Spatiotemporal changes in the chromatin accessibility landscape are essential to cell differentiation, development, health, and disease. The quest of identifying regulatory elements in open chromatin regions across different tissues and developmental stages is led by large international collaborative efforts mostly focusing on model organisms, such as ENCODE. Recently, the Functional Annotation of Animal Genomes (FAANG) has been established to unravel the regulatory elements in non-model organisms, including cattle. Now, we can transition from prediction to validation by experimentally identifying the regulatory elements in tropical indicine cattle. The identification of regulatory elements, their annotation and comparison with the taurine counterpart, holds high promise to link regulatory regions to adaptability traits and improve animal productivity and welfare.We generate open chromatin profiles for liver, muscle, and hypothalamus of indicine cattle through ATAC-seq. Using robust methods for motif discovery, motif enrichment and transcription factor binding sites, we identify potential master regulators of the epigenomic profile in these three tissues, namely HNF4, MEF2, and SOX factors, respectively. Integration with transcriptomic data allows us to confirm some of their target genes. Finally, by comparing our results with Bos taurus data we identify potential indicine-specific open chromatin regions and overlaps with indicine selective sweeps.Our findings provide insights into the identification and analysis of regulatory elements in non-model organisms, the evolution of regulatory elements within two cattle subspecies as well as having an immediate impact on the animal genetics community in particular for a relevant productive species such as tropical cattle.The online version contains supplementary material available at 10.1186/s13059-021-02489-7. C. elegans [Chromatin is a complex of DNA and proteins (nucleosomes) found in the nucleus of eukaryotic cells. The non-uniform topological organization of nucleosomes across the genome, as well as their post-translational modifications, reflects a dynamic process that controls chromatin accessibility, switching between transcriptionally active euchromatin and inactive heterochromatin . The lan elegans , 8.Bos taurus indicus and Bos taurus taurus [B. indicus) are highly adapted to tropical environments, including resistance to disease and parasites, heat stress, and severe drought conditions. Considering more than half of livestock heads are found in tropical and subtropical environments [Recently, the Functional Annotation of Animal Genomes (FAANG ) and itss taurus , 15. Indronments , understB. taurus), chicken, goat, and pig [B. t. indicus) through ATAC-seq. The liver was chosen for being a central organ of metabolism, including bilirubin, bile acids, carbohydrates, lipids, xenobiotics, protein synthesis, and immunity [In the quest of detecting chromatin accessibility, the Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) has become increasingly popular . The lib and pig , 19. Herimmunity . SimilarUsing several bioinformatics approaches, such as motif discovery, as well as publicly available datasets, we aimed to functionally characterize regulatory elements in indicine tissues as well as their underlying regulatory code. That is the combination of transcription factor binding sites (TFBS) that govern the spatiotemporal regulatory activity and gene expression. This improved knowledge of regulatory annotation sheds high promise in linking sequence to phenotype and posing new questions on our current understanding of productive traits of agricultural relevance.To annotate regulatory elements in tropical cattle, we generated ATAC-seq data from liver, hypothalamus, and muscle of three post-puberty Brahman heifers –23. AfteOpen chromatin regions were identified through consensus peaks across biological replicates in each tissue, resulting in 78,528 peaks for hypothalamus, 40,104 peaks for muscle, and 22,291 peaks for liver , which can be seen in Fig. The identified peaks were compared between tissues and tissue-specific peaks were defined. Conversely, overlapping regions in all three tissues were considered constitutive. We were able to identify 2213, 11,439, and 53,289 tissue-specific peaks for liver, muscle, and hypothalamus, respectively, and 11,983 constitutive regions , and HNF1A/B NES = 10.59)  presented significant co-expression with HNF4 Fig. . Also, HThe 11,439 muscle-specific ATAC-seq peaks were converted into 10,067 human genome regions which were enriched for a family of master regulators of muscle differentiation Fig. , namely The 53,289 hypothalamus-specific ATAC-seq peaks were converted into 48,067 human genome regions which were enriched for an important family of transcription factors for neuronal development Fig. , namely Considering RNA-seq data in hypothalamus, from the 2241 predicted SOX targets, 1632 presented gene expression, including the top TFs with the only exception of