The cohesin complex is a multi-subunit protein complex initially discovered for its role in sister chromatid cohesion. However, cohesin also has several other functions and plays important roles in transcriptional regulation, DNA double strand break repair, and chromosome architecture thereby influencing gene expression and development in organisms from yeast to man. While most of these functions rely on protein–protein interactions, post-translational protein, as well as DNA modifications, non-coding RNAs are emerging as additional players that facilitate and modulate the function or expression of cohesin and its individual components. This review provides a condensed overview about the architecture as well as the function of the cohesin complex and highlights its multifaceted interplay with both short and long non-coding RNAs. Spatial and temporal regulation of gene expression is a complex process that involves diverse players and has to be orchestrated on multiple layers. Epigenetic and transcriptional regulators are complemented by post-transcriptional and post-translational modulators to facilitate tight control of RNA and protein abundance in cells. While epigenetic enzymes and transcription factors play a major role in gene expression control in the nucleus, the importance of the cohesin complex is becoming increasingly recognized. Next to its canonical role in sister chromatid cohesion, the cohesin complex also functions in transcriptional regulation and DNA repair ,2. HowevIn this review, we aim to shed light on the interplay between ncRNAs and the cohesin complex. First, we will provide an overview about the cohesin complex and its architecture followed by a description of its diverse biological functions. Ultimately, we will present recent insights into the role of ncRNAs in modulating the expression as well as the functions of the cohesin complex.Drosophila melanogaster and were shown to be part of the “cohesive” force opposing the “splitting” force exerted by microtubules [The chromosome-associated cohesin complex is a multi-subunit protein complex which is deeply conserved in eukaryotes with close homologs also found in bacteria. The complex consists of four core subunits as well as several associated proteins . By leveotubules ,13,14,15Two of the four core subunits of cohesin, namely SMC1A and SMC3, belong to the structural maintenance of chromosomes (SMC) protein family of ATPases. An unusual characteristic of these SMC proteins is their domain organization. In detail, a central hinge domain enables back-folding of the polypeptide chain on itself resulting in an extended anti-parallel coiled-coil structure and the juxtaposing of the N- and C-terminal regions that jointly form a globular ATPase head domain ,17. ImpoOf note, cohesin complexes in meiotic cells slightly differ from their somatic counterparts. While the core complexes are very similar, individual subunits are being exchanged for their meiotic counterparts: instead of SA1/STAG1 or SA2/STAG2 the meiotic complex contains SA3/STAG3, SMC1A is substituted for SMC1B, and RAD21 is replaced by either the meiotic Recombination Protein 8 (REC8) or RAD21-Like protein 1 (RAD21L1) to form the final cohesin ring ,23.Next to the four core subunits of the cohesin complex, additional accessory proteins are involved in regulating chromatin binding and dissociation of the ring-shaped multi-protein complex. In dividing cells, Scc2 and Scc4 proteins (Nipped-B-Like protein (NIPBL) and MAU2 in vertebrates) form a heterodimeric cohesin deposition complex which facilitates loading of cohesin onto chromatin during telophase . ImportaThe cohesin complex is best known for its function in mediating sister chromatid cohesion. The complex is loaded onto chromatin after exit from mitosis in telophase or early G1 phase . LoadingDNA-damaging events that are triggered by extra- and intracellular agents occur frequently during the cell cycle and can lead to diverse defects including DNA double strand breaks (DSBs). In order to repair DSBs, two major pathways exist in eukaryotes, namely non-homologous end-joining (NHEJ) and homologous recombination (HR). The NHEJ pathway re-joins the ends of damaged DNA, which can result in an imperfect restoration of the genetic information due to the random gain or loss of one or more nucleotides at the DSB site. In contrast, the HR pathway is able to restore the original sequence by utilizing the nearby sister chromatid as a template to faithfully repair the damaged DNA. However, the HR pathway is only active in S- and G2-phase whereas the NHEJ pathway can be used to repair DSBs throughout the cell cycle ,50. GiveIn addition to its role in checkpoint activation, cohesin is also required to suppress active transcription at DSB throughout the entire interphase. Importantly, failure to repress transcription near DSBs can lead to large-scale genome rearrangements, such as translocations. However, while SMC3, RAD21, PDS5B, and STAG2 are required for efficient suppression, loss of PDS5A or STAG1 does not affect gene transcription suggesting that PDS5B and STAG2-cohesin complexes have a specific role in transcription suppression at damaged DNA and chromatin regions . IntriguLast but not least, cohesin and its subunits were also shown to be involved in NHEJ during immunoglobulin class switch recombination (CSR). CSR is initiated by the transcription-coupled recruitment of activation-induced cytidine deaminase (AID) and the subsequent generation of DSBs. These breaks activate the DNA damage response and are resolved through classical and alternative NHEJ pathways. Importantly, cohesin subunits  were shown to interact with nuclear and chromatin-bound AID and are required for efficient CSR .To facilitate chromosome-structure compaction and gene regulation, the genomes of eukaryotic cells are organized into compartments, topologically association domains (TAD), and loops ,65. NextIn summary, the loop extrusion activity of the cohesin complex and its regulation is fundamental for genome organization and constitutes a source of chromatin contacts with important implications for gene expression regulation .With the event of large-scale RNA sequencing, both small and long non-coding RNAs have been found to constitute a large portion of the human transcriptome. While our understanding of the molecular role of these transcripts is far from being complete, it is becoming more and more obvious that fundamental questions in biology might only be resolved by considering the action of non-coding RNAs. In fact, several studies over the last decade demonstrated the multidimensional functions of lncRNAs. For example, cytosolic miRNAs and lncRNAs are thought to mainly regulate mRNA decay and translation, protein stability, as well as interfere with each other’s function. In contrast, nuclear ncRNAs have been shown to epigenetically regulate chromatin remodeling and structure as well as gene transcription through their interaction with chromatin modifying enzymes and transcription factors or by adopting an architectural role in the construction of the cell nucleus and maintenance of the three-dimensional organization of the genome ,104,105.MicroRNAs (miRNAs)—short ~22 nts long non-coding RNAs—are important regulators of post-transcriptional gene silencing. MiRNA genes are transcribed by RNA polymerase II yielding an initial primary transcript, called pri-miRNA, which consists of a stem-loop structure containing the mature miRNA sequence . The priBesides these general connections between components of the endogenous RNAi pathway and cohesin, a regulatory impact on cohesin core and accessory subunits has also been assigned to individual miRNAs. For example, a decreased STAG1 expression in colorectal cancers among African Americans has recently been linked to an exonic single nucleotide polymorphism (SNP), namely rs34149860. Intriguingly, the SNP allele contains a binding site for miR-29b and functional inhibition of this miRNA could rescue STAG1 mRNA levels . In addiIn addition to the core subunits of the cohesin complex, accessory components of the cohesin network have been shown to be regulated by miRNAs as well. For example, NIPBL was identified as a direct target of miR-187-3p and NIPBL downregulation was shown to facilitate sensitivity towards treatment with propranolol in infantile hemangioma . In non-These examples highlight an intriguing connection between the RNAi pathway with its role in post-transcriptional gene expression control and the nuclear chromatin structure and function as organized by the cohesin complex.Regulation of the cohesin component expression through different miRNAs represents one possible way to control cohesin-dependent effects in cells. However, various other non-coding RNAs are expressed in cells and could modulate the function of the cohesin complex as well. In particular, lncRNAs are implicated in various biological processes such as proliferation, gene expression regulation, and apoptosis. They achieve transcriptional control through interaction with DNA, RNA, and proteins and have the ability to alter chromosomal architecture . Due to in cis versus in trans. Given the short half-lives of the majority of eRNAs, eRNAs are generally expected to act in cis [in trans. An example of an eRNA acting in trans was also recently identified in mice [DDReRNA originates from an enhancer region of the myogenic master regulator Myoblast Determination Protein 1 (MYOD1) and interacts with different subunits of the cohesin complex, namely SMC1A, SMC3, NIPBL, and PDS5B, to ensure spatially appropriate cohesin loading in trans to regulate expression of Myogenin thereby controlling myogenic differentiation. The trans-activating function and SMC3 binding ability of DDReRNA was shown to be implemented through two of a total of four domains that were identified via in silico RNA secondary structure prediction [Over a decade ago, enhancer regions were shown to support transcription and give rise to a special class of non-coding transcripts called enhancer RNAs (eRNAs) and there have been numerous reports about cell-type and signal-dependent expression of eRNAs ,160,161.t in cis ,162,165. in mice . The DDRediction . Of noteediction ,168,169.While certain single non-coding RNAs can directly or indirectly influence their targets, there are known interactions between different kinds of ncRNAs which affect each other’s regulatory functions. For example, lncRNAs as well as circular RNAs (circRNAs) can serve as miRNA sponges. These sponge RNAs contain complementary binding sites for specific miRNAs which allows them to sequester these small inhibitory RNAs thereby preventing the silencing of miRNA targets. An example of this competition for miRNA binding was revealed in Autism Spectrum Disorder (ASD) patient samples. Here, RNA-Seq data of ASD cortex samples were analyzed for circRNA deregulation using available circRNA detection tools. A circRNA of interest, namely circARID1A, was identified through correlation analysis of the circRNA–miRNA–mRNA axis with miR-204-3p as its predicted interactor. Knockdown or overexpression of circARID1A both had no effect on expression of its collinear counterpart (ARID1A), while miR-204-3p was significantly increased or decreased, respectively. Further analysis of ASD implicated genes revealed the downregulation of STAG1 by miR-204-3p overexpression and circARID1A knockdown . Taken tThe cohesin complex and its cohesion-mediating network portray a highly interesting yet incompletely understood mode of regulation that is crucial to any cell. With its binding to chromatin, this ring-shaped complex collectively influences critical pathways and biological processes such as mitotic segregation, DNA damage repair, and transcriptional regulation allowing inter-chromosomal interactions. Furthermore, combining this knowledge with the modulatory function of non-coding RNAs which were shown to interact with cohesin through diverse mechanisms creates an intricate network. In addition to the aforementioned classes of non-coding RNAs, there are many more such as unusually small RNAs (usRNAs) that are also involved in regulation of the cohesin subunits . However


