Archaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques.Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes  and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control.In this study, experimentally verified promoter sequences of the organisms Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.The classification models were employed to perform genomic annotation into the archaea The online version contains supplementary material available at 10.1186/s12859-022-04714-x. The righteous introduction of the archaeal domain to the tree of life dates no longer than half a century. Since then, a lush path towards discovering new insights in order to benefit archaeal genome annotation arose. The archaeal domain is diverse , 2, rangSingle cell organisms rely on finely regulated cellular processes. The production of the right nutrient at the right moment grants the cell survivability. Instances of these processes include the transcription of an RNA molecule. This mid-step operation is carried out by the RNAP enzyme and configures a central process in the genetic information flux across all domains. The way the transcription occurs in archaea roughly resembles the eukaryotes . In facti) a wTTATwww set of nucleotides, located at − 25, matching the TBP binding site, where w means A or T in the IUPAC code; (ii) an ssnAA sequence located around two nucleotides upstream TATA and a TAC sequence located in the range of − 1/ − 10, due to its two-extremity binding, TFB stabilizes TBP and the two combined create the Pre Initiation Complex (PIC), where s means C or G and n means any nucleotide in the IUPAC code; (iii) a TFE protein has the function of assisting PIC formation, hence, its binding preference varies according to the promoter and organism [The recruitment of RNAP to the DNA is mediated by a DNA segment defined as a promoter sequence whose presence is necessary for the initiation of the transcription. The typical archaeal promoter element possesses three basal transcription factor binding sites. These additional proteins are TATA-box Binding Protein (TBP), Transcription Factor B (TFB), and Transcription Factor E (TFE) and they are needed for correctly directing RNAP to its precise site of action . On a nuorganism , 10.The conservation found around the binding site of transcription factor proteins in the archaeal genome might be used as input in a way that the recognition of these regulators is able to provide a more reliable annotation. The promoter prediction task is well developed in other branches of life than archaea. Such tools have succeeded in classifying these regulators in eukarya and bacteria. However, due to the particularities archaea have, a universal promoter classifier is an open scientific question.In this work, we systematically locate the potential promoters of unannotated archaea by using their structural properties in comparison to random and sequences where no promoters have been identified. To do so, the well-conserved nature of archaeal promoters is employed and stressed.Haloferax volcanii, 1048 sequences of Sulfolobus solfataricus, and 1248 sequences of Thermococcus kodakarensis. These are model organisms in the Euryarchaea and TACK superphylum. These organisms were selected because there is available transcriptome information, which enables the extraction of promoter sequences associated with a given transcript.A total of 3630 experimentally validated promoters of three different archaea were employed in this study: 1340 sequences of H. volcanii and S. solfataricus [https://zenodo.org/record/5137551.The original data contains 1001 nucleotides per sequence with their Transcription Start Sites (TSS) mapped. Only primary TSS (pTSS) from the published transcriptomic data was considered. Next, a sub-sequence containing 100 nucleotides, i.e. − 80 to + 20 was extracted. This region comprises the reported core promoter in ataricus , 12 and ataricus . AnnotatThe classification methods of this study were stressed with three forms of control. First, we, through a self-developed Python script, shuffled the 100-nucleotides original sequences. A second control dataset was used by selecting the downstream sequences from + 21 to + 121. By this, we wanted to test the validity of our method by assessing sequences that do not indicate promoter activity nor have a TATA-box; and finally, we performed a second method for shuffling sequences, proposed by ; i.e., wThe totality of the sequences of this study (promoters and controls) were submitted through a structural coding in order to represent genetic information into numeric attributes. This representation captures specific sequence properties associated with regulatory regions such as promoters . The parFirstly, position-specific slices of 8 nucleotides  were extracted and averaged in each dataset . Then, an interval was set ranging from the plus and minus values of the standard deviation formed upon the promoter dataset  simulations took place in the R environment through the  package . The alg package . The num package . The num package . Finallyd class correctly predicted as a member of d class; True Negatives (TNs), which are elements that do not belong to a d class and have been assigned as non-d class; False positives (FPs), which comprises a member of d class classified as a non-d class; and False negatives (FNs), which encompasses members that do not belong to a d class and have been assigned as d class. With the error matrix, it is possible to calculate performance metrics of a binary classification.A binary classification might get assessed through an error matrix with predicted and actual values of a classification; this enables the performance of a classification technique to be evaluated. The elements that compose the matrix belong to two classes, positives and negatives and they are: True Positives (TPs), which correspond to the number of elements of a The first metric is Accuracy Eq. , which mThe second metric is Precision that verifies how many of the observations predicted as positive are actually positive and it is calculated through Eq. Next, Recall, which assesses how many of the TPs are actual TPs, is obtained through Eq. Finally, Specificity that calculates the detection rate of TNs throughout the entire dataset. It is obtained by Eq. http://embnet.ccg.unam.mx/rsat/) in its Sep 23 12:30:06 2021 version. The database contains upstream regions for 211 archaeal organisms. Two archaeal genomes, Aciduliprofundum boonei (741 sequences of 400 nucleotides each) and Thermofilum pendens (1926 sequences of 400 nucleotides each) exhibited a promoter-like profile in a previous study [To provide a validation for the methods proposed in this study, upstream sequences whose promoter activity has not been experimentally described yet, were downloaded from the RSAT prokaryotic database , More GC would indicate less potential binding sites for such proteins, as reported in [Next, we have observed conserved binding sites of promoter recruitment transcription factor proteins in archaea with varied GC content  seven neurons in the input layer; (ii) two neurons in the hidden layer, and; (iii) one neuron in the output layer. Table In order to achieve more robust classification scores, ANNs were used. In the ANN simulation, the architecture that protruded satisfactory scores follows: .A second application of ANNs was conducted to evaluate if the pattern of one archaeon might be employed to classify another. Following this rationale, a new simulation was achieved in which the ANN was trained with one organism and tested with another. The results of this new simulation are available in Table The results brought by the ANN classification suggest the model succeeded in classifying archaeal promoters, distinguishing them from three variations of control. In fact, this machine learning approach has succeeded in encountering promoters , 22, 23.By outshining the statistical classification, the mathematical robustness of the ANN method  has provS. solfataricus and T. kodakarensis share similarities, evidenced by the acceptable classification scores between these two archaea. The high values of recall observed in H. volcanii vs. S. solfataricus and T. kodakarensis suggest that very few False Positives were identified, meaning that it was rare for the model to incorrectly classify H. volcanii promoters, this is due to the divergent amount of GC in this organism, reported in [H. volcanii and S. solfataricus (and vice-versa) shows that the S. solfataricus model correctly identified non-promoters of the H. volcanii dataset, meaning the model correctly identifies promoters with conserved binding sites. However, the H. volcanii ANN architecture failed in classifying non promoters of the S. solfataricus dataset, indicating that the rationale of classification of this halophilic archaeon only performs well with organisms with higher GC%. In general terms, due to the higher amount of GC in H. volcanii and consequently, less conserved binding sites of transcription factors, the promoter sequences of this organism are unparalleled.The verification of inter-organism rationale of classification has evidenced that orted in . The bumAciduliprofundum boonei and Thermofilum pendens were selected in order to extract potential promoters from. The statistical and ANN models found in S. solfataricus and T. kodakarensis were employed in the validation dataset. H. volcanii was left out due to its unparalleled AT content; such inclusion would have jeopardized the validation. An upstream region was considered as a promoter if the statistics of S. solfataricus and T. kodakarensis and the ANN of S. solfataricus and T. kodakarensis flagged the given sequence as a promoter. From the 742 and 1927 sequences from A. boonei and T. pendens, respectively, the method encountered 145 promoters of the Euryarchaea and 243 promoters of the Crenarchaea. The lists containing sequence ID, the nucleotide sequences, and functional annotation are available at https://doi.org/10.5281/zenodo.5729308.Upstream regions of A. boonei and T. pendens as well as the other three archaea. In Fig. A. boonei resemble the averages of S. solfataricus, while T. pendens match T. kodakarensis. The whole analysis of the datasets present a p = 3.241^ − 14.To validate the newly identified promoters, they have been compared with experimentally verified promoters. In this sense, Fig. A. boonei and T. pendens. To do so, we systematically characterized promoters from well-known archaea [The method proposed in this study was able to hand in regulatory annotation upon the genomes of  archaea  and systin-silico approaches provide help to experimental biology by curating data [T. kodakarensis and A. boonei are Euryarchaea while S. solfataricus and T. pendens belong to the Crenarchaeota division, the statistical resemblance of these organisms requires further analysis. We also suggest using the model of H. volcanii in order to locate promoters in archaea that have high GC content. The statistical similarity found between verified and potential promoters advocate the robustness of the method proposed.Many factors such as the diversity of archaea and their relatively recent discovery creates the need for high quality genome annotation. This is the moment when ing data . The boxA. boonei and T. pendens.The results gathered in this study reveal the classification of promoter sequences in archaea susceptible to the percentage of GC in specific organisms. Moreover, the classification indicates a novel way of predicting promoter sequences in unannotated archaeal genomes through a combination of artificial neural networks and statistics. In this regard, the structural parametrization of genetic information has been able to locate key areas within upstream regions, successfully classified in Additional file 1: Table S1. The following table contains the performance metrics of the ANN classificatory approach of three archaea with the three forms of control implemented in this study. The three controls have been averaged to present a single value/metric.