SOX3. From the expressed targets included in the co-expression analysis, 360 (22%) presented significant results  not only to identify regulatory elements but to annotate their possible biding sites and targets in each tissue. We were able to identify HNF4 as a key regulator in liver, MEF2 in muscle and SOX in hypothalamus, and support those results based on gene co-expression and publicly available ChIP-seq and FAIRE-seq data. In addition, we compared B. indicus and B. taurus data and identified potential indicine-specific open chromatin regions, which mostly correspond to distal regulatory elements.The vast non-coding portion of the bovine genome, the one that regulates epigenetics changes, responds to environmental stimuli, and determines cell and tissue activity, is only starting to be characterized , 18, 19.The peaks detected in the ATAC-seq data are expected to correspond to regulatory regions harboring functional combinations of regulatory elements, which dictate their spatiotemporal function . UnravelThe proportion of peaks falling in promoters and intergenic regions changes when we look at tissue-specific peaks and constitutive regions. Although we can still see an enrichment around TSS for all subsets, the percentage of tissue-specific peaks within promoter regions drop dramatically . First, reads quality was accessed using the tool FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). After checking no adapter contamination existed, sample reads were aligned to the cattle reference genome (ARS-UCD1.2) using HISAT2 v2.1.0 with -k 10 to allow for multiple alignments [ATAC-seq data processing and alignment was completed using the Harvard pipeline (ignments . Summaryignments .https://github.com/jsh58/Genrich) including all biological replicates per tissue and parameters -j (ATAC-seq mode) -r (remove PCR duplicates) -e MT  -p 0.01 . Genrich analyzes reads that map to multiple locations in the genome by adding a fractional count to each location, allowing for peak detection in regions that are otherwise inaccessible to the assay. Moreover, it calls peaks for multiple biological replicates collectively by first analyzing the replicates separately and then combining the multiple replicates’ p values at each genomic position using Fisher’s method to identify significant consensus peaks per tissue.Peak calling was performed using the tool Genrich v0.6.1 (available at Bos taurus annotation libraries TxDb.Btaurus.UCSC.bosTau9.refGene [https://www.bioconductor.org/packages/release/bioc/vignettes/ChIPseeker/inst/doc/ChIPseeker.html). Briefly, the ChIPseeker covplot function was used to calculate and visualize the coverage of peak regions over chromosomes. Then, the profile of peaks binding to TSS regions was visualized by first defining the TSS regions as ± 3 kb of TSS sites, and then aligning the peaks that were mapped to these regions using the ChIPseeker getTagMatrix function and TxDb.Btaurus.UCSC.bosTau9.refGene database as reference. Heatmaps of peak profile around TSS were produced using ChIPseeker tagHeatmap function and peak distribution profiles were produced using ChIPseeker plotAvProf which generate confidence intervals estimated by bootstrap method. Peak annotation to functional categories was performed by ChIPseeker annotatePeak function, which reports the genomic region of the peak , the position and strand of the nearest gene, and the distance to TSS of the nearest gene using the org.Bt.eg.db database as a reference. ChIPseeker plotDistToTSS was used to calculate the percentage of peaks upstream and downstream from the TSS of the nearest genes and visualize the distribution.After peak calling, peak location was accessed by the R package ChIPseeker  using as.refGene  and org..refGene    for eachcisTarget v6.0 [http://iregulon.aertslab.org/collections.html#motifcolldesc). The i-cisTarget tool contains motif information across seven species, including cow, and those motifs were previously scored for the enrichment of homotypic clusters of PWM using a Hidden Markov Model from the tool Cluster-Buster [To identify enriched TFBSs within tissue-specific peaks, peaks coordinates in each tissue were converted first to human hg38 coordinates using the liftOver tool   regions predicted as targets for the top TF in liver, muscle, and hypothalamus were converted back to ARS-UCD1.2 coordinates using the liftOver tool as described before . Next, twww.ebi.ac.uk/biosamples) under the submission identifiers GSB-113 and GSB-8708 [To validate the relationships between the master regulator of each tissue (top TF) and its predicted targets at transcriptional level, we used the previously described RNA-seq data of liver and hypothalamus to investigate gene co-expression –23. In aGSB-8708 . RNA-seqGSB-8708 . The EdgGSB-8708  was usedGSB-8708 . PCIT deGene Regulatory Network visualization was performed using Cytoscape 3.6.0 . Genes hB. taurus and B. indicus and identify indicine-specific regions, we used data from [Bos indicus and Bos taurus cattle, Padj < 0.05). For this comparison, we used bedtools intersect -wa to identify overlaps.To compare open chromatin regions between ata from . Brieflyata from . To idenata from  as descrata from  and are Additional file 1. Summary mapping statistics per sample.Additional file 2. Identified peaks and annotation for hypothalamus. