The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as “junk” DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years. At first sight, the question “what is a gene?” would seem to elicit a simple answer: genes transmit inherited characteristics and must be the cause of them. However, since the time when the idea of gene was first mooted, the advent of novel Next-Generation Sequencing (NGS) technologies have complicated and expanded this view into an ever-evolving concept Figure .conditions, foundations and determiners which are present in unique, separate and thereby independent ways by which many characteristics of the organisms are specified (…) precisely what we wish to call genes”  immediately suggests a possible copying mechanism for the genetic material.” Overall these discoveries led to the realization that the genetic material is made up of a chain of polynucleotides, called DNA, and to the establishment of the central Dogma, which states that polypeptides are translated from RNA which is transcribed from DNA. Together with the Watson-Crick double helix model, the relation between DNA and polypeptide synthesis provided a mechanistic model of gene and gene activity and inaugurated the molecular biology era.The existence of genetic traits inherited as discrete entities was first intuited by Darwin,  and Men in the mIn eukaryotes, the discovery of the diverse modalities of RNA maturation evolved the awareness of colinear relationship of DNA, RNA, and polypeptides. The existence of alternative splicing, 5' and 3'-ends alternative maturation and RNA editing processes allow a single gene to produce multiple proteins by means of a single act of transcription. That only the minority of the transcribed genes in higher eukaryotes encode for proteins suggests that the genome has a potential which extends beyond the discrete coding loci. The rest of the genome (at least 60%) is transcribed independently of its coding capabilities  regulation  operon of together?” A partial answer came when the International Human Genome Sequencing Consortium, in the framework of the Human Genome Project (HGP) provided a more accurate quantification of the number of genes by publishing the complete human genome sequence  a whole geography of distributions (…) a storybook that's been edited for a couple billion years. And you could take it to bed like A Thousand and One Arabian Nights, and read a different story in the genome every night.” Thus, each cell of the same individual tells a different story, even if the storybook is the same. This observation can draw on the Aristotelian theory of potentiality and actuality. Potentiality represents the genotype, which contains all the information needed to develop the function, while actuality is the motion of the genotype into a phenotype that is the composite of an organism's observable features. The driver of the switch from potentiality to actuality, however, was unknown. In the late nineteenth century, the question of how a fertilized egg can give rise to a complex organism with cells of varied phenotypes was the object of long debates between two main schools of embryologists. The “pre-formationists,” who thought that each cell contains preformed elements that enlarge during development, while the “epigenesists” thought that chemical reactions among soluble components execute the developmental plan    by silencing transcription through the recruitment of several chromatin modifying complexes, including PRC1 and PRC2  databases  and in vitro occurring interactions  the endogenous RNA pull-down, which is based on the purification of the endogenous transcript under native or ultraviolet (UV) cross-linking conditions. In the first method   thed Figure , left, tin vivo are better preserved. However, also in this case, the occurrence of background interactions cannot be excluded  washing conditions  has recently been developed. In iCLIP experiments, occasional transcription arrests are mapped through circular ligation based methods. Since this step is often inefficient, Van Nostrand and collaborators have modified this part with the addition of two different adapters in two separate steps. The 3' RNA adapter is ligated directly to the crosslinked RNA after immunoprecipitation, while the 3′ single-stranded DNA adapter is ligated after reverse transcription to the 3'end of the cDNA , structural  and regulatory  RNAs.LncRNAs represent the most recently discovered class of regulatory RNAs which exert their roles through a variety of mechanisms without being translated into proteins. The interaction with proteins enables RNAs to operate through distinct means and to exert a wide-range of functions across diverse biological processes Figure . One intIn the attempt to find order within the RNA landscape we could define pervasive transcription as a cocktail of disordered sounds that are selected by evolution to be turned into music Figure . This isAC wrote the manuscript and selected the literature. MB proposed the topic, wrote the manuscript and reviewed the text.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Instead we discover pervasive binding with enrichment at regions of higher AT content irrespective of local variation in chromatin modifications.Genomic location can inform on potential function and recruitment signals for chromatin-associated proteins. High mobility group (Hmg) proteins are of similar size as histones with Hmga1 and Hmga2 being particularly abundant in replicating normal tissues and in cancerous cells. While several roles for Hmga proteins have been proposed we lack a comprehensive description of their genomic location as a function of chromatin, DNA sequence and functional domains. Here we report such a characterization in mouse embryonic stem cells in which we introduce biotin-tagged constructs of wild-type and DNA-binding domain mutants. Comparative analysis of the genome-wide distribution of Hmga proteins reveals pervasive binding, a feature that critically depends on a functional DNA-binding domain and which is shared by both Hmga proteins. Assessment of the underlying queues instructive for this binding modality identifies AT richness, defined as high frequency of A or T bases, as the major criterion for local binding. Additionally, we show that other chromatin states such as those linked to cis-regulatory regions have little impact on Hmga binding both in stem and differentiated cells. As a consequence, Hmga proteins are preferentially found at AT-rich regions such as constitutively heterochromatic regions but are absent from enhancers and promoters arguing for a limited role in regulating individual genes. In line with this model, we show that genetic deletion of Hmga proteins in stem cells causes limited transcriptional effects and that binding is conserved in neuronal progenitors. Overall our comparative study describing the  in vivo. Importantly this preferential binding to AT-rich sequences occurs throughout the genome irrespectively of other local chromatin features. Genomic location and loss of function experiments challenge the view that Hmga proteins act as local modulators of transcriptional regulation but rather argue for a role as structural components of chromatin.We investigated the chromosomal location of a group of highly abundant nuclear proteins. Our genome-wide results for Hmga1 and Hmga2 reveal a unique binding modality indicating preference for DNA rich in A or T bases  With the advent of genomics techniques, the understanding of the many roles of histone proteins and their modifications has increased rapidly ,2. HowevInitially described as small proteins (< 30 KDa) associated with chromatin , they weHmga proteins are robustly expressed during embryonic development and in rapidly replicating cells (such as hematopoietic lineages) ,10 but hFrom a functional perspective, Hmga proteins have been mainly implicated in regulating chromatin architecture through direct interaction with histone proteins or with the transcriptional machinery . Additioin vitro to a primate major satellite sequence [in vitro.Such functional models make predictions on the chromosomal location of Hmga proteins and suggest preferential binding to regulatory regions. Attempts to determine genomic localization have however remained limited. The Hmga1 protein was discovered due to its ability to bind sequence  and its sequence ), howevein vivo genome-wide binding determination of Hmga proteins, data is only available for Hmga2. In the first in vivo determination of Hmga2 binding preference, the identified motif (consensus of 49 sequences) was a simple repetition of W nucleotides (either A or T) [in vivo for DNA and chromatin recognition by Hmga proteins will help shed light on the many, partially opposing functions that have been described.Regarding the  A or T) . This is A or T) . In anot A or T) , yet bin A or T)  did not  A or T) . We reasHere we use an antibody-free ChIP-seq approach to investigate the location of Hmga1-2 proteins genome-wide. We adopted a flexible cellular system in mouse embryonic stem cells that allows stereotyped expression of different protein constructs. This reveals widespread DNA-binding throughout the genome, with a preference for DNA with high AT content. Interestingly this binding appears neither affected by chromatin state, nor linked to regulatory regions and is conserved upon neuronal differentiation, underscoring the robustness of the primary sequence readout. Accordingly regions that show a compositional bias towards AT, like heterochromatin found at major satellites , show onin vivo DNA and chromatin-binding preferences of Hmga1 and Hmga2 proteins. Given the discordant results obtained using antibodies [We aimed to investigate the tibodies ,28, we dtibodies ,32. Impoin vivo [We designed recombination constructs for the main isoforms of Hmga1 and Hmga2, respective DBD-mutants and a GFP control, each flanked by inverted lox sites to enable site-specific targeting by the Cre recombinase. Utilizing this comprehensive sample set enabled unambiguous assessment of whether genomic location was a reflection of genuine DNA binding. As DBD-mutants we generated Hmga variants that are mutated at the conserved arginines of the central Arg-Gly-Arg motif of the DBDs , previouin vivo . As a coin vivo .After transfection, individual cell clones of mouse embryonic stem cells (ESC) were isolated and characterized for targeted integration at a previously utilized chromosomal location that confers stable and homogeneous expression . RNA-seqNext we determined the subcellular localization of Hmga1-2 proteins by immunofluorescence. In line with previous observations ,35, we dIn light of the correct subcellular localization of the biotinylated proteins we proceeded with bioChIP. After pull-down, a considerable amount of DNA was retrieved for both Hmga proteins , potentially hinting at