Rhodococcus opacus PD630 has considerable potential as a platform for valorizing lignin due to its innate “biological funneling” pathways. However, the transcriptional regulation of the aromatic catabolic pathways and the mechanisms controlling aromatic catabolic operons in response to different aromatic mixtures are still underexplored. Here, we identified and studied the transcription factors for aromatic degradation using GFP-based sensors and comprehensive deletion analyses. Our results demonstrate that the funneling pathways for phenol, guaiacol, 4-hydroxybenzoate, and vanillate are controlled by transcriptional activators. The two different branches of the β-ketoadipate pathway, however, are controlled by transcriptional repressors. Additionally, promoter activity assays revealed that the substrate hierarchy in R. opacus may be ascribed to the transcriptional cross-regulation of the individual aromatic funneling pathways. These results provide clues to clarify the molecule-level mechanisms underlying the complex regulation of aromatic catabolism, which facilitates the development of R. opacus as a promising chassis for valorizing lignin. Rhodococcus opacus and substrate hierarchy in aromatic mixtures for applications in lignin valorization.Gene knockout studies with analysis of aromatic sensors identify transcription factors involved in regulating aromatic degradation pathways in  Lignocellulose, which consists of carbohydrate polymers  and aromatic polymers , represents the most abundant raw material for the potential production of next-generation renewable biofuels and chemicals2. To date, the major components of lignocellulose—cellulose, and hemicellulose—have been efficiently converted into various chemicals through biochemical routes3. Lignin, which is the second most abundant polymer on earth, holds promise as a renewable feedstock for the production of fuels and platform chemicals, due to its high carbon-to-oxygen ratio (above 2:1) and great energy density7. Moreover, recent research indicates that converting lignin to high-value fuels and chemicals would improve the overall competitiveness of biorefineries12. However, due to the structural heterogeneity of lignin, the depolymerization process typically results in diverse aromatic products, which are challenging to valorize13; consequently, lignin is still under-utilized and treated primarily as waste14. To date, the most predominant strategies used for valorization—including depolymerization and fragmentation—require extensive separation and purification procedures, which are commercially non-profitable due to low yields and the low quality of their final products.The serious environmental problems caused by society’s dependence on fossil fuels, including climate crisis and ocean pollution, make it important to seek alternative methods to produce chemicals and fuels, particularly from renewable non-food biomass16. Diverse aromatic streams can be funneled into uniform compounds (catechol (CAT) and protocatechuic acid (PCA)) and then be degraded through the β-ketoadipate pathway17, a process that can potentially overcome the challenges associated with the heterogeneity of lignin breakdown products18. Rhodococcus opacus PD630 , a Gram-positive soil bacterium that has the natural ability to tolerate and consume toxic aromatic compounds, has been considered a promising chassis for producing valuable products from aromatics23. Previous transcriptomic analysis of R. opacus cells grown on single aromatic compounds has identified the distinct funneling pathways for five lignin model compounds 24. Moreover, the roles of the two branches of the β-ketoadipate pathway in the degradation of those five compounds have been confirmed by gene knockout experiments18.Beyond chemical processing, biological treatment is a promising choice for lignin valorization; particularly, bacterial systems are increasingly attracting attention due to their inherent “biological funneling” processes25. As genomic, genetic, and biochemical data have been accumulated, various regulatory proteins that control the expression of the aromatic degradation pathways have been identified and classified into different protein families26. Of additional interest are the sensory mechanisms of the regulatory elements, because regulatory proteins and their cognate promoters have the potential to be developed into biosensors that can be used in many applications, including drug discovery, biomedicine, food safety, defense, security, and environmental monitoring27. In R. opacus, comparative transcriptomics has revealed that the five aromatic funneling pathways previously identified were significantly upregulated in response to a subset of the aromatic compounds tested, suggesting that the expression of those pathway genes is likely to be tightly controlled by specific regulatory mechanisms. Furthermore, via the application of whole genome sequencing and comparative genomics, we have identified different families of transcription factors (TFs) that are located adjacent to R. opacus’s proposed aromatic catabolic pathways24. However, the specific roles of these TFs in regulating the aromatic degradation pathways—such as the signals that trigger pathway expression and the exact mechanisms of activation and/or repression—are still unclear.Constitutively maintaining this catabolic flexibility could impose a metabolic burden on the host microbe, but this handicap is typically overcome by arranging the genes of each degradative pathway as operons whose summed expression is controlled by specific regulators and inducers. Thus, the success of a particular catabolic pathway depends not only on the efficacy of the catabolic enzymes but also on the specific regulatory elements governing their expression30, but the number of studies conducted on substrate combinations containing only aromatic compounds is limited. To date, most studies have focused on benzoate and 4-hydroxybenzoate, which are commonly metabolized via the two parallel branches of the β-ketoadipate pathway. For example, in the γ-proteobacteria Pseudomonas putida PRS2000 and Acinetobacter sp. Strain ADP1, benzoate has been found to be the preferred substrate of the two32. Similarly, in the β-proteobacterium Cupriavidus necator JMP134, the same utilization hierarchy between benzoate and 4-hydroxybenzoate has been observed33. In Rhodococcus sp. strain DK17, a catabolite repression-like response has been reported when cells are simultaneously provided with benzoate and phthalate34. Moreover, benzoate catabolite repression of phenol degradation has been observed in Acinetobacter calcoaceticus PHEA-235. Finally, in R. opacus, in a mixture of the five lignin model compounds previously described, benzoate was found to be consumed preferentially18, which suggests the existence of a substrate hierarchy. Although it is important for microbial lignin conversion strategies, this sequential consumption order is still underexplored.In natural environments, carbon sources are commonly found as heterogeneous mixtures. To handle these mixtures, including the portion of aromatic compounds that are toxic, most bacteria have evolved a hierarchy of substrate utilization that enables them to quickly adapt their intracellular metabolic network toward a preferred substrate, which is vital for competition in these environments. This phenomenon, termed carbon catabolite repression (CCR), has been extensively reported in the utilization of sugar mixtures and non-sugar substratesR. opacus promoters that can detect aromatic compounds and by comparing the cell growth and aromatic consumptions of these TF deletion mutants to those of the wild type (WT), we evaluated the roles of the candidate TFs in regulating the degradation pathway of each lignin model compound. To establish the substrate hierarchy of the tested lignin model compounds, we performed time-course analyses of the consumption of individual aromatics in the mixture, revealing that these compounds were consumed in a distinct order. Moreover, by testing the responses of the funneling pathways in WT cells grown on both individual lignin model compounds and a mixture, we confirmed that R. opacus can differentially and specifically regulate the funneling pathways in response to specific compounds, which is critical for the utilization of the preferred aromatic substrate. Taken together, these results advance our understanding of the regulatory patterns of the aromatic degradation pathways, which is critical to constructing a more efficient bacterial chassis for comprehensively utilizing lignin. Moreover, these insights into the mechanism of hierarchical utilization of aromatics in R. opacus are of great significance for achieving rapid consumption of complex aromatic mixtures, enabling more cost-effective conversion of lignin into fuels, chemicals, and materials.In this study, to identify those TFs involved in regulating the degradation of lignin model compounds, we selected potential TFs in the genomic neighborhood of aromatic operons and knocked them out via homologous recombination. By using metabolite sensors derived from native catB  strain was unable to grow using phenol, guaiacol, and benzoate as sole carbon sources, demonstrating that those three compounds are metabolized through the CAT branch of the β-ketoadipate pathway in R. opacus18. Additionally, by using RNA-Seq, several funneling pathway clusters, including those for phenol, guaiacol, and benzoate, were identified18. Because those genes were found to be upregulated by R. opacus in the presence of the relevant compounds, it was hypothesized that the induction of the funneling pathways is controlled by specific regulatory mechanisms. To identify the respective protein regulators, potential TFs, encoded by genes adjacent to the catabolic clusters, were selected  were identified in the R. opacus genome, and these genes were significantly upregulated in the presence of phenol24. Furthermore, two AraC-type TFs, pheR1 (LPD06739) and pheR2 (LPD06574), which are located adjacent to these clusters but transcribed divergently  with the gfp+ reporter gene were constructed, and expressed in the WT strain  and measured the promoter activity of PpheB1 and PpheB2, respectively, using the previously-described gfp+ constructs. In ∆pheR1, expression from promoter PpheB1 was severely inhibited but remained detectably ON , the fluorescence output of the promoter PpheB1 was null, suggesting that this promoter may have crosstalk with TF pheR2 . In the strain expressing sgRNA PHE_1 (targeting pheB2), no significant increase in OD600 was observed under the induced condition, but growth was comparable to that of the control strain when uninduced —which is located adjacent to the deduced guaiacol funneling cluster—was selected as a knockout target  with the guaiacol funneling pathway  was identified, which is responsible for the conversion of 4-hydroxybenzoate to protocatechuate. Adjacent to this gene are two proposed TFs, IclR-type regulator hbaR1 (LPD06765) and Tet/AcrR-type regulator hbaR2 (LPD06763) . In the WT strain, the promoter activity was markedly increased during cultivation on 4-hydroxybenzoate, but not on PCA or on CAT  Fig. . To studain Fig. . These rcer Fig. . We alsoain Fig. . These rvanR1 (LPD00562) and RrfR-type regulator vanR2 (LPD00561), are found adjacent to the vanillate funneling pathway  are clustered but transcribed divergently  were found 80 nt and 127 nt upstream of the translation start sites of catA and catR, respectively. Additionally, the −35 and -10-like elements of the promoter regions were found with