–6) , which lT 5.8–6) , therefot pH ∼ 5 . This net pH ∼ 5 : this art pH ∼ 5 ,21.Since iMs and G4s can in principle form in complementary sequences, a long-debated question is whether formation of the two structures occurs at the same site and whether it is interdependent. The CUT&Tag technique used here cannot determine whether complementary genomic regions can fold into both quadruplex structures, since the detection limit size is 150 bp, i.e. the size of the immunoprecipitated sequences, where typically multiple G4s/iMs can form on the same and complementary strands. However, our analysis clearly indicates that iMs and G4s do form independently, as only one of the two structures was found in several immunoprecipitated sequences (unique peaks).At the same time, the iM sequences identified with the highest degree of confidence  were always found in both iM and G4 pools (shared peaks) . These dThis new evidence of the existence of iMs and their interplay with G4s in cells could be exploited in the study of most human diseases, such as cancer, infections and neurodegenerative disorders, where G4s have already been reported to play a key role ,69–72. TOur findings, by revealing the similar yet unique properties of iMs and G4s within the human genome and their distinctive relevance within cell types, expand the field of quadruplex structures and stimulate research into their role in disease and their use as drug targets.gkad626_Supplemental_FileClick here for additional data file.


All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning. The finite resources of our planet have driven us to shift from a petrochemical-based towards a bio-based industry. Industrial biotechnology plays a key role in this transition by providing ways to sustainably produce a broad range of enzymes, pharmaceuticals, foods, fuels and chemical building blocks with numerous applications . The ranThe development of these MCFs usually involves multiple iterations of the Design-Build-Test-Learn (DBTL) cycle to find an optimal producer . The cur2/O2, metal ions, pH, light and temperature [A transcriptional biological sensor is a detection tool based on prokaryotic transcription factors (TFs). These proteins are the key component in transcriptional regulatory mechanisms. They enable cells to detect and respond to a whole range of internal and external stimuli, such as antibiotics, fatty acids, secondary metabolites, different carbon sources, COperature ,6,7,8,9.perature . From a perature . By usinperature ,12. In aperature ,17,18,19perature ,21.With this plethora of possibilities and applications, it is not surprising that biosensing is seen as one of the ten key technological advances for the next generation of synthetic biology . DespiteEscherichia coli based on transcription factors from four different transcription factor families to illustrate the versatility of the developed technology. The result is a new, standardized portfolio of fully characterized biosensors.In this research paper, a modular plug-and-play platform, called MoBioS or Modular Biological Sensors, was developed to speed up the standardized construction of biosensors . Based oE. coli TOP10 cells . All products were purchased from Sigma-Aldrich  unless otherwise stated. Lysogeny Broth (LB) composed of 10 g/L tryptone , 5 g/L yeast extract (Becton) and 5 g/L NaCl was used to grow strains for routine cloning at 30 °C with shaking. LB agar plates of 12 g/L agar were used for single colony plating. All plasmids used in this study contained kanamycin resistance markers; thus, media were supplemented with 50 µg/mL kanamycin for selection. LB media were autoclaved before use. Experiments were performed in MOPS EZ Rich Defined Medium  with 2% glucose as the carbon source. The following chemical inducers were added to the medium: N-(3-oxododecanoyl)-L-homoserine lactone (O9139), N-(β-ketocaproyl)-L-homoserine lactone (K3007), chlorohydroquinone  (224081), L-homocysteine (≥98.0% purity) (69453), calcium lactate heptahydrate from VWR (5551), vanillic acid with purity ≥97.0% (HPLC) (94770), copper(II) sulfate pentahydrate 98% (209198) and zinc sulfate heptahydrate from VWR (1884). A detailed description of inducer preparation and storage is given in Plasmid construction and experiments were performed in All plasmids were constructed using Golden Gate, as described in Prior to analysis, 4 biological replicates of each strain were inoculated in wells of 96-well microtiter plates  containing 150 µL LB with antibiotics and grown overnight on a Compact Digital