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [IPseeker , and theAdditional file 3. Identified peaks and annotation for liver. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [IPseeker , and theAdditional file 4. Identified peaks and annotation for muscle. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [IPseeker , and theAdditional file 5. Distribution of peaks by chromosome for muscle (A), liver (B) and hypothalamus (C).Additional file 6. Complete distribution of genomic features overlapping peaks identified in muscle (A), liver (B) and hypothalamus (C).Additional file 7. Identified peaks and annotation for constitutive regions. The first 10 columns correspond to ENCODE narrowPeak format and the following columns are the annotation output of ChIPseeker [IPseeker .Additional file 8. Functional enrichment of gene ontology terms for tissue-specific peaks.Additional file 9.  - Enriched regulatory features in liver-specific peaks according to i-cisTarget online tool [ine tool .Additional file 10. Annotation of possible binding sites of NHF4 in cattle liver according to Cluster-Buster [r-Buster .Additional file 11. Significant partial correlation between HNF4 and its possible targets in liver using RNAseq data.Additional file 12. Enriched regulatory features in muscle-specific peaks according to i-cisTarget online tool [ine tool .Additional file 13. Annotation of possible binding sites of MEF2 in cattle muscle according to Cluster-Buster [r-Buster .Additional file 14. Enriched regulatory features in hypothalamus-specific peaks according to i-cisTarget online tool [ine tool .Additional file 15. Annotation of possible binding sites of SOX8/9/10 in cattle hypothalamus according to Cluster-Buster [r-Buster .Additional file 16. Hypothalamus-specific master regulator SOX and its predicted targets. Dotted edges represent predicted targets, continuous edges and red borders represent targets with significant co-expression using RNA-seq data.Additional file 17. Significant partial correlation between SOX8/9/10 and its possible targets in hypothalamus using RNAseq data.Additional file 18. Indicine-specific peaks in liver, muscle, and hypothalamus.Additional file 19. Indicine-specific peaks in liver, muscle, and hypothalamus which overlap selective sweeps in indicine cattle.Additional file 20. Peer review history.


Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts.Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C 2 yields during the OCM reaction.Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C Here, graph theory is proposed as a means to represent the information and knowledge found within catalyst big data where the relationships within catalyst data are represented as complex networks.18 Doing so would thus assist in revealing the underlying knowledge in catalyst big data in a comprehensive manner, leading towards a more informed way of designing catalysts.The introduction of catalyst informatics has innovated how catalysts are designed and understood based on the trends and patterns that lie within catalyst data.4 to C2H4 and C2H6.19,20,23 Big data focused on OCM catalysts are previously collected using high throughput experiments where the dataset consists of 291 catalysts with experimental conditions that result in maximum catalytic performance.4,5 If the relationships between chemical element combinations in catalysts and experimental conditions as well as catalytic performance are uncovered, it becomes possible to find key combinations for chemical elements and corresponding experimental conditions that result in high C2 yields. Here, the relationships within the OCM catalyst big data are expanded into networks that provide a basis for designing and understanding the OCM reaction from complex networks.Catalyst big data for oxidative coupling of methane (OCM) is investigated where OCM aims to directly convert CH5 It has two important features that few other catalyst datasets possess. The first feature is the consistency, which arises from the fact that the catalysts were prepared and evaluated by the exactly same methods. When datasets consist of catalyst data collected from multiple references, data inconsistency due to discrepancies in catalyst preparation and evaluation methods is a major obstacle. There are few datasets of this scale that are collected in a consistent manner, and this was achieved through high-throughput experimentation.4,5 The second feature is that these 291 catalysts are randomly selected from 36 540 compositions that can be created by combining 28 elements and 9 oxides. The frequency of appearance of individual elements and supports is uniform, without any biases toward known effective