| Transcription factors(TFs)drive various biological processes.Approximately 1,500 transcription factors are encoded in the mammalian genome.TFs can be grouped into different families,depending on the structure of their DNA-binding domains(DBDs),and each family prefers to bind a specific DNA consensus sequence.Specific recognition of a response element(RE)is essential for gene regulation in response to developmental cues and environmental signals.Tissue specificity is enabled by temporal and spatial gene expression patterns,which are,in turn,driven by TFs.This process involves important roles,such as DNA-binding TFs interacting with the cis-elements,including promoters,enhancers and silencers,of the genes they activate or repress.Transcription factors play a decisive role in the development and maintenance of tissues and organs,and other normal functioning.Previously,a variety of genome/transcriptome technologies have been employed to investigate mammalian TFs at high resolution and depth.Many studies have inferred TF expression through mRNA expression profiling using RNA sequencing(RNA-seq)combined with genome promoter analysis.Chromatin immunoprecipitation(ChIP)coupled with microarrays or sequencing is another revolutionary strategy that enables the genome-wide analysis of TF binding.Through the development of a series of powerful data analysis tools and in-depth data mining,the transcription factor binding site research and expression profile at the mRNA level has been completed.While these studies have led to the construction of models of TF actions,there are critical issues that remain unresolved.First,the correlation coefficient between mRNA and protein abundance is low and is insufficient to predict protein expression levels from quantitative mRNA data.Second,ChIP-seq measures the binding sites for one TF at a time and can only obtain data for limited numbers of TFs because of constraints by reagents and experimental throughput.To date,screening TFs and subsequently illuminating their activities at proteome scale remains challenging.Thus,one clear and immediate task is to map TFs at the protein level and determine their DNA-binding activities in different organs/tissues.In recent years,proteomic research techniques,especially the rapid development of mass spectrometry,have made it possible to examine the entire proteome.At the beginning of the 20 th century,the rise of the "shotgun" proteomics greatly enhanced the ability of proteomics to identify and study of complex proteomics.The first two draft of the human proteome were published in Nature in 2014,which made our understanding of the life sciences a step forward.Transcription factor,as the ultimate regulator of signal transduction,is relatively absent at the level of proteomics.Towards this goal,we recently developed an approach that permits identification and evaluation of the DNA-binding activity of endogenous TFs at the proteome scale.Using a synthetic DNA containing a concatenated tandem array of the consensus TFREs(catTFRE)as an affinity reagent,the TF sub-proteome can be identified to the TF sub-transcriptome level in cell lines and tissues using mRNA-seq.In this study,we analyzed the activated TF in 24 adult and 8 fetal mouse tissues on a proteomic scale using the catTFRE method we developed.In the data set,a total of 941 TFs was identified,representing more than 60% of the TFs were encoded in the mouse genome.By integrating multiple-omics data,we present a TF network of major organs of the mouse,allowing data mining and generating knowledge to understand the roles of TFs in various biological processes,including the maintenance of physiological functions of tissues and characteristic transcription factors representing a variety of physiological systems.We demonstrated that the use of catTFRE enrichment,combined with proteomics,has very high transcription factor enrichment efficiency at the tissue and organ level,and can be very effective in identifying the transcription factors necessary for the proper functioning of tissues and organs.The transcription factors we identified cover all transcription factor families and we classify these transcription factors into ubiquitous and specifically expressed transcription factors based on their expression activity in various tissues and organs.Afterwards,we used the nuclear protein receptor family transcription factor as an example to analyze the expression of the transcription factor family in various organs,confirming that the research methods through system biology can be applied to biological organisms in addition to single cell types and specific tissue types.In contrast to the studies using mRNA data,some of the abnormal findings due to transcriptome defects were corrected.We constructed a co-expression map of transcription factors and used co-expression to predict the function of unknown transcription factors.A total of 37 closely related transcription factor co-expression modules were discovered and screened.In some specific co-expression modules,we were able to predict the function of some of these transcription factors.For example,the transcription factor Zfp655 is closely co-expressed with Hnf1 a,Hnf1b,Hnf4 a and Nr1h4 and is likely to play an important role in liver function.This possibility is further enhanced by the mRNA data and functional analysis of GSEA.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,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 found that transcription factors with high specificity tend to have fewer interactions,whereas ubiquitous transcription factors have more interactions.These observations suggest that ubiquitous transcription factors interact with different specific transcription factors to expand their regulatory capacity and perform regulatory functions in different tissues.The transcription factor subproteome is the driving force for the formation of tissue proteomes.We identified the tissue-type-maintenance transcription factor(ttmTF)by combining transcription factor activity maps and corresponding tissue and organ mRNA data using the relationship between transcription factors and their regulated target genes.ttmTFs are critical in maintaining tissue properties,and their target genes,particularly target genes that are commonly regulated by multiple ttmTFs,directly indicate tissue function.The ttmTFs identified in this study encompass most of the disclosed key TFs that are necessary for direct converting fibroblasts to the major cell types of the tissues.We examined the dynamic changes in transcription factors when the tissue was disturbed and it drastically changed by the liver regeneration model.Based on the temporal changes of transcription factors in this process,we divided the liver regeneration process into four phases,namely,the start-up phase(0-12 h),early progress(24-48 h),late progress(48 h-3 days)and termination(5-7 days),where transcription factors correspond to immune and stimuli responses,development and differentiation processes,nuclear receptors and metabolism,inhibitory and repressor signals,respectively.We found that ttmTF tend to be down-regulated when tissue changes drastically,suggesting that hepatocytes lose their homeostasis and identity or initiate dedifferentiation procedures when severe disturbances occur.In addition to systematically studying transcription factor proteomics using model animal mice,we also conducted a similar study on the human cancer cell line NCI60.A total of 849 transcription factors were identified in 59 cancer cell lines,with an average of 380 per cell line.The quantitative range of the entire transcription factor spans approximately 6 orders of magnitude.We studied the molecular characterization of these cells based on the activity spectrum of each cell transcription factor.Surprisingly,according to the expression of transcription factors,using unsupervised hierarchical clustering,essentially every tissue-derived cell line can spontaneously accumulate in one class.These results imply that transcription factors are more hierarchical in their regulation of cellular functions and characteristics,and that the function of maintaining tissue “identity” is also largely regulated by the activation of transcription factors.In order to reveal the characteristics of transcription factors that are significantly associated with drug sensitivity and resistance,we used elastic network regression analysis to reveal the relationship between transcription factor expression status and antitumor drug sensitivity in NCI-60 cell lines.Our data shows that the expression of transcription factors in cancer cells can be used to predict a biological index of drug sensitivity or drug resistance of cells,providing effective support for subsequent basic research or clinical application.In conclusion,our study constructed a map of DNA binding activity of transcription factors in mouse tissues,thereby expanding the existing knowledge of proteomic large-scale transcription factors.The transcription factor expression pattern will influence our understanding of how transcription factors perform their regulatory functions in tissues.In addition,through the study of cancer cell line transcription factors,new ideas have been provided for the identification of cancer cells escaping from effective inhibitors or chemical therapeutic compounds. |
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