a high intrinsic affinity for chromatin. Interestingly, we noted that for DBD-mutants much less DNA was recovered , indicating that mutations in the DBD indeed compromised the ability to bind chromatin.In order to stringently account for systematic biases, we generated paired input controls for each IP condition. Additionally, we used similar numbers of PCR cycles for both IP and input chromatin during library preparation in order to minimize biases arising from different rounds of exponential amplification . SubsequAs a first step, we performed principal component analysis (PCA) on log2 enrichments of IP over input for two replicates of WT proteins, DBD-mutants, and a GFP sample, calculated over 1kb tiling windows along the genome. Strikingly, the first principal component (PC1) accounts for almost 50% of the total variance in the data  while noTo further explore the link to AT content, we directly performed hierarchical clustering of input normalized data from all samples and AT content. This identifies one cluster which groups WT samples and their replicates together with AT content, and a second cluster consisting of the DBD-mutant and GFP control samples . The robin vivo is variable between different genomic regions but dependent on a functional DNA-binding domain.Since at megabase scale, the genome shows clear structures in terms of AT content due to the presence of isochores ,38, HmgaTo further dissect the nature of the AT dependence, we tested whether specific AT-rich motifs were preferentially bound by Hmga1 and Hmga2 proteins. As the absence of focal binding prevents a motif finding-based analysis, we instead, using ridge regression, modelled signal dependence in 1kb tiling windows as a function of nucleotide frequencies of increasing complexity  for which we and others have already generated a variety of epigenomic maps –51. In tIndeed, similarity in binding as compared to ESC can be appreciated visually by inspecting the binding pattern along an entire chromosome . The absHaving established that binding of Hmga1-2 is not influenced by sites of open chromatin or presence of local histone marks, we set out to characterize in more detail Hmga1-2 targets in the genome. First, we specifically profiled binding over distal regulatory regions since it was previously reported that Hmga proteins bind subsets of these . Fig 4A Since Hmga1-2 binding seems largely invariant between cell types and at low resolution appears to cover broad regions defined by high AT content, we next asked whether enriched regions coincide with broad chromosomal features that are known to be largely invariant. These include constitutive heterochromatin, which is characterized by low histone acetylation, high H3K9me2 and high cytosine methylation . AdditioWhile Hmga1-2 binding was only weakly correlated to H3K9me2, it displayed good correlations with replication timing  and the As Hmga binding appears domain-like at a large scale, we wondered whether binding is simply a function of the particular genomic distribution of AT-rich DNA or whether some form of spreading could play a role. Towards this goal we built a linear model that predicts binding over a 1kb window based not only on the AT content of the window in question, but also its immediately neighbouring windows . Importantly, the correlation of a window’s AT content to the AT content of its flanking windows is not above 0.6 , thus coin vitro [Given that Hmga proteins have been shown to bind mouse major satellite DNA in vitro ,65 and sin vitro ) we alsoIn summary, these findings reinforce our observation that Hmga proteins bind the genome preferentially at regions of higher AT content, which, as a consequence of genome evolution, tend to overlap with large, heterochromatic domains. As we do not detect a dependence of binding on sequence composition of the surrounding regions at the kilobase scale, Hmga1-2 binding to AT-rich DNA thus appears to be determined locally, whilst the organization of mammalian genomes in isochores ,38 explain vitro and in vivo studies that suggested that Hmga1 functions as a co-activator by stabilizing the pre-initiation complex or the enhanceosome, an enhancer-associated protein complex contacting active promoters [Our results thus far reveal that Hmga binding to the genome mainly occurs outside of regulatory regions. This is somewhat in contrast to previous romoters ,19,21. INext we determined the global effect of loss of Hmga function. Since Hmga2 is not expressed in mouse ESC this analysis was limited to Hmga1 KO cells, which consequently do not contain any Hmga protein. We observe in ESC a lack of significant changes in colony formation ability and morphology , cell cySimilar to the absence of transcriptional deregulation at the gene level, we did not observe deregulation of repeat elements , which cGiven the lack of detectable protein products in the Hmga frameshift mutant and matching changes in RNA abundance and structure , it seemin vivo binding preferences of Hmga1 and Hmga2, we applied RAMBiO [in vivo DNA and chromatin substrate of Hmga proteins in both cell types.In order to shed light on the d RAMBiO  to mouseThe observed absence of focal binding of Hmga proteins is backed up by several controls and normalization steps to account for potential contributions of biases that are frequently observed in genome-wide ChIP experiments ,36. ThisPCA analysis showed that a single principal component was able to explain almost half of the total variance and this first principal component was strongly correlated to AT content. Visualization of the actual data highlighted reproducible binding between replicates and loss of this binding upon mutation of the DNA-binding domain. These controls were necessary to convincingly expose that the genome-wide distribution of Hmga1-2 is a direct function of DBD affinity for DNA. The dependence on AT content was further confirmed by a direct comparison of Hmga1-2 enrichments to AT content. For both proteins the majority of the genome contains a sufficient density of A or T nucleotides to elicit a response in terms of binding. However, the binding to a highly abundant sequence feature directly explains the overall lack of focal binding and thus precludes the use of algorithms for peak detection. This is highly reminiscent of our previous experience with proteins reading or writing methylated CpGs ,32. In tOur findings reveal similar AT-dependence for both proteins and a comparable range of enrichments. However, Hmga1 shows higher noise levels in AT readout, pointing to either lower affinity for DNA or higher sensitivity to chromatin cues. The latter however seems unlikely given the absence of genome-wide correlations with features other than AT content. Together these results argue that genomic binding of Hmga1 and Hmga2 in stem cells is entirely encoded in the respective and highly similar DBDs.To further evaluate to which extent DNA sequence was the sole determinant of binding, we asked whether a different chromatin environment was able to modulate affinity for AT-rich DNA, either at subsets of regions in the same cell-type or genome-wide in a differentiated post-mitotic cell. In both cases, enrichments were not modulated by the different chromatin states. Importantly, this finding argues that the Hmga1 and Hmga2 binding modality is conserved in other cell-types and cellular states, provided that a functional DBD is expressed. While it seems unlikely, we cannot exclude that Hmga might show varying binding behaviors in other cell types, e.g. due to post-translational modifications ,68, whicn or W-rich . Alternatively, Hmga proteins may recognize specific DNA shapes. AT-rich sequences are indeed often associated with altered minor groove shapes and in particular A-tract, ApT and ApA (TpT) sequences induce narrowing of the minor groove [In vitro studies however tend to support the base readout mechanism [in vivo.From a biochemical perspective, mechanisms of AT recognition could be read out of base or shape. Indeed the pattern of hydrogen bond donors and acceptors in the minor groove does not allow a discrimination of A:T and T:A nor G:C and C:G base pairs . Thus mir groove . In suchechanism , which iechanism  even thoechanism . Given tIt is known that regions of higher AT content can overlap with heterochromatic DNA. Accordingly, in ESC, regions of higher Hmga1-2 enrichment are broad, replicate late in S-phase and correlate weakly with methylation of lysine 9 of histone H3, all of which are known hallmarks of heterochromatin. Another feature of heterochromatin is binding to the nuclear periphery, which can be assessed through the quantification of the interaction with LaminA, a protein localizing at the inner nuclear membrane. Indeed LaminA displays the second highest correlation with Hmga protein enrichment (after AT content), opening the possibility that Hmga1-2 might be involved in the sub-nuclear localization of heterochromatin since peripheral domains are maintained in a lamin-independent fashion in ESC . It is tAs heterochromatic regions tend to be gene-poor, Hmga1-2 binding appears to be depleted in regions of high gene density and indeed shows no enrichments at active promoters or distal regulatory regions. This observation is in contradiction to the recently reported enrichment of Hmga2 at gene regulatory regions, albeit observed in a different model system . While wIn agreement with this model, we observe very limited transcriptional effects when deleting Hmga1 in ESC. Taken together, these results challenge the notion of a central role for the Hmga family of proteins in transcriptional regulation . InsteadThe RAMBiO approach has previously been described . Here beESC , were cultivated on feeder cells or 0.2% gelatine coated dishes. ESC growth medium consisted of DMEM (Invitrogen) supplemented with 15% fetal calf serum (Invitrogen), 13 nonessential amino acids (Invitrogen), 1 mM L-glutamine, LIF, and 0.001% beta-mercaptoethanol. Multipotent Pax6-positive radial glial neuronal progenitors were obtained as described previously ,52.For construct design cDNAs were amplified from a random hexamer reverse transcription cDNA library  generated from RNeasy extracted total RNA  and sequence verified or alternatively ordered for gene synthesis. Mutations in the DBD of Hmga1-2 where targeted to the core RGR motif of the three AT-hooks. Basic and bulky arginine residues were substituted with small and polar cysteines. Mutations targeting the same residues were previously shown to impair DNA binding in vitro and in vivo ,79,80. TConstructs were then cloned into pL1-CAG-bio-MCS-polyA-1L. The two inverted L1 Lox sites allowed CRE-mediated integration into a unique genomic site. Gancyclovir (6 μM) resistant clones were selected and tested for direction of the integration through junction-PCR. The parental cell line expresses BirA-V5 biotin ligase under