compositions, i.e. free of sampling bias. The performance of a catalyst as part of a chemical process is sensitive to reaction conditions. Evaluation under specific conditions tends to favor the catalyst that is best suited to those conditions rather than the catalyst that is truly superior. In this dataset, each catalyst is evaluated under 135 reaction conditions with different temperatures and gas compositions, and the data points with the best C2 yield are extracted and collected.The dataset used in this study is a collection of the OCM data for 291 quaternary catalysts represented by M1–M2–M3/support.5 Briefly, catalysts are prepared based on a wet impregnation method: a specified support (1.0 g) is loaded with precursors of the elements specified as M1–M3 (0.37 mmol for each), followed by drying and calcination at 1000 °C to obtain a catalyst. Support materials and precursors used are the same as those described in the literature.5 The OCM performance of the catalysts is acquired using a high-throughput screening instrument developed by some of us.4,5 The instrument automatically acquires the performance of 20 catalysts under a pre-programmed set of reaction conditions in a fixed-bed flow reactor configuration. Catalyst beds consist of quartz reaction tubes with an inner diameter of 4.0 mm filled with catalyst powder at a bed height of 10 mm. A gas mixture of a specified composition is simultaneously flowed through 20 catalyst beds heated at a specified temperature, and the composition of the effluent gas is measured using a quadrupole mass spectrometer (QMS) equipped with an auto-sampling system. The catalyst performance is obtained for 135 reaction conditions differing in the temperature and feed volume of CH4, O2, and Ar, where Ar serves as a carrier gas as well as an internal standard in QMS. As in the original dataset, the data point corresponding to the best C2 yield out of the 135 conditions is extracted, which represents the performance of a catalyst.Validation experiments are performed on the catalysts proposed from the analysis of network information. The methods of catalyst preparation and evaluation are exactly the same as those used to create the original dataset.via Gephi.21 Data from the dataset are extracted and preprocessed to account for graph nodes, edges, and edge weights. Here, graph nodes are objects that represent the catalysts, catalyst supports, corresponding experimental conditions, and the resulting C2 yields when tested via high throughput experiments. Edges represent the connections shared between two nodes while the edge weight is set to 1. In particular, the following data are extracted for network analysis: atomic elements, catalyst supports, C2 yields of the individual catalysts, C2 yield groups , CH4/O2 ratio , CH4 flow, O2 flow, Ar flow, and temperature . Note that in the case of C2 yield groups, each catalyst is assigned to a C2 yield group according to the individual C2 yield produced during the high throughput experiments . Based on previous reports, cut-off points are based on catalyst-free OCM which produces a C2 yield of 10% with a ±2% range for the “neutral” group (“C2 yield 8–12%”).5 C2 yields that are less than 8% can be seen as yields that are negatively affected by catalytic activity while catalysts with C2 yields greater than 12% can be seen as exhibiting higher degrees of catalytic activity. The preprocessed data are then transformed into an undirected graph through the Force Atlas 2 algorithm where node placement is influenced by how often nodes access other nodes (e.g. nodes that share many connections are closer to each other within the network).22 Note that node sizes and colors are adjusted for visualization purposes.Networks of the created datasets are constructed 2 yield group “C2 yield 12+%” or are found in grey areas between C2 yield groups but are found to be closer to the C2 yield group “C2 yield 12+%”. Additionally, element combinations are chosen based on how often certain element pairs appear near the C2 yield group “C2 yield 12+%” and how likely they are to pair with particular supports.Proposed catalysts are designed based on observations and information gathered from the catalyst networks illustrated in 2 yields and can be found in 4 flow (blue), O2 flow (red), Ar flow (brown), temperature (pink), CH4/O2 ratio (purple), and C2 yield group (yellow). Individual C2 yields are listed by their value.High-throughput experimental data of catalysts used towards the OCM reaction are preprocessed and transformed into a network in order to analyze how various components of data relate to others. By visualizing the data as a network, it becomes possible to make several valuable observations about the catalyst that would otherwise be difficult to obtain when analyzing it in textual format. A network is generated from the collected and preprocessed data using Gephi where individual atomic elements of each catalyst are plotted with their corresponding supports, experimental conditions, and C2 yield groups