Microplate shaker (Thermo Scientific) at 800 rpm and 30 °C. These cultures were diluted 1:300 in a final volume of 150 µL MOPS EZ Rich Defined Medium containing antibiotics and chemical inducers and grown in our Inheco Incubator Shaker MP  at 30 °C and 800 rpm for 24 h. The strains were measured every 30 min for optical density at a wavelength of 600 nm and using mKate2 fluorescence with excitation and emission wavelengths set at 588 nm and 633 nm, respectively, in our PerkinElmer Ensight multimode plate reader (integrated in our explorer G3 workstation). TritonX-100, which was dissolved at 0.05 V% in 20 V% ethanol, was used to coat the corresponding plate lids to prevent condensation.The SynMetR RBS library was analyzed with a similar set-up as described previously. A total of 76 RBS library colonies as singlets were grown prior to analysis, after which the library variants were diluted 1:300 in two plates containing either no ligands or 0.1 mM L-homocysteine. The control strains without MetR-related plasmids were grown in 4 biological replicates and further diluted in medium containing no ligands. All medium preparations and measurements were conducted as described for the ligand induction assay.Fluo), normalized for optical density (OD), was calculated as follows:The fluorescence output signal , and normalizing for its optical density OD. Inter-plate variability caused by technical variation was accounted for by comparing the output of a reference culture containing the MoBioS platform with a constitutive promoter controlling mKate2 production. The mean of the corrected 600Fluo/OD over the 4 biological replicates was fitted with the Hill function as described in [To correct the measurements for the background fluorescence and optical density of the medium, represented as ribed in .http://bccm.belspo.be/, accessed on 21 April 2023). The platform architecture is designed to allow a one-step, one-pot Golden Gate (GG) biosensor construction in which three DNA components are combined: (1) the MoBioS platform plasmid containing two cloning sites for insertion of biosensor-specific parts, (2) the transcription factor coding sequence (TF) of choice, including a synthetic RBS sequence, and (3) the corresponding responsive promoter with its transcription factor binding sites (PTFBS) of choice. All three elements have PaqCI GG restriction sites to enable the insertion of these parts into the platform. This assembly results in a synthetic biosensor architecture, here called SynSens  platform and the corresponding biosensor construction step(s) is depicted in  SynSens , which c SynSens . The TF ion time . Other tion time .In natural transcriptional regulatory circuits, the TF coding sequence is often found co-localized with a member of its regulon. In such cases, the TF and PTFBS region can be positioned in a flanking, bidirectional manner in which the intergenic PTFBS region also holds the promoter and RBS sequences for controlling the TF’s expression ,32. ThisHowever, constructing and characterizing the natural architecture of such a regulatory circuit can be of great interest for gathering fundamental knowledge. In addition, the bidirectional nature of the promoter region could be used as an additional biosensor tuning method by re-introducing this autoregulation of the TF, thereby mimicking the gene architecture of cis-acting TFs. To allow easy construction of these ‘natural’ gene architecture biosensors, here called NatSens, a novel feature was developed within the MoBioS system, namely the GoldenRBS. This synthetic RBS sequence is included in the TF part and regulates the translation initiation rate of the TF coding sequence. The GoldenRBS contains a BsaI GG site, which results in the restriction of the DNA exactly at the start of the CDS. A second BsaI GG site was introduced in the PTFBS part outside of the biologically relevant sequences, and it results in a four-base pair overhang that overlaps with the start codon of the CDS. When performing a GG reaction on the pSynSens biosensor plasmid, the two GG sites are cleaved, and the TF is linked to its original promoter-RBS region in a scarless manner, thereby generating the pNatSens biosensor plasmid . This alE. coli strains were transformed with the resulting eight pSynSens plasmids. We refer to these strains by ‘Syn’, followed by the name of the transcription factor, e.g., SynLasR for the biosensor with transcription factor LasR.To demonstrate fast and efficient assembly through the MoBioS platform, the developed workflow was applied