the CAG-promoter, which leads to stable biotinylation of the tagged protein throughout differentiation .Protein expression was assessed by transcriptomics  and 10% normal goat serum in PBS, detection was performed with Streptavidin-AF568 (ThermoFisher) or primary antibodies over night at 4°C using a Z1 (Zeiss) epifluorescence microscope.Nuclear-enriched cell preparations were obtained as follows: cell pellets from 1 confluent 10 cm plate were washed twice in cold PBS, lysed in nuclear extraction buffer (n.e.b.) A , washed once with n.e.b. A, homogenized 10X with Dounce homogenizer, resuspended in n.e.b. B , homogenized 10X with Dounce homogenizer and cleared supernatant was saved for analysis. All buffers were cooled to 4°C and supplemented with 1mM dithiothreitol and protease inhibitors before use.Primary antibodies used were Lamin B1 Santa Cruz (C-20) or Abcam (ab16048), Hmga1 Active Motif (39615) and Hmga2 R&D Systems (AF3184).BD Pharmingen BrdU FITC Flow Kit (BD Biosciences) was used for cell-cycle profiling, whereas CellTrace Violet Cell Proliferation Kit (Thermo Fisher) was used for assaying cell proliferation capability. In both cases, manufacturers’ protocols were followed and data was acquired with a LSR II Flow Cytometer (BD Biosciences).KO strategy relied on introducing frame-shift mutations in the coding sequence of Hmga1 via CRISPR-Cas9 induced indels. We targeted an intron-exon junction in the Hmga1 gene in order to avoid off-targets caused by the presence of pseudogenes .http://crispr.mit.edu and http://www.e-crisp.org/E-CRISP/, which led to the selection of the guide GTCCCCTAGGAGGCTCACCC. A pX330 plasmid expressing CRISPR-Cas9 and guide RNA together with a reporter expressing Puromycin-2A-mCherry were co-transfected in ESC. On the following day, Puromycin (2 μg/mL) was added and cells were kept under selective media overnight. Media was refreshed the next day and after single cell plating, clones were isolated. After PCR amplification of a 700 bp region centered on the CRISPR guide, indels were analyzed by Sanger sequencing. WB with a specific antibody was performed to confirm absence of the targeted protein.Tools used for CRISPR-Cas9 guide design were For the full-deletion cell line, the same approach was used as for obtaining the KO cells, with the simultaneous addition of GTGAGTCTGGGGGAGATGCA (5’ UTR) and GAAGTTAGCCTTGTCAGGAT (3’ UTR) sgRNAs.Hmgb1 locus, GTGTTCTCCTTACTATATGAC and GTAGTGATATACTGTGCAAAG).Primers used for the screening PCRs described in BioChIP experiments were performed as described  except tFor replicate C of Hmga1, cross-linked cell pellets were resuspended in 50 mM Hepes-KOH (pH 7.5), 140 mM NaCl, 1 mM EDTA, 10% Glycerol, 0.5% NP-40, 0.25% Triton X-100 for 10 min on ice (membrane lysis). Nuclei were collected by centrifugation and resuspended in 10 mM Tris-HCl (pH 8.0), 1 mM EDTA, 200 mM NaCl, 0.5 mM EGTA for 10 min RT . Nuclei were collected by centrifugation and resuspended in 10 mM Tris-HCl (pH 8.0), 1 mM EDTA, 0.1% Deoxycholate, 200 mM NaCl, 0.25% N-Lauroylsarcosin, 0.5 mM EGTA.Crosslinked chromatin was subjected to sonication in a Bioruptor instrument (Diagenode). Triton X-100 to 1% final concentration was added before SAV-IP. Subsequent steps were performed as for the other replicates.Libraries of extracted DNA from the IP and input (50 μl of IP) fraction were prepared according to the manufacturer’s protocol using either the NEBNext ChIP-Seq Library Prep Master Mix Set for Illumina  or the NEBNext Ultra DNA Library Preparation Kit .DNA was measured using NanoDrop 3300 Fluorospectrometer (Witec AG) and Qubit dsDNA HS Assay Kit (ThermoFisher). Size-selection was performed using Agencourt AMPure XP beads  before PCR amplification with NEBNext Multiplex Oligos for Illumina . PCR amplification was performed for 6 to 12 cycles using indexed primer and cycling conditions according to Illumina recommendations. Adapter-ligated and amplified DNA was purified using AMPure XP beads. Before pooling, the size distribution was checked on an Agilent Bioanalyzer 2100 using Agilent High Sensitivity DNA kit .For RNA-seq, two micrograms of total RNA was used from at least two independent cultures harvested on different days. RNA was isolated with the RNeasy mini kit (Qiagen) with on-column DNA digestion and ribosomal RNA was depleted using the Ribo-Zero rRNA removal kit (Epicentre). Strand-specific RNA-seq libraries were prepared from rRNA-depleted samples using the ScriptSeq v2 protocol (Epicentre) following producer’s instructions. Up to 7 samples with different barcodes were mixed at equimolar ratios per pool. Sequencing was performed on an Illumina HiSeq 2500 machine .RepeatMasker Open-4.0, 2013–2015. http://www.repeatmasker.org) repeat annotation was downloaded from UCSC (https://genome.ucsc.edu). CpG islands were retrieved from UCSC (“cpgIslandExt” table) using rtracklayer [n such that FDR < 5%) as previously described [http://chromosome.sdsc.edu/mouse/download/mESC.zip)). For the classification into primed and active enhancers, broad peaks for H3K27ac from [https://www.ncbi.nlm.nih.gov/geo/, accession GSM1000099).Promoters were defined using the UCSC knownGene annotation via the R package TxDb.Mmusculus.UCSC.mm9.knownGene , R package version 3.2.2., 2015). For each transcript, promoters were defined as +/-1000 nucleotides around transcription start sites and only promoters that were at least 80% mappable were retained (see section ChIP-seq for details). For each gene, the promoter with highest levels of PolII ChIP-seq enrichment over input was selected (see section ChIP-seq for details). For this initial annotation, ChIP-seq and input reads were shifted by 60nts, assuming a fragment size of 120nts. Promoters on chrM and random chromosomes (chr*_random) were removed and only promoters that did not overlap with any other promoter were retained for the downstream analysis. RepeatMasker  and finding the shift that minimizes the root-mean-square deviation between the two profiles.For all samples, fragment lengths were estimated by calculating average profiles over promoters separately for reads mapping to the same strand and to the opposite strand of the respective promoters . Enrichments over input were determined as log2(nsIP+ 8)–log2(nsinput+ 8), where nsIPand nsinput are the library-size normalized counts of the IP and corresponding input samples in a given promoter, window or repeat. The library-size normalized counts were determined as nsIP= min*(nIP/NIP) and nsinput= min*(ninput/Ninput) where nIP and ninput are the raw counts per promoter, window or repeat and NIP and Ninput are the total number of reads mapping to the genome in the IP and input respectively. The counts were normalized to the smaller library size. The pseudo-count of 8 was used to decrease noise levels at low read counts. Enrichment over the DBD-mutant was defined as the difference in log2 enrichments over input (as defined above) of the wild-type and the corresponding DBD-mutant sample. In all analyses, replicates “a” were paired and replicates “b” were paired . Hmga1-2 add-back samples were paired with the respective DBD-mutant samples of the wt background.The number of alignments in promoters, repeats and tiling windows (1kb or 10kb) along the genome were counted using the QuasR function qProfile using the estimated read shifts. Normalization to input and DBD-mutant as performed analogously to the normalization in promoters, windows and repeats (see above) where the counts here correspond to the sum of reads mapping to a window of size 51 (201 in the case of (TA)n simple repeats due to the small number of instances and thus higher noise levels (see below)) centered at each position in the profile, summed over all regions of the respective type.Average profiles over ESC-specific, NP-specific, constitutive LMRs, enhancers, CpG islands and (TA)n simple repeats were calculated using the QuasR function For LaminA DamID, Dam ratios of the loess and quantile normalized data were downloaded from GEO. For the replication timing data, the wavelet-smoothed signal was downloaded from ENCODE . For both datasets, window levels were calculated by averaging the signal for each probe mapping to the respective window. For the replication timing data, the average coverage was 1.65 probes per window and 98.4% of windows were covered by at least 1 probe. For the LaminA data, the average coverage was 8.94 probes and 99.96% of windows were covered by at least one probe.prcomp. This resulted in the first principal component (PC) loading being negatively correlated to AT content. Since the signs of the PC loadings are arbitrary and a positive correlation to AT content can be more intuitively appreciated, all principal component loadings and, accordingly, all principal component scores were multiplied by -1.Principal component analysis was performed on mean-centered log2 enrichments over input in 1kb tiling windows of the genome using the R function glmnet [glmnet [cv.glmnet was used to determine the regularization parameter λ via 10-fold cross-validation on the training set. More specifically, λ was set to “lambda.1se”, which results in the most regularized model such that the error is within one standard error of the minimum mean cross-validated error [To check whether more complex sequence features than simple AT content are predictive of Hmga binding, we modeled Hmga binding as a function of the mono-, di-, tri- and tetranucleotide frequencies of each 1kb tiling window with ridge regressions using the R package glmnet . Regular [glmnet . The funed error . The modHaving established that Hmga binding could be well explained by AT content, we wondered whether predictions could be improved by including information about the clustering of As and Ts along the DNA sequence. To this end, we determined, for the same set of windows as for the nucleotide models, the number of stretches of consecutive Ws (W = A or T) of length 1 to L (where L is the largest length found in any of the windows. L = 20 in this case) and used these as predictors for the ridge regression (with offset term). Model fitting and the evaluation of predictive power was done as in the case of the nucleotide models. The improvement in terms of predictive power was only minor compared to the simple mononucleotide model.. DNAshapeR calculates for each pentamer in a given DNA sequence the minor groove width (MGW), the propeller twist (ProT), the helix twist (HelT) and the DNA roll (Roll), using values derived from all-atom Monte Carlo simulations. The former two correspond to the estimated MGW and ProT at the central nucleotide of the pentamer, whereas the latter two correspond to the estimated HelT and Roll between the second and third as well as the third and fourth nucleotide of the pentamer. For modelling, the two values of HelT or Roll were averaged to end up with 4 numbers for each mononucleotide