rather than others. For example, atomic element nodes such as Pd and Cu are close to the C2 yield group “C2 yield 0–8%” while atomic element nodes such as Ti and Nd are close to the C2 yield group “C2 yield 8–12%”. This suggests that these elements have a clearer tendency to result in a particular range of catalytic activity, e.g. Pd and Cu tend to result in lower degrees of catalytic activity while Ti and Nd tend to result in a neutral level of catalyst activity when compared to the catalytic activity of other catalysts in this study. In the case of the C2 yield group “C2 yield 12+%”, it becomes less obvious where the boundaries between the C2 yield groups lie. Their location between C2 yield groups “C2 yield 8–12%” and “C2 yield 0–8%” results in many elements being placed in the shared spaces between “C2 yield 0–8%” and “C2 yield 12+%” and between C2 yield groups “C2 yield 0–8%” and “C2 yield 12+%”. Further analysis of the data reveals that atomic elements that fall within these grey areas between C2 yield groups “C2 yield 0–8%” and “C2 yield 12+%” and between C2 yield groups “C2 yield 0–8%” and “C2 yield 12+%” will result in varying levels of C2 yields depending on their companion elements, supports, and experimental conditions. From this, one can understand that elements that fall within these so-called grey areas can be treated as elements whose catalytic performance is influenced by other elements or experimental conditions. Thus, the figure successfully illustrates the importance of combinatorial effects in the design of high-performance catalysts.From 4 flow, O2 flow, and Ar flow conditions are found to closely associate with particular conditions. For instance, nodes representing the CH4 flow, O2 flow, and Ar flow tend to congregate around the nodes representing temperature. For example, CH4 flows 6.0 and 11.33, O2 flows 2.83 and 3.0, and Ar flow 6.0 are found in close proximity to the node representing“700 °C” and, as a set of conditions, are close to node “C2 yield 8–12%”, this suggests that these particular experimental conditions are likely to be the conditions that elicit the best catalytic performance of the catalysts that fall within this range. Similarly, the network illustrates that the nodes representing gas flows tend to congregate around temperature nodes where particular temperatures will show closer proximity to certain C2 yield groups. Given these observations, one can understand two points: (1) gas flows tend to have share more connections with particular temperatures as seen by their congregation patterns, and (2) temperatures show more connections to some C2 yield groups over others. One can therefore treat these gas flow/temperature combinations as sets of conditions that have a stronger correlation with particular C2 yields.2 yields that fall under 8% become strikingly clear. Immediately, one can see that a temperature of 900 °C is strongly related to the C2 yield group “C2 yield 0–8%” along with CH4/O2 ratios of 4 and 6. One can also see that a large array of CH4 flow and O2 flow nodes also exhibit a strong correlation with the C2 yield group “C2 yield 0–8%” along with atomic elements Cu, Pd, Zn, and Ni. Thus, the network better illustrates elements and supports that associate with conditions that correlate with low C2 yields and therefore it may be better to avoid them when designing high-performance catalysts.While the development of the network illustrated in 2/O2 ratios. The location of the node representing the CH4/O2 ratio of 2 within the network reflects how commonly this ratio is involved with the various types of catalysts, supports, and experimental conditions that were tested through high-throughput experimentation. Given its location at the center of the network, one can assume that this particular ratio does not show preference to any particular C2 yield outcome, thereby suggesting that other factors may be at play when determining C2 yields for the cases where the CH4/O2 ratio of 2 is involved. Meanwhile, CH4/O2 ratios of 6 and 4 are clearly close to the C2 yield group “C2 yield 0–8%”, suggesting that using these particular ratios when designing experiments to test catalysts will likely hinder catalytic performance.Interestingly, transforming catalytic data into a network clarifies the outcomes of choosing different CH2 yield groups. Various elements and experimental conditions are found in areas where they share equal or similar distances between more than one C2 yield group, suggesting that particular elements or experimental conditions may associate with a particular C2 yield group depending on the other elements, supports, and experimental conditions that they may be paired with. For instance, elements such as Sr or Cs can lead to C2 yields that fall within the C2 yield range of 8–12% or lead to a yield greater than 12% depending on what they are coupled with. Similarly, elements such as Zr, Mg, and Ba fall within a grey zone between C2 yield ranges of less than 8% and greater than 12%, suggesting that the