appropriate spacing within the sequences upstream of the TSSs , this finding suggests that the real effector compound may be cis-cis muconate.To study the roles of ins Fig. . In the sor Fig. . MoreoveCAT Fig. . Surprisate Fig. . As it iRhodococcus erythropolis, expression of the TF responsible for regulating the CAT degradation cluster is regulated by auto-regulatory repression37. To examine whether the regulation pattern of catR (LPD06569) in R. opacus is comparable, the promoter activity of PcatR was examined with cultivation on various carbon sources. In the WT strain, promoter PcatR showed constitutive activity when glucose was present, but in ∆catR, only basal expression from this promoter was observed under the same condition. Positing that ligand binding could change the conformation of catR, leading to the detachment of protein from the cognate promoter, we hypothesized that with ligand present in the media, the promoter activity of PcatR would be decreased. As expected, with the addition of cis-cis muconate, the output of this promoter was significantly decreased , PpheB2-GFP + (phenol responsive), and Pcyp255-GFP+ (guaiacol responsive) were expressed in WT and the mutant ∆catR. In the absence of catR, the promoter activities of PbenA and PpheB2 were identical to that of the WT strain; however, the output of the promoter Pcyp255 was significantly decreased in ΔcatR, implying that catR may work as a weak co-activator in regulating the guaiacol funneling pathway  is located in the PCA operon. To investigate how this single TF regulates the entire PCA operon, PpcaH-GFP+ and PpcaI-GFP+ constructs were expressed in both WT and the corresponding TF deletion mutant strains. For the orientation corresponding to coding region pcaH-pcaF (LPD05450-05455), promoter PpcaH was responsive to all the compounds we tested , in the presence of pcaR, carbon source did not make a significant difference in the promoter activity of PpcaI. However, in the mutant ∆pcaR, the promoter activity was dramatically enhanced on glucose, compared to that of WT, indicating that in regulating this coding region, pcaR works ligand-free as a repressor , PvanA-GFP+ (vanillate responsive), or Pcyp255-GFP+ (guaiacol responsive) were selected to test the activities of each promoter when treated with different combinations of aromatic mixtures —a transcriptional activator responsible for regulating the expression of 4-hydroxybenzoate monooxygenase pobA (LPD06764)—was knocked out. The fusion construct Pcyp255-GFP+ was expressed in this ∆hbaR1 mutant, and the resulting fluorescence data indicated a lack of transcriptional repression, regardless of whether cells were cultivated with or without 4-hydroxybenzoate . In this mutant, the conversion of 4-hydroxybenzoate to PCA is abolished, whereas TF hbaR1 is still active. Monitoring the promoter activity when cells were cultivated on different aromatic combinations showed no transcriptional repression in ∆pobA strain  is also highly upregulated when treated with guaiacol (121-477-fold)18, another compound which is degraded through the CAT branch of the β-ketoadipate pathway.Unraveling the complex transcriptional regulation of the catabolism of aromatics in rs Figs.  and 2. O26; however, assays of promoter activity in both the WT and the mutant strains indicated that the IclR-type TF hbaR1 and vanR1 both work as transcriptional activators in regulating the expression of 4-hydroxybenzoate and vanillate funneling pathways, respectively , is responsible for regulating the entire PCA operon -type activator pcaO44.IclR-type TFs are generally recognized as transcriptional repressorsly Figs.  and 4. Trs Figs.  and 6. F26. In this study, however, we found that the location and transcription orientation of IclR-type regulators varied. For instance, the catR regulator is located upstream of the CAT degradation operon, and is transcribed divergently  and shares a promoter with this operon  coding region and thus shares the promoter, resulting in a negative feedback loop (NFL). Because some of the pca genes  are also involved in the degradation of CAT, this NFL allows the enhanced PcaR to downregulate the expression of those genes, which consequently serves as a metabolic node for controlling the carbon flux of the β-ketoadipate pathway towards the TCA cycle to reduce succinate overflow47.In general, the coding gene for an IclR-type TF lies upstream of its target gene cluster and is transcribed in the opposite directiontly Fig. ; howeverway Fig. . Notablyns Figs. , arrangeron Fig. , an arra48. In this work, we found that benzoate is the most-preferred substrate among the model compounds we tested. Additionally, we were able to firmly establish the substrate hierarchy of all tested compounds in R. opacus H34. Energetic considerations cannot, however, explain the preference for 4-hydroxybenzoate, since the initial steps in the metabolisms of 4-hydroxybenzoate, phenol, and guaiacol have similar requirements for reducing equivalents. To facilitate understanding the complex intracellular dynamics, a cybernetic model has been previously developed to describe the resource allocation and microbial kinetics that influence the hierarchical utilization of carbon sources49; different variants of this model have been able to account for a variety of instances of preferential carbon uptake in E. coli50. From this resource-allocation point of view, it is reasonable to find that R. opacus prefers 4-hydroxybenzoate over phenol and guaiacol. Compared to phenol, which has redundant funneling pathways, and guaiacol, which requires an accessory demethylation pathway to detoxify formaldehyde released from the funneling process, the 4-hydroxybenzoate funneling pathway’s “single regulator-single enzyme” unit is relatively simple and thus less resource-intensive.In nature, carbon and energy resources are often limited. Thus, specific bacteria that are more efficient or more selective in utilizing the carbon sources in their habitat will have significantly higher growth rates and therefore greater competitive success than other microorganismsus Figs. –9. The p51. Specifically, in a mixture of vanillate and guaiacol, the proposed PFL regulation pattern for vanillate keeps the TF vanR1 preferentially expressed, which in turn could further promote the induction of the vanillate funneling pathway, supporting fast cell growth. Fast growth on vanillate may result in dilution of guaR, thus preventing effective induction of the guaiacol funneling pathway. While these models developed in E. coli can be used to provide a coarse-grained description of the preferential utilization of aromatics, we believe that a new generation of models, specifically and precisely tuned for R. opacus, is still needed.Intriguingly, although the vanillate and guaiacol funneling pathways both require reducing equivalents and the accessory demethylation pathway, our results indicated preferential utilization of vanillate over guaiacol , no transcriptional repression of the phenol or guaiacol funneling pathway was observed   was used as the strain (WT) to construct mutants. Unless otherwise indicated, cells were grown at 30 °C and 250 rpm in a previously described minimal salts medium B52 with different carbon sources. All the plasmids constructed in this study were confirmed by DNA sequencing and are summarized in Supplementary Table 53. ~600 bp regions up- and downstream of the selected transcriptional regulator were amplified from the genome. These two pieces of DNA fragments were assembled into a plasmid containing an E. coli origin of replication (p15a) and a chloramphenicol selection cassette. To analyze the potential function of the selected TF, the upstream region containing the promoter and ribosome-binding site (RBS) of the proposed target genes was cloned in front of gfp+. The resulting transcriptional construct was then expressed in both WT and the corresponding TF deletion mutant strains. Unless otherwise described, all plasmids were assembled in E. coli DH10B using Gibson Assembly54.For constructing the TF deletion strains, the deletion construct was assembled as described previously36. Specifically, the T7 RNAP gene was integrated into the R. opacus chromosomal neutral site (ROCI3) under the control of the phenol inducible promoter (PpheB2). The codon-optimized dcas9 from Streptococcus thermophilus (dcas9Sth1) was placed under the control of the pBAD promoter. The expression of the guide RNA was driven by T7 promoter. One sgRNA (sgRNA PHE_1) was designed to target pheB2 (LPD06575), and a strain with dcas9Sth1 and T7 RNAP but no sgRNA was used as a control. The uninduced condition represented 0 mM arabinose and 0.3 g/L PHE, while the induced condition represented 50 mM arabinose and 0.3 g/L PHE.The T7 RNAP expression platform was developed in our previous study52. Cells were transformed with ~500 ng plasmid DNA by electroporation. To generate R. opacus knockout mutants, a previously developed homologous recombination method was applied with modifications53. Briefly, a helper plasmid expressing modified viral recombinases was introduced into the strain via electroporation, and electrocompetent cells were made from this strain. The cells were transformed with ~2 μg suicide plasmid DNA. Positive colonies were verified by colony PCR. All the strains used in this study are summarized in Supplementary Table Competent cells were made as described previously600), absorbance at 600 nm (Abs600), and fluorescence were measured using a Tecan Infinite M200 Pro plate reader. For the measurements of GFP+ fluorescence, the excitation and emission wavelengths were 488 and 530 nm, respectively52. Fluorescence values were normalized using Eq.  of the test strain, Fluorescencesample is the fluorescence value of the test strain, A600control is the absorbance of the empty vector control strain, and Fluorescencecontrol is the fluorescence value of the empty vector control strain.The optical density at 600 nm  and a UV detector (at 280 nm). The temperature was set at 60 °C, and the flow rate was 1 mL/min. Mobile phase A (water with 0.1% formic acid) and mobile phase B (acetonitrile with 0.1% formic acid) were used as follows (A%/B% with a gradient elution): 92/8 at 0 min, 74/26 at 5 min, 50/50 at 8 min, and 92/8 at 10 min. Concentrations were determined by comparing UV absorbance values to a standard curve for each lignin model compound.To measure the aromatics, 200 μL samples from each cell culture were centrifuged at 3500 rcf for 5 min, and the cell culture supernatant was subsequently analyzed. For cell cultures using a single aromatic compound as the carbon source, the concentration of each lignin model compound was measured by comparing UV absorbance values to a standard curve for each lignin model compound. For cultures using the aromatic mixture, overnight cultures of WT All experiments were conducted with at least three biological replicates. Differences between the control and engineered strains were analyzed using GraphPad Prism 9.