corresponding to the estimated MGW, ProT, (average) HelT and (average) Roll. For each mononucleotide, each structural feature was binned into 5 equally sized bins, and the counts per bin of each structural feature were used as predictor values for the modelling. This resulted in 5*4 = 20 predictors per nucleotide and thus 80 predictors in total. Model fitting and the evaluation of the predictive power were done as in the case of the nucleotide models. Also in this model, the improvement in terms of predictive power was small compared to the simple mononucleotide model.To evaluate whether the local structure of the DNA has an effect on Hmga binding, we extended the mononucleotide model by incorporating structural information as predicted by the R package DNAshapeR To estimate the effect of spreading of Hmga binding, we used a linear (ordinary least squares) model that predicts binding (log2 enrichment over DBD-mutant as defined above) in 1 kb windows based on the AT content of the respective windows and their 0–3 neighbouring 1kb windows up- and downstream. We used all windows outside of CpG islands and retained only windows where all 3 windows up- and downstream did not overlap CpG islands and were at least 80% mappable. Windows overlapping repeats could not be removed as this would have too strongly reduced the set of possible windows on which to train the model. As in the nucleotide models, windows were sampled such that the distribution of enrichments of the central window were roughly uniform over the entire range of enrichments and windows were randomly split into training and test sets of equal size (for each sample separately). Performance was assessed using Pearson correlation.Importantly, the correlation of a window’s AT content to the AT content of its flanking windows is not above 0.6 , thus coRepeat analysis was restricted to the subset of repeats from RepeatMasker that have a minimal length of 300 nts due to the limitations in ChIP-seq resolution and the general abundance of As and Ts in the genome that make it difficult to distinguish binding signal stemming from a short repeat versus its surrounding region. Repeats were further filtered out if the fraction of mappable bases was below 80%. Finally, in order to be able to estimate robust enrichment values, only repeat names with at least 10 instances in the genome (after applying the previous filters) were used. This leads to more than 50 reads on average across all input samples  in every enrichment calculation.To determine whether there are any differences in karyotype between KO and WT cell lines, we calculated, for each WT and KO ESC input sample, the number of reads in (at least 80% mappable) 1 Mb tiling windows of the entire genome. The log2 counts were then for each sample separately normalized to the mean across all windows. Since GC biases confound the copy number signal, we then performed, for each sample separately, loess regression of mean-normalized log2 read counts against GC content. The resulting residuals were then averaged per chromosome. All WT and all KO values from the respective samples were averaged to determine mean ratios (KO/WT) per chromosome. Individual WT and KO values were all very similar.qAlign alignmentParameter set to "—trim5 3 -m 100—best—strata"). For multi-mapping reads, one randomly chosen alignment was used for quantification. The first 3 nucleotides from the 5’ end were trimmed (“—trim5 3”) as they showed higher error rates in an initial round of mapping without trimming. Gene-level counts were determined using the UCSC knownGene table [TxDb.Mmusculus.UCSC.mm9.knownGene. As a stranded RNA-seq protocol was used, only reads on the same strand as the respective genes were counted ). To determine significantly changing genes, limma-trend as part of the limma R package was used [www.bioconductor.org). Only genes that had a mean count of at least 10 reads, averaged over all samples, were used. To account for batch effects , a batch variable was included in the linear modelling which grouped samples if library preparation had been done on the same day and they had been sequenced on the same flow cell. Genes with an adjusted p-value < 0.01 (Benjamini-Hochberg) and an absolute fold-change of at least 2 were called as significantly changing. For RNA-seq reads were mapped to the mm9 assembly of the mouse genome using QuasR. Bowtie was run with mapping parameters "—trim5 3 -m 100—best—strata"  and ignoring all repeats that overlap with an annotated transcript in TxDb.Mmusculus.UCSC.mm9.knownGene (on the same or opposite strand of the corresponding gene). Repeats mapping to transcripts were excluded as their changes in read counts may be due to expression changes of the corresponding genes. Linear modelling and significance calculations were done as for genes. For normalizeQuantiles function from the limma package [Repeat quantification was done on the level of Repeat masker repeat “names” using the  package . This waTo create Sashimi plots, RNA-seq reads were first realigned using SpliceMap[, using the QuasR function qAlign with default value for the alignmentParameter argument, maxHits = 100 and spliced = TRUE.SpliceMap, using thttps://pypi.python.org/pypi/misopy)). For visualization of Hmga1, Hmga2 and Uty gene models, the pre-processed gene annotation in GFF3 format (mm9 Ensemble genes [http://genes.mit.edu/burgelab/miso/annotations/ucsc_tables/mm9/ensGene.gff3.Sashimi plots were generated using the sashimi_plot function  as part le genes  downloadle genes ) was dowTo determine the enrichments for the single locus tracks in www.bioconductor.org, [maptools: Tools for Reading and Handling Spatial Objects. R package version 0.9–2. https://CRAN.R-project.org/package=maptools)).All data analysis was performed in R , using mtor.org, ). The motor.org, , Gviz fotor.org, , the pactor.org,  and mapthttps://www.ncbi.nlm.nih.gov/geo/) under accession GSE100407.Data sets generated for this study are available from GEO (https://www.encodeproject.org/files/ENCFF001JUP/ and https://www.encodeproject.org/files/ENCFF001JUQ/ (Replication Timing) or from GEO with the following accession numbers: GSM1531435, GSM1531436 (LaminA); GSM1314605, GSM1314606, GSM1543602, GSM1543603 (H3K9me2); GSM632032, GSM632033, GSM632034 (H3K27me3); GSM1000099 (H3K27ac); GSM747542 (H3K4me1); GSM632035 (H3K4me2); GSM747534, GSM747535, GSM747536 (CTCF); GSM747547, GSM747548 (RNA PolII); GSM671103 (input DNA); GSM1657364, GSM1657365 (DNaseI); GSM748786, GSM748787 (DNA methylation).Additional ESC data used in this study was downloaded from Additional NP data used in this study was downloaded from GEO with the following accession numbers: GSM748788, GSM748789 (DNA methylation).S1 Text(DOCX)Click here for additional data file.S1 Fig(A) Protein alignment between Hmga1 (top row) and Hmga2 (bottom row) obtained with Uniprot through Clustal Omega . Highlig(B) WB with anti-Hmga2 Ab of whole cell lysate from cells differentiated to the neuronal progenitor stage. A band is visible at the expected height for Hmga2 .(C) WB with anti-SAV conjugated HRP of whole-cell lysate from parental cell line and cells expressing Hmga1 and Hmga2. Although slightly different, expression levels are comparable between both proteins.(D) Clonogenicity assay for the indicated cell lines showing similar pluripotent potential for cell lines expressing the biotinylated constructs. Displayed are the mean and standard deviation of 3 replicate counts of alkaline phosphatase positive colonies 5 days after plating to clonal density (number of single cells in brackets). None of the engineered lines show a significant difference to the parental cell line .(E) WB with anti-SAV conjugated HRP of whole cell lysate from parental cell line and cells expressing Hmga1 or mutated Hmga1. In the last lane, a lower molecular-weight band  representing mutated bioHmga1 is visible and shows a comparable expression level to bioHmga1. Blotting with SAV was chosen because the mutated bioHmga1 protein runs at the same position as the untagged Hmga1.(F) WB with anti Hmga2 Ab of whole-cell lysate from parental cell line and cells expressing Hmga2 or mutated Hmga2. As expected from the lack of mRNA signal , Hmga2 ihttps://imagej.nih.gov/ij/), for bioHmga1 (left) and bioHmga2 (right) expressing cell lines. High levels of spatial correlations are detected in both instances.(G) Co-localization analysis of bioHmga1 and Hmga1 by IF: SAV-conjugated fluorophore (pseudo-coloured red) and secondary anti-rabbit (coloured green). DAPI was used to stain the cell nuclei (coloured blue). Top-left, Hmga1 KO sample (see (H) Correlation heatmap of the PC loadings, various chromatin marks as well as AT content. While the PC1 loading is strongly associated with AT content, all other PC loadings show no or only weak correlations with the investigated features.(TIF)Click here for additional data file.S2 Fig(A) Barplot showing genome-wide correlations for all Hmga1 and Hmga2 replicates in ESC and NPs (log2 enrichments over input) with respect to genomic AT content.(B) Log2 enrichments over input for the depicted samples on chromosome 14. Each dot represents the log2 enrichment of IP over input in a window of size 10kb. Gaps indicate regions with low mappabilty (below 80%). Top and bottom 1% of data range are not shown to enhance readability. Data for replicate c of Hmga1 and Hmga1 DBD-mutant was obtained using a different ChIP protocol and highlights the robustness of the results see .(C) Hmga binding is depleted at CpG islands as it follows AT content. Average profiles were calculated relative to CpG island starts (nt). Enrichment denotes enrichment over the corresponding DBD-mutant. AT content is shown in grey (dashed line). AT content is not shown directly around CpG island starts (position 0) since the start position is by definition a CpG, resulting in an artificial local dip in AT content. All values denote a running mean over 51 nucleotides.(D)-(E)-(F) Single locus tracks of log2 enrichments over the respective DBD-mutants at three (TA)n simple repeats with strong binding. To increase coverage, the counts of all Hmga1 samples, all Hmga2 samples, all Hmga1 DBD-mutant samples and all Hmga2 DBD-mutant samples were separately pooled and aggregated in a running window of 401 nts before determining enrichments see . The loc(TIF)Click here for additional data file.S3 Fig(A) Inferred ridge regression coefficients are internally consistent. Shown are scatter plots of the inferred coefficients for each mono-, di-, tri- or tetranucleotide versus the coefficients of its reverse complements for the respective models. As the ChIP-seq data does not contain any strand information, the inferred coefficient for a nucleotide should be very similar to the coefficient of its reverse complement (the nucleotide frequencies that act as predictors for the ridge regression are determined from the plus strand sequence only). This is indeed the case as evidenced by the high Pearson correlation coefficients (R).