elements' ability to invoke a higher C2 yield may depend on the elements or experimental conditions that they are partnered with. While these grey zones provide insights towards designing catalysts that result in higher C2 yields, the pairing effect that occurs between elements is still largely unknown.Finally, by analyzing 2 yield of a given catalyst. Not only can one understand the likely C2 yield outcome of using different elements when designing a catalyst, but can also understand which experimental conditions can enhance the catalytic activity of the catalyst in question. Visualizing the data in this manner can therefore improve the efficiency of the catalyst design process and allow researchers to extract knowledge and apply it towards new catalysts and experimental designs.From these results, it becomes clear that transforming catalytic data into a network provides a wealth of information regarding how various components affect the C2 yields, the network is redesigned by representing element combinations as element pairs. The catalytic data are preprocessed in the same manner as previously discussed; however, catalysts are represented by the possible element pairs that can be made with the individual elements of the catalyst instead of individual atomic elements. For instance, catalyst LiKMn–MgO, which was previously represented in the network as nodes “Li”, “K”, “Mn”, and “MgO”, is now represented as the following: “LiK”, “LiMn”, “KMn”, and “MgO”. By representing catalysts by their element pairs, the ability to design new catalysts that elicit a high C2 yield based on the network visualization becomes possible as it can potentially help clarify positive combinations of elements that may have otherwise fell within the “grey areas” between C2 groups as found in 2 yields. Supports, CH4 flow, O2 flow, Ar flow, CH4/O2 ratios , temperatures , and C2 yield groups “C2 yield 0–8%”, “C2 yield 8–12%”, and “C2 yield 12+%” are also defined as nodes. Nodes are also colored according to the type of information they represent and are colored as the following: atomic element pair (light green), support (dark green), CH4 flow (blue), O2 flow (red), Ar flow (brown), temperature (pink), CH4/O2 ratio (purple), and C2 yield group (yellow). Edges represent the connections shared between two nodes while the edge weight is set to 1. For the new network, individual C2 yield values are excluded in order to focus on the element pair nodes.In order to better understand the effects of element pairing in relation to various experimental conditions and resulting C2 would be represented as LiEu, LiW, and EuW, respectively, while its support ZrO2 is represented separately. By representing the elements in this manner, the pairing effect becomes clearer. For instance, in 2 yield 12+%” and “C2 yield 0–8%”. However, when represented as pairs, one can see that element pair BaEu correlates more with the yield group “C2 yield 12+%” than with the C2 yield group “C2 yield 0–8%”. Cases like W also prove to be interesting when comparing the location of nodes between networks. In 2 yield 12+%”. In 2 yield 12+%” when paired with elements such as Cs, Mo, Hf, and Li. Meanwhile, W more closely relates to the yield group “C2 yield 0–8%” when paired with elements Pd and Sr. This therefore illustrates that the catalytic performance of elements is affected by the elements they are paired with, which can improve or worsen the catalytic activity of the catalyst.2 yield group “C2 yield 0–8%”; however, 2 yield group “C2 yield 8–12%”. The elements Ti, Ba, and Co, in the meantime, are positioned near the C2 yield group “C2 yield 8–12%” or within the grey zone between C2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. This suggests that elements that may be considered to traditionally have poor catalytic performance could potentially be improved by pairing with elements that are typically viewed as having good catalytic performance. Furthermore, the network in 2 yield groups in 2 yields higher than 12% by combining elements and experimental conditions that fall within the vicinity of the C2 yield group “C2 yield 12+%”.Representing elements in this manner also helps dispel preheld ideas that particular elements are considered to be poor. As seen in via high-throughput experiments. Atomic element combinations and potential experimental conditions are proposed using the networks illustrated in 2 yield group “C2 yield 12+%” while atomic elements such as Ca, V, Mn, and Tb are found in a grey area between C2 yield groups “C2 yield 8–12%” and “C2 yield 12+%”. Given that these elements are involved in designing catalysts that result in various C2 yields, a more detailed network like the one shown in 2 yield.In order to test the efficiency of designing catalysts based on network visualization, 32 catalyst combinations are designed and then tested 2O3 are strongly associated with the C2 yield group “C2 yield 12+%”, suggesting that these supports have a higher likelihood of resulting in C2 yields when used