The balance between coactivators and corepressors fine-tunes target gene transcription. This Research Topic aims to collate original research, review, and perspective articles relating to the structure and function of transcription factors and coregulators. Keywords : Transcription factor, transcription coregulator, transcription coactivator, transcription corepressor, protein structure, protein function, transcription regulation, structure biology, ChIP-seq, NMR, X-ray crystallography, cryo-EM, SAXS, computational biology.
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Find out more on how to host your own Frontiers Research Topic or contribute to one as an author. A key component of the overall cellular stress response includes adjustments in the gene expression program in favor of proteins that manifest activities capable of frustrating and eventually eliminating the molecular constituents of the stress condition.
One protein providing such cytoprotective activity is heme oxygenase-1 HO-1 , an enzyme that catalyzes the rate-limiting reaction in heme catabolism i. Because of the potent antioxidant, anti-inflammatory, and signaling properties of the reaction products, the HO-1 gene hmox1 is frequently activated under a variety of cellular stress conditions.
Cells use multiple signaling pathways and transcription factors to fine-tune their response to a specific circumstance. See the Methods section for details. In the adult and fetal tissues, and TFs were identified, respectively; among them, and 34 TFs were unique to the adult tissues and fetal tissues, respectively Supplementary Fig.
TFs with multiple DBDs were classified in each of their respective families. The colour bar on the right indicates the relative expression abundance. The abundance of the TFs spans almost seven orders of magnitude. The names of the top three most abundant TFs are listed.
The number of TFs identified in each tissue varied, ranging from in skeletal muscle to in thymus Fig. Geiger and colleagues reported a proteome profiling of 28 mouse tissues When compared with the proteome profiling data set, the catTFRE showed a clear advantage in enriching endogenous TFs with the identification of compared to identified by proteome profiling Supplementary Fig. We mined hundreds of published literatures Supplementary Data 1 to construct a library for well-characterized TFs in the 13 overlapped tissues.
As shown in Supplementary Data 1 , the catTFRE successfully detected most of these TFs in tissues 76 out of 85 , while profiling only identified few of them 6 out of Transcriptional co-regulators TCs play critical roles in transcriptional regulation by interacting and cooperating with the TFs.
An L-shaped distribution pattern was observed among the 32 tissues Supplementary Fig. We calculated the median and maximum FOT values in all tissues where they were detected and used them to perform a density plot analysis of the TF expression patterns across the 24 adult tissues. Among them, the expression of 27 TFs exhibited a maximum value of less than ten times the median value, indicating a ubiquitous-uniform distribution 27 TFs, 3. Consistent with their wide distribution, the ubiquitous-non-uniform TFs mainly function in generic biological processes, such as circadian rhythms, cell cycle, cell growth and chromatin remodelling.
We define TFs that are expressed in a tissue at levels that are at least ten times higher than the median value of all adult tissues as tissue type restricted TFs ttrTFs. The extreme case is the tissue-specific TF, which is expressed only in one tissue. A large number of ttrTFs were identified in the central nervous system, the immune system thymus , and the reproductive system testis , whereas a smaller number of ttrTFs were identified in the metabolic system intestine, liver, stomach and adipocytes Fig.
Studies on the mRNA expression profile of TFs offered a simple and powerful way to obtain highly relational information regarding the physiological functions of the individual proteins and the protein families. The mRNA profiling of NRs defined a ring of NR physiology, dividing the NR regulatory network along two physiological paradigms: 1 reproduction, development and growth; and 2 nutrient uptake, metabolism and excretion. These studies reveal a transcriptional circuitry that extends beyond individual tissues to form a mega network governing physiology on an organismal scale.
The comparison with mRNA is shown on the right colour block. The number of NRs expressed in various tissues is indicated in the pie chart. The dendrogram is depicted based on the hierarchical clustering, revealing NR clusters in different tissues. Blocks with different colours represent different physiological systems.
The annotations in the blocks are the GO enrichment terms for the TGs that were co-regulated by at least two TFs in the block. Unsupervised hierarchical clustering of the NR DNA-binding activity revealed two major clusters, which can be further divided into five sub-clusters Fig. Cluster IB includes NRs that are ubiquitously expressed in adult tissues and are expressed at low levels in embryos. In general, our clusters are consistent with the previous one derived from the mRNA profiling 23 but show some differences Fig.