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Digestive system tumors have a poor prognosis due to complex anatomy, insidious onset, challenges in early diagnosis, and chemoresistance. Epidemiological statistics has verified that digestive system tumors rank first in tumor-related death. Although a great number of studies are devoted to the molecular biological mechanism, early diagnostic markers, and application of new targeted drugs in digestive system tumors, the therapeutic effect is still not satisfactory. Epigenomic alterations including histone modification and chromatin remodeling are present in human cancers and are now known to cooperate with genetic changes to drive the cancer phenotype. Chromatin is the carrier of genetic information and consists of DNA, histones, non-histone proteins, and a small amount of RNA. Chromatin and nucleosomes control the stability of the eukaryotic genome and regulate DNA processes such as transcription, replication, and repair. The dynamic structure of chromatin plays a key role in this regulatory function. Structural fluctuations expose internal DNA and thus provide access to the nuclear machinery. The dynamic changes are affected by various complexes and epigenetic modifications. Variation of chromatin dynamics produces early and superior regulation of the expression of related genes and downstream pathways, thereby controlling tumor development. Intervention at the chromatin level can change the process of cancer earlier and is a feasible option for future tumor diagnosis and treatment. In this review, we introduced chromatin dynamics including chromatin remodeling, histone modifications, and chromatin accessibility, and current research on chromatin regulation in digestive system tumors was also summarized. Following a 19-year effort to sequence the full human genome, the landscape of human cancers began to be revealed. One of the most valuable results of this genome sequencing effort was that epigenetic and chromatin remodeling-centered processes were closely linked to cancer development . These dDigestive system cancers rank first in tumor-associated death and rank second in the new case chart after reproductive system cancers . Since eHere, we provide a brief overview of chromatin structure, chromatin remodeling, and chromatin accessibility, the landmark studies pertaining to their roles in digestive system tumors, and we also summarize relevant clinical trials and posit new directions for future research and therapeutic approaches.The concept “chromatin” was first coined by W. Flemming in 1880 . The strChromatin remodeling and histone modifications may induce altered chromatin accessibility, and these three make major contributions to genome rearrangements. Chromatin accessibility was once termed as “a window into the genome,” which refers to other factors’ degree to physically rebind eukaryotic chromatinized DNA after histones and chromatin-binding factors bind to it . DynamicHistone tail modifications and the proteins that control them represent important components of chromatin regulation. Various types of chemical modification of histones such as histone acetylation, methylation, and ubiquitination give dynamic changes to nucleosome occupation and chromatin stages . HistoneO-acetyl-ADP-ribose during the process of deacetylation  and first histone deacetylase (HDAC) were discovered in 1996 . HATs i. Thus, htylation , 48. Dystylation . In colotylation . HDAC2 ctylation . HDAC5, tylation .SOD2) transcription by acting as a scaffold to recruit the WDR5 and KAT2A complexes to the SOD2 promoter, increasing levels of H3K4 trimethylation and H3K9 acetylation in the SOD2 promoter region and leading to increased chromatin accessibility  promoter regions of H3K18Ac and H3K27Ac levels, leading to decreased chromatin accessibility and antitumor effects (Triggering changes in chromatin accessibility is another important mechanism by which histone acetylation affects (positively or negatively) tumor progression. Histone acetylation reduces the positive charge of histones and disrupts electrostatic interactions between histones and DNA. This leads to a more accessible chromatin structure, thereby facilitating DNA access by molecules such as TFs or protein elements. Evidence for this can be found in loci within a hyper-acetylated and transcriptionally competent chromatin environment that shows higher DNase sensitivity and therefore are generally accessible . Gastricsibility . P300/CB effects . The bro effects . JQ1 is  effects . It has  effects .S-adenosyl-l-methionine (SAM) as the substrate to transfer methyl groups onto the lysine residues of histones. The amine group of lysine residues may bind one (mono-), two (di-), or three (tri-) methyl moieties (N-methyltransferase (PRMT) family leads the process of histone arginine methylation, which is considered less specific than the lysine methyltransferases  that are individually associated with GC susceptibility through a synergistic triad of actions  residues of histone H3 and H4 by adding methyl groups, which is one of the most important post-transcriptional modifications. The methylation is catalyzed by the histone methyltransferase (HMT), which uses moieties , 55. Themoieties . Histonemoieties . Methyltsferases . By incrsferases . JMJ famsferases . Regardiignaling . In addi actions . As repoogenesis . Furtheritiation .p53 and c-Myc were also decreased  seem to be necessary for the binding of TFs. Several studies have shown that histone methylation can affect the higher-order chromatin structure by recruiting chromatin remodeling complexes. For instance, BPTF, the component of the chromatin remodeler NURF, contains a PHD finger that recognizes H3K4me3 . DPF3, tecreased . In coloof c-Met .Histone phosphorylation, one of the most common post-translational modifications (PTMs), occurs at serine and tyrosine residues of histone proteins. Histone phosphorylation plays a similar role to histone acetylation in modulating nucleosome dynamics. Modified residues are imparted with a negative charge by phosphorylation, creating charge repulsion between histone and negatively charged DNA backbone, so that the association between DNA and histones can loosen and is less able to inhibit DNase I digestion , 65. In via a thioester bond. Finally, ubiquitin is transferred from the E2 enzyme to target lysine residues of specific substrate proteins by ubiquitin-protein isopeptide ligase (E3)  by affecting H3K27ac and H2Bub1 occupancy on the SPARC gene in inflammation-associated colorectal cancer  first activates ubiquitin in an ATP-dependent manner and then binds to a cysteine residue of the ubiquitin-conjugating enzyme (E2) ase (E3) . The proase (E3) . Immunohase (E3) . In addirognosis –76. Notal cancer . In hepaBAP1 gene encodes a DUB and is identified as a tumor suppressor in many types of cancers including cholangiocarcinoma  is an important TF for carcinogenesis in chronic inflammatory diseases and plays a key role in promoting inflammation-associated carcinoma in the gastrointestinal tract . 5-hmC content was positively correlated with chromatin state, and the phenomenon of decreased 5-mC content but increased 5-hmC occurred at multiple gene active transcription sites –100. Resr cancer .To achieve dynamic access to packaged DNA, cells have evolved a series of tailored regulation factors, named chromatin remodeling complex. The contribution of chromatin remodelers in regulating replication and transcription is obvious: i) specific remodelers can space nucleosomes correctly after replication to guarantee rational nucleosome position and properly arrange the whole genome. ii) Critical cis DNA elements are hidden among the densely packed nucleosomes, which lose the opportunity to interact with DNA-binding factors. Remodelers are able to slip the nucleosomes away and transiently expose the elements on the binding side. iii) The activities of DNA polymerases and RNA polymerases can be barriers to nucleosomes. Remodelers may help eject the nucleosomes or chaperone the histone octamers around the running polymerases . Becausep53, Rb, and beta-catenin. HELicase, lymphoid-Specific (HELLS), also known as LSH, SMARCA6, or PASG, is a chromatin remodeling enzyme of the SNF2 family  family remodelers consist of 8 to 14 subunits. These family remodelers generally altered more than 20% of human malignancies . BAF and2 family . Abnorma2 family . At the tastasis . SMARCB1tastasis .ARID1A encodes a subunit of SWI/SNF, and its deletion in hepatocellular carcinoma induces conversion of the A/B region, remodeling of TADs, and a reduction in chromatin loops. RAD21 is a structural subunit of the chromatin structural element cohesin, and the ATPase BRG1 of the SWI/SNF complex can physically interact with RAD21. Lack of ARID1A markedly reduces BRG1–RAD21 coupling, leading to increased chromatin accessibility and promoting hepatocellular carcinoma metastasis , upregulating ALDH1A1 expression, and attenuating KRAS-induced senescence , SNF5, BAF57, and BAF155 . ARID1A tastasis . mTORC1  in vivo . Remarka lesions . In pancnescence .ISWI (imitation switch) family remodelers include 2 to 4 subunits and are conserved from budding yeast to humans . This favia recruitment to the promoter, while HDACis can dissociate NuRD from the promoter to activate 15-LOX-1 transcription  family includes two chromodomains tandemly arranged at the N-terminal of catalytic subunits in addition to ATPase . CHD famcription .ino1 (INO80 (inositol requiring 80) family contains more than 10 subunits and was originally discovered as a protein necessary for transcriptional activation of the gene ino1 . “Split”ino1 . INO80 fino1 . HoweverKRAS mutation . In colorectal cancer, up to 20% of patients were found to carry mutations in one or more of the TET genes . However, it seems like mutation types of TET genes in solid tumors are often missense mutations with no significance.DNMT enzymes, mainly DNMT1, DNMT3A, and DNMT3B, catalyze/regulate DNA methylation. DNMT1 maintains the methylation status of newly replicated DNA strands, while DNMT3A and DNMT3B are responsible for e factor . DNA metEP300, P300, CBP, MOZ, etc.) in many cancers. The EP300 protein is a HAT that regulates transcription and chromatin dynamics. Six mutations of EP300 gene were analyzed in 193 epithelial cancers  as a highly mutated gene encoding histone H3K27 demethylase has been reported in several cancer cell lines including colorectal adenocarcinoma and pancreatic adenocarcinoma  and Epstein–Barr virus (EBV) infection and also poorly differentiated grade and advanced tumor depth  is the first technique to be applied to large-scale epigenetic mapping, followed closely by ChIP-chip to enable genome-wide detection of DNA–protein interactions . ChIP-chAs our understanding of the structure and dynamics of chromatin has improved, techniques for detecting chromatin accessibility have also made great strides. MNase , an endo- and exo-nuclease, could preferentially digest naked DNA between nucleosomes, releasing nucleosomes from chromatin and retaining the DNA fragments that are protected by nucleosomes . Early iHighly active regions of genomes commonly have an altering chromatin structure, thereby generating DNase hypersensitive sites (DHSs), which are chromatin accessible and can be cut by DNase1 . In the in vivo protein–DNA binding and then shearing chromatins with sonication, followed by phenol–chloroform extraction and detection of DNA within the aqueous phase. The regions where nucleosomes are depleted will be released into the aqueous phase of the solution, and subsequently, the chromatin-accessible subgroups of fragments can be detected by real-time PCR, tiling DNA microarrays or paired-end/single-end NGS  and class II HDACs . Several clinical trials have been conducted to validate the use of vorinostat in combination with other chemotherapeutic agents  in colorectal cancer, pancreatic cancer, and other gastrointestinal tumors. Patients with gastrointestinal tumors (NCT00455351) showed better tolerability and stability when treated with vorinostat alone with a reduced dose (vorinostat 300 mg bid for 3 consecutive days followed by 4 days of rest) or combined with radiotherapy , 