(B)-(C) Inferred ridge regression coefficients for the mono- (left) and dinucleotides (right) for each sample. For Hmga WT proteins high coefficients are apparent for A and T nucleotides and combinations thereof. The “CG” dinucleotide coefficients are high in all samples which may reflect the unspecific recruitment to accessible regions, which tend to be CpG- and GC-rich .(D)-(E) Inferred ridge regression coefficients for tri- (left) and tetranucleotides (right) consisting of only As and/or Ts. The inferred contributions of the different tri- or tetranucleotides are highly comparable. Nonetheless, there is a subtle, but reproducible preference of polyA or polyT stretches over polyAT or polyTA stretches. However, this and other subtle differences are too small to substantially improve the predictive power of the tri- or tetranucleotide model over the simple mononucleotide model .(TIF)Click here for additional data file.S4 Fig(A) The improvement in predictive power of the AT-stretch model over the simple mononucleotide model is small. R stands for the Pearson correlation coefficient.(B) Inferred coefficients of AT-stretches of length L scale linearly with L. This is in agreement with a model where the addition of one extra A or T leads to a constant increase in binding, which is in agreement with the predictions of the simple mononucleotide model.(C) The improvement in predictive power of a model that incorporates local structural features of the DNA see  in addit(TIF)Click here for additional data file.S5 Fig(A) Scatterplots of log2 enrichments over DBD-mutant versus chromatin features and AT content for a representative Hmga1 replicate .(B) Same as in (A) for a representative Hmga2 replicate.(C) Correlation heatmap of cut frequency of DNaseI, AT content, chromatin marks, CTCF and bioChIP samples . Hmga1-2 correlate most strongly with AT content and form a separate cluster, in agreement with the PCA.(D) Distribution of DBD-mutant-normalized log2 enrichments for the indicated samples over promoters stratified into different categories: promoters overlapping (CGI) or not overlapping a CpG island (not CGI), expressed or not expressed. Hmga1 and Hmga2 enrichments are generally lower at CpG island promoters. Non-expressed non-CpG island promoters (blue boxplots) appear to have slightly larger enrichments than expressed non-CpG island promoters (in red).(E) Correlation heatmap of log2 enrichments over input for all NP samples, showing that as in ESC, WT and DBD-mutant samples form separate clusters and WT samples show good correlations to AT content.(F) Scatterplot and Pearson’s correlation of the log2 difference between NP and ESC enrichment values (over DBD-mutant) for two replicates of Hmga1 (left) and Hmga2 (right) over 1kb tiling windows indicating that there are no reproducible differences in binding between ESC and NPs.(G) Scatterplot and Pearson’s correlation of Hmga1 and Hmga2 samples versus AT content in ESC (top row) and NPs (bottom row), illustrating the nature of the positive correlation for all samples .(TIF)Click here for additional data file.S6 Fig(A) Average profiles at LMRs  of log2 (B) Average profiles at primed enhancers (marked by K4me1 and not marked by K27ac (K4me1+K27ac-)) and active (marked by both K4me1 and K27ac (K4me1+K27ac+)) enhancers for two Hmga1 replicates in ESC. Enrichments denote enrichment over the respective DBD-mutants.(C) Same as in (B) for two Hmga2 replicates in ESC.(TIFF)Click here for additional data file.S7 Fig(A) Scatterplot of log2 enrichment values over DBD-mutant in 10kb tiling windows for one representative Hmga1 (top) and Hmga2 (bottom) replicate versus the indicated heterochromatic features, AT content or DNaseI cut frequency (same data as in (B) Genome-wide correlation heatmap of all measures in (A) and all ESC Hmga1 and Hmga2 replicates .n to the n-th neighbouring window downstream or upstream, respectively. The correlation between neighbouring windows is not very strong, thus containing potential additional information for linear modelling.(C) Correlation heatmap of AT content in the set of neighbouring 1kb windows used for linear modelling see . 0 refer(D) Example scatterplot of AT content in the central versus the directly neighbouring downstream window (+1). R indicates Pearson correlation coefficient.(E) Pearson correlation of predicted log2 Hmga enrichments (over DBD-mutant) versus measured values for linear models that use only the AT content of the window itself or the AT content of the window itself as well as of 1–3 neighbouring windows both up- and downstream. There is no substantial increase in predictive power when the AT content of neighbouring windows is taken into account.(TIFF)Click here for additional data file.S8 Fig(A) Hmga enrichment at repeat families as defined by RepeatMasker. Log2 enrichments over the respective DBD-mutants  are shown for all families of a minimal length of 300nts and at least 10 occurrences in the genome. Average AT content is shown in green. In general, binding is increased with increasing AT content. The two seeming outliers, simple and satellite repeats, are heterogeneous in terms of AT content and also show a trend towards increased binding as a function of AT content when further subdivided into their corresponding different subtypes .(B) as in (A) but for all types of simple repeats of minimal length of 300nts and at least 10 occurrences in the genome.(C) as in (A) but for all types of satellite repeats of minimal length of 300nts and at least 10 occurrences in the genome. GSAT_MM repeats represent major satellites (indicated by an arrow).(TIFF)Click here for additional data file.S9 Figrd exon of Hmga1. The downstream intron, which contains a premature stop-codon, is shown in yellow.(A) Allelic summary of Sanger sequencing of PCR products of the region targeted by CRISPR-Cas9 against the 3(B) WB of Hmga1 KO, Hmga1 add-back and parental ES cell lines , blotted against Hmga1.(C) WB of Hmga1 KO and Hmga2 add-back ES cell lines , blotted against Hmga2.(D) DBD-mutant-normalized log2 enrichments for Hmga1-2 either in the WT or in the Hmga1 KO background . Each datapoint is calculated over a 10kb tiling window on chromosome 14. For better readability, top and bottom 1% of data range are not shown.(E) Pearson correlation values for DBD-mutant-normalized Hmga1 and Hmga2 replicates and AT content in the Hmga1 KO background. All samples show a similar degree of positive correlation, in line with what we observe in the WT background.(F) Scatterplots and Pearson correlation for one Hmga1 and one Hmga2 replicate and AT content in the Hmga1 KO background. Y-axis values show DBD-mutant (in the WT background)-normalized log2 enrichments.(G) log2 changes in enrichment (over input) between WT and KO background for two replicates of Hmga1 (left) and Hmga2 (right) in 1kb tiling windows. There are no reproducible changes in binding between add-backs and WT.(TIFF)Click here for additional data file.S10 Fig(A) Clonogenicity assay of Hmga1 KO and parental cell line. Triplicate biological replicate staining with alkaline phosphatase for puripotency, 4 days after plating. No significant change in colony morphology or staining intensity can be seen.(B) Cell cycle distribution profile of a representative WT (top) and Hmga1 KO sample (bottom), respectively.(C) Percentage of parent gate and geometric mean of the Cell-Cycle Violet dye (d = day).(D) Average GC-corrected read ratio per chromosome of WT and KO (see (TIFF)Click here for additional data file.S11 Fig(A) Transcriptomic comparison of Hmga1 and Hmga2 add-backs vs. parental WT cell line at the gene level. Gene names are indicated if the gene is significantly differentially expressed .(B) Correlation heatmap of RNA-seq samples illustrating the very high correlation between all samples (colour indicates Pearson correlation).(C) Scatterplots showing reproducibility of the transcriptional differences between 2 different KO, Hmga1 addback or Hmga2 addback samples and their corresponding WT samples (sequenced on the same lane). The significantly changing genes are shown in red.(D) Scatterplots and Pearson correlations for all RNA-seq samples, illustrating the high similarity between all samples.(TIFF)Click here for additional data file.S12 FigSashimi plot  over the Hmga2 gene in the Hmga1 KO condition. Hmga2 gene expression is unchanged in the KO compared to WT.(TIFF)Click here for additional data file.S13 Fig(A) The mutated Hmga1 locus described in (B) In accordance to the splice junctions highlighted in the Sashimi plot, a shorter mRNA transcript, and not a longer one, can be detected by PCR in the KO condition. The PCR primers used anneal to the first and the last coding exon over the following regions: GAGAATGAGCGAGTCGGGCTC and GATCACTGCTCCTCCTCAGAGG. Marker 1 Kb Plus DNA Ladder (Invitrogen).(C) Western Blot with TBP (loading control) and Hmga1 recognizing antibodies against the indicated samples. By Western Blot, a shorter protein (expected ~ 8.8 kDa) that could originate from the shorter transcript described in S13A and S13B Fig can neither be detected in the cytosolic + free nuclear fraction Click here for additional data file.S14 Fig(A) Transcriptomic comparison of Hmga1 and Hmga2 add-backs vs. parental WT cell line at repetitive regions of the genome as defined by RepeatMasker, excluding repeats lying on the same or opposite strand of annotated transcripts . Repeats were not quantified at the level of single repeat instances but on the level of Repeat masker repeat “names”. No significant changes can be detected .(B) Correlation heatmap of RNA-seq samples illustrating high similarity between samples on the level of repeats (colour indicates Pearson correlation). As in the case of genes , the mos(C) Scatterplots showing reproducibility for the indicated samples of the transcriptional differences at repeats between 2 different replicates and the corresponding WT samples. In agreement with the absence of significant changes in (A) and (D) Scatterplots and Pearson correlations at repeats for all samples.(TIFF)Click here for additional data file.S15 Fig(A) WT (left) and Hmga1 KO (right) embryoid bodies 2 days after retinoic acid addition. No apparent differences can be detected.(B) WT (left) and Hmga1 KO (right) neuronal progenitors at day 6 after plating, which marks the start of synaptic firing . No appa(C) WT (left) and Hmga1 KO (right) terminal neurons at day 10 after plating, when pruning has occurred and neurons are mature and stable . No appa(D) DAPI staining of nuclei of WT (left) and Hmga1 KO (right) plated neuronal progenitors. No apparent differences can be detected.