S1-Casein (encoded by the CSN1S1 gene) is associated with food allergy more than other milk protein components. Milk allergy caused by αS1-casein is derived from cow milk, goat milk and other ruminant milk. However, little is known about the transcription regulation of αS1-casein synthesis in dairy goats. This study aimed to investigate the regulatory roles of signal transducer and activator of transcription 5 (STAT5) on αS1-casein in goat mammary epithelial cells (GMEC). Deletion analysis showed that the core promoter region of CSN1S1 was located at −110 to −18 bp upstream of transcription start site, which contained two putative STAT5 binding sites . Overexpression of STAT5a gene upregulated the mRNA level and the promoter activity of the CSN1S1 gene, and STAT5 inhibitor decreased phosphorylated STAT5 in the nucleus and CSN1S1 transcription activity. Further, GAS site-directed mutagenesis and chromatin immunoprecipitation (ChIP) assays revealed that GAS1 and GAS2 sites in the CSN1S1 promoter core region were binding sites of STAT5. Taken together, STAT5 directly regulates CSN1S1 transcription by GAS1 and GAS2 sites in GMEC, and the mutation of STAT5 binding sites could downregulate CSN1S1 expression and decrease αS1-casein synthesis, which provide the novel strategy for reducing the allergic potential of goat milk and improving milk quality in ruminants.α S1-, αS2-, β- and κ-casein, and whey proteins mainly include α-lactalbumin and β-lactoglobulin [S1-casein is highly susceptible to human allergies, which is sensitive for approximately 65% of patients suffering from a milk protein allergy [S1-casein content  compared to cow milk (36–40%) [S1-casein gene transcription to decrease milk allergic reactions is necessary to be elucidated.Dairy products are rich in nutrients such as essential amino acids and bioactive peptides needed by human body, which are important sources of dietary protein. Milk proteins are divided into caseins and whey proteins . Caseinsglobulin . Milk prglobulin . The preglobulin ,6. Among allergy ,8. Goat (36–40%) ,10. TherCSN2 gene promoter and promotes the transcription and synthesis of β-casein.Transcription factors that play key roles in regulating the transcription of milk protein genes mainly include signal transducer and activator of transcription 5 (STAT5), glucocorticoid receptor (GR), activator protein 1 (AP-1), CCAAT enhancer binding protein (C/EBP) and YinYang1 (YY1) in mammals. Among these transcription factors, STAT5 plays a central role in the transcription of milk protein genes . During S1-casein in ruminants, it is essential to clarify the transcriptional regulation of αS1-casein (CSN1S1). Although previous studies have shown that STAT5 plays crucial roles in the processes of milk protein synthesis; however, whether it could regulate αS1-casein transcription in ruminants remains obscure. Therefore, the objective of the present study was to dissect the transcriptional regulation mechanism of CSN1S1 by STAT5 in goat mammary epithelial cells (GMEC). Here, we found that STAT5 directly regulated the transcription of the CSN1S1 gene by GAS binding sites in GMEC, which provides the novel way to reduce milk allergic reactions through the mutation of these GAS sites in ruminants.To address the situation of milk allergic reactions due to αAll experimental procedures were approved by the Institutional Animal Care and Use Committee of Northwest A&F University, YangLing, China (protocol number 15-516).CSN1S1 promoter region (GenBank no. NC_030813.1) was used for primer design. Genomic DNA extracted from goat blood samples by using TIANamp Genomic DNA Kit . The 2201 bp fragment of the CSN1S1 gene promoter was amplified and ligated to pMD-19T vector . The sequences of forward −2018 and reverse +183 primers were shown in http://jaspar.genereg.net/, accessed on 20 April 2020) and TRANSFAC databases  were used to predict the putative binding sites of the transcription factor in the CSN1S1 promoter region.Goat genome sequence of the CSN1S1 promoter for −1715, −1354, −1049, −598, −346, −110 and −18 bp were designed  and cloned into a pGL3-Basic vector. The plasmids were confirmed by gel electrophoresis  medium, 5 mg/L insulin , 1 mg/L hydrocortisone , 10 μg/L epidermal growth factor , 1 × 105 U/L streptomycin/penicillin  and 10% FBS . To promote lactogenesis, GMEC were switched to a lactogenic medium supplemented with 2 mg/L prolactin  for 48 h before performing the experiments.The GMEC were isolated from 3 healthy Xinong Saanen dairy goats at the peak of lactation, as previously described . The pro4 cells per well and transfected with the CSN1S1 promoter vectors by X-treme GENE HP DNA Transfection Regent  for 48 h. Similarly, cells were incubated with pcDNA3.1-STAT5a expression vector [CSN1S1 promoter plasmids transfection, respectively. The pRL-TK vector (renilla luciferase) was the internal control of pGL3-Basic vector (the mass ratio was 1:50). For quantitative real-time PCR (qRT-PCR) assays, cells were cultured in 12-well plates at a density of 2 × 105 cells per well and treated with pcDNA3.1-STAT5a vector or STAT5-IN-1 (200 μM) for 48 h, respectively. For western blot analysis, cells were cultured in 60 mm dish and incubated with STAT5-IN-1 (200 μM) for 48 h, then cellular nuclear and cytoplasmic proteins were extracted, respectively. In addition, cells seeded in the culture medium were incubated with STAT5-IN-1  followed by prolactin (2 mg/L) treatment for 48 h, then total RNA or protein was extracted. Similarly, cells seeded in the culture medium were co-treated with pGL3-CSN1S1 vector and prolactin (2 mg/L) for luciferase assays.The GMEC were approximately 80% confluence before applying treatments. For luciferase assays, cells were cultured in 48-well plates at a density of 5 × 10n vector  or STAT5The GMEC cultured in 48-well plates were harvested and lysed for luciferase activity measurement. The luciferase activity was detected by the Dual-Luciferase Reporter System  on a Fluoroskan Ascent . The relative luciferase activity was calculated as the ratio of firefly compared with renilla luciferase activity.RPS9) and ubiquitously expressed transcript (UXT) were used as internal control genes [−ΔΔCt method [The total RNA of GMEC was extracted by Trizol reagent . The concentration and purity of RNA were measured by NanoDrop2000 (ThermoFisher Scientific). The optical density 260/280 was 1.9–2.1, and the concentration of RNA was 400–500 ng/μL. The purified RNA was used to synthesize cDNA by PrimeScript RT kit . qRT-PCR were detected by SYBR green  in a CFX96 sequence detector . The qRT-PCR reaction condition was performed at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Ribosomal protein S9 , mouse anti-p-STAT5a , mouse anti-STAT5a , mouse anti-Histone H3  and mouse anti-β-actin . Horseradish peroxidase-conjugated goat anti-mouse-IgG  and goat anti-rabbit-IgG  secondary antibodies were performed. Signals were examined by the chemiluminescent kit . ImageJ  was used for measuring the intensities of bands. Relative expression of αS1-casein was normalized to β-actin, and p-STAT5a expression was normalized by comparison to STAT5a or Histone H3.Total protein of GMEC was harvested using RIPA lysis buffer  containing phosphatase and protease inhibitor cocktails . Nuclear and cytoplasmic proteins were extracted separately according to the protocol of protein extraction kit . Protein concentration was measured by BCA kit . The protein was separated using 10% SDS-PAGE and blotted onto a PVDF membrane (Roche) by a Bio-Rad Trans-Blot SD. Nonspecific binding sites were blocked for 2 h by 5% skim milk , then probed overnight at 4 °C with primary antibodies: rabbit anti-α6 cells per well until 80% confluence before cell collection. Cells were crosslinked with a final concentration of 1% formaldehyde for 10 min at 37 °C, then washed in cold PBS containing phosphatase and protease inhibitors. Chromosomal DNA was sheared into lengths of 100 to 500 bp by Bioruptor UCD-200 . The chromatin–protein complexes were treated with protein A/G beads and rabbit p-STAT5 antibody  or normal rabbit IgG antibody  at 4 °C for 12 h. The primers used in the ChIP assays for amplification are shown in The ChIP assays were performed according to the protocol of a commercial kit . GMEC were cultured in 100 mm dish at a density of 2 × 10n = 3). The data were processed using SPSS20.0 . Statistical analysis was performed using Student’s t-test for two group comparison, and one-way ANOVA was performed with Tukey test for multiple comparisons. Data were shown as means ± SEM. Significant differences were declared at p < 0.05 .All experiments were carried out with each goat as a biological replicate (CSN1S1 promoter containing 2018 bp upstream of the transcription start site (+1), the first exon (+1 to +107 bp) and part of the first intron. Using the JASPAR and TRANSFAC database analysis of the CSN1S1 promoter sequence, we revealed some consensus binding sites of transcription factors, such as STAT5 , AP-1 , C/EBP , GR , YY1  and TATA-box .The pGL3-CSN1S1 vector was transfected into GMEC, and luciferase activity was measured. The result shows that the S1-casein transcription, GMEC were treated with pcDNA3.1-STAT5a or STAT5-IN-1. Compared with control, mRNA abundance of STAT5a and CSN1S1 was effectively increased in STAT5a overexpression group , as well as αS1-casein synthesis in cytoplasm by STAT5 inhibition  versions of the CSN1S1 promoter were transfected into GMEC for promoter activity assays. Compared with the wild-type promoter vector, GAS1 or GAS2 mutation decreased the CSN1S1 promoter activity by an average 40% and 48%, respectively  resulted in very low promoter activity  and the site-mutated vectors. Results showed that STAT5a overexpression could also increase the CSN1S1 promoter activity during the individual GAS1 or GAS2 site mutation compared with the pcDNA3.1-NC control  may also exist in −1354~−1049 bp upstream. Sequence analysis of the CSN1S1 promoter region in bovine showed that there were three conserved STAT5 binding sites similar to the sequences of dairy goats [CSN1S1 promoter activity in dairy goats could also provide the theoretical support for CSN1S1 transcriptional regulation in bovine, which contribute to the reduction of αS1-casein content in ruminant milk.In ruminants, the active center of gene promoter plays a vital role in transcription activity ,40. In oart site . When thpromoter . The corry goats . TherefoS1-casein [STAT5a activity, and milk protein synthesis was significantly reduced after the phosphorylation of STAT5a was blocked [STAT5a binds to CSN2 promoter and promotes the transcription of β-casein [STAT5a binds to the CSN2 promoter region, and STAT5 activity inhibition markedly reduced CSN2 promoter activity and β-casein synthesis [STAT5a in the nucleus, which functions as a transcription factor, and downregulated CSN1S1 mRNA level and αS1-casein synthesis. Site-directed mutagenesis of the three potential STAT5 binding sites in the CSN1S1 promoter region revealed that GAS1 and GAS2 sites in the core region could affect the promoter activity, while the distal GAS3 site had no significant effect on the promoter