to eight different transcription factors (TFs) and corresponding responsive promoters. Furthermore, the TFs originated from four different TF families to establish the platform’s applicability across a wide range of the bacterial regulatory landscape . Two TFs600OD values were still below 1 and, thus, linearly correlated with the number of cells. The growth of SynLinR was influenced by the level of chlorohydroquinone concentration, resulting in lag phases of differing lengths  family show co-localization with a gene of their regulon . TherefoDifferences in response curves between the synthetic (SynSens) and natural (NatSens) architecture for any biosensor can be attributed to either the re-establishment of any naturally present autoregulatory mechanism or the difference in TF expression levels caused by the change in the promoter–RBS combination that controls this expression level.The transcriptional regulatory mechanisms used for the development of biosensors have evolved in nature for specific metabolic or environmental sensing purposes. As such, the response curves are fine-tuned for their specific functions in vivo and do not necessarily meet the sensor characteristics required for specific applications. In addition to allowing fast and efficient conversion of the synthetic biosensor architecture to its original natural architecture (NatSens), the GoldenRBS is designed to be easily changed in a designated region without losing its GG functionality. This allows efficient and customized RBS strength tuning, modulation of TF expression levels and, thus, response curve characteristics. The GoldenRBS can be divided into three regions: two invariable flanking regions (one containing the GG site) and one variable region of 13 nucleotides that can be replaced by any specific or degenerate sequence of choice . Two des6. Seventy-six variants from the obtained library were screened for their fluorescent response both in the absence and presence of the ligand  was screened. The fluorescence intensity was measured in the absence and presence of the ligand data for future biosensor constructs.The platform also includes several ways to alter the biosensors’ characteristics. First, because of the independent detector and effector module in the SynSens architecture, engineering of the TFBS, RBS or promoter in the effector module will not compromise the expression of the transcription factor in the detector module, and vice versa. Therefore, PTFBS parts and/or TF parts can consist of libraries of sequence variants to construct libraries of biosensor variants in a high-throughput, sequence-independent manner using the implemented Golden Gate assembly. Second, as the concentration of the regulator has a great impact on the response curve, the GoldenRBS was designed to be easily altered in strength by simple PCR based techniques. Last, the incorporation of a GG site in the GoldenRBS, allows for a one-step, scarless recreation of the natural gene architecture for gaining more fundamental knowledge.


Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.Fungal infections are a major global health burden where  Invasive candidiasis poses a major threat with more than ~700,000 cases worldwide  provide large scale profiles of the cellular transcriptome, proteome and phosphoproteome. Phosphoproteomics enables the characterization of proteins that are post-translationally modified by addition of a phosphate group and phosphopeptide enrichment methods can identify several thousands of phosphoproteins in one experiment . To dateS. cerevisiae but has been described to play a regulatory role during stress response in C. albicans  protein kinases are highly conserved in eukaryotes . They aralbicans . However nucleus . In addi nucleus . Recentlic fungi . In thisy1 group). Furtherrevisiae . In contrevisiae . IdentifC. albicans and established integrated modules of Sky1 and Sky2 kinase signaling, focusing on downstream effects on gene expression. Therefore, we performed RNA-Seq to generate transcriptional profiles of sky1Δ and sky2Δ mutant strains and integrated data from a phosphoproteome study of these mutants, both comparing mutants with wild type (WT) controls. Subsequently, we performed pathway and transcription factor enrichment analysis to identify key components of transcriptional regulation. Next, we integrated computational predictions of kinases for the phosphoproteome data and further assembled information on protein-protein interactions to provide linking information between the signaling proteins and