(TIFF)Click here for additional data file.S16 Fig(A) Strategy used for obtaining alleles carrying a “full-deletion” of the coding portion of Hmga1. Indicated are the location of designed sgRNAs and primers used for PCR screening of the single cell clones. Thicker boxes stand for the coding portion of Hmga1 exons.(B) Sequencing results from the PCR product of the “external PCR” . Top: 3’ UTR junction. Bottom: 5’ UTR junction.(C-E) PCRs showing presence/absence of respectively, from left to right, the external and internal Hmga1 regions  and an unrelated control region see  for the (F) WB on the indicated cell lines. Loading control and Hmga1-specific antibody staining highlight absence of Hmga1 in the KO and “full-deletion” clones.(G) Sashimi plot over the Hmga1 locus for WT and Hmga1 “full-deletion” replicates, showing absence of transcription from the deleted locus.(TIF)Click here for additional data file.S17 Fig(A) Transcriptomic comparison of Hmga1 full-deletion vs. parental cell line at the gene level. Gene names are indicated if the gene is significantly differentially expressed .(B) Transcriptomic comparison of Hmga1 full-deletion vs. parental cell line at repetitive regions of the genome as defined by RepeatMasker, excluding repeats lying on the same or opposite strand of annotated transcripts . Repeat elements show no significant changes . Quantification was performed on the level of RepeatMasker repeat “names”.(C)-(D) Scatterplots and Pearson correlations for all RNA-seq samples, illustrating the very high similarity between all samples at genes (C) and the high similarity at repeats (D).(E) Scatterplots showing reproducibility of the transcriptional differences in the pairwise comparisons of WT and KO samples . The significantly changing genes are shown in red.(TIFF)Click here for additional data file.S18 FigUty gene, which already in the WT condition is transcribed at very low levels (note the missing exon-exon junctions). For comparison with the expression levels of a transcript that is efficiently translated see Sashimi plot over the only gene significantly changed in both Hmga1 KO and full-deletion clones. Depicted are reads in the WT and full-deletion condition over the (TIFF)Click here for additional data file.


Medicago truncatula, PALM1 encoding a Cys(2)His(2) transcription factor is a key regulator of compound leaf patterning. PALM1 negatively regulates expression of SGL1, a key regulator of lateral leaflet initiation. However, how PALM1 itself is regulated is not yet known. To answer this question, we used promoter sequence analysis, yeast one-hybrid tests, quantitative transcription activity assays, ChIP-PCR analysis, and phenotypic analyses of overexpression lines and mutant plants. The results show that M. truncatula AUXIN RESPONSE FACTOR3 (MtARF3) functions as a direct transcriptional repressor of PALM1. MtARF3 physically binds to the PALM1 promoter sequence in yeast cells. MtARF3 selectively interacts with specific auxin response elements (AuxREs) in the PALM1 promoter to repress reporter gene expression in tobacco leaves and binds to specific sequences in the PALM1 promoter in vivo. Upregulation of MtARF3 or removal of both PHANTASTICA (PHAN) and ARGONAUTE7 (AGO7) pathways resulted in compound leaves with five narrow leaflets arranged in a palmate-like configuration. These results support that MtARF3, in addition as an adaxial-abaxial polarity regulator, functions to restrict spatiotemporal expression of PALM1, linking auxin signaling to compound leaf patterning in the legume plant M. truncatula.Diverse leaf forms can be seen in nature. In  KNOXIs), which are required to promote and maintain the meristematic activity of SAM, are downregulated at the incipient sites of leaf primordia (KNOXI genes are permanently downregulated (Solanum lycopersicum) and Cardamine hirsuta, KNOXI genes are reactivated in developing leaf primordia, and this reactivation is required for compound leaf development (Pisum sativum) and Medicago (KNOXI genes are not reactivated during compound leaf development (UNIFOLIATA (UNI) and SINGLE LEAFLET1 (SGL1), orthologs of the Arabidopsis thaliana LEAFY (LFY) gene, are required for the initiation of lateral leaflet primordia . The class I Knotted-like homeobox transcription factor genes  acts as a key determinant of trifoliate leaves in Medicago truncatula. Loss of function mutations of PALM1 result in palmate-like pentafoliate leaves in contrast to trifoliate leaves in WT plants (PALM1 gene encodes a novel Cys(2)His(2) zinc finger transcription factor, which can binds to the SGL1 promoter sequence and negatively regulates SGL1 transcription (It has been reported that T plants . PALM1 gcription .PHANTASTICA (PHAN) was first identified as a key adaxial-abaxial polarity gene in Antirrhinum majus  and ROUGH SHEATH2 (RS2) in A. thaliana and maize (Zea mays), respectively, only have mild effects on the adaxial-abaxial polarity of leaves , tasiR-ARF. tasiR-ARF is derived from the non-coding TAS3 gene, whose RNA is initially targeted by miR390 for cleavage , SUPPRESSOR OF GENE SILENCING3 (SGS3), RNA-DEPENDENT RNA POLYMERASE6 (RDR6), and DICER-LIKE4 (DCL4) proteins  mutant in leaf adaxial-abaxial partitioning and margin development  in the promoter region of PALM1 and represses expression of reporter gene activities in transcription activity assays in tobacco leaves.It is still not clear whether and how ARF3 regulates leaf patterning in Medicago truncatula phan (mtphan), palm1 , mtago7 (mtago7-1), and sgl1 (sgl1-2) mutants were described previously  for 1 h and then incubated at 4°C overnight. Plant tissues were further fixed with 1.0% osmium tetroxide in the same phosphate buffer overnight and dehydrated in a graded ethanol series. Scanning electron microscopy (SEM) was performed as described previously .15 primer (Promega). Real-time RT-PCR was performed using a 7900HT fast real-time PCR system (Applied Biosystems), as previously described . Residual genomic DNA was removed using a DNA-free Kit (Ambion). cDNA synthesis was performed using SuperScript III reverse transcriptase (Invitrogen), starting with 2 μg of total RNA in a 20-μL reaction mix with oligo(dT)escribed . SDS2.2.escribed , and traPALM1 promoter sequence was PCR-amplified, cloned into the pAbAi vector and integrated into the Y1H Gold genome to create the bait strain, according to the manufacturer’s instructions. Coding sequences of MtARF3 were PCR-amplified from cDNA templates, cloned into the pGADT7 vector and introduced into the bait strain Y1H Gold/PALM promoter. Positive clones grown on double selection media (SD-Ura-Leu) were tested on SD-Ura-Leu plates supplemented with Aureobasidin A (100 ng/ml). Primers are listed in Supplementary Table The Matchmaker Gold System (Clontech) was used in yeast one-hybrid assays. The MtARF3 was generated by RT-PCR from total RNA extracted from vegetative shoot buds of M. truncatula. MtARF3m with mutations in tasiR-ARF recognition sites was created by overlapping PCR, cloned into the pENTR/D vector and then transferred into the pEARLEYGATE 202 vector using Gateway cloning technology. M. truncatula transformation was performed as previously described . Briefly, after inoculation and a transient incubation for 2 days, 1 cm leaf disks were harvested and ground in 100 μl of Passive Lysis Buffer. 5 μl of this crude extract was assayed in 100 μl of Luciferase Assay Buffer, and the first chemiluminescence was measured for firefly luciferase activities. 100 μl of Stop and GlowTM buffer was then added, and a second chemiluminescence measurement was made for Renillia luciferase activities. Absolute RLU was measured in a GloMax® 96 Microplate Luminometer with Dual Injectors, with 5-s delay and 15-s measurement. Data were collected as ratios of LUC/REN.Transcription activity assays were carried out essentially as described previously , with thA. thaliana from TAIR10 (The Arabidopsis Information Resource) were used to identify ARF protein sequences from M. truncatula genome project v4.01. Multiple sequences were aligned using ClustalX  protein sequences in ClustalX . Phylogeications .in situ hybridization was performed as described previously . This was followed by centrifugation at 14,000 g for 20 min. The pellet was resuspended in extraction buffer 3  and centrifuged at 20,000 g for 40 min. The resulting pellet was resuspended in nuclei lysis buffer . Sonication, 6 X 15 pulses with a 1 min break between each 15 pulses, was used to shear the DNA. Cell debris was removed by centrifugation. ChIP was performed by incubation with rabbit anti-Flag polyclonal antibody (Abcam) and followed by incubation with Protein A/G magnetic beads (Pierce). The beads were washed twice with high-salt wash buffer , and the chromatin was eluted in the elution buffer (1% SDS and 0.1 M NaHCO3) and de-cross-linked at 65°C for 16 h. Cellular RNA and protein were then removed by RNase (Qiagen) and proteinase-K (Applied Biosystems) treatments, respectively. DNA was purified through a purification column. Enrichment of DNA targets in each sample was determined by PCR using primers listed in Supplementary Table ChIP was performed using FLAG-MtARF3escribed  with somPALM1; AY928184, SGL1; XM_003593616, MtARF3; DQ468322, MtPHAN; XM_003613868, MtAGO7; JF929904, MtNAM; XM_003602497, ACTIN.HM038482, PALM1 gene expression, we first analyzed whether any specific cis-elements are present within the promoter sequence of the PALM1 gene. This led to the discovery of 18 TGTCXX elements within the 2.5-kb promoter region of PALM  (M. truncatula ARF3 (MtARF3) can interact with the PALM1 promoter sequence in the yeast one-hybrid system. The results show that MtARF3 interacted strongly with the PALM1 promoter sequence in yeast one-hybrid assays .To identify potential regulators of gure 1A) , 1997a. gure 1A) , resemblgure 1A) , 1997a. PALM1  reporter activity assays in tobacco leaves . Because ARF3 is post-transcriptionally regulated by trans-acting short interfering RNA (tasiR-ARF) (MtARF3 (MtARF3m) by mutating the tasiR-ARF targeting sites without changing the amino acid sequence . To assess transcription repressor activities of the effector protein, we compared 35S-Es::LUC activities in the presence of the MtARF3m effector protein and the unrelated β-glucuronidase (GUS) protein . The results show that (1) 35S::LUC activities were not significantly different; (2) several potential AuxRE elements, i.e., E3, E4, E9 and E10, exhibited moderate but not significant inhibitory effects on the luciferase activities; and (3) other AuxRE elements, namely E1, E2, E5, E7 and E8, significantly reduced the luciferase activities in the presence of the MtARF3m effector protein compared with that in the presence of the unrelated GUS protein control . These results indicate that MtARF3 can recognize several putative AuxREs that are present in the PALM1 promoter and function as a transcription repressor in the transcription activity assay in tobacco.It is notable that the canonical AuxRE element (TGTCTC) is present