The current classification is consistent with their functions that were uncovered in more recent studies. For example, Nr1h2 regulates genes involved in liver metabolism and cholesterol uptake 26 , 27 , and Nr6a1 plays an important role in germ cell development during gametogenesis Nr2c1 and Nr2c2 form the direct repeat erythroid definitive complex, which plays a fundamental role in early embryogenesis and embryonic stem cell proliferation Notably, these two NRs were classified to the same cluster but were divided into two groups based on the transcriptome data.
A minor difference in classification between current and previously proposed one revealed the diverse and complementary information provided by TF DNA-binding activity at protein level and gene expression at mRNA level. For instance, a cluster of the Fox family Foxa1, Foxa2, Foxa3 and Foxf1 was enriched in the digestive system; another cluster of the Hmg family Sox4, Sox14, Hmga2, Sox2, Sox1 and Hmgn3 was predominantly expressed in the nervous system Fig.
We investigated the co-expression of TFs in 24 adult tissues using TFs that were expressed in more than four tissues. Using Pearson correlation coefficient of 0. We summed the average intensity of each module and then performed hierarchical clustering to reveal the relationship among the 37 TF modules.
These modules segregate into six distinct clusters Fig. Twenty-two out of the 37 modules appeared to have functional enrichment, and predicted functions of of the TFs matched their functions revealed by the previous publications. For example, the Module 1 contains six TFs that are mainly expressed in tongue and skin, was related to muscle contraction and keratinocyte differentiation.
Functional roles for Module 5 proteins Tead2, Bach1, Notch3 and Dlx5 in the nervous system have reported; this suggests that the other member of Module 5, Fosl2, may have the similar function. Thirty-seven co-expression blocks of TFs were identified in 24 adult organs. Six clusters were grouped according to their different tissue distribution patterns.
The correlation coefficients between the TFs in Module 12 are listed in the table. Similarly, Zfp in Module 12 was tightly co-expressed with Hnf1a, Hnf1b, Hnf4a and Nr1h4, all of which play important roles in liver Fig. This finding suggested that Zfp, whose function is unknown, may play important roles in liver function.
The GSEA terms indicated that Zfp was positively associated with both metabolism-related pathways and the immune processes Fig. Mef2 and Bhlha15 Mist1 have been reported to play diverse roles in skeletal and cardiac muscle.
Mist1 mainly represses MyoD 34 , a major TF that regulates muscle differentiation, whereas Mef2a and MyoD can cooperatively activate muscle genes In our study, Mist1 and Mef2a expression had a negative correlation coefficient of 0. Combinatorial TF interactions are critical for cellular functions and are important determinants of different cell types. The TF interaction network constructed from protein—protein interaction assays, such as the mammalian and yeast two hybrid assay 22 , 37 , have revealed many important regulatory features of TFs.
One limitation of such heterologous assays is that the over-expressed proteins may or may not be co-expressed in the same cells. TF atlas allows us to survey the combinatorial TF interactions among different tissues from the perspective of endogenously expressed proteins.
We re-analysed the TF interaction network of the 24 adult tissues by matching differential TF expression patterns to the TF network map 22 Fig. Thus, tissue-restricted TF—TF interactions were identified in no more than 12 tissues, and ubiquitous TF—TF interactions were identified in more than 12 tissues Supplementary Data 5.
The TFs were binned into five groups of approximately equal size based on tissue specificity x axis. The stacks of coloured segments represent the number of interactions for each bin. Centre line and box limits represent median value and lower or upper quartile, respectively. As a facilitator hub, Meis1 interacts with different TFs in the Hox family in different tissues, showing the specificity of the interaction was determined by Homobox TFs, namely the ttr TFs.
The TF expression patterns are similar between tissues of the same physiological systems. Ubiquitous TFs, such as Jun, Smad3 and Rxra, were involved in large number of TF—TF interactions implying that they are involved in wide variety of cellular transcriptional programs through protein—protein interactions. In contrast, ttrTFs, such as Nr1h4, Irf4 and Ptf1a, have fewer interaction partners, suggesting these TFs may function in tissue-restricted processes Supplementary Fig.