the transcription factors. Combining all data into a joint network we derived signaling modules for each mutant strain, which allow the analysis of transcriptomic and phosphoproteomic profiles within in this network context. This provides a detailed view on the interface between cellular signaling cascades and transcriptional regulation which offers the opportunity to investigate not only direct but also indirect effects of SR-like kinases in C. albicans. With that, we analyze specific and common functions of the two Sky kinases in a comparative manner, with focus on their influence on transcriptional regulation. We derive and discuss potential mechanisms and functional implications of various regulatory interactions. Here, we demonstrate how the combination of experimental data with network information can offer a broader perspective on C. albicans Sky kinases and can help to generate novel hypotheses about how signaling and transcription can shape the fungal phenotype.Here, we set out to investigate the effects of SR-like kinase on transcriptional regulation in 22.1C. albicans strains used in this study are listed in The 2.22O. Strains were adjusted to an optical density of 600 nm (OD600) of 1.0. 5 µl of each 10-fold serial dilutions were spotted onto the YPD plates without or with Hygromycin B (250 µg/ml or 300 µg/ml) and incubated for 3 days at 37°C.Sensitivity of the strains to the antibiotic Hygromycin B was tested by spot dilution assays on YPD agar plates. YPD overnight cultures of the wild type and mutant strains were centrifuged  and washed with dH2.32O, adjusted to an OD600 of 0.5 and 5 µl of each cell suspension was spotted on solid arginine medium. The plates were incubated for 3 days at 37°C. Two different types of arginine media were used: 0.17% (w/v) yeast nitrogen base without ammonium sulfate and amino acids, 0.5% (w/v) ammonium sulfate, 1% (w/v) arginine (pH 6), and 0.17% (w/v) yeast nitrogen base without ammonium sulfate and amino acids, 10 mM Arginine (w/v), 0.2% glucose .YPD overnight cultures of the wild type and mutant strains were centrifuged , washed with dH2.4C. albicans wild type as well as the sky1Δ and sky2Δ mutant strains were diluted in 50 ml fresh YPD medium with a starting OD600 of 0.3 and grown for 3 h at 37°C to logarithmic growth phase. Subsequently the strains were centrifuged , washed with dH2O and were adjusted to an OD600 of 0.2 in 30 ml YPD medium and grown at 37°C for 4 h. Cells were collected by centrifugation and the cell pellets were shock frozen with liquid nitrogen. The cell pellets were resuspended in 400 µl AE buffer  and transferred to screw cab tubes followed by adding 40 µl 10% SDS solution, 500 µl glass beads and 440 µl Phenol-Chloroform-Isoamylalkohol 125:24:1 . Cells were mechanically disrupted on a FastPrep-24 cell-homogenizer  with two 40 s runs at 6.0 m/s and with 5 min on ice incubation between each run. Samples were centrifuged at 12,000 g for 2 min and the upper phase was transferred into a new reaction tube and mixed with 1/10 volume 3 M sodium acetate (pH 5.3) and one volume 2-propanol. To precipitate the RNA, the samples were incubated for 30 min at -20°C and subsequently centrifuged for 10 min at 12,000 g. The pellets were washed twice with 100 µl 70% ethanol and resuspended in 100 µl RNase-free water. RNA quality and quantity were determined using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system  and the NanoPhotometer® spectrophotometer , respectively. RNA sequencing was performed by Novogene  using the Illumina NovaSeq 6000 Sequencing System. The mRNA was enriched by a polyA capture followed by reverse transcription of cDNA to prepare an RNA library. Illumina PE150 technology was used for 150 bp paired-end sequencing.YPD  overnight cultures of the 2.5C. albicans genome  or ‘moderate to high’ (absolute log2FC ≥ 0.5) for further analyses. All transcriptomic analyses have been performed with the statistical software package R (version 3.6.3). Transcription factor enrichment was performed via the PathoYeastract+ website  and protein-protein interaction data. Significant differentially phosphorylated kinases and transcription factors  were used to predict site-specific phosphorylation for 617 different classifiers on the level of group, family and subfamily. To obtain kinase-specific information, the proteins of the C. albicans kinome were mapped to the corresponding classifiers, matching the kinase group, family and subfamily provided by the GPS prediction with that classified by Kinannote 1.0  by linking two