experimentally. From there, element combinations that are found close to these supports are analyzed. Closer analysis of 2 yield group “C2 yield 12+%” in 2 yield group “C2 yield 12+%”. Similar observations are made for elements such as Ca and Tb with pairs such as CaK, CaTi, CaNd, FeTb, MoTb, and TbTi. By listing the atomic elements according to the additional atomic elements they are paired with, it becomes easier to understand which particular combinations of elements may result in a higher C2 yield. This can help clarify cases where atomic elements fall within grey zones as the element pairs can clarify which particular combinations of elements will fall under different C2 yield groups.An initial glance at 2 yield group “C2 yield 12+%” or are found in grey areas between C2 yield groups but also show affinity for “C2 yield 12+%”. 2 yield group “C2 yield 12+%” as seen in 2 yield 12+%” range. Also, element combinations are chosen based on elements that are found to be common in element pairs near a particular support.Designing catalysts according to node placements within the networks is further investigated in order to determine the accuracy and efficiency of designing catalysts in this manner. 2 yield that can be categorized as “C2 yield 12+%”, 8 cases result in a C2 yield that can be categorized as “C2 yield 8–12%”, and 1 case results in a C2 yield that can be categorized as “C2 yield 0–8%”. From this, one can see that over half of the suggested elemental combinations result in high C2 yields; more specifically, 70% of the catalysts produced a C2 yield of 12% or greater when tested via high throughput experiments. In particular, catalysts EuMgZr–BaO, MoKW–BaO, and KYV–BaO result in C2 yields (%) of 18.82, 18.36, and 18.21, respectively, while catalysts MoCsZr–BaO, KYMO–BaO, TiCsW–BaO, MoCsW–BaO, CsZrW–BaO, and TiTbW–BaO resulted in C2 yields (%) of 17.96, 17.60, 17.45, 17.39, 17.32, and 17.14, respectively. One can therefore understand that using the constructed network to represent catalysts and experimental conditions with their respective yields can help increase the likelihood of designing a catalyst with higher C2 yields.The catalysts suggested in 2 yields of 18% – Eu, Mg, Zr, Mo, K, W, Y, and V – are highlighted in 2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. Elements that make up the catalysts that result in C2 yields of 17% – Mo, Cs, Zr, K, Y, Ti, W, Li, and Tb – are also not only found within the grey areas between C2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”, but in some cases are also between C2 yield groups “C2 yield 12+%” and “C2 yield 8–12%”. From this, one can come to the understanding that the efficiency of these elements is affected by the elements that they are paired with.The elements of these catalysts are compared against their locations within the created networks in order to better understand the reliability of network-based catalyst design. To start with, the elements that make up the catalysts that result in C2 yield groups when represented by their element pairs as listed in 2 yields become clearer. For instance, in the case of proposed catalyst “EuMgZr–BaO”, the element pair “EuMg” is found closer to the C2 yield group “C2 yield 0–8%” while element pairs “MgZr” and “EuZr” are found closer to the C2 yield group “C2 yield 12+%” and in the grey area between groups “C2 yield 12+%” and “C2 yield 0–8%”, respectively. Here, one can see that while “EuMg” may be more associated with catalysts that result in C2 yields that are low, their combination with element Zr improves the C2 yield (as seen by the placements of “MgZr” and “EuZr”). This effect is also seen with proposed catalysts MoKW–BaO and KYV–BaO, where element pairs “MoK” and “VY” share association with the C2 yield group “C2 yield 8–12%” and the remaining element pairs are found near the C2 yield group “C2 yield 12+%”. By studying the locations of these element pairs, it becomes possible to not only improve the efficiency of a designed catalyst by choosing element combinations that strongly associate with high C2 yields but also can potentially improve the efficiency of catalysts with poor performance by selectively replacing elements with other elements that result in higher catalytic performance.2 yields that fall within the category of “C2 yield 12+%” while the remaining two produce C2 yields that fall within the category “C2 yield 8–12%”. No catalysts produce yields that would fall within the C2 yield category “C2 yield 0–8%”. Thus, one can see that using the created networks to design catalysts in an informed manner can help decrease time and resources spent on catalyst development and testing while also have a higher chance of successfully returning a C2 yield that is considered to be high.A second batch of catalysts are then proposed and are presented in 2 yields of 20.38% and 19.03%, respectively, which outperform those of the remaining proposed catalysts and have also not been previously reported. Further analysis is conducted in order to better understand why these combinations may have resulted in such high yields. 