activity. Simultaneous mutation analysis of multiple sites showed that GAS1 and GAS2 sites could independently affect the promoter activity. The activity of the CSN1S1 promoter was severely weakened after simultaneous mutation of the two sites in the core region, and even the overexpression of STAT5a could not enhance its activity.Previous studies have shown that STAT5 plays the crucial regulatory role in mammalian milk protein synthesis . After t1-casein ,45. In b blocked ,47. In bβ-casein . In primynthesis . ConsistCSN2 gene [CSN1S1 gene was severely inhibited after the mutation of GAS1 and GAS2 sites. In this case, prolactin could also not activate its transcriptional activity, indicating that these two sites were the main binding sites of STAT5 in the CSN1S1 promoter. Compared with the site mutation analysis, the ChIP assay is the direct evidence to confirm the binding of transcription factors to the promoter region [CSN1S1 gene. These results indicate that STAT5 can directly regulate the transcription of the CSN1S1 gene and the synthesis of αS1-casein in GMEC. In addition, the complicated mechanism under which STAT5 binding site mutation at goat genome affects the synthesis of αS1-casein and other milk proteins requires further investigation.Prolactin is a key hormone regulating milk protein synthesis in mammals . In boviSN2 gene . Therefor region ,42. In tCSN1S1 gene is located at −110~−18 bp upstream of transcription start site, which contains two STAT5 binding sites (GAS). STAT5 activates CSN1S1 gene promoter activity and αS1-casein synthesis. Furthermore, GAS1 and GAS2 sites in the core region of the CSN1S1 promoter are the binding sites of STAT5. Overall, this study reveals that STAT5 can directly regulate CSN1S1 transcription and αS1-casein synthesis through GAS1 and GAS2 sites. Our findings provide the novel basis for decreasing milk allergic reactions by mutating the STAT5 binding sites on αS1-casein gene promoter in ruminants.The core promoter region of the goat


The best recognized role of S100A10 is the regulation, via interaction with annexin A2, of plasminogen conversion to plasmin. Plasmin, together with other proteases, induces degradation of the extracellular matrix (ECM), which is an important step in tumor progression. Additionally, S100A10 interacts with 5-hydroxytryptamine 1B (5-HT1B) receptor, which influences neurotransmitter binding and, through that, depressive symptoms. Taking this into account, it is evident that S100A10 expression in the cell should be under strict control. In this work, we summarize available literature data concerning the physiological stimuli and transcription factors that influence S100A10 expression. We also present our original results showing for the first time regulation of S100A10 expression by grainyhead-like 2 transcription factor (GRHL2). By applying in silico analysis, we have found two highly conserved GRHL2 binding sites in the 1st intron of the gene encoding S100A10 protein. Using chromatin immunoprecipitation (ChIP) and luciferase assays, we have shown that GRHL2 directly binds to these sites and that this DNA region can affect transcription of S100A10.S100A10, a member of the S100 family of Ca In a Ca2+-bound form, they interact with a variety of protein ligands which, in consequence, leads to regulation of numerous processes inside and outside the cell [The S100 protein family consists of over 20 low molecular weight (10–12 kDa), Catebrates ,4. S100 the cell ,6.2+ and is insensitive to changes in Ca2+ concentration. Both “EF hand” motifs in S100A10 have amino acid substitutions that allow them to maintain a conformation, which resembles that of other S100 proteins in a Ca2+-bound form. This means that S100A10 is permanently locked in an active state [2+ and phospholipids [S100A10 must be flexible enough to meet these functional demands. In this work, we present literature data concerning various factors that influence S100A10 expression and our original results that show that the gene is regulated by GRHL2.S100A10 is a unique member of the S100 protein family in that it does not bind Cave state . Inside holipids . The annholipids . Dysreguholipids ,12. The holipids ,14. It iThe gene encoding S100A10 is highly inducible and its expression seems to be regulated by numerous external and internal factors. Studies performed by different research groups identified many physiological stimuli that promoted S100A10 expression, among them transforming growth factor-β (TGF-β), gonadotrophin (GD), epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), interleukin 1β (IL-1β) , brain-dS100A10 to inflammatory cues has also been studied. It was shown that, in response to interferon γ stimulation, STAT1 binds to two GAS sequences located at positions −1219 and −1090 of the gene promoter [S100A10 expression was assessed by the luciferase assay, which demonstrated that overexpression of wild-type STAT1 in two epithelial cell lines, BEAS-2B and HeLa, increased luciferase activity, while a phosphorylation deficient (Tyr 701) STAT1 mutant had no effect.The mechanism of the responsiveness of S100A10 expression following HIF1α upregulation in glioblastoma cells and samples from glioblastoma patients as compared with normal tissue [S100A10, 103 bp downstream of the transcription start site, was shown by ChIP. This binding occurred following hypoxia or after treatment of glioblastoma cells with anti-cancer drugs [S100A10 expression with hypoxic conditions characteristic for many cancer tissues.Chedeville et al.  observedl tissue . Accordier drugs . The invS100A10 expression involves glucocorticoid receptor (GR) binding sites in the promoter sequence [S100A10 promoter region; at least two of them were found to be functional by means of mutational analysis. The binding of GR to these sites was shown by ChIP after dexametasone stimulation of neuroblastoma SH-SY5Y cells [It was also revealed that dexametasone-induced sequence . Three p5Y cells .S100A10 expression are listed in S100A10 transcription according to cell’s needs.The stimuli and transcription factors known to be involved in regulation of GRHL2 is a member of the grainyhead-like transcription factor family . GRHL2 rhttps://www.genomatix.de/solutions/genomatix-software-suite.html (accessed on 2 July 2021)). This program identified five potential, but only two highly conserved, binding sites for GRHL2 located in the 1st intron of S100A10  was performed using the MatInspector program  containing the luciferase gene and a 536 bp long intronic sequence (chr1:151 990 668-151 990 132), comprising the identified GRHL2 binding sites, was constructed as described in S100A10. Cells were co-transfected with plasmid encoding GRHL2 with a FLAG tag (EX-W2222-M12-GRHL2-3xFLAG) or control plasmid (EX-NEG-M12-3xFLAG). Luciferase activity was assessed after 24 h using a Glomax 20/20 luminometer. As shown in S100A10 expression.To assess the influence of GRHL2 on To confirm these results, each potential binding site for the GRHL2 transcription factor was subsequently deleted using pTA-Luc-S100A10-GRHL2-WT as a template in a PCR reaction with appropriate primers . The resS100A10, a ChIP assay was performed. For that HEK293 cells were transfected with plasmid EX-W2222-M12-GRHL2-3xFLAG and, 24 h later, were fixed with 1% formaldehyde, lysed, and sonicated into about 500 bp long fragments  , a clearS100A10 expression, we used kidney epithelial RC-124 cells with diminished level of this transcription factor (transfected with GRHL2 shRNA) [GRHL2 mRNA in these cells and in control ones (transfected with scrambled shRNA) was analyzed using RT-qPCR. The TaqMan Fast Universal Master Mix with TaqMan Probes (ID: Hs02800695_m1 for HPRT and Hs00227745_m1 for GRHL2) was used and the reaction was performed in a 7900HT Fast Real-Time PCR System.To check the effect of the GRHL2 transcription factor on 2 shRNA) . The levCt method and gene expression was normalized to the HPRT1 housekeeping gene. As can be seen in GRHL2 mRNA in RC-124 with silenced GRHL2 expression was diminished when compared to control cells.The obtained results were analyzed using the comparative ΔΔS100A10 mRNA level was then analyzed by RT-qPCR using the SYBRGreen system and appropriate primers  and other genes [S100A10. Moreover, RT-qPCR showed that in cells with silenced GRHL2 expression, S100A10 mRNA level was lower than in control cells. Altogether, in this work, we show for the first time that GRHL2 transcription factor regulates expression of S100A10. Since both proteins are involved in proliferation/tumorigenesis/metastasis, their interrelated regulation might have some impact on cancer development and progression.In summary, by applying in silico analysis, we identified two highly conserved GRHL2 transcription factor binding sites in the 1st intron of the gene encoding the S100A10 protein. Such intronic location of the binding sites conforms to the results of ChIP-seq analyses, which show that GRHL2 binding occurs more frequently within intragenic and intronic regions than gene promoters ,30. Intrer genes . The preer genes . For inser genes . Additioer genes . By perf2+-binding protein family. S100A10 is involved in regulation of various processes, among them in plasmin production and regulation of the level of matrix metalloproteinases. Thus, in consequence, S100A10 has an effect on degradation of the extracellular matrix (ECM), which is an important step in tumorigenesis/metastasis.S100A10 is a member of the S100 CaS100A10 expression. Based on all these data, it is evident that it can be influenced by many different stimuli, which, together with a range of transcription factors, ensure that the S100A10 protein level meets the demands of a given cell. Our work has identified yet another transcription factor, that is GRHL2, a protein which, among others, plays a role in tumorigenesis. Expression of GRHL2 is increased in many cancers [S100A10 expression by GRHL2 might have an impact on cancer progression and metastasis.Up to now, several reports have been published concerning regulation of  cancers  and the  cancers . Cancer  cancers ,26. In t