The positive correlation between number of tissues expressed and the number of TF—TF connections was significant Fig. As exemplified by the connection between Meis1 and Homobox the ubiquitous TF Meis1 was expressed in 23 out of 24 adult tissues and has the potential to connect to 15 ttr-Homobox TFs Fig.
A signature TF networks of each tissue type were also constructed Supplementary Fig. For example, Hnf4a interacted with Smad, Hnf1a and Nr2c2 in the stomach, small intestine and colon, whereas it was also connected to Esr1 in the liver, white adipose tissue WAT and kidney Supplementary Fig.
Our analysis also revealed similar TF expression patterns between adjacent tissues in the nervous system brain, eyes and spinal cord , digestive system stomach, colon, intestine and liver , adipose tissue brown adipose tissue BAT and WAT and immune system thymus, spleen and blood Fig.
We identified top 30 differentially expressed TFs for each of the ten physiological systems Figs 1a , 5g and Supplementary Data 5 and analysed their downstream TG.
Our results revealed that they were significantly enriched in the predominant biological processes of the corresponding systems Supplementary Fig.
We sought to identify TFs that may be required to maintain the identities of the tissue types tissue-type-maintenance transcription factor ttmTFs. We reasoned that ttmTFs should not only be specifically enriched in the tissue but should also dominantly control the transcription of their downstream genes in that tissue.
A total of ttmTFs were identified in 21 adult tissues, ranging from 62 in thymus and 4 in pancreas, and none in thyroid and seminal vesicle Fig. Network of TFs with a three-tiered organization was shown on the right. Plus signs indicate the cluster centroids. The radius of the circle indicates the average distance from the centroid. The silhouette value was used to measure separation between the three tissue classes.
Accuracy was defined as the ratio of tissues with silhouette value more than zero. Green: ectoderm; Red: mesoderm; Blue: endoderm. The silhouette value right was used to measure how similar an object is to its own cluster cohesion relative to the other clusters separation. The 1,time permutation test was performed. The tracks include two tissues from ectoderm, four tissues from mesoderm and six tissues from endoderm. The tissues were ordered according to Hnf4a DNA-binding activities increasing.
Notably, a number of ttmTFs identified here were consistent with previously reported roles of directly converting fibroblasts into the major cell type of the tissue Table 1. For example, Hnf4a, Hnf1a and Foxa2 had been reported to drive the direct conversion of fibroblasts to hepatocytes 9 , 40 and Myt1l, Lhx3 and Isl1 have been shown to convert MEF to spinal motor neurons These activities indicate dominant roles that ttmTFs play in determining tissue identity.
The identification of ttmTFs of tissues provides a conceptual framework for understanding how tissue identity is maintained by TFs. It is logical to predict that genes controlled by two or more ttmTFs may represent the biological processes of particular tissues relatively specifically.
Reactome terms that were enriched in TGs controlled by dual ttmTF represent the major functions of the tissue Fig. For example, TGs of multiple ttmTFs in eye, brain and spinal cord were enriched in neuron-related items, whereas TGs related to immune functions were enriched in spleen and thymus Supplementary Data 6.
All tissues are developed from three germ layers: ectoderm, mesoderm and endoderm. Because adult tissues are terminally differentiated, they cannot be clearly clustered into their germ layer origins based on their global proteome.
As ttmTFs play central roles in regulating gene expression in tissues, we investigated the upstream regulatory factors controlling ttmTFs in tissues. DNA methylation is an epigenetic mark that is critical for mammalian development and tissue lineages, and tissue-specific differentially methylated regions tsDMRs occur at distal cis-regulatory elements.
For example, Hnf4a locus was hypomethylated in the digestive system, particularly in the liver and colon, compared with other tissues Fig. We asked whether the TF hierarchy would change and particularly ttmTF regulation, under physiological and pathological conditions. As PHx is a dynamic process and prone to experimental variations, we included more initial experimental conditions to define a more accurate initial state.