at the -2129 position and a majority of TGTCXX elements are present in the proximity of the translation initiation codon (ATG) of o leaves . For thireporter , betweensiR-ARF) , we genePALM1 promoter in M. truncatula plants, using chromatin immunoprecipitation coupled with polymerase chain reaction (ChIP-PCR). For this, we first generated independent transgenic Medicago plants that overexpress MtARF3m-FLAG fusion proteins. ChIP-PCR analysis shows that P1, P2 and P4 regions, but not the P3 region of the PALM1 promoter were significantly enriched in MtARF3m-FLAG-bound chromatins . Consistent with these results, P1, P2 and P4 regions, but not the P3 region, of the PALM1 promoter contain a number of putative AuxREs . Although three different putative AuxRE elements  that are present in P2 exhibited moderate but not significant inhibitory effects on luciferase activities in the tobacco system , the ChIP-PCR results clearly show that four copies of these elements in P2 are sufficient to be recognized by MtARF3 in vivo . These results confirm that PALM1 is a direct target of ARF3 in M. truncatula plantsNext, we tested whether MtARF3 can bind to the ARF3 belongs to a large gene family in both A. thaliana and M. truncatula . These phenotypes are consistent with the role of ARF3 in leaf adaxial-abaxial polarity regulation in Arabidopsis and resemble the phenotype of mtphan and mtago7 mutants . These results suggest that a high level of MtARF3 overexpression results in proliferation of compound leaves that resemble palmate-like pentafoliate leaves of the palm1 mutant  distinct epidermal cell shapes are present on the adaxial and abaxial surface of each needle-like structure ; (2) each needle-like structure has stomata and trichomes on its surface ; and (3) each needle-like structure is connected to a petiole or rachis through the motor organ, pulvinus .Scanning electron microscopy analysis showed that the needle-like structures in the strong pulvinus  , and the severity of the leaf phenotype was correlated with the expression level of MtARF3 . In the strong mMtARF3 overexpression line, the MtARF3 transcript level was upregulated about 36 fold over the wild type level, supporting that a high level of MtARF3 expression is associated with the palmate-like pentafoliate leaf phenotype .Reverse transcription (RT)-PCR results show that ARF3 transcript level is regulated both at the transcriptional level by the MYB domain protein, ASYMMETRIC LEAF1 (AS1) . And, the PALM1 transcript level was slightly but significantly reduced in both mtago7 and mtphan mutants compared with wild type . Similar results on ARF3 gene expression were obtained in developing leaves in mtphan and mtago7 mutants . Quantitative RT-PCR results further show that the MtARF3 transcript level was approximately eight times that of the wild type level in the mtago7;mtphan double mutant . Most significantly, mtphan;mtago7 double mutants developed palmate-like pentafoliate leaves with narrow blades . The palmate-like pentafoliate leaf phenotype of the double mutant resembles that of loss-of-function palm1 mutants, although the palm1 mutant did not exhibit adaxial-abaxial polarity defects . Interestingly, in the R108 background, the petiolules of the ectopic lateral leaflets appear to diverge from secondary rachises in the mtphan;mtago7 double mutants and to a less degree also in the palm1 mutants , whereas in the Jemalong A17 background, the petiolules of lateral leaflets appear to come from the same location on the petiole (Since F1 (AS1)  and at tasiR-ARF  in Arabiescribed . Quantit petiole .mtphan;mtago7 double mutant were subtended by the motor organ, pulvinus, similarly as in WT (R108), and mtphan and mtago7 mutants . In addition, the two lateral leaflets had elongated petiolules, resembling the phenotype of the palm1 mutant, particularly in the R108 background  . These results suggest that transcriptional and post-transcriptional mechanisms act synergistically in restricting the expression of MtARF3, which, in turn; negatively regulate the expression of PALM1.Scanning electron microscopy analysis shows that all five leaflets of pentafoliate leaves in the  5A,E,H) . Consistin situ hybridization results show that in the vegetative shoot apex in wild type, MtARF3 transcripts were detected in the SAM and the abaxial domain of leaf primordia . Its expression domain is opposite from MtAGO7  , consistent with the notion that both AGO7 (tasiR-ARF pathway) and PHAN restrict the ARF3 expression in the abaxial domain. Similarly, in the mtphan;mtago7 double mutants, MtARF3 transcripts were detected in both adaxial and abaxial domains of developing leaf primordia; and MtARF3 transcripts appeared much higher in the adaxial domain than the abaxial domain and corresponding domains in wild type, and mtphan and mtago7 single mutants .RNA gure 6A) . In mtphpalm1 mutant, leaf developmental process is indistinguishable from wild type until the P3 stage, when an additional pair of lateral leaflet primordia (LLp) starts to initiate at the base of the lateral leaflet primordia (LLd)   . And, lateral leaflets expanded and trichomes covered all of the terminal and lateral leaflet primordia . Subsequently, at the P3 stage, an additional pair of lateral leaflet primordia (LLp) initiated at the base of the distal lateral leaflet primordia . Although LLp initiated at similar position and time as in the palm1 mutant, they were narrower than those formed in the palm1 mutant starting from the P2 stage. In addition, stipule primordia were sometimes not initiated in the mtphan;mtago7 mutant. These results suggest that the initiation of ectopic lateral leaflets was similar between the mtphan;mtago7 double mutant and the palm1 mutant.In the gures F) . We invegure 7G) . HoweverA. majus, loss of function phan mutant develops abaxialized needle-like leaves , compound leaves remain pinnate, with individual leaflets abaxialized . Thus, the genetic interaction results confirm that the pentafoliate leaf phenotype of the mtphan;mtago7 double mutant is dependent on SGL1. Thus, the epistatic interaction between sgl1 and mtphan;mtago7 is similar to that between sgl1 and palm1 /CUP-SHAPED COTYLEDON2 (CUC2) has been shown to regulate organ boundary establishment . The epistatic interaction shows that MtNAM/CUC2 is required for the elaboration of palmate-like pentafoliate leaves in the mtphan;mtago7 double mutant, similar to that in the palm1 mutant , and the transcript level of MtNAM/CUC2 was significantly increased in the mtphan mutant and even more so in the mtphan;mtago7 double mutant .In lishment . Our pheC. hirsuta, the KNOXI genes are reactivated in developing leaf primordia and this reactivation is required for compound leaf development in these species. In some legume species including pea and M. truncatula, the KNOXI genes are not reactivated in developing leaf primordia and thus not likely involved in the development of compound leaves. Instead, UNI in pea and SGL1 in M. truncatula act in place of KNOXI in mediating the initiation of leaflet primordia in these legume species. In both simple and compound leaf species, it has also been shown that local gradients of auxin (auxin maxima) mediated by the auxin transport PIN-FORMED (PIN) proteins are required for the initiation and positioning of leaf primordia along the periphery of the SAM  in incipient leaf primordia. In compound leaf species, including tomato and  the SAM . In comp the SAM . HoweverTIR1/AFBs 26S proteasomes, and thus releasing their inhibitory effects on ARF’s transcriptional activities , which activate or repress downstream gene expression, and Aux/IAA transcription repressors that interact with ARF proteins. At a high auxin level, Aux/IAA proteins are ubiquitinated and degraded by the SCFtivities . In tomae plants . In M. t margins .sgl1 or uni mutants develop simplified leaves  in M. truncatula. Our bioinformatics analysis shows that there are 18 TGTCXX cis-elements, resembling the canonical auxin response element  in the 2.5 kb promoter region of the PALM1 gene. We show that in the presence of three tandem repeats of several TGTCXX elements, ARF3 significantly repressed reporter gene expression in the transient transcription activity assay in tobacco. We noticed that there are different background activities when different elements were assayed in tobacco. We speculated that this may be due to the activity of endogenous ARF3 proteins in tobacco cells. By comparing the reporter gene expression in the presence of ARF3 and the unrelated β-glucuronidase (GUS) protein, we reasoned that any influence of background activity on the assay should be minimized. In M. truncatula, chromatin immunoprecipitation (ChIP) assay shows that P1, P2 and P4 regions, which harbor the TGTCXX elements, but not P3 region, which does not contain any TGTCXX elements, of the PALM1 promoter sequence were enriched in ARF3-bound chromatin. The P2 region of the PALM1 promoter sequence contain four TGTCXX elements. However, in the transcription activity assay in tobacco, three tandem repeats of individual elements did not confer ARF3 inhibitory effects on reporter gene expression. There may be several possibilities to account for the discrepancy between the two assay results: (1) three tandem repeats of the same element may not be optimal for the binding of ARF3 in tobacco cells; (2) the spacing between tandem repeats may not be optimal for ARF3 binding in tobacco; and (3) the heterologous system (tobacco cells) may not be optimal for ARF3 binding of the TGTCXX elements. Nevertheless, the combined results strongly support that M. truncatula ARF3 interacts with the TGTCXX cis-elements in the PALM1 promoter sequence in vivo.Here, we show that AUXIN RESPONSE FACTOR3 (ARF3) in Medicago plants and the trans-acting short interfering RNA, tasiR-ARF pathway negatively regulates the transcript level of ARF3  , in addition to narrow blades. The change in the dissected leaf patterning is associated with a drastically increased level of ARF3, decreased level of PALM1  and a greatly increased level of SGL1 . Independent evidence that supports this conclusion includes (1) when the tasiR-ARF resistant mARF3 is highly overexpressed, dissected leaf patterning changes from trifoliate to palmate-like pentafoliate leaves, albeit with radialized blades ; (2) sgl1;mtphan;mtago7 triple mutant exhibits simple leaves with narrow blades, similar to sgl1;palm1 double mutant  , negatively regulates the expression of igure 9) . Both PHigure 8) ; and (3)PALM1-SGL1 module is important for the development of trifoliate leaves in M. truncatula. Our results that the PHAN and tasi-ARF pathways, which are known adaxial-abaxial regulators, are recruited to fine tune the expression level and domain of PALM1 and therefore regulate the compound leaf patterning in M. truncatula  are consistent with the observation that auxin and AS1 converge at the incipient leaf primordia to control leaf development in A. thaliana that has simple leaves .The e leaves . In concJP and RC: designed experiments; JP and AB: performed the experiments; JP, FM, and RC: analyzed data; JP and RC: wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mycobacterium smegmatis, the kdpFABC (encoding Kdp-ATPase) expression was found to be affected by low concentration of K+, high concentrations of Na+, and/or kdpFABC operon to positively regulate kdpFABC expression. The KdpE binding motif was highly conserved in the promoters of kdpFABC among the mycobacterial species. 