PI3KCA gene. This is codified by what is seen for the class I PI3K catalytic subunit p110α, a common feature of many cancers. The metastasis suppressor protein NM23-H1 (NME1), whose ability to suppress the metastasis activities of different tumors has been widely described and was previously reported to alter phosphatidylinositol signaling. Here, we show interaction of NM23-H1 with the p110α subunit and the functional consequence of this interaction. This interaction is predominantly localized at the plasma membrane with some signals seen in the cytoplasmic compartment. Analysis of NM23-H1 levels showed a negative correlation between NM23-H1 expression and Akt phosphorylation, the key marker of PI3K pathway activation. Investigating the functional consequence of this interaction using cell motility and clonogenicity assays showed that expression of NM23-H1 reversed the enhanced migration, invasion, adhesion, and filopodia structure formation in cells expressing the p110α catalytic subunit. A similar trend was seen in anchorage-independent assays. Notably, differential analyses using NM23-H1 mutants which lacked the enzymatic and metastasis suppressor activity, showed no detectable interaction between p110α and the NM23-H1 mutant proteins P96S, H118F, and S120G, as well as no dysregulation of the PI3K-AKT axis.The PI3K pathway is one of the most deregulated pathways in cancer, which is predominantly due to gain of function mutations or altered expression of the  NM23-H1 (NME1) gene was shown to be a negative “Master Regulator” of cancer cells motility. This occurs either through multiple enzyme activities (nucleoside diphosphate kinase (NDPK), protein histidine kinase, and a serine/threonine-specific protein kinase 3′-5′ exonuclease activity), or through its extensive interactome, that negatively regulates signaling pathways which leads to metastasis. NM23-H1 was characterized as a metastasis suppressor by Steeg et al.1 during an analysis of genes differentially expressed in the murine melanoma cell line (K1735), where highly metastatic clones showed the lowest NM23-H1 expression1. The functional characterization of different NM23-H1 protein mutants, which include P96S, a site corresponding to the k-pn motif responsible for developmental defects in Drosophila, exhibited normal autophosphorylation and nucleoside-diphosphate kinase (NDPK), but is deficient in phosphotransfer activity. The H118F mutation lies within a site crucial for NDPK and histidine protein kinase activity, and the S120G mutation which is found in aggressive Human Neuroblastoma retains NDPK activity, but lacks histidine dependent serine phosphorylation, link the anti-motility activity of NM23-H1 to its enzymatic activity. However, it was shown that the P96S and S120G mutations showed a reduced hexameric and increased dimeric oligomerization compared to the wild type. This has been associated with a reduction in colonization and invasion suggesting that the metastasis-suppressor activity of NM23-H1 may depend on its oligomeric structure, which can affect its enzymatic activities, as well as its protein-protein interaction network4.Cancer progression is a dynamic process that reaches its peak with the formation of a metastatic clone at a different position from the primary site. In this context, the P or PI3P)5. The p110α and p85α are the most characterized PI3K catalytic and regulatory subunits, respectively, because of their association with cancer progression and tumorigenesis while the other subunits are rarely associated with tumorigenesis6. Production of PI3P which initiates from PI2P is the main function of the p110α kinase7. This process is a starting point for recruitment of effector proteins which contain a Pleckstrin Homology Domain (or PHD). Among these proteins, PDK1 (3-phosphoinositide-dependent kinase 1) and its binding partner, the serine-threonine kinase Akt are the main effectors. After PI3P-PDK1 mediated activation, Akt is fully activated by phosphorylation at Ser473 and Thr 308 thus activating the signaling cascade8. Indeed, gain of function mutations of the p110α gene (PI3KCA) occurs in up to one-third of human colorectal cancers (CRCs). The most frequent p110α cancer-specific gain of function mutation is the H1047R mutation and is responsible for increased migration, filopodia formation, and significant changes in cell morphology9. Notably, Qian et al. have shown that activation of PI3K alone is sufficient to remodel actin filaments and increase cell migration through the activation of protein kinase B (PKB) or Akt10. This preliminary characterization of the interaction between NM23-H1 and the p110α kinase puts forward an interesting scenario towards understanding the complex mechanism that underlying the anti-metastatic behavior of the NM23-H1 protein.The Class I phosphoinositide 3-kinases consist of four different p110 catalytic subunits, -α-β, -ɣ, and -δ and only this class is involved in production of Phosphatidylinositol 3-phosphate  was generated and incubated with in vitro-transcribed/translated 35S-labeled p85α or p110α. While a GST control protein was unable to bind either p85α or p110α, GST-NM23-H1 bound only to p110α  and PDK219. To confirm a possible role of NM23-H1 in modulating p110α kinase activity which results in Akt phosphorylation (p-Akt), Western blot analyses of cell lysates from HEK293T, MDA-MB-435, and MDA-MB-231 cell lines stably expressing NM23-H1 were performed  that acts as a docking site for proteins containing the Pleckstrin homology (PH) domain. An example is the Akt protein, which is the most characterized protein that interacts with either PI2P or PI3Pmed Fig. . All celmed Fig. . Westernmed Fig. . The mutmed Fig. . To detemed Fig. . The eff20. For example, the epidermal growth factor receptor (EGFR) can activate PI3K signaling21. Furthermore, EGF-stimulated induction of actin barbed ends, as well as lamellipodia extensions specifically require the p85/p110α complex in breast cancer cells22. Therefore, EGF was chosen as an activator of the PI3K pathway due to its ability to activate cell motility through activation of the PI3K pathway. To demonstrate if NM23-H1 can downregulate p-Akt upon EGF stimulation, Western blot analyses of cell lysates from cell lines MDA-MB-435 as well as MDA-MB-231 expressing NM23-H1 treated with EGF was performed  in favor of catalytically inactive dimers28. To date, the main mechanism by which NM23-H1 acts as an anti-metastatic protein is still unclear. Analysis of the mutated P96S, H118F, S120G isoforms showed no detected evidence of protein–protein interaction with p110α, although they preserved the same subcellular localization. Expression of these mutants did not induce any obvious downregulation of Akt phosphorylation, suggesting that the mutants are not able to inhibit the PI3K-AKT axis as observed with the NM23-H1 WT. The data therefore suggested that interaction between these two proteins had a clear functional role. We focused on the concomitant expression of p110α and NM23-H1 and compared the data obtained with the same experimental setting with the P96S mutant. Consistent with the role of p110α in cancer progression, we showed an increase in motility and invasiveness as well as adhesion in MDA-MB-435 expressing p110α. This activity decreased when NM23-H1 was also expressed and was abrogated when NM23-H1 was expressed in MDA-MB-435 cells that were knockdown for p110α. These results support a role for p110α activation as a major stimulus for cancer progression, and that restoring NM23-H1 expression can effectively neutralizes its pro-metastatic activity.The biochemical and functional characterization of the mutated isoforms of NM23-H1  linked the antimotility activity of NM23-H1 to its different enzymatic activities30. Furthermore, an increased number of filopodia in Human colon cancer cells HCT116 positive for the p110α gain of function point mutation H1047R has been previously described9. The analysis of the structures associated with cell motility provides an important clues as regards to the dynamic processes associated with actin remodeling in the context of cell migration38. Our analysis of filopodia structures showed an increase in the number of filopodia when p110α was expressed alone or with the P96S mutant, while we did not observe this increase when p110α was expressed along with NM23-H1 WT suggesting that NM23-H1 was necessary to negatively regulate the formation of filopodia. When NM23-H1 expression was restored the results aligned with previous studies which showed that expression of NM23-H1 affected clonogenicity in an anchorage independent growth assay39. Expression of both NM23-H1 and p110α led to a reduction in the clonogenicity induced by p110α expression, and this reduction was evident in MDA-MB-435 Sh-p110α but expressing NM23-H1. Notably, we did not see a similar trend when the P96S mutant was expressed.The NM23-H1 interactome also extends to proteins directly involved in actin remodeling with a key role in regulation of cell motility36. Future studies will focus on the characterization of this interaction from a biochemical and structural perspective. A translational approach will also be interesting as it was previously shown that treatment of MDA-MB-231 with Medroxyprogesterone acetate increases the expression levels of NM23-H1 with reduction of metastasis formation40. Therefore, a strategy which involves both a biochemical and translational approach will likely expose novel areas for new therapies that target the PI3K complex using small molecule inhibitors, and may prove a promising anti-metastatic tumor strategy41.To comprehensively describe the role of NM23-H1 in regulation of the metastatic process is still very difficult, due to the numerous biological processes associated this protein. However, characterization of this interaction may be fundamental to our understanding of previously described mechanisms including the phosphatidylinositol pathwayNM23-H1 antibody (sc-514515) and p110α antibody (sc-293172) used for Western blot, Co-IP and Confocal Microscopy were purchased from Santa Cruz Biotechnology, Inc. . p-AKT1 (Ser 473), AKT1 (sc-1618), GAPDH (sc-47724), and GFP (sc-53882) were obtained from Santa Cruz Biotechnology, Inc . Mouse anti-Myc (9E10) and mouse anti-HA (12CA5) were prepared in our lab from the hybridoma cells. Human anti-Fascin (clone 833223) was purchased from Novus Biological. MDA-MB-435 (Melanoma cell line), MDA-MB-231  and HEK-293T (human embryonic kidney cell line) were provided by Professor Jon Aster . MDA-MB435, MDA-MB231 and HEK-293T were maintained in Dulbecco’s modified Eagle’s medium  supplemented with 5% Bovine Growth Serum .42.HEK293T, MDA-MB-435, and MDA-MB-231 cells were transfected with jetPRIME  according to the manufacturer’s instructions. Co-IP and Western blotting were performed as described previouslypCMV-NM23-H1, pCMV-NM23-H1 (P96S), pCMV-NM23-H1 (H118F), and pCMV-NM23-H1 (S120G) were obtained from Dr. Patricia Steeg . NM23-H1 mutants P96S, H118F, S120G were cloned into the pCDNA HA-tagged vector at the BamHI-EcoRI sites. Wild type NM23-H1 was cloned into pEGFP, pA3M, pCDNA-HA, and pET-28a vectors at the BamHI-NotI sites using the primers CGGATCCGGAAGGAACCATGGC and CTGTCATTCATAGATCCAGTTCTGAG, the pSG5-p110α vector was obtained from Dr. Downward . p110α was cloned in pGex-6p1 vector at BamHI-XhoI sites. cDNA for individual domains of p110α  were amplified by PCR using the following primers: p85BD, CGGGATCCATGCCTCCAAGACCATCA and CGGCGGCCGCACGGTTGCCTACTGG; RBD, GCGGATCCACCATGGAAGAAAAGATC and CGGCGGCCGCGCTTTCTTTAGCCATC; C2 domain, GCGGATCCACCATGCTCTATTCTCAA and CGGCGGCCGCTACCACACTGCTGAA; helical domain, GCGGATCCACCATGAAGTTTCCAGAT and CGGCGGCCGCCCCACATGCACGGCA; and catalytic domain, GCGGATCCACCATGTATCTGAAG and CGGCGGCCGCTCAGTTCAAAGCATG. PCR products of each domain were subcloned into Not1 and BamH1 sites of pGex-6p1 vector.Sh-RNAs targeted p110α and NM23-H1 were constructed by annealing two pairs of primers. The primers are: 5′tcgagtgctgttgacagtgagcgaCCAGATGTATTGCTTGGTAAAtagtgaagccacagatgtaTTTACCAAGCAATACATCTGGgtgcctactgcctcggaa3′(Shp110α.1); 5′cgagtgctgttgacagtgagcgaGCATTAGAATTTACAGCAAGAtagtgaagccacagatgtaTCTTGCTGTAAATTCTAATGCgtgcctactgcctcggaa3′(Shp110α.2); 5′tcgagtgctgttgacagtgagcgaGTCTGAAGTTTCTGCAGGCTTtagtgaagccacagatgtaAAGCCTGCAGAAACTTCAGACgtgcctactgcctcggaa3′(ShNM23-H1.1); tcgagtgctgttgacagtgagcgaCGGCCTGGTGAAATACATGCAtagtgaagccacagatgtaTGCATGTATTTCACCAGGCCG gtgcctactgcctcggaa (ShNM23-H1.2).44.The cloning strategy, Lentivirus production and transduction have been described previously5 cells/well in 12-well culture plates and allowed to form a confluent monolayer. The layer of cells was scraped with a 20–200 μL micropipette tip to create a wound of ~1 mm width. Images of the wounds were monitored under a phase-contrast microscope at ×100 magnification at T0h and T24h. All values were expressed as mean ± SEM.MDA-MB-435 cells were seeded at a density of 1–5 × 102. Subsequently, cells were seeded into wells (2 × 104 per well) and allowed to adhere for 30 min. Wells were washed four times with 1 mg/mL BSAin DMEM and adherent cells were fixed, stained with crystal violet, and dried overnight. Cells were then counted with ImageJ software .Ninety-six-well plate were coated with 