We combined 26 data sets of liver TFs obtained by catTFRE in physiology conditions and use them as the control experiment to construct a liver TF reference map that defines abundance range for each TF Supplementary Data 7. We found that the intensities of TFs after PHx were greater than the upper quartile values Q3 of the TF reference map in at least two out of the three individual measurements, and the TFs were defined as outliers.
The ttmTFs were significantly downregulated. The levels of downregulated and upregulated non-ttm TFs were used as the baseline. One third of the outlier TFs can be classified into the four major functions categories, namely immune and stimulus response, development and differentiation, nuclear receptors and metabolism, repressors and brakes.
We found that ttmTFs tended to be downregulated when the fate of the tissue was altered. Profiling of the global liver proteome during PHx identified downregulated proteins, which could be used as the TG products to trace back TFs that regulate them Supplementary Fig. These results suggested that the liver lost ttmTFs during the process of tissue expansion—a trait that normally is not ascribed to liver, and implicated the importance of losing ttmTFs when the identity of the organ is perturbed during liver regeneration.
Here, we present an atlas of mouse TF DNA-binding activities from 24 adult tissues and 8 fetal tissues www. TFs are considered relatively low-abundance proteins in the proteome; however, we found that the TF sub-proteome spanned almost seven orders of magnitude in abundance, revealing a large variation in the abundance of TFs.
More than half of the detected TFs were enriched in a certain tissue, and few non-specific or ubiquitous TFs were identified. TFs often interact with each other to regulate gene transcription 22 ; these combinatorial interactions are commonly identified by the yeast two-hybrid method. Using the endogenous TF expression data in different tissues, these combinatorial interactions among TFs derived from the artificial over-expression system in yeast could be verified, such that an interaction between two TFs could be ruled out if they were not co-expressed in the same tissue.
Interestingly, TFs with high specificities tend to have less connectivity, whereas ubiquitous TFs engage in more interactions than other TFs. As the largest node, Smad3 was expressed in 23 out of the 24 adult tissues and was connected with 38 TFs, ranging from the Smad3—Max interaction in 23 tissues to the Smad3—Pax8 interaction in the kidney alone. These observations suggested that a ubiquitous TF interacts with different specific TFs to expand its regulatory repertoire and perform regulatory functions in different tissues.
The TF sub-proteome is the driving force that shapes the tissue proteome. We presented different ways of using our uniformed data set of TF atlas for correlative bioinformatics analysis. Interestingly, most ttmTFs are among the most highly abundant TFs in the tissue, indicating that they play a dominant role in the tissue and may be candidates for tissue engineering and regenerative medicine research.
A more precise approach, for example, using single TFRE to purify TF—TC complexes may be applied to further validate functional modules drawn in this study. Taken together, our study constructed an atlas of TF DNA-binding activities in mouse tissues, expanding the existing knowledge of TFs on the proteome wide scale. The TF expression patterns will have implications for our understanding of how TFs execute their regulatory functions in tissues.
Twenty-four adult mouse organs and tissues eye, skin, bladder, blood, thyroid, seminal vesicle, WAT, BAT, tongue, pancreas, colon, small intestine, stomach, spinal cord, liver, heart, kidney, brain, lung, muscle, spleen, thymus, adrenal gland and testis males were used in this study. Fetal tissues were collected from pregnant mice at different time points 1. The mice were killed by cervical dislocation. Fresh tissue samples were collected to prepare nuclear extracts NEs.
Approximately 0. In each biological replicate, tissues from 3 to 4 mice were pooled for each sample to further minimize the individual differences between mice.
Dynabeads M streptavidin were purchased from Invitrogen. Six bands were excised according to the molecular weight ranges and then subjected to in-gel trypsin digestion, as previously described All partial hepatectomy surgeries were performed between and The PHx mouse model two-thirds hepatectomy was established as described by Mitchell and Willenbring Up to two missed cleavages were allowed for protease digestion, and the minimal required peptide length was set to seven amino acids.
Carbamidomethylation of cysteine was set as a fixed modification, and N-terminal protein acetylation and methionine oxidation were set as variable modifications. Homemade software was used to estimate the protein quantities based on the precursor area under the curve. The amount of each gene product was estimated with a label-free, intensity-based absolute quantification iBAQ approach
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