173. PaClinical trials targeting histone methylation modifiers have focused on the effects on hematological malignancies such as stomatous lymphoma and non-Hodgkin’s lymphoma. As for digestive system cancer, guadecitabine alone has been examined closely in phase I/II clinical trials of colorectal cancer and hepatocellular carcinoma . Guadecitabine was administered at two doses in patients with advanced hepatocellular carcinoma who had failed sorafenib treatment (NCT01752933). The median survival of included patients was 294 and 245 days, and the most serious adverse reaction was hematopoietic system dysfunction. Future research should pay more attention to these aspects in order to identify new treatment options for cancers of the digestive tract.SWI/SNF has the broadest function of the four chromatin remodeling complexes, and drugs targeting this complex have been involved in several clinical trials, such as palbociclib, olaparib, rucaparib, bortezomib, and abemaciclib. Pancreatic neuroendocrine cancer patients with palbociclib alone had an overall survival of 33 months (NCT02806648). In patients with PDAC treated with abemaciclib+LY3023414+gemcitabine+capecitabine in different combinations, the overall survival was only about 6–10 months (NCT02981342).Conclusion and perspectiveThe regulation of chromatin dynamics by transcriptional elements and related complexes affects various pathways of digestive system tumor development, metastasis, and drug resistance and provides complex and precise control of various biological behaviors including cell cycle, metabolic program, and tumor microenvironment. The individual heterogeneity of tumors poses a very serious challenge for clinical treatment, and chromatin, which integrates genetic and epigenetic information, is a promising avenue to realize personalized treatment.In the past decade or so, tremendous progress has been made in the field of chromatin regulation and cancer mechanisms, owing to in-depth investigations of chromatin regulatory factors, how these regulatory elements act on tumors, and attempts of targeting drugs in clinical therapy. In parallel, the invention of sequencing technologies such as ATAC-seq has further advanced our understanding of chromatin regulatory features, histone modifications, etc.The current exploration of chromatin dynamics is primarily restricted to the regulation of extra-chromatin factors. It is worthwhile to consider whether chromatin already has potential accessible features in the early stage of formation. What is more, chromatin modulation-based tumor treatment strategies are rarely used in clinical training. First, there are still many limitations in regulating gene networks at the chromatin level, for example, the escape mechanisms and complexity of tumor signaling pathways under various stress stimuli, as well as diversities in the expression of a gene in a large patient population and the individual heterogeneity of downstream signaling pathways in each patient. Second, existing techniques for determining chromatin status still have many drawbacks and limitations, and there are no methods that can present the complete and dynamic genomic status of tumor patients and intervene. Application to individualized tumor treatment still requires much exploration and a long-term course of clinical trials. With the development of chromatin characterization and application, individualized tumor therapy is becoming unveiled.ZL and BZ contributed equally to this review. WW, BZ, and ZL raised the concept. ZL conducted the literature review and wrote the first draft. BZ and CQ drew the figures. TL and YW organized the tables. All authors revised and approved the final manuscript.The research was funded by the National Natural Science Foundation of China, grant number (Nos. 81773215 and 82173074) and CAMS Innovation Fund for Medical Science (CIFMS), grant number (2021-12M-1-002).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In eukaryotic cells, the genome is organized in the form of chromatin composed of DNA and histones that organize and regulate gene expression. The dysregulation of chromatin remodeling, including the aberrant incorporation of histone variants and their consequent post-translational modifications, is prevalent across cancers. Additionally, nuclear envelope proteins are often deregulated in cancers, which impacts the 3D organization of the genome. Altered nuclear morphology, genome organization, and gene expression are defining features of cancers. With advances in single-cell sequencing, imaging technologies, and high-end data mining approaches, we are now at the forefront of designing appropriate small molecules to selectively inhibit the growth and proliferation of cancer cells in a genome- and epigenome-specific manner. Here, we review recent advances and the emerging significance of aberrations in nuclear envelope proteins, histone variants, and oncohistones in deregulating chromatin organization and gene expression in oncogenesis. Each chromosome occupies a unique sub-volume in the interphase nucleus, referred to as a chromosome territory . A chromThe double-membraned nucleus functions as the primary protector of the genome. In metazoans, the nucleus not only harbors the genome but also works in tandem with the differentially-compacted chromatin to regulate its tissue-specific spatial and functional organization. The nuclear envelope comprises the nuclear lamina that maintains nuclear integrity and regulates gene expression and is interspersed with Nuclear Pore Complexes (NPCs), whose primary function is to regulate nuclear transport . Though In metazoans, type V intermediate filament proteins, the lamins, maintain the structural and functional integrity of the nucleus . The nucChromatin in eukaryotes is organized as DNA wrapped around histone octamers, forming nucleosomes. Further, the linker histone H1 is incorporated with the nucleosomes constituting the fundamental units of the chromatin fiber—the chromatosomes . HistoneCancer cells exhibit a remarkable interplay between aberrant genome organization and deregulated transcription. The cancer genome harbors mutations in both coding and non-coding regions, selectively providing tumorigenic cells with a proliferative advantage to outcompete normal cells . For insvia the interaction with the SUN and KASH domain proteins  and NPCs. The INM is lined on the inner side by a protein meshwork referred to as the nuclear lamina, which is composed of type V intermediate filament proteins—the A- and B-type lamins. The A-type lamins, lamin A/C, are produced as two somatic isoforms of prelamin A by alternative mRNA splicing at exon 10 of the LMNA gene . Lamin Aproteins . These lAberrations in nuclear morphology, such as invaginations, blebs, and micronuclei, serve as histological markers for grading tumor progression and often correlate with carcinogenesis. For instance, Haematoxylin-Eosin (HE) staining of papillary thyroid carcinoma cells and colon adenocarcinoma revealed enlarged nuclei with irregular morphology compared to normal cells with smaller and spherical nuclei . FurtherThe stability and integrity of LINC complex proteins and nuclear lamins are crucial for maintaining chromatin organization and genome stability, aberrations of which are associated with various cancers . For insRemarkably, nuclear morphology plays a vital role in modulating cell fate in the continuum of cancer progression . In part−/− mice, cells derived from EMD-null mice showed a compensatory increase in lamin A expression  pathway proteins such as Rad51 in U2OS cells . Corresper cells . FurtherNuclear pore complexes (NPCs) are ∼120 nm-wide structures in the nuclear envelope, which mediate selective transport in and out of the nucleus. In vertebrates, NPCs comprise nucleoporins (Nups), a group of ∼30 proteins, to form a ∼125MDa protein complex . In addivia a similar mechanism  have been identified and characterized across a wide range of cancer types using high-density single nucleotide polymorphism (SNP) arrays . DetermiIt is well established that CIN and aneuploidy involving whole chromosomal and focal amplifications and deletions in the genome are defining features of cancer initiation and progression . InteresInterestingly, the LMNB1 gene shows a significant number of deletions across cancers. It is unique that LMNB1 and LMNB2 genes showed only deep deletions in ovarian cancers . In ovarWe examined the status of mutations in genes that encode the nuclear envelope proteins across cancers using cBioPortal. This analysis revealed recurrent mutations in the Nesprin-1 gene—SYNE1 . MarkedlThe genome is a highly dynamic collection of genes, their regulators, and massive stretches of DNA whose function is yet to be discovered. Maintenance of genome organization involves the concerted function of numerous proteins required for the regulation of chromatin organization and gene expression. The aberrant function of chromatin organizers is associated with cancers . Here, wCCCTC-binding factor (CTCF) is a conserved zinc-finger protein that functions as a chromatin organizer and transcription factor. In association with cohesin, CTCF regulates the organization of gene loci and alternative splicing primed by its sequence-specific binding to CTCF sites. In addition, CTCF functions as an insulator to restrict the expansion of repressive marks . The humAberrant expression or occupancy of CTCF is associated with breast, lung, endometrial, gastrointestinal, prostate, and skin cancers . Of noteAnalyzing patient data sets from TCGA reveals frequent loss of the CTCF gene in breast and prostate cancer patients, correlating with hypermethylation of CpG islands and hypomethylation of other parts of the genome. CTCF depletion in a prostate cancer cell line, HPECE6/E7, shows hypermethylation of CTCF binding sites, further downregulating respective gene expression . This inCTCF functions as a double-edged sword, acting both as an oncogene as well as a tumor suppressor in a cancer subtype-specific manner. Ovarian cancers exemplify the oncogenic potential of CTCF, where metastatic lesions display elevated CTCF expression. Further, the depletion of CTCF in ovarian cancer cell lines (SKOV3 and A2780) decreased cell migration by consistently downregulating three metastasis-associated genes, including CTBP1, SRC, and SERPINE . In contin vivo remains an area of active study. Moreover, it is intriguing that environmental factors, such as diet and social interaction, also impinge on CTCF function and, therefore, chromatin organization and function  proteins, SMC2 and SMC4, but differ in their non-SMC components. Interestingly, decreased condensin expression triggers CIN, consequently driving colorectal cancer progression . In addil ploidy . This agl ploidy . HoweverApart from chromatin compaction, condensins also play moonlighting roles that include facilitating enhancer RNA transcription and enhancer-promoter looping in condensin-bound ERα (Estrogen Receptor α)-sensitive enhancers in breast cancers by recruiting p300 and RIP140 . ImmunopNon-canonical histone variants occasionally replace canonical histones in the genome, often serving two main purposes. First, histone variants are dynamically incorporated throughout the interphase with the regular nucleosomal turnover of canonical histones to sustain nucleosomal stability. Secondly, additional regulatory domains, interactors, and PTMs in non-canonical histones offer supplementary mechanisms for the control of epigenetic regulation. Since cancers are characterized by large-scale remodeling of their epigenetic landscape, canonical