10 μg/mL mouse collagen IV  for 30 min at 37 °C. Wells were then washed with 1 mg/mL BSA DMEM and blocked for 1 h with 5 mg/mL BSA DMEM at 37 °C, 5% CO42.The Matrigel invasion assays were done using BD Biocoat Matrigel-coated invasion chambers . FBS (2%) was applied as a chemoattractant. Cells that had invaded through Matrigel to the other side of the 4-μm porous membrane were fixed and stained with crystal violet. Membranes were then affixed to glass slides and examined microscopically at ×100 magnification. Cells in a representative region of each membrane were counted with ImageJ. MDA-MB-435 cell line was assayed in triplicate chambers and in duplicate experiments. The colony formation and soft agar assays were performed as described previouslyFor immunofluorescence, cells were seeded on glass coverslips in 24-well plates before transfection. After treatment, cells were fixed with 4% paraformaldehyde at 4 °C for 60 min and permeated with 0.2% Triton X-100 in PBS for 10 min. The nuclei were visualized by staining with DAPI for 2 min. Images were acquired using a Fluoview FV300 confocal microscope and Fluoview software was used for image analysis.42. The used primers for NM23-H1 and p110α expression detection are: p110α forward 5′-CTGCAGTTCAACAGCCACAC-3′; p110α reverse 5′-ACAGGTCAATGGCTGCATCA-3′; NM23-H1 forward 5′-TCTGGCCTTTTCTTCACAGC-3′; NM23-H1 reverse 5′-GCTCCCGCTTTGTGTTTATT-3′.The total RNA extraction and quantitative real-time PCR analysis were performed as described previouslyT-test”. The program GraphPad Prism 8.0.2 software was used. A value of at least p < 0.05 was considered statistically significant. .All experiments in the present study were performed at least two times and similar results were obtained. The statistical analysis were performed using the “two-tailed paired Student’s Supplemental Figures LegendsSupplemental Figure 1Supplemental Figure 2Supplemental Figure 3Supplemental Figure 4Supplemental Figure 5Supplemental Figure 6Supplemental Figure 7Supplemental Figure 8


Chromatin architecture influences transcription by modulating the physical access of regulatory factors to DNA, playing fundamental roles in cell identity. Studies on dopaminergic differentiation have identified coding genes, but the relationship with non-coding genes or chromatin accessibility remains elusive. Using RNA-Seq and ATAC-Seq we profiled differentially expressed transcripts and open chromatin regions during early dopaminergic neuron differentiation. Hierarchical clustering of differentially expressed genes, resulted in 6 groups with unique characteristics. Surprisingly, the abundance of long non-coding RNAs (lncRNAs) was high in the most downregulated transcripts, and depicted positive correlations with target mRNAs. We observed that open chromatin regions decrease upon differentiation. Enrichment analyses of accessibility depict an association between open chromatin regions and specific functional pathways and gene-sets. A bioinformatic search for motifs allowed us to identify transcription factors and structural nuclear proteins that potentially regulate dopaminergic differentiation. Interestingly, we also found changes in protein and mRNA abundance of the CCCTC-binding factor, CTCF, which participates in genome organization and gene expression. Furthermore, assays demonstrated co-localization of CTCF with Polycomb-repressed chromatin marked by H3K27me3 in pluripotent cells, progressively decreasing in neural precursor cells and differentiated neurons. Our work provides a unique resource of transcription factors and regulatory elements, potentially involved in the acquisition of human dopaminergic neuron cell identity. Chromatin modifications alter its packing level, resulting in mainly two distinct environments: ‘open’ accessible regions, also referred to as euchromatin, and ‘closed’ compact regions or heterochromatin. These regions are enriched or depleted in specific histone modifications. Euchromatin is permissive for gene activation, whereas genes in heterochromatin are mainly silenced. Interestingly, regions bound by certain proteins such as CTCF, have a role in maintaining the boundary between heterochromatin and euchromatin2. Studies have revealed the role of CTCF in regulating chromatin loops’ formation and, hence, in controlling three-dimensional nuclear landscape to regulate gene expression5.In eukaryotic cells, DNA is bound to histones forming chromatin, whose structure is dynamic and suffers reversible chemical changes, mainly DNA methylation and histone post-translational modifications. These epigenetic modifications have gained increasing interest since they play fundamental roles in modulating gene expression throughout development, differentiation, and in response to environmental cues6. Euchromatin contributes to the pluripotency of embryonic stem cells (ESCs), whereas heterochromatin regions increase upon differentiation8. Epigenomic regulation and lineage-specific gene expression orchestrate cellular differentiation; however, the temporal interplay between these mechanisms remains elusive. Several large-scale efforts have characterized transcriptional and epigenetic landscapes of cell lines and tissues10. Even though these studies have identified putative regulatory elements, none of them focused on dopaminergic differentiation.Chromatin accessibility is highly dynamic and plays essential roles in establishing and maintaining cellular identity11. Currently, protocols to obtain mDA from ESCs have been described12. Differentiated neurons in vitro express several dopaminergic markers, like Tyrosine Hydroxylase (TH), GIRK2, LMX1A, EN1 and FOXA2. These neurons have electrophysiological activities that resemble mDA. Furthermore, ESC derived neurons release dopamine, survive after grafting in parkinsonian non-human mammalian models, and improve the behavioral test performance of these animals, when compared with non-grafted condition13. Therefore, differentiation of human ESCs to mDA provides an important model for studying the correlation between the dynamic chromatin accessibility landscape and gene expression changes.Mesencephalic dopaminergic neurons (mDA) are of great medical interest because they are lost in Parkinson’s disease (PD)14, making the identification of causal polymorphisms a difficult task. Disease-associated SNPs in psychiatric and neurological diseases reside in open chromatin regions (OCRs) during neural development16. Familial PD results from a single mendelian recessive or dominant mutation on one of 11 identified genes, and accounts for roughly 5% of cases17. Unlike familial PD, sporadic PD is more complex and results from a combination of environmental and genetic predisposition linked to SNPs.PD is the second most common neurodegenerative disease and its main pathological characteristic is the loss of mDA. Genome-wide association studies (GWAS) have revealed that > 80% of disease-associated SNPs are found within poorly annotated regionsHere we set out to generate a more detailed genome-wide map of the transcriptional changes (RNA-Seq) and chromatin accessibility (ATAC-Seq) of dopaminergic induction. We integrated epigenomic and transcriptomic profiles to infer the activity of transcription factors (TFs) and DNA regulatory regions such as enhancers and long non-coding RNAs (lncRNAs). Our work provides a unique resource of TFs and regulatory elements, potentially orchestrating human mDA specification.18 expressing OCT4 and SOX2 into mDA  were present at D14  with known regulatory functions. We found that 56% of DE lncRNAs (54 out of 96) had significant gene expression correlation . For visualization purposes, we contrasted FCs of DE lncRNAs with their related protein-coding genes . The high number of lincRNAs suggests potential regulatory functions in ans Fig. b. Genes nes Fig. c. Exampl6. The majority of reads were located in regions less than 100 bp, indicating OCR. Mononucleosome, dinucleosome, and trinucleosome patterns were also observed.We used ATAC-Seq to identify genomic chromatin accessibility depicting active (open) and inactive (closed) chromatin during DA induction. We identified OCRs in samples from D0, D14, and D28 collected simultaneously from the same experiments used for RNA-Seq. Nucleosome packing and an apparent periodicity of approximately 200 bp in insert size distribution plots were evident  of the consensus peaks were specific to D0, 150,787 (32%) were D14-specific, and 89,235 (19%) were D28-specific. Only 49,126 (11%) of consensus peaks were shared among all time-points  of D0 specific OCRs overlapped GeneHancer annotated regions. In contrast, 31%  and 42%  of D14 and D28 specific OCR regions respectively, overlapped GeneHancer regions.To analyze global accessibility, we used + /- 5 kb of identified OCRs. Higher chromatin accessibility in D0, compared to D14 and D28 was observed . Significant gene-sets enriched in D0-specific peaks are diverse but related to negative regulation of differentiation, stem cell division, and endodermal/mesodermal/ectodermal differentiation  showed a simultaneous significant downregulation of RNA expression and a decrease in OCRs at their promoters. Both loci were also marked by an enrichment of promoter histone mark H3K4me3, the activating histone modification H3K27ac, and DNase I hypersensitivity in H9 undifferentiated hESCs. We also identified a decrease in chromatin accessibility at an upstream region coincident with enhancer histone modification H3K4me1, histone activation modification H3K27ac, and DNase I hypersensitivity. Interestingly, H3K27ac is absent in the same location in hESC-generated NPC. This putative enhancer region is already annotated in GeneHancer database22; our results suggest that this region is a pluripotency-related enhancer . Some of these regions have already been annotated in GeneHancer database.Analyzing individual loci, we observed changes in both chromatin accessibility and expression levels of expected genes at D0 and D28. We complemented our analysis by using public histone modification and DNase I hypersensitivity datasets. Pluripotency genes like M14 Fig. b and NANcer Fig. b. In con27. Hence, we compared both consensus and time-point specific OCRs in our experiments with PD-associated single nucleotide polymorphisms (SNPs) derived from GWAS analysis 28. Interestingly, significant overlaps were found between PD-related SNPs28 and OCRs in consensus peaks and D0 specific peaks only in promoters. Whereas, OCRs specific to mDA (D28) depicted significant overlap with PD-related SNPs only in intronic and intergenic regions. The significancy of overlaps was determined using a hypergeometric statistical test . SNCA expression and accessibility in OCR overlapping TSS, presumably at the promoter, were invariant through differentiation. In contrast, in the same locus there was an increase in chromatin accessibility at intergenic and intronic OCR by D14 and a higher one at D28  are TFs. We searched for TF binding motifs in promoter regions (± 1 kb up- and downstream) of protein-coding and lncRNA DEGs. For motif search, we used FIMO (FDR < 0.1)To compare the frequency of TF binding motifs in promoter regions of DE genes from the previously defined expression clusters, we counted the number of genes enriched by each TF binding motif per cluster. This process resulted in a matrix of TFs (rows) and clusters (columns) whose values represent the percentage of genes in each cluster which had a TF binding motif. After filtering out motifs of TFs which were not expressed (FPKM < 1) and TFs whose frequencies remained constant (median absolute deviation < 0.05) in all clusters, we obtained a list of 24 TFs Fig. . We obseTo investigate