5′-RACE data indicated a transcriptional start site (TSS) of the kdpFABC operon within the coding sequence of MSMEG_5391, which comprised a 120-bp long 5′-UTR and an open reading frame of the 87-bp kdpF gene. The kdpE deletion resulted in altered growth rate under normal and low K+ conditions. Furthermore, under K+ limiting conditions, a single transcript (kdpFABCDE) spanning kdpFABC and kdpDE operons was observed. This study provided the first insight into the regulation of kdpFABC operon by the KdpD/KdpE TCS in M. smegmatis.Kdp-ATPase is an inducible high affinity potassium uptake system that is widely distributed in bacteria, and is generally regulated by the KdpD/KdpE two-component system (TCS). In this study, conducted on  It plays important roles in a wide variety of cellular functions such as turgor pressure maintenance, osmo-regulation, acid-base homeostasis, membrane potential adjustment, enzyme activation, and gene expression regulation  is required than that of extracellular environment   under salt stress in this osmo-tolerant bacterial species  that play a vital role in the pathogenicity of S. aureus. It was found that KdpE specifically bound to the cap promoter region and deletion of kdpDE resulted in the decreased transcription of cap genes  have been well-characterized in E. coli, Kdp-ATPase was found to be widely distributed among different bacterial and archeal genera  at a specific site in M. smegmatis. In addition, we also predicted the coding sequence of KdpF in M. smegmatis as well as in many other mycobacterial species.Based on sequence homology to E. coli strains DH5α and BL21 (DE3) were grown in Lysogeny broth (LB) media. M. smegmatis wild type strain MC2155 and its mutants were grown at 37°C in Middlebrook 7H9 broth  supplemented with 0.2% (v/v) glycerol and 0.05% (v/v) Tween80 or on Middlebrook 7H10 agar  supplemented with 0.5% (v/v) glycerol 2SO4, supplemented with 0.2% (v/v) glycerol and 0.05% (v/v) Tween 80, pH 7.0]  and the kdpE complementary strain (CΔkdpE) are provided in Supplementary Material  (Novagen) to construct expression plasmid pET28-kdpE. The 6-His-tagged KdpE protein was heterologously expressed in E. coli BL21(DE3)-pET28-kdpE (Table The pE Table  and quanpE Table .2ATP to activate phosphorylation of KdpE. Unlabeled probes were used for competition experiments. The reaction mixture was incubated at 25°C for 1 h and then subjected to 6% native polyacrylamide gel electrophoresis at 150 V for 1 h in a 0.25 × Tris-borate-EDTA (TBE) running buffer. The gel image was obtained using Typhoon Scanner .DNA probes were amplified with and without 6-FAM-labeled primers Table  and the kdpF, the fluorescence labeled probes and the reaction system were the same as used in EMSA. The mixtures were treated with 0.02 U DNase-I for 2 min. Further procedures of purification and analysis of samples were same as described previously  to the late logarithmic phase. Total RNA was extracted by TRIzol method  was used as a reference gene for the determination of relative expression.For real time qPCR reaction (RT-qPCR), gene specific primers were used Table . ApproprkdpFABC operon, 5′-RACE analysis was performed with RNA extracted from M. smegmatis cells grown in HdB media (with and without K+) using 5′-Full RACE kit  according to manufacturer's instructions and as reported in our previous studies  PCR from RACE cDNA libraries using 5′-RACE outer primers and PkdpF specific outer primers  of rs Table . The resrs Table . Finally+) and 7H9 broth with different concentrations of salts , sucrose and with different pH-values containing kanamycin (30 μg/mL) at 37°C for 20 h (late-logarithmic phase). The collected culture was assayed for β-galactosidase activity as previously described  and KEGG server (http://www.genome.jp/kegg/). The multiple sequence alignments of KdpF proteins and KdpE binding motifs were conducted using BioEdit program, and final output was prepared by ESPript 3.0 server (http://espript.ibcp.fr/ESPript/ESPript/)   and kdpD (first gene of the kdpDE operon) when cDNA from wild type strain grown under K+ limiting condition was used as a template . These results showed that in M. smegmatis, the kdpFABC and kdpDE operons were co-transcribed as a single transcript (kdpFABCDE) from PkdpF under K+ limiting condition but not under normal K+ condition.Next, we sought to investigate the transcription patterns of e Figure . In contkdpFABC operon, we determined the binding of KdpE to the promoter PkdpF. The in vitro EMSA of KdpE with the candidate promoter of kdpF was carried out in the presence of ATP, and a dramatic shift of the labeled DNA probe in the presence of KdpE was observed  (data not shown).To gain further insights into the regulation of d Figure . It was g Figure . In S. alacZ as the reporter gene. Surprisingly, no β-galactosidase activity was obtained when 500-bp upstream of MSMEG_5391 (P5391)  was selected as the candidate promoter  appeared to be protected from DNase-I digestion as a result of KdpE binding  were constructed from the wild type strain. Amplification of expected RACE inner fragment was only possible when RT-PCR was performed using the K+ limiting library, which revealed a sharp band on agarose gel, whereas no such band was detected using the normal K+ library (data not shown). First nucleotide base identified next to the 5′-RACE adaptor was an adenosine residue (A) that was considered as the TSS of the kdpFABC operon  shared significant homology to the kdpF of M. tuberculosis (93-bp). Furthermore, this candidate kdpF was aligned to the upstream regions of kdpA in various mycobacterial species. Almost all species in which KdpE binding sequence was conserved, contained a kdpF gene which shares significant identity to the kdpF of M. smegmatis and M. tuberculosis  was very likely to be annotated incorrectly. In contrast, this genomic locus (MSMEG_5391) was believed to contain a long 5′-UTR and a functional kdpF gene.Two 5′-RACE cDNA libraries . The growth patterns of wild type and ΔkdpD were mostly similar without much difference, whereas ΔkdpE showed a slightly faster growth rate  and HdB (with 0 mM K+) using the complementary strain of ΔkdpE. The growth curve of the CΔkdpE strain was found to be similar to the wild type strain  was used for EMSA and DNase-I foot printing studies, considering MSMEG_5391 as a part of the kdp operon. However, the newly identified TSS was the fourth nucleotide of MSMEG_5391 hence, we believed that the original annotation of ORF (MSMEG_5391) might be inaccurate since the first three nucleotides of this frame were not the part of mRNA. The downward sequence of MSMEG_5391 was therefore searched for another potential ORF of kdpF gene. The sequence similarity of this newly identified kdpF of M. smegmatis with those of other species in genus Mycobacterium was noteworthy, although in many mycobacterial species, kdpF is still un-annotated. Our findings provide a strong foundation to identify and characterize kdpF in other species of genus Mycobacterium. Previously, KdpF was reported as a regulatory peptide  -GGGTA motif was predicted as SigF-dependent promoter consensus after inspecting promoter sequences of numerous SigF-regulated genes through bioinformatics analysis by the same group -GGGCA). In the previous study, PkdpF was not identified as a SigF-regulated promoter  in K+ limiting condition was reported in Clostridium acetobutylicum  in Acinetobacter baumannii  were co-transcribed at least to some extent from PkdpF, whereas under normal K+ condition, kdpDE was transcribed to very basal level and this transcription was initiated from PkdpD.In our study on E. coli, S. aureus, C. acetobutylicum, M. tuberculosis, S. typhimurium, Leptospira interrogans, Alicyclobacillus acidocaldarius, and A. baumannii for its role in K+ uptake . The C. acetobutylicum KdpE binding motif shared significant sequence similarity with that of E. coli (23 bp T-rich). However, M. smegmatis KdpE binding motif (22 bp A-rich) did not show noticeable sequence similarity with any other previously reported KdpE binding motif  and TrkB (MSMEG_2769) subunits. In addition, TrkA domain-containing protein (MSMEG_3665) and ion channel membrane protein (MSMEG_1945) were found as possible K+ channels. Expression of these genes was down-regulated in K+ limiting (0 mM K+) condition when compared to K+ non-limiting conditions  in wild type and mutant strains (Figure + limiting condition, the Kdp-ATPase appeared to be functional to scavenge K+ as compared to other K+ systems.In s Figure . These rM. smegmatis, Kdp-ATPase plays a vital role in the bacterial physiology during K+ limitation. The system is positively regulated by KdpD/KdpE TCS and triggered for osmo-regulation during low K+ and salt stress. The regulation of kdpFABC operon via KdpD/KdpE TCS appeared to be dependent upon SigF. Based on growth discrepancies of ΔkdpD and ΔkdpE, it can be proposed that some other histidine kinases may also activate KdpE, which in return regulates genes other than kdpFABC. However, a detailed study is required to verify this hypothesis.It can be concluded that in kdpF was identified in genus Mycobacterium. Based on sequence similarity of the DNA motif, the binding of KdpE protein to positively regulate Kdp-ATPase can be expected in other mycobacterial species such as M. avium, M. fortuitum, M. marinum, and M. chubuense. On the other hand, a different mechanism might be speculated for the regulation of Kdp-ATPase in M. tuberculosis and M. bovis, which lack this KdpE-binding DNA motif and disclose different genomic organization for the kdpFABC and kdpDE operons.For the first time, DNA sequence required for successful binding of KdpE with PkdpFABC for M. smegmatis. It would be worthy of further studies to see whether this 5′-UTR region exhibits some kind of regulatory role for the translation of Kdp-ATPase transporter.In the present manuscript, we also observed a comparatively long 5′-UTR region in the transcript of MKA performed most of the experiments and made most of the data evaluation. XLi, QT, XLiu, FC, JX and MA participated in partial experiments and interpretation of the data. MKA, QT, and JH conceived the study and drafted the manuscript. JH, SC, and MA revised the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.