histones in cancers are occasionally interchanged with histone variants . StructuThe histone variant H3.3 functions as a space-filling histone when canonical H3 is evicted from the nucleosome, thus maintaining nucleosomal stability . The incin vitro and in vivo studies reveal that both the oncohistones, H3.3K27M and H3.3G34W/L/V/R, reduce H3K27me3 levels, resulting in the aberrant expression of Polycomb-group (PcG)-mediated heterochromatinized genes, though the results are more promising in in vitro systems  . H3G34V/ systems . H3.3K27 systems . Likewis systems . A possi systems . Intrigu systems . It rema systems . The ratAll histones exist as multiple variants that modulate gene expression, barring histone H4, which has only one variant. Histone H2A and H2B cumulatively have 15 non-canonical histone variants, out of which 11 are H2A variants—H2A.X, H2A.Z.1, H2A.Z.2.1, H2A.Z.2.2, H2A.Bbd, H2A.J, H2A.B, TH2A, H2A.P, macroH2A.1.1, macroH2A.2, and macroH2A1.2, and four are H2B variants—H2BE, H2B.S.M, TH2B, and H2B.W , which aBoth H2A.Z and H3.3 maintain an open conformation of chromatin in nucleosome-depleted regions of the promoter for transcription factors to interact with gene promoters resulting in their transcriptional upregulation . ConsistH2A.Bbd, a member of the short H2A family, is a testis and brain-specific histone variant overexpressed predominantly in Diffuse Large B-cell Lymphomas (DLBCLs) . InteresEssentially, tail-domain mutants are well-characterized, but not limited to, H3.3. The same is true for globular domain mutations, which are better documented for histone H2 . MutatioApart from the role of histones in regulating transcription, histones are essential for modulating DNA damage response, genome organization, and chromosome maintenance. CENP-A, a centromere-specific H3 variant, is necessary and sufficient to ensure the structural and functional organization of the centromere. Heterochromatinization at the centromere is achieved by recruiting RNAi-based DICER machinery and SUV methyltransferases . MoreoveThe two fundamental functions of CENP-A include 1) centromere formation and maintenance and 2) nucleation of checkpoint assembly proteins involved in chromosomal segregation. The organization of the centromere is dynamic during the various cell cycle stages, contributing to chromatin reorganization. During the early G1 phase, CENP-A molecules form a rosette-like structure nucleated by HJURP, facilitating a 3D ring-like organization during the G1 phase . During INK4A, a cell cycle blocker (Cellular senescence is a state of dormancy where the cell ceases to divide. Senescence involves shortened telomeres, increased DNA damage, stalled replication, nuclear deformities, mitochondrial dysfunction, and aberrant genome organization . After a blocker .Remarkably, extensive topological changes in chromatin compartmentalization accompany senescence, obfuscating the spatial separation between the A and B compartments. Microscopy and polymer modeling of chromatin reveals that the spatial organization of chromatin compartments is drastically altered in tumor cells. Following this finding, an additional intermediate Compartment I has been proposed that interacts with both A and B compartments in normal tissue and inclines toward the B compartment in cancerous tissue . Cells uThe induction of senescence in cancer cells serves as a traditional therapeutic approach by targeting p53, mTOR, PI3K, and BCL-2 family proteins using senolytic agents . HoweverThe LADs are parts of chromatin domains that are largely in a state of repression. Surprisingly, the simultaneous loss of all lamin forms in mouse embryonic stem cells did not change the overall TAD structure but reorganized the inter- and intra-TAD interactions, further altering transcriptional output . It is iOvarian cancers harbor homozygous deletion in the LMNB1 gene, while the loss of lamin A/C leads to poor prognosis and enhanced metastatic potential of cells . In the Additionally, TCGA data retrieved from GEPIA2 shows that most of the cancers show upregulated levels of lamin B1. It is known that lamin B1 overexpression sequesters 53BP1, a crucial mediator of the NHEJ pathway . It remaLamin A regulates gene expression by interacting with various chromatin modifiers, the deregulation of which promotes cancers. For instance, lamin A directly interacts with and prevents the proteasomal degradation of SUV39H1, the writer of the H3K9me3 inactive mark , the dysIn cancers, chromatin organizers harbor mutations in various domains, resulting in their deregulated activity and altered binding to chromatin or lamins. For instance, sarcomas harbor high-frequency H3.3G34R and H3.3K36M mutations that directly prevent the binding of H3K36me2/3 writer NSD1/2, thus reducing PRC2-H3K36me2 interaction and increasing H3K27me3 levels . The intThe nuclear lamina is primarily associated with inactive histone marks at the nuclear periphery. However, lamins also modulate active euchromatin . How theIn summary, it is beyond any doubt that genetic mutations, aberrations in chromatin organization, incorporation of oncohistones, deregulated transcription, and defects in nuclear envelope organization and function collectively deregulate the cellular and molecular processes of cancers. Novel therapeutic targets will be identified by leveraging high-resolution single-cell approaches, such as sc-ChIP-seq, sc-ATAC-seq, and sc-Hi-C, with high-content imaging, including high-resolution FISH. Furthermore, molecular and biochemical assays remain the mainstay for the elucidation of molecular mechanisms. Collectively, these lines of evidence reveal that aberrant genome architecture serves as a precursor and promoter of cancer initiation and progression.


Escherichia coli strains designed to allow for precise control of the concentration of individual TFs enabling the study of the role of TF concentration on physiology and regulation. We demonstrate the usefulness of this resource by measuring the regulatory function of the zinc‐responsive TF, ZntR, and the paralogous TF pair, GalR/GalS. For ZntR, we find that zinc alters ZntR regulatory function in a way that enables activation of the regulated gene to be robust with respect to ZntR concentration. For GalR and GalS, we are able to demonstrate that these paralogous TFs have fundamentally distinct regulatory roles beyond differences in binding affinity.Predicting the quantitative regulatory function of transcription factors (TFs) based on factors such as binding sequence, binding location, and promoter type is not possible. The interconnected nature of gene networks and the difficulty in tuning individual TF concentrations make the isolated study of TF function challenging. Here, we present a library of  Escherichia coli strains where the concentration of individual transcription factors (TFs) can be independently tuned and measured, allowing for the analysis of copy number‐dependent TF roles in regulatory networks and cellular physiology.This study presents a library of  Techniques such as DNaseI footprinting  will repress the expression of the TF until the inducer, anhydrous tetracycline (aTC) is present. Upon addition of aTC, the TF gene is expressed and is tracked by measuring the mCherry levels at a given aTC concentration  in E. coli genome. Of these two pseudogenes, gatR is inactivated by an “IS elementâ” inserted in the middle of the gene , and hence that variant is included in our library. Despite repeated trials, the construction of relE, toxin gene, from a native toxin/antitoxin pair was unsuccessful. It is possible that even the “leaky” levels of RelE are enough to overwhelm the native expression of the toxin, RelB. As a result, relE and relB are also excluded from this library. Finally, the TF alaS is essential and the corresponding knockout is not available in the keio collection  and the number of fluorophores (N), I = vN. The arbitrary fluorescence signal measured from a microscope or flow cytometer can be converted to the number of fluorescent proteins by estimating the calibration factor, v. The techniques to measure v typically involve measuring either the fluorescence of a single molecule in photobleaching experiments  to measure the calibration factor for nine strains , we chose to use the mean of the nine measured calibration factors to estimate the number of TFs in each strain throughout the library. However, it is important to note that there may be cases where this estimate is significantly off. As such, the measurement of the TF number for other strains should be thought of as an estimate; if precise knowledge of TF numbers in a specific strain is required, an individual measurement in that specific library strain should be made. In Fig E. coli from the work of Schmidt et al  involved in rewiring the function of clusters of genes within the cell. Clearly, there will be physiological consequences for altering the concentration of some of the TFs. For instance, some TFs such as Crp, ArgR, CysB, and MetJ are critical for essential metabolic pathways, and under‐producing or deleting these TFs may seriously affect the fitness of the corresponding strains under certain induction conditions. On the other hand, some TFs such as Nac and PdhR may be toxic when expressed at high concentrations  and is also found in TFs from other organisms such as bltR of Bacillus and merR of transposable elements  is a fundamental indicator of the overall regulatory activity of a gene. Elucidating the mechanisms of common promoter architectures can help lay ground rules to build well‐defined genetic parts. For instance, the architecture of promoters involved in sensing heavy metals such as copper, zinc, gold, and mercury are very similar across different bacterial species Fig . Interprdt et al  the concl et al, . AccumulPzntA promoter contains the binding site for ZntR as the spacer sequence between the −35 and −10 box for RNAP binding , as seen from the black dashed curve in Fig PzntA promoter clearly responds to the titration of external zinc and shows repression at low zinc levels and activation at higher zinc concentrations with a maximum fold change of 10 when compared to the expression from a zntR knockout strain at the same zinc concentration. We next tested the effect of zinc titration in our library strain for several fixed TF copy numbers  for low concentrations of TF and up to roughly 2‐fold in the presence of hundreds of TFs. Our library strains reveal a very interesting feature of this circuit; while the repression levels seen at low zinc concentrations strongly depend on the number of TFs in the cell, activation of the promoter by the TF is largely independent of the total number of TFs; the curves collapse for concentrations above roughly 1 µM of zinc  from DL5*P are 40 times higher than from PzntA . From previous work on a handful of TFs, we anticipate that TFs regulate at these locations through pure steric hindrance where the fold change will be repressive and a reflection only of a single parameter, the occupancy of the TF /Nns), which we set equal regardless of TF‐state /Nns is altered). We solve this model for steady‐state and calculate the fold change of the target gene (the zinc transporter itself) as a function of effective zinc concentration (see r0 and r) while other parameters could be derived from the experiments (kon/koff and γ). Interestingly, when we consider the role of different promoters in this problem, we associate changes to the promoter with changes to r0 and possibly r. Since, we did not alter both the sequence and the location of the TF‐binding site , we kept the value