the potential binding of TFs in OCR, we performed a motif search in consensus peak regions. We filtered out motifs with FDR > 0.1. Next, for each TF motif, we calculated its frequency in peaks defined as promoter-TSS and enhancer regions . Based on ENCODE’s motif matrix, TFs may have more than one motif, thus we selected the most enriched motif representative of a TF. This strategy is intended to identify potentially important regulators of dopaminergic differentiation. We obtained a list of 171 TFs, which were further filtered to contain only TFs in the list of 4009 previously defined DE genes. We found 23 and 18 DE TFs with a higher frequency of binding motifs in promoter or enhancer peaks, respectively Fig. c. The meThe top 8 TFs with motif frequencies annotated to promoter regions were ESRRA, CTCF, EBF1, POU2F2, BHLHE40, HEY1, SP8, and HIC1. Motifs of ESRRA, CTCF, EBF1, and POU2F2 were also the most enriched in enhancer regions. We calculated the gene expression Pearson correlation of these TFs with all 4009 DEGs considering significant correlations at FDR < 0.05. ESRRA, whose expression decreases from D0 to D28, is positively (18%) and negatively (23%) correlated with DEGs, Fig. d. ConverACTL6B, which codes for BAF53B, a subunit of the BAF remodeling complex. Notably, most of the genes associated with 3D chromatin structure show a consistent downregulation from D0 to D14 and an upregulation from D14 to D28. To further analyze chromatin states, we selected CTCF, a major component of the nuclear organization machinery that shows a dynamic expression pattern during DA induction  showed a positive correlation, suggesting that these lncRNAs participate in positive or negative feedback loops. However, their roles in dopaminergic differentiation remain elusive. LncRNAs such as linc-ROR (lincRNA-ST8SIA3) can reprogram somatic cells to pluripotency35. Further studies with simultaneous analysis of TFs, lncRNAs, miRNAs, and epigenetic marks are required to better understand regulatory networks controlling the transitioning from pluripotent stem cells to NPC and mDA.Our characterization of transcriptional changes allowed us to generate a repertoire of DE lncRNAs with potential regulatory functions in pluripotency and mDA commitment. Unexpectedly, there was a high proportion of DE lncRNAs. The majority of DE lncRNAs found were lincRNAs, or antisense relative to the closest protein-coding gene. Notably, most were lincRNAs showing downregulation. Loss-of-function assays in mouse ESCs showed that lincRNAs have key roles in maintaining pluripotency and repressing differentiation programs12, and also a comprehensive approach was performed in human DA development with single-cell resolution36. However, gene expression is not sufficient for elucidating regulators of cell identity. The integration of ATAC-Seq and the greater resolution of RNA-Seq is a powerful resource to address regulatory heterogeneity between different cellular populations6. We found higher levels of global chromatin accessibility in D0 when compared with D14 and D28. These results confirm that ESC have a more open or “loose” chromatin correlating with a more permissive transcriptional state7. Differentiation is associated with heterochromatinization and global reorganization of heterochromatin structure, density and position8. A clear relationship between chromatin accessibility and gene expression has been demonstrated37. We observed a moderate correlation when comparing these parameters, suggesting the participation of other elements in the regulation of gene transcription such as TF binding, DNA methylation, histone tail modifications, miRNAs, and lncRNAs39.Previous studies profiled gene expression by microarrays with the same differentiation protocol40. By studying DA differentiation in mice, researchers concluded that expression of lncRNAs and accessibility of enhancer regions represent a more accurate molecular signature associated with cell specificity than the expression of protein-coding genes41. We found a higher overlap of ATAC-Seq D0-specific OCRs with GeneHancer regulatory elements than with D14 and D28 OCRs suggesting that cell type-specific enhancer regions are potentially involved in dopaminergic differentiation. D14 and D28 OCRs not annotated by GeneHancer are interesting candidates for experimental validation.We identified known and novel TFs potentially orchestrating neurogenesis and dopaminergic specification. We found a higher density of TF motifs in promoter regions compared to enhancer regions, consistent with previous data42 and contributes to reprogramming fibroblasts to pluripotency43. Recent research revealed a pioneering role of ESRRB in binding silenced enhancers to promote a permissive chromatin state for pluripotent core TF recruitment44. Expression profiling studies of nuclear receptors in mouse and human ESCs showed that ESRRB is unique to mice. In contrast, ESRRA is present in both mice and humans45, suggesting that ESRRA might subserve biological functions in hESCs similar to those of murine ESRRB.Overall, 6 out of 8 DE TFs, whose motifs were most frequently found in OCR, have expression patterns significantly correlated with a considerable number of DE genes. Interestingly, these TFs have diverse profiles with their highest expression in D0, D14, or D28, denoting their roles in pluripotency, neural precursor commitment, or mDA specification. ESRRA and SP8, upregulated in D0, might participate in pluripotency maintenance. ESRRA is a nuclear receptor, but its participation in self-renewal and pluripotency has not been elucidated yet; however, another family member, ESRRB is a crucial regulator of pluripotency in mouse ESCs46 and enhancer regions47 and may interact with p300/CBP regulating gene transcription48. EBF1 is expressed in post-mitotic neurons and has an important role in mouse striatal differentiation49. Given that computationally predicted motif binding sites do not always indicate physiological binding of TFs50, additional ChIP-seq experiments are needed to address the in vivo validation of TF binding in progenitors and mDA51.Motifs of ESRRA, CTCF, and EBF1 were enriched in enhancer regions, suggesting their role in distal regulatory mechanisms. Some of the TFs identified in this study have not been previously implicated in dopaminergic differentiation, but they have a role in neurogenesis. Early B cell factor (EBF1), highly expressed in D28, has been shown to bind to the promoter27, suggesting that these SNPs mark variations that might impact regulatory functions increasing the risk for PD. Concordantly, we observed that OCRs in differentiated DA neurons overlap significantly with SNPs in non-coding regions. Specifically differentiated DA neurons show that OCRs in SCNA overlap with reported PD-related SNPs. Further studies are required to pinpoint the functional effect of enhancer region SNPs with TF binding.Genome-wide association studies (GWAS) have identified associations between SNPs and human pathologies. The majority of identified SNPs lie within non-coding regions including lncRNAs and enhancer regionsACTL6B transcript that codes for BAF53B subunit and is significantly upregulated from D14 to D28. Interestingly, cell cycle exit in NPCs is accompanied by a subunit exchange from BAF53A to BAF53B52, consistent with our results. To elucidate molecular triggers driving chromatin accessibility changes, we queried expression profiles of diverse chromatin remodelers and selected the architectural protein CTCF, given its importance in brain development53. CTCF is ubiquitously expressed in numerous cell types and developmental stages and has been implicated in regulating widespread functions54.We observed marginal changes in gene expression of several subunits of the chromatin remodeling complex BAF, except for the 55. Notably, we observed a transient decrease at both protein and mRNA levels of CTCF in samples from D14. Beagan et al. found a decreased genome-wide CTCF occupancy in the transition from mouse ES cells to NPCs53. They also showed down-regulation in the protein levels of CTCF when comparing mouse pluripotent cells with NPCs, but they did not analyze differentiated neurons; we observed an increase in CTCF when human NPCs progress to differentiated neurons. Heterochromatin regions can be identified by intense Hoechst staining; however, a more specific epigenetic mark for Polycomb-mediated facultative heterochromatin is H3K27me356. Through further assays, we found the lowest co-localization of CTCF with intense Hoechst staining at D0, increasing at D14 and D28. However, the co-localization of CTCF with H3K27me3 in pluripotent cells is highest. At D14, there was a discrete, although significant decrease in this coincidence. In contrast, there was a significant dissociation of CTCF presence with H3K27me3 deposition at D28. Previous work showed that the majority of CTCF sites present in ESC-derived NPC are already present in pluripotent ESC, suggesting that neural commitment is accompanied by a massive loss of CTCF occupancy instead of an extensive CTCF site acquisition or reshuffling53. Reduced CTCF presence in heterochromatin at D0 may result from the heterochromatin-free nuclei of ESCs. Increased co-localization of CTCF with heterochromatin might be due to the extensive heterochromatinization that takes place through differentiation8 or to CTCF acting as an insulator to prevent excessive repression. Further experimental assays will unveil the role of CTCF in chromatin looping and its changes during DA specification. Our results indicate that CTCF relocates to euchromatin regions in differentiated neurons.The leading hypothesis denoting CTCF’s diverse regulatory functions is by conforming chromatin loopsIn summary, we generated genome-wide maps of transcriptional changes and chromatin accessibility and through their integration, we derived a list of TFs and lncRNAs with potential functions in pluripotency maintenance or DA differentiation. We pinpointed important chromatin remodelers and demonstrated the dynamics of CTCF protein and mRNA. Importantly, we highlight the coordinated functions of chromatin accessibility, chromatin remodelers, TFs and lncRNAs to regulate spatiotemporal gene expression. Our work provides a useful resource of TFs and regulatory elements, potentially orchestrating human mDA specification.For detailed protocols, refer to Supplementary Information.18 and cultured until 75% confluency. These GFP-expressing cells are useful for transplantation studies13 and behave as its parental H9 hESC. At day zero (D0), cells were considered pluripotent. We followed a dopaminergic induction protocol12 with minor modifications57. At D14, neural precursors are present, whereas at D28, cells presented a neuronal morphology. Samples were analyzed at D0, D14 and D28, some plates were used for RNA extraction while others for nuclei isolation and chromatin assays.H9 human embryonic stem cells (WA09) of female origin were purchased from WiCell , transduced with lentiviral vectors to express GFP as describedUndifferentiated H9-GFP cells were cultured for 2–3 days. Neural progenitors were re-seeded on induction day 13 or 21 on 24-well plates and fixed the next day. Cells were permeabilized, washed, blocked, and incubated with primary and secondary antibodies. Incubation with Hoechst 33258 (1 ng/ml) was used for nuclear labeling.58.Standard immunoblot protocols were performed for CTCF and TH. Images were analyzed using ImageJ-Fiji56. Also, the co-localization of CTCF with H3K27me3 was determined as previously described59.Photographs of CTCF- and Hoechst-stained cells, or confocal images were normalized, and analyzed with