proteins in the target species by an interaction (A’ and B’) if the orthologous proteins in the template species are known to interact    were tract A-B;  from thee (IMEx)  via PSICy, 2020) . Additioy, 2020) . The infy, 2020)  in Janua33.1SKY1 and SKY2 knockout effects on gene regulation, we performed a genome wide transcription profiling of sky1Δ and sky2Δ mutant strains in comparison to the SC5314 wild type (WT) control  genes measured in at least one sample. Differential expression analysis yielded a total of 1,580 (25.4% of the entire transcriptome) differentially expressed genes  for sky1Δ and 452 (7.3% of the entire transcriptome) differentially expressed genes for sky2Δ . The analysis shows a largely symmetric effect on up- and downregulation for sky1Δ (51.4% up and 48.5% down) whereas in sky2Δ the majority of genes (63.9%) were downregulated  the symmetric and asymmetric response patterns remain similar . This suggests that the deletion of one C. albicans Sky kinase does not lead to a compensatory transcriptional activation of the other Sky kinase gene.To investigate the sky1Δ to be differentially expressed  whereas in sky2Δ 14 (5.0% of 282) transcription factors were differentially regulated . Similar to the small overlap of all differentially regulated genes, only five transcription factors  were differentially expressed by both skyΔ mutant strains, among which only EFH1 show an expression log2FC change greater than 0.5 in both mutant strains . Since phosphorylations play a major role in the direct and indirect regulation of transcription factors we searched for differentially regulated kinases in sky1Δ and sky2Δ. Out of the 115 kinases in C. albicans were 29 kinases (25.2%) in sky1Δ and six kinases (5.2%) in sky2Δ differentially regulated compared to the wild type. Only two kinases  were differentially expressed in both knockout strains albeit only with a low expression change. This indicates, that both Sky kinases have only small similarities on transcriptional level regarding regulated kinases and transcription factors.The major key players in signaling and transcriptional regulation are kinases and transcription factors. In total, we found 71/282 (25.2%) transcription factors in 3.2sky1Δ this revealed an enrichment of mitochondrial respiratory associated categories like ‘mitochondrial respiratory chain complex assembly’, ‘mitochondrial intermembrane space’ and ‘organelle envelope lumen’ as well as RNA processing (‘U2 snRNP’) for upregulated genes  categories, focusing again on genes with moderate to high effects. For ransport . In addi defects . In thisC. albicans. Similar to S. cerevisiae, Sky1 seems to be, mainly involved in mRNA processing as well as nuclear and mitochondrial related processes, whereas Sky2 seems plays a distinct role in the cell periphery and is involved in the transcriptional regulation of glutamine and arginine metabolism.In contrast to that, we found an enrichment of metabolic and biosynthetic processes for arginine and glutamine for sky2Δ Figure 23.3skyΔ mutant strains showed a significant upregulation of the NADPH-dependent glutamate dehydrogenase (GDH3) which catalyzes the conversion of α-ketoglutarate and ammonium to glutamate. However, sky1Δ and sky2Δ mutants show different regulation of glutamate utilizing pathways like the biosynthetic and catabolic arginine pathway. So, sky2Δ upregulates most genes responsible for the conversion of glutamate to ornithine and further Arg4 which is the final step in the arginine biosynthesis pathway. This enzyme catalyzes the reversible breakdown of argininosuccinate to arginine and fumarate. Additionally, large parts of the arginine catabolic pathway with the initial conversion of arginine (Car1) to ornithine and further to proline, to glutamate  and to α-ketoglutarate as well as ammonium (Gdh2) are downregulated in sky2Δ. Since in this arginine catabolic pathway glutamate is used to fuel the TCA cycle .3.4sky1Δ and sky2Δ knockouts on protein phosphorylation in a network context we integrated previously published mass spectrometry phosphoproteome profiles of sky1Δ and sky2Δ mutants  . Apart from direct phosphorylation events, protein-protein interactions are an important part of signaling networks. Hence, we collected experimentally validated interactions of C. albicans from databases for the set of differentially expressed or phosphorylated proteins, focusing on interactions involving kinases and transcription factors. This yielded 85 interactions between 74 proteins for sky1Δ and 51 interactions between 50 