of r fixed and changed the basal rate r0 proportionately. We plot to fold change as a function of effective zinc concentration for different ratios of r0 and r in Fig PzntA regulation; we see TF‐dependent repression and activation that is mostly copy number‐independent. Changing the promoter to DL5*P, we expect r0 to increase 38 fold  relative to the active TF‐bound rate of expression r. This implies that ZntR acting on a strong promoter will exhibit stronger repression/weaker activation whereas a weaker promoter will exhibit weaker repression/stronger activation. Consistent with our model the measured constitutive value for DL5*P is roughly 35–40 fold higher than for PzntA Fig DL5*P assuming the only change to the system is in r0. while this does not perfectly capture the DL5*P data from Fig DL5*P in the −35 and −10 boxes and consistent with our model, we see weaker repression/stronger activation for mutations that weaken constitutive expression and stronger repression for mutations that strengthen the constitutive expression . We then measured five native GalR‐GalS responsive promoters in these strains at a range of induction levels. The native promoters each have between 1 and 6 binding sites for GalR/GalS. The response of each of these promoters to titration of GalR or GalS is shown in Fig PgalP (BS1 and BS2), one site from PgalS (BS3), and the last from PgalR (BS4) for regulatory differences in response to GalR or GalS. The natural location of these sites is at −243.5 for BS1, −61.5 for BS2 and BS3, and +8.5 for BS4. We measured fold change for each individual GalR/GalS‐binding site placed at different locations along with a synthetic promoter. In this case, the binding site we introduce represents the only known TF‐binding site on the promoter. Similar to our approach with ZntR, we first introduce each binding site directly downstream of the promoter. These data are shown in Fig We next tested four distinct binding site sequences; two sites that natively regulate et al, max represents the maximum fold change at saturating TF concentration, exp(‐Δε) is the binding affinity measured as in the previous plots and NTFexp(‐Δε)/Nns is the effective TF concentration, i.e., TF copy number normalized by the binding site affinity. Importantly, the steric model fit to that data are recovered by setting FCmax = 0. Figure max, the regulatory function of the TF, as the sole fit parameter. Figure Next, we infer the regulatory effect of GalR/GalS at other binding locations on the promoter. For this, we use a general model of gene regulation that quantifies the regulatory role of the TF  are moved by P1 transduction into E. coli MG1655 expressing constitutive TetR at the gspI locus, and the kanamycin cassette associated with the keio knockout is flipped using the frt flippase expressed from the pCP20 plasmid. These strains serve as no fluorescence (and no TF) control strains for the corresponding titratable TF strain in our library. Unless otherwise stated, all steps are performed in 96‐well plates. Primers to amplify TFs are designed using customized matlab codes. Individual TF genes are cloned by Gibson assembly into pSC101 plasmid between the Ptet and aek‐linker‐mCherry sequence. Gibson clones were confirmed by sequencing and used as a template to amplify the Ptet‐tf‐aek‐mCherry fusion gene for integration at the ybcN locus. Chromosomal integration is assisted by the lambda red recombinase proteins  expressed from pKM208  that was later reported to have an internal start codon  and grown in 2 ml volume 96‐well plates at 37°C and 250 rpm until it reaches OD600 of 0.4. Ten microlitres of these cells are then diluted into 190 µl of the same media in 300 µl volume 96‐well plates and transferred to a plate reader with an automated setting (TECAN MPro‐200). OD600 values and mCherry fluorescence are measured every 30 min for up to 20 h. The growth rate is then calculated in the exponential phase of growth. Background subtracted OD600 values are log‐transformed and a polynomial fit is performed over a sliding window. A plot of the fit values across the sliding window gives characteristic regimes: a noisy regime for the lag phase, a distinct peak in the growth phase, and a plateau for the stationary phase. The maximum of the peak value corresponds to the growth rate of the particular strain. The growth rates of the test strains are normalized by the growth rate of MG1655 (grown in the same aTC concentration as the test strains) measured on the same day. Normalized growth rates are used in hierarchical clustering to determine different characteristic features of TF titration on growth rates.For each experiment, 10 different TFs from the library (no TF control and the corresponding titratable TF strains) and wild‐type MG1655 are grown in 96‐well plates to characterize the growth and mCherry fluorescence in M9‐minimal media supplemented with glucose. The strains are grown overnight in LB and diluted 10v, for the conversion of mCherry fluorescence to TF copy number is quantified as described in Brewster et al (I1 and I2). After one doubling time (roughly 1 h or depending on the doubling time for different TFs), the microscope stage was returned to the same field of view using the saved position matrix and is imaged again (and named as daughter finder) now using both phase and mCherry channels. The exposure time for mCherry channel was set to 1 s and all nine TFs were measured simultaneously with identical experimental settings. For partition statistics to estimate the calibration factor cells saved as daughter finder are segmented using a modified version of Schnitzcells code  are picked manually by matching the segmented daughter finder with the phase image in the lineage tracker. The mean pixel intensity and area of the daughter pairs are obtained using region props. The background fluorescence is estimated as described in Ali et al  and grown in 2 ml volume 96‐well plates at 37°C and 250 rpm until it reaches OD600 of 0.2–0.4. Cells are then washed twice in 1X M9‐minimal media with no sugar and supplemented with spectinomycin to ensure cells are arrested and there is no more protein synthesis. Cells are then spotted on 2% agarose bed made with 1X‐M9‐minimal media with no sugar (and with spectinomycin) and imaged using conditions identical to that for calibration factor measurements described above. Six beds are imaged per sample in both phase and mCherry channel. Phase images are segmented using a modified version of Schnitzcells code . Each data point is a mean of single‐cell mCherry values of the given TF grown at the specified aTC concentration. Error bars are the standard error in single‐cell fluorescence. Roughly, 150–1,000 cells are analyzed per sample. The data from Schmidt et al  is used as an autofluorescent strain for microscopic measurement and the same strain transformed with the corresponding reporter plasmid serves as the constitutive strain. The ZntR‐TF‐titration strain from the library is directly transformed with different ZntR reporter plasmids. For GalR‐GalS reporter assays, the autofluorescence control strain is a double knockout of 5‐fold into fresh M9‐minimal media supplemented with glucose and different concentration of aTC. These strains are grown for 16–18 h until it reaches a OD600 of 0.2–0.3. For experiments with zinc as a co‐factor, 0.1 M stock of fresh zinc sulfate is added to the media at a concentration of 125 µM and serially diluted to the desired zinc concentrations. As zinc sulfate is volatile the stocks are stored at −20°C and thawed right before use. We observe significant degradation of zinc even when stored at −20°C and hence comparisons are usually made for experiments performed with the same batch of zinc. Unless otherwise specified, the aTC concentration used for all microscopic experiments are 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.5, 3 ng/ml and 300 µl cells are grown in 2 ml‐deep well 96‐well plate. Once in steady state, cells from different aTC concentrations are pooled and washed twice with 1X PBS and spotted on 2% low melting agarose pad made with 1X PBS. The constitutive strains and autofluorescent strains are processed the same way as the pooled library strain and all samples are imaged on an automated fluorescent microscope (Nikon TI‐E) with a heating chamber set at 37°C. For constitutive and autofluorescence sample, 15 unique fields of view are imaged resulting in roughly 100–300 cells per sample. For pooled titration strains, 40 unique fields are imaged per sample resulting in 1,000–2,000 cells per sample.For microscopic analysis, control strains and their respective constitutive and titratable strains are grown overnight in LB media and diluted 10et al, Segmentation of individual cells is performed using a modified version of the matlab code, Schnitzcells (Rosenfeld Zincout) is transported inside the cell with a rate βin whereas the exporter facilitates the export of internal zinc (Zincin) outside the cell with a rate βout. We assume that the TFs inside the cell can either be zinc bound (TF*) or free (TF). The basal expression from a TF‐free promoter (Poff) and a TF*‐bound promoter (Pon∗) are r0 and r, respectively. We further assume that the free TF‐bound promoter (Poff) completely represses the promoter. Here, by modulating the values of r with respect to the basal expression rate r0, we can study the effect of different promoter strengths, e.g., for a saturating zinc concentration, a promoter with r > r0 will act as an activator whereas a promoter with r < r0 will act as a repressor. The set of ordinary differential equations (ODEs) describing the dynamics of the system is given below.Based on the observation in Fig γ1 and γ2 are the rates at which the TF binds and unbinds intracellular zinc. Pon and Pon∗ are the concentrations of promoters bound by TF and TF*. γ is the degradation rate of the transporter, E. For simplicity, the transporter (E) itself is the reporter gene here. In Fig kon and koff) of TF and TF* to the promoter is the same or equivalently, both the TF and TF* bind DNA with a rate kon and unbind with a rate koff. The right‐hand side of the above equations is set to zero in order to obtain the steady‐state values of each component. Interestingly, the steady‐state solutions require only few free parameters, r, r0, and experimentally observed parameters, the ratio kon/koff and the degradation rate γ = log(2)/cell cycle. In Fig Zoutγ1βin/γ2βout).Here, Vinuselvi Parisutham: conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, and writing—review and editing. Shivani Chhabra: conceptualization, data curation, investigation, and methodology. Md Zulfikar Ali: software and methodology. Robert C Brewster: conceptualization, formal analysis, supervision, funding acquisition, investigation, writing—original draft, project administration, and writing—review and editing.CRediT author contributions listed above, the contributions in detail are:In addition to the RCB conceptualized the library. VP and SC performed all the experiments for library construction. VP performed all the case study. MZA helped with the model. RCB and VP developed the manuscript.The TF library strains are available for distribution, free of cost.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Table EV1Click here for additional data file.Source Data for Figure 1Click here for additional data file.Source Data for Figure 2Click here for additional data file.Source Data for Figure 3Click here for additional data file.Source Data for Figure 4Click here for additional data file.Source Data for Figure 5Click here for additional data file.