ImageJ. Co-localization of CTCF with heterochromatin regions stained with Hoechst was determined by the overlapping area from merged images as reported60. After cell lysis, tagmentation, and adapter incorporation, transposed DNA was purified and quality was assessed. Paired-end 75 cycle sequencing reads were acquired on the Illumina HiSeq 2500 sequencer. A total of 50, 25, and 40 million reads were obtained from D0, D14, and D28 samples respectively.The assay for transposase-accessible chromatin (ATAC) libraries were prepared on D0, D14, and D28, following the original methodTotal RNA was extracted using TRIzol following manufacturer’s instructions. Samples with RNA integrity number greater than 8 were used. For library construction, Illumina TruSeq RNA Sample Prep Kit (Illumina) were used. Samples were run on MiSeq and HiSeq 2500 Illumina sequencers using paired-end 75 cycle mode. A total of 80.4 million reads were obtained and distributed among 3, 4, and 3 replicates from D0, D14, and D28 time points, respectively.61 and trimmomatic62 we obtained 36–126 bp paired-end ATAC-Seq reads for each time-point samples. The quality of trimmed sequencing datasets was verified using FASTQC63. We followed the ENCODE ATAC-Seq pipeline (http://www.encodeproject.org/atac-seq/) to process samples. Paired-end reads were aligned to the human reference genome (GRCh38/hg38) using Bowtie2 with parameters ‘-X 2000 -k 5’64. Next, we used SAMTools65 to remove unmapped reads, fragments with unmapped mates, non-primary alignment reads, and reads failing platform quality checks. Low quality reads (MAPQ < 30) and reads which mapped to mitochondrial DNA were also excluded. Optical and PCR duplicates were also removed using Picard tools (http://broadinstitute.github.io/picard). To accurately locate the center of each transposon-binding event, the remaining reads were offset by + 4 and − 5 for positive and negative strands respectively66. To evaluate the expected periodicity of DNA winding around nucleosomes, we obtained insert size histograms using Picard tools |> 1.5 and p-value < 0.05. Promoter-TSS was defined as 1 kb up- plus 100 bp downstream of the transcript start site (TSS). HOMER annotatePeaks function was used72, to associate peaks to genes (GENCODE v29) or genomic regions . We used Mann Whitney tests to compare the number of normalized reads found in open chromatin regions (OCR) of specific loci between ATAC-Seq samples (D0 vs D14 and D0 vs D28). Asterisks indicate statistical significance  in IGV tracks  and a subset of gene-sets from MSigDB73 combined with Wikipathways 201975 were used. Gene-sets were considered significantly enriched with FDR < 0.05.The hypergeometric statistical test in R  from Gene Expression Omnibus accession numbers indicated in Supplemental information. We used GeneHancer database from GeneCards Suite v4.1431. Promoter regions of differentially expressed (DE) genes (DEGs) (1 kb up- and downstream of the TSS) and open chromatin regions  were scanned for motif occurrences using FIMO30 with FDR < 0.1. The most frequently occurring DE TFs were correlated with DEGs using the Pearson coefficient. Only correlations with FDR < 0.05 were considered significant.We performed a motif search using position weight matrices from the ENCODE TF ChIP-seq datasets61, trimmomatic62, and FASTQC63. Reads were mapped to hg38 (https://www.gencodegenes.org/) using a previously described pipeline76. Assembly of mapped reads was performed with Cufflinks v2.2.177 and values of Fragments Per Kilobase of transcript per million Mapped reads (FPKM) were calculated for all annotated genes and transcripts . We used HTSeq70 to calculate read counts for annotated genes and transcripts. We adjusted batch effect  if at least one of the replicates in the comparison had FPKM Standard quality procedures were performed using cutadaptect Fig.  and perfSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Video 1.Supplementary Video 2.Supplementary Video 3.Supplementary Information 11.


Gammaproteobacteria group, although its sequence is poorly conserved. We propose that FeoC, which may couple FeoB NTPase activity with pore opening, was an ancestral element that has been dispensed with through mutations in FeoA and FeoB in some lineages. We provide experimental evidence supporting this hypothesis by isolating and characterizing FeoC-independent mutants of the Vibrio cholerae Feo system. Also, we confirmed that the closely related species Shewanella oneidensis does not require FeoC; thus, Vibrio FeoC sequences may resemble transitional forms on an evolutionary pathway toward FeoC-independent transporters. Finally, by combining data from our bioinformatic analyses with this experimental evidence, we propose an evolutionary model for the Feo system in bacteria.Iron acquisition is essential for almost all living organisms. In certain environments, ferrous iron is the most prevalent form of this element. Feo is the most widespread system for ferrous iron uptake in bacteria and is critical for virulence in some species. The canonical architecture of Feo consists of a large transmembrane nucleoside triphosphatase (NTPase) protein, FeoB, and two accessory cytoplasmic proteins, FeoA and FeoC. The role of the latter components and the mechanism by which Feo orchestrates iron transport are unclear. In this study, we conducted a comparative analysis of Feo protein sequences to gain insight into the evolutionary history of this transporter. We identified instances of how horizontal gene transfer contributed to the evolution of Feo. Also, we found that FeoC, while absent in most lineages, is largely present in the  However, the latter component, FeoC, is present in only a few species, primarily in the Gammaproteobacteria group .Since FeoC is the least-conserved protein of the Feo system, its phylogenetic distribution and how its sequence varies among species may inform us about its role and origin. To this end, we sought to identify FeoC orthologs in reference databases; however, a BLASTP search failed in this task due to the poor conservation of FeoC amino acid sequences. For instance, a BLASTP search of ng HMMER . We usedng HMMER . The smaThe distribution of Feo proteins that we found resembles that reported in previous studies , 13 and feoB gene and at least one copy of feoA or an FeoA-like domain fused to the N terminus of FeoB. We also found instances in which FeoA has an additional domain or two FeoA-like domains are combined in a single protein  between positions 57 and 60 stands out as the most conserved motif, and it is embedded in a region with several insertions ranging from 2 to 5 amino acids (aa).Next, we explored the sequence conservation of FeoC. The FeoC family in Pfam comprises 523 sequences, and its HMM profile was built based on a seed alignment of 15 sequences from diverse bacterial phyla, all annotated as either FeoC domain-containing proteins or FeoC-like transcriptional regulators. The sequence logo representation of this protein family shows that only a few amino acids appear to be conserved . Namely,Two possible scenarios may explain the limited and uneven presence of FeoC. First, FeoC could be an element recently acquired in some lineages that has been stabilized by positive selection. Alternatively, the ancestral form of the Feo system may have had an FeoC-like component that has been lost in most clades. To evaluate each of these hypotheses, we examined how FeoA, FeoB, and FeoC are distributed across different groups of organisms and whether specific features of FeoA or FeoB correlate with the absence of FeoC.feoC have a small “FeoB-associated Cys-rich membrane protein” (PF12669 in Pfam) annotated downstream of feoB. This oligopeptide is predominant in Firmicutes and Bacteroidetes, and in some species, it is present as a C-terminal domain of FeoB . It contains a cysteine-rich motif (3–5Cx2–3Cx6–8CCx) commonly associated with iron binding , some of them followed by gene loss events. For example, this would be the case for Shewanella species if the common ancestor of the Proteobacteria group acquired feoC via HGT.FeoC proteins from distant species are likely homologs since they can be gathered by a single HMM profile and are located downstream of an  of FeoC . While tfeo genes. Namely, Xanthomonas and Xylella species, both Gammaproteobacteria, localize within the Betaproteobacteria group, with bootstrap support of 99%. These plant pathogens likely acquired feoABC from Betaproteobacteria via HGT, possibly from other pathogens that occupy the same niche, such as Ralstonia species . This association remains when analyzing FeoA and FeoC sequences separately , indicating that this potential HGT event moved the whole Feo system rather than independent genes. Overall, the FeoA tree largely recapitulates the same associations as those in the FeoC tree, suggesting that FeoA and FeoC have evolved together; thus, there is no evidence suggesting that feoC was acquired independently in other species via HGT.HGT is a major force shaping genome evolution in bacteria , 19. To  species . InteresfeoA genes. Besides HGT events and mutations that might have led to dispensing with FeoC, our tree shows evidence of a complex evolutionary history. For instance, some Feo systems encoded in the same genome do not cluster together in our tree. Rather, they are grouped into clades with Feo systems from different species. This is the case for Feo systems found in Firmicutes, Cyanobacteria, and Alphaproteobacteria. Although these phyla form monophyletic groups in the organismal tree , one of which has two feoA genes and an feoC gene and is conserved in Rhodobacter sphaeroides, while the other one, which is found only in Rhodobacter capsulatus, lacks feoC and likely functions as an Mn2+ transporter  into the pACYC184 vector to determine whether these genes encode a functional Feo system on their own. V. cholerae EPV6 carrying this construct was able to grow in Luria-Bertani (LB) medium without heme supplementation, indicating that S. oneidensisfeoAB encodes a functional Feo system and does not require a separate FeoC   .E. coli FeoC contains four cysteines arranged as a 56CX4CX2CX5C70 motif in proximity to its C terminus. We replaced the two central cysteines with serines, obtaining the 61SxxS64 mutant, and assessed the phenotype of V. cholerae EPV6 carrying a plasmid with this mutation. The strain harboring the FeoCSxxS mutant showed a phenotype similar to that of the ΔFeoC mutant, indicating that the conserved C residues in E. coli FeoC are necessary for full function . Unlike most Gammaproteobacteria, the Shewanellaceae, Alteromonadaceae, and Pseudoalteromonadaceae species in the tree lack FeoC, but their closest neighbors within the Alteromonadales family  have FeoC. Moreover, unlike most species, including other Vibrionaceae , FeoC sequences found in Vibrio spp. have no cysteine residues ; thus, they contain neither the CxxC nor the 4–7CCxxGxCKxCPx archetypic motif. Taken together, these findings show that cysteineless FeoC is a characteristic feature of the Vibrio lineage, which likely evolved after the divergence of this genus within the Vibrionaceae family  to FeoC-independent transporters in other groups (such as in Shewanella).In our phylogenetic reconstruction of Feo,  species . This clVibrio Feo is at an intermediate stage between the FeoC-requiring ancestor and the FeoC-independent phenotype, then FeoA and FeoB from both Shewanella and Vibrio might share some of the mutations needed to bypass the requirement for FeoC. To determine whether spontaneous changes may render the V. cholerae Feo system independent of FeoC, we induced random mutations into the V. cholerae Feo operon containing