proteins for sky2Δ.To investigate the effects of  mutants . The exp system)  with a h3.5sky1Δ and sky2Δ mutants with the PathoYeastract+  database for C. albicans . In total, we detected a significant enrichment for 45 transcription factors in sky1Δ and 67 transcription factors in sky2Δ. To identify candidates with a potential impact on transcriptional response in sky1Δ and sky2Δ, we selected those transcription factors which are either differentially expressed or differentially phosphorylated compared to wild type. This revealed five transcription factors for sky1Δ  and in sky2Δ almost all  genes with moderate to high expression changes. For sky1Δ two transcription factors were downregulated and one was upregulated in transcriptomics, whereas two showed an increased phosphorylation . For sky2Δ four transcription factors were transcriptionally regulated with three downregulated and one upregulated while four  showed decreased phosphorylation. This contrasts with sky1Δ, where all sites showed an increase in phosphorylation    or by phstract+) . We compstract+)  and havestract+) . Here, tstract+) , while Fstract+) . Based ostract+)  , Flo8 (wf19.3626) and Efg1f19.3626)  where they have been shown to be phosphorylated by the yeast Cyclin-dependent kinase 1 (CDC28)  , suggestnditions . Furthernditions .C. albicans, supporting the notion that the changes induced by the knockout of SKY1 and SKY2 modulate parts of pathways that maintain hyphal development and morphology in different ways.Altogether, this indicates, that the transcription factors Ace2, Efg1 and Flo8, are central players in the transcriptional response of both kinases but different regulation of these proteins by phosphorylation or interactions might alter their functionality. In addition all three transcription factors have been reported to be involved in several processes comprising a broader range of cell morphology  in C. al3.7sky1Δ signaling module  display increased phosphorylation. The signaling backbone of this module consists of 22 kinases and 15 transcription factors. Around this core module we found seven additional proteins with experimentally validated interactions in C. albicans which could potentially play a modulating role in the signaling cascade . Both networks are dominated by a few kinases . These are not necessarily direct substrates of the Sky kinases but might rather be phosphorylated by other kinases acting downstream in the signaling cascades. To investigate the regulation of the transcriptional responses and elucidate the effects of Sky kinases knockouts, we constructed integrated network modules for both kinase mutants  are targeted by the kinases Hog1 and orf19.4518 in the in sky2Δ module. This indicates a differential adjustment of Rim11, orf19.4518 and Hog1 in sky1Δ compared to sky2Δ potentially leading to distinct downstream effects on the transcriptional responses.Four transcription factors which have been detected in the transcription factor enrichment analysis 5 as can complex . In addi complex . Interes2FC = 1.5 and adjusted P-value < 0.05) in sky1Δ and S234  in sky2Δ which are both conserved in S. cerevisiae and are targeted there by the kinase Cdc28  are transcriptionally downregulated in sky1Δ but none of the genes are transcriptionally regulated in sky2Δ. On the protein level a decreased phosphorylation of Kic1, Mob2 can be observed in sky1Δ (compared to the wild type), in contrast to an increased phosphorylation in sky2Δ   but not in sky2Δ, suggesting that sky1Δ and sky2Δ differentially fine-tune Cdc28 kinase activity to regulate the RAM pathway and SBF complex. Since Swi4 and Swi6, similar to Ace2, influence morphogenesis including hyphae differentiation in C. albicans  in the sky1Δ and 40.0% (30/75) of the proteins in the sky2Δ module are connected to filamentous growth. Additionally, 72% of the sky1Δ proteins are associated with resistance to chemicals whereas only 30% of the sky2Δ proteins were associated with this knockout phenotype.For Ace2, the network modules depict the phosphorylation of the sites S565   cell cycevisiae)   induces hypersensitivity  under the accession number PRJNA941841.The data presented in the study are deposited in the Sequence Read Archive (SRA) at NCBI (CL, MD, TM analyzed the data. PB performed the wet lab experiments. CL, PB designed the figures. MD, TD, TM, SV discussed the results. MD, SV, TD supervised the project. CL, MD, PB wrote the manuscript in consultation with SV, TD, TM. All authors contributed to the article and approved the submitted version.