AE3 activity evoked Cl- influx in neurons which was generally associated with increased excitability and susceptibility to pain. We demonstrated that the S-palmitoylation status of Cys47 could affect the interaction between PRDX6 and the C-terminal domain of AE3, thereby regulating the activity of AE3 anion exchanger enzyme in the nervous system. The results highlight a central role for PRDX6 palmitoylation in protection against diabetic neuropathy.Diabetic neuropathy is regarded as one of the most debilitating outcomes of diabetes. It can affect both the peripheral and central nervous systems, leading to pain, decreased motility, cognitive decline, and dementia. S-palmitoylation is a reversible posttranslational lipid modification, and its dysregulation has been implicated in metabolic syndrome, cancers, neurological disorders, and infections. However, the role of S-palmitoylation in diabetic neuropathy remains unclear. Here we demonstrate a potential association between activating protein palmitoylation and diabetic neuropathy. We compared the proteomic data of lumbar dorsal root ganglia (DRG) of diabetes mice and palmitoylome profiling data of the HUVEC cell line. The mapping results identified peroxiredoxin-6 (PRDX6) as a novel target in diabetic neuropathy, whose biological mechanism was associated with S-palmitoylation. Bioinformatic prediction revealed that PRDX6 had two palmitoylation sites, Cys47 and Cys91. Immunofluorescence results indicated PRDX6 translocating between the cytoplasm and cell membrane. Protein function analysis proposed that increased palmitoylation could competitively inhibit the formation of disulfide-bond between Cys47 and Cys91 and change the spatial topology of PRDX6 protein. Cl Diabetes mellitus (DM), with an increasing prevalence, has systemic implications for health and quality of life. Among the various complications of diabetes, diabetic neuropathy (DN) is of the most importance, which can affect the peripheral nervous system, central nervous system, pain receptors, gastrointestinal systems, etc. Diabetic peripheral neuropathy (DPN), is characterized by mechanical allodynia, spontaneous pain, and paresthesia , affecting 25 to 30% of patients with diabetes over the course of the disease , 2. In c-HCO3- ion channel, which can cause chloride influx as CaCCs, is rarely studied in diabetic neuropathic pain.Although the precise cellular mechanisms of DN remain poorly understood, some promising clues have emerged. Recently, various ion channels are promising topics on the mechanisms of pain inducing. The dynamic of ion channels, including sodium, potassium, calcium and chloride channels, can regulate transmission and processing of pain signals –5. AmongS-Palmitoylation is a dynamic and reversible posttranslational modification of palmitate onto cysteine residue of a protein, which is catalyzed by palmitoyltransferases (PATs), a family of integral membrane enzymes . The hum-/HCO3- flux inducing pain in D-DRG. This study will advance our understanding of S-palmitoylation in diabetes and will provide a new nutritional approach for diabetes associated neuropathy.In this study, we investigated the potential role of S-palmitoylation in DN by the proteomic and palmitoylomic data, and we identified PRDX6 as a novel candidate of S-palmitoylation in diabetic lumbar dorsal root ganglia (D-DRG). Furthermore, we analyzed the gene expression of PRDX6 in human nervous system. The results also showed that S-palmitoylation modified the Cys47 of PRDX6, and this modification enhanced its interaction with anion exchanger 3 (AE3) and activated the Clhttps://hiplot.org).The D-DRG protein set was obtained from Marc’s research . They fiThe RNA-sequencing (RNA-seq) data of the AEs family for 55 tissue types was obtained from the Consensus dataset, which was based on a combination of RNA-seq data from Internally generated Human Protein Atlas (HPA) and the Genotype-Tissue Expression (GTEx) project. The transcript per million (TPM) was normalized separately using Trimmed mean of M values (TMM) to allow for between-sample comparisons. The normalized expression (nTPM) levels were calculated for each gene in every sample.The single cell RNA-sequencing (scRNA-seq) dataset was retrieved from the Single Cell Type Section of HPA. The data of scRNA-seq was performed on single cell suspension from tissues without pre-enrichment of cell types, and pseudo-bulk gene expression profiles were highly correlated with bulk RNA-seq profiles. The protein-coding genes were classified according to specificity into cell type enriched genes, group enriched genes and cell type enhanced genes. The cell type enriched genes were at least fourfold higher expression levels in one cell type as compared with any other analyzed cell type. The group enriched genes were enriched expression in 2-10 cell types. The cell type enhanced genes were only moderately elevated expression. These cluster results were visualized by the UMAP plot.We performed the protein-protein interaction (PPI) analysis by STRING 11.5 . The inpWe clustered the nodes (proteins) in the PPI network by the Markov Cluster Algorithm (MCL) using the STRING tool. The MCL inflation parameter was set as 3. Edges between clusters were shown in dotted line, and each dot color was coded by its cluster.Strength = lg (b/e), which was used to describe the enrichment effect. Here b refers to the number of proteins in the D-DRG network that were enriched in a term; e denotes the number of proteins that we expected to be enriched with this term in a random network of the same size from the genome. We used the False Discovery Rate (FDR) to describe the significance of the enrichment. FDR was the p-values corrected for multiple testing within each category using the Benjamini–Hochberg procedure.We performed the Gene Ontology (GO) Functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment for the 88 D-DRG proteins and the 23 ZDHHCs. We defined Strength as We used the CSS-Palm software (version 4.0) to predict the S-palmitoylation site of PRDX6 and CANX based on their protein sequence in FASTA format. CSS-Palm 4.0 included a forth-generation of Group-based Prediction System (GPS) algorithm and the training data set contained 583 palmitoylation sites from 277 distinct proteins. The Particle Swarm Optimize (PSO) was also integrated to GPS. The leave- one-out validation and 4-, 6-, 8-, 10-fold cross-validations were performed to evaluate the prediction performance and system robustness.We performed protein domain analysis for PRDX6 based on its protein sequence by the HMMER tool. HMMER was a tool for biosequence analysis using profile hidden Markov models. The functional domain analysis of the AE family was performed based on their protein sequence in the InterPro protein families and domains database .The Immunochemistry (IHC) data of PRDX6 was obtained from the HPA database. We chose the IHC results of PRDX6 stained by the antibody of CAB008663. The antibody staining in the cell types of glial cells and neuronal cells in the human brain was reported as not detected, low, medium, or high. This score was based on the staining intensity and fraction of stained cells.The indirect immunofluorescence (IF) microscopy was used to determine the subcellular location of PRDX6 and CANX  in human cancer cell lines. The staining of nuclei was labeled by 4’,6-diamidino-2-fenylindol (DAPI) in blue, and the endoplasmic reticulum (ER) was labeled by Alexa Fluor 647 staining for calreticulin (yellow). All IF data was obtained from the HPA database.Marc et al. have established type 2 diabetes model (BKS-db/db mice) and characterized the proteome of D-DRG by tandem mass tag labelling and mass spectrometry analysis . In the DM can affect not only the peripheral nervous system such as the DRG, but also the central nervous system. We analyzed the gene expression of PRDX6 in the brain, to explored whether PRDX6 could play a certain molecular function in the central nervous system. The bar-plot showed PRDX6 mRNA expression distributed in all the 13 regions of human brain sample (the HPA Human brain dataset), but there was low region specificity  for the D-DRG protein set and the ZDHHCs family  and Molecular Function (MF) was also performed for the D-DRG proteins and the ZDHHC family , and PRD-/HCO3-exchanger, and conformed that PRDX6-AE1 interaction could be disrupted by the Cys47Ala  mutation of PRDX6 , the four AEs have the same domain in the C-terminal. However, only AE3 has been proved to present in DRG; AE3 expression enhanced during short and long-lasting formalin-induced nociception , a Clof PRDX6 . Howeveriception , and spaiception . Since Pse of pH Figure 6+-activated chloride channels of the anoctamin , CLC chloride channels and transporters, CFTR (cystic fibrosis transmembrane conductance regulator), Best1 (bestrophin) family, as well as VRACs (volume-regulated anion channels). Besides, Na+-independent Cl–HCO3–anion exchanger, which is proved for the intracellular Cl– accumulation, is expressed in 60% of peptidergic and 30% of non-peptidergic DRG neurons  family, including AE1, AE2, AE3, and AE4, are transporters exchanging one intracellular HCO3iception . Furtheriception . TherefoThe PRDX family, with peroxidase and antioxidant activity, was comprised of six members. Among those, PRDX6 was distributed in tissues of the brain, heart, kidney, lung and testis. Recently, studies had proved that PRDX6 was involved in cancer , inflammWe demonstrated that the S-palmitoylation of Cys47 and Cys91 could affect the steric structure of PRDX6 and promote the PRDX6-AE3 interaction. On the one hand, the Cys47 and Cys91 sites of PRDX6 could form a disulfide bond, and mutation of Cys91 alone suppressed the formation of disulfide bond without affecting the function of the Cys47 site for PRDX6 . As S-pain vitro and in vivo studies that the PLA2 activity of Prdx6 was related to the secretion of neuroinflammatory factors IL-1β, IL-17 and IL-23, and could upregulate the expression of TLR2/4 and induce the activation of NF-κB (Previous researches had shown that PRDX6 had multiple functions including glutathione peroxidase (GPx) activity, lysophosphatidylcholine acyl transferase activity and phospholipaseA2 (PLA2) activity . The curof NF-κB . Currentof NF-κB . HoweverAt present, the research on the pathogenesis of diabetes and S-palmitoylation mainly focused on the bidirectional regulation of S-palmitoylation on pancreatic islet function. Under normal physiological conditions, specific G-proteins of H-Ras, Rac1 and Cdc42 were critical S-palmitoylation substrates for islet function and insulin secretion . AdditioIn our research, we compared the proteomic data of lumbar DRG of diabetes mice and palmitoylome profiling data of the HUVEC cell lines. Since palmitoylation was a non-specific reaction and thousands of proteins had been proved which could be modified by S-palmitoylation , so we cIn this study, the protein interactions included both functional and physical protein associations, which was based on different kinds of data sources. The interaction score was based on the detailed information of these sources, which would affect the result of each PPI network generated by the STRING tool. The links only indicated the connections between each dot with medium confidence, as we set 4 as the minimum required interaction score. Most of the S-palmitoylations in the human body were catalyzed by PATs of the ZDHHC family. So, potential but weaker connections of ZDHHCs could also exist, in addition to the links visualized in this network. The PPI networks revealed potential interactions among proteins based on prior knowledge, which could potentially explain that ZDHHC6 was not even connected to the cluster of their own gene families.Palmitoylation substrates were generally paired with PATs, and this correspondence was generally associated with both tissue specificity and subcellular colocalization. To search for the key PATs corresponding to PRDX6 in the nervous system, we analyzed the subcellular localization and tissue specificity of 23 human ZDHHCs. Immunofluorescence results demonstrated that PRDX6 was mainly localized in the cytoplasm and cell membrane, so we focused on the ZDHHCs localized in the cell membrane or cytoplasm and detected in the nervous system at the translation level Table 1.–/HCO3– currents through AE3. This advanced our knowledge of the association between S-palmitoylation and DN. As such, these outcomes may offer novel insights for alleviating pain in DN.The current work identifies a post-translation regulatory mechanism for DN, PRDX6 breaks the disulfide bond (Cys47-Cys91) and translocates to the cellular membrane, which depends upon the S-palmitoylation of PRDX6 at Cys47. This mechanism causes stable PRDX6-AE3 interactions, and is responsible for the enhanced the Clhttps://www.proteinatlas.org/.Publicly available datasets were analyzed in this study. This data can be found here: YZ: conception and design. YC: survey and collection of data. WW: administrative support. XZ: data analysis and interpretation. All authors manuscript writing and final approval of 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.