an in-frame deletion of feoC. Next, we selected those mutants in which ferrous iron transport was rescued. Specifically, we transformed plasmids carrying the mutagenized operons into the iron transport-deficient V. cholerae strain EPV6, which grows only in medium supplemented with heme or when it harbors a functional Feo system , partially bypassed the requirement for FeoC as well , indicating that different additional mutations are needed to fully recapitulate the wild-type phenotype in the ΔfeoC background. We also evaluated whether the Feo(AB) fusion led to an FeoC-independent phenotype in other species by constructing such a fusion in the E. coli Feo system. However, the Feo(AB) fusion was insufficient to render the E. coli Feo system independent of FeoC . Similarly, we were unable to isolate FeoC-independent mutants for ΔfeoC in the E. coli Feo system. Together, these results support the hypothesis that V. cholerae has already accumulated some of the mutations that might have led to FeoC independence in Shewanella so that only a few additional changes in FeoA and FeoB are needed to replace the diminished role of FeoC.Since several Feo systems lacking FeoC exhibit an FeoA-like domain at the N terminus of FeoB , we also as well . HoweverV. cholerae  fusion allowed V. cholerae to obtain iron via Feo in the absence of FeoC. For comparison, the fusion of FeoA and FeoB did not bypass the requirement for FeoC in E. coli. This fusion requires the deletion of 19 nucleotides in E. coli  but only a single nucleotide insertion in V. cholerae (because feoA and feoB overlap one another). Thus, the Feo system of V. cholerae may be more likely to naturally acquire a mutation that results in an Feo(AB) fusion and eventually evolve to dispense with FeoC. We have previously shown that this fusion protein from V. cholerae is stable and active  fusion proteins and lack FeoC.Because gene fusion/fission is the major factor governing the evolution of multidomain proteins , we propVibrionaceae family. Previous studies suggest that this family of bacteria evolved from a common ancestor that existed about 600 million years ago in the Devonian, from which the genera Vibrio and Aliivibrio diverged . Some authors have proposed that the widespread cysteine-rich motif in FeoC must have functional significance . Therefore, besides the role of the cysteine-rich motif, future studies could investigate whether other motifs have evolved to have critical functional significance in certain bacterial lineages.It has been shown that the E29 and M35 residues in  species . Our anaXanthomonas and Xylella species acquired the Feo operon from Betaproteobacteria, and it was then fixed by positive selection. Instances of HGT between these plant pathogens have been documented, especially involving genes relevant for virulence , but whether they are translated into functional peptides is still an unanswered question. Also, cysteine-rich motifs are a common feature in most FeoC-like elements. Still, this feature could have arisen multiple times and may reflect convergent evolution for metal-binding or redox-sensing functions. Equally possible is that these alternative FeoC-like elements reflect the transition from systems with FeoC to systems lacking this component. In this regard, Vibrio species seem to be the exception that proves the rule insofar as this genus has an FeoC protein that is required for iron uptake but lacks cysteine residues. Overall, the role of FeoC remains elusive, but we propose that it may be coupling nucleotide hydrolysis with pore opening  for small peptides prevalent in  opening . This ac2+ and Mn2+ in bacterial redox homeostasis .Bacterial metal transporters often have promiscuous specificity toward isovalent cations. Most importantly, there is an interplay between Feeostasis , and traeostasis . In this system” . Namely,nsporter , 22, 25.ransport , 24. In ransport , 23. In Firmicutes display a remarkable multiplicity and diversity of Feo proteins, having up to three operons in a single genome, some with several feoA genes. In our Feo tree, some Feo clusters from Firmicutes form a monophyletic group with the P. gingivalis Mn2+ transporter but separate from that of R. capsulatus mentioned above. If these systems are true Mn2+ transporters, these results indicate that specialization toward Mn2+ may have resulted from gene duplication events that occurred in several lineages. Alternatively, an ancestral Mn2+ transporter derived from Feo may have been horizontally transferred between these groups, although they do not cluster together as a monophyletic group in our Feo tree. Further research is needed to evaluate whether these systems can support the transport of metals other than Fe2+. The functional significance of multiple FeoA proteins in a single operon also merits further study.Archaea and Neocallimastigomycota genomes (see Tables S1 and S2 at https://doi.org/10.6084/m9.figshare.17082758). The latter group is the earliest-diverging fungal phylum, which comprises anaerobic unicellular symbionts that inhabit the gastrointestinal tract of large mammalian herbivores  were used as queries to search in the UniProt Reference Proteomes database through HMMER , specifihttps://doi.org/10.6084/m9.figshare.17082758.Results from fungal genomes were manually inspected to rule out spurious sequences. With this aim, BLAST analysis of the amino acid sequences under inspection was performed (by BLASTP) against the nonredundant protein sequence database (NCBI). Those sequences with significant hits in genomes other than those retrieved in the original search  were considered potential contaminants or annotation errors and were not used in further analyses. Those results that passed this curation are shown in Table S1 at Amino acid sequences (see Table S6 at the URL mentioned above) were aligned using MUSCLE through MEGAX software , with alfeo genes in Helicobacter species are not adjacent to one another, but since no other copies were detected in Helicobacter genomes, they were concatenated as a single sequence to construct this tree.For the Feo system tree, the amino acid sequences of all the Feo proteins encoded in the same locus were concatenated and then analyzed as described above. Of note, feoC genes were overlooked because they were not annotated as reading frames, those genomes with no feoC were analyzed by TBLASTN against the FeoC-like sequences of the closest species. Additionally, for those Feo clusters lacking feoC, the 50 last 3′ bp of feoB together with the next downstream 450 bp were analyzed using ORF Finder (https://www.ncbi.nlm.nih.gov/orffinder/), and any potential ORF longer than 40 aa was used as a query in BLASTP against the nonredundant protein sequence database.To check that no E. coli and V. cholerae strains were grown in Luria-Bertani (LB) broth  or on LB agar  at 37°C and at 200 rpm for liquid media.All chemicals and growth media were purchased from Sigma-Aldrich Chemical Company. Bacterial strains were preserved at −80°C in tryptic soy broth (TSB) with 20% glycerol. Unless otherwise stated, https://doi.org/10.6084/m9.figshare.17082758. For strains harboring plasmids, the following antibiotics were used: 50 μg mL−1 ampicillin, 50 μg mL−1 kanamycin, and 12.5 μg mL−1 tetracycline for E. coli and 25 μg mL−1 ampicillin, 6.25 μg mL−1 tetracycline, and 5.0 μg mL−1 gentamicin for V. cholerae. When indicated, heme was added at 10 μM.Bacterial strains and plasmids used in this study are described in Tables S7 and S8, respectively, at https://doi.org/10.6084/m9.figshare.17082758. Point mutations in V. cholerae were generated in the corresponding parental plasmids by the QuikChange protocol (feo(AB) fusion in the V. cholerae operon was generated through site-directed mutagenesis with the primers Vcfeo(AB)fus-F and Vcfeo(AB)fus-R. For E. colifeo genes, the feo(AB) fusion was generated in pEcFeoAB by PCR-mediated deletion . Primer pair SoFeoA-EcoRI-F and SoFeoA-EcoRI-R and primer pair SoFeoA-EcoRI-F and SoFeoB-NotI-R were used to amplify S. oneidensisfeoA (SofeoA) and SofeoAB, respectively. The PCR product for SofeoA was digested with EcoRI and cloned into the corresponding restriction site in pACYC184 in the same direction as the Pcamr promoter. The PCR product for SofeoAB was digested with EcoRI and NotI and cloned into the corresponding restriction sites in pWKS30 in the same direction as the Plac promoter. To generate pSoFeoB, SofeoAB was amplified with SoFeoA-EcoRI-F and SoFeoB-EcoRI-R and cloned into pACYC184 in the same direction as the Pcamr promoter. Next, SofeoA was removed by PCR-mediated deletion using the primers SoFeoA-del-F and SoFeoA-delR. PCRs were done using high-fidelity Phusion Taq polymerase (New England BioLabs). The sequence and directionality of all the constructs were verified by Sanger sequencing at the DNA Sequencing Facility at The University of Texas at Austin.Genomic DNA from V. cholerae EPV6 (O395 ΔvibB feoABC::kan vctP::gent fbpA::cam) (pA::cam)  cells ca650) in triplicate cultures in EZ rich defined medium (RDM) broth  supplemented with 2% (wt/vol) sucrose, 5 μM FeSO4, and the appropriate antibiotics. The absorbance was measured every hour for 6 h.Growth rates in liquid medium were measured as the optical density at 650 nm , 5-bromo-4-chloro-3-indolyl-β-d-galactopyranoside , and the appropriate antibiotics. In this assay, β-galactosidase activity negatively correlates with iron transport activity; therefore, white colonies are read as a positive result, while blue colonies are considered a negative result.Iron transport by different Feo constructs was evaluated in lleagues . In shorfeoABΔC operons from E. coli or V. cholerae were cloned into pWMB1 (a mobile derivative of pWKS30 with oriT). E. coli BMH71-18mutL was transformed with either of these constructs. Next, one transformant colony was picked and grown overnight in 5 mL of LB broth to be used as a donor strain in a triparental mating with V. cholerae EPV6 (recipient) and E. coli MM294 carrying pRK2013 (helper). Cells from 1 mL of a mid-log-phase culture of each strain were pelleted and washed three times with saline to remove traces of antibiotics. Afterward, the cells were resuspended in 100 μL of fresh LB broth, and a mixture containing 25 μL of each resuspension was spotted onto an LB agar plate and incubated upside up at 37°C overnight. The cells were then scraped into 1.0 mL LB broth and plated onto LB agar plates supplemented with ampicillin, kanamycin, and gentamicin to recover V. cholerae EPV6 colonies.The feoC-independent mutants were isolated and sequenced to identify mutations in the feo genes. These mutations were then inserted de novo into the initial construct (pFeoΔC) by site-directed mutagenesis to be tested in V. cholerae EPV6.Plasmids from the presumptive Average colony sizes from different Feo constructs were compared to that of the control group (FeoABC) by the one-way Kruskal-Wallis test with Dunn’s test for multiple comparisons. For 2-group comparisons, the Mann-Whitney test was used. These analyses and the corresponding graphs were made using R statistical software version 4.1.0  and the https://doi.org/10.6084/m9.figshare.17082758, and supplemental figures can be found at https://doi.org/10.6084/m9.figshare.17082773.No new sequences were generated in support of this study. All the sequences and HMM profiles underlying this article are available in the NCBI, Pfam, or UniProt public databases; their corresponding accession numbers and other data supporting this study are available within the document and in its online supplemental material. Supplemental material for this article is publicly available at Figshare. Supplemental tables can be found at