2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. Given that the element pair nodes are located in the grey area between the two C2 yield groups, it is likely that the success of these elements is in someway dependent on the supports and gas flows that accompany them. For instance, supports BaO and CaO are seen to have a strong correlation with the C2 yield group “C2 yield 12+%” while support CeO2 strongly correlates with “C2 yield 0–8%”. A similar effect is also seen with LiTiW–BaO, where element pairs LiTi and TiW are seen near the C2 yield group “C2 yield 8–12%” and LiW is found within the grey area between C2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. Interestingly, the network did not include a case where any of these element pairs are connected with the support BaO. Given that the node for support BaO correlates strongly with the C2 yield group “C2 yield 12+%”, it is reasonable to believe that pairing the mid-level performing elements with a potentially high-level performing element with a support like BaO can improve the catalytic performance of the proposed catalyst. Further studies, however, are required in order to determine the long-term stability of these catalysts. These results thereby show that targeted design of new catalysts can be carried out more efficiently with the relational information that can be extracted through studying a network representation of catalytic data.Catalysts KVEu–BaO and LiTiW–BaO are also found to elicit C2 yields produced during the OCM reaction. One can see that elements either have clear associations with a particular C2 yield group or are found in areas between groups, which suggests that the performance of these so-called “grey-area” elements is influenced by other factors such as the elements they are paired with or other experimental factors such as temperature. The pairing effect of elements on the performance of catalysts is easier to understand when the data are retransformed into a network where catalysts are represented by their possible element pairs. Thirty-two catalysts are then designed using the constructed networks and then tested via high-throughput experiments with the aim of producing catalysts that result in high C2 yields during the OCM process. Out of the 32 catalysts, 23 are found to result in C2 yields greater than 12%, with 9 catalysts resulting in C2 yields of 17% or greater. Further analysis of these catalysts shows that elements that are found in grey areas are improved by elements that had correlations with high yield-producing catalysts, thereby demonstrating that catalyst performance can be enhanced through deliberate elemental pairings. Additional catalysts are designed and tested in order to confirm the efficiency of catalyst design via a network, where 7 out of the 9 catalysts are found to have C2 yields greater than 12%. Two catalysts in particular – KVEU–BaO and LiTIW–BaO – are found to elicit C2 yields of 20.38% and 19.03% and have not been previously reported, though long-term stability requires further investigation. Catalyst knowledge networks provide a way to design catalysts based on the relationships provided by catalyst data. In particular, this enables the ability to design highly active OCM catalysts. One can consider that the networks can assist further developments of catalysts, e.g. through doping or optimization of composition ratios, by providing information that could potentially lead to the enhancement of catalytic activity. If catalyst big data contains doping and ratio of composition information of catalysts, a further detailed catalyst knowledge network can, in principle, be developed. Thus, by creating networks of catalysts and experimental conditions of data produced via high-throughput experiments, catalysts with high performance can be designed in a much more efficient manner with a higher likelihood of success than traditional methods used during the catalyst design process.Transforming catalyst data generated from high-throughput experiments into networks has proven to be beneficial in several ways. To start with, by visualizing the transformation of catalyst data into networks, it becomes easier to understand correlations between atomic elements, their supports, and corresponding experimental conditions in relation to CData used to construct the networks presented in Fig. 1 and 2 have been uploaded as part of the ESI.†LT and KT conceived the idea for this analysis, determined methodologies, and wrote and reviewed the published work. LT curated catalyst data and applied network-related methods for formal analysis and visualization. TNN, SN, and AF tested designed catalysts in experiment. TT reviewed the published work and provided resources for experimental investigations. KT acquired funding for this published work.There are no conflicts of interest to declare.SC-012-D1SC04390K-s001SC-012-D1SC04390K-s002SC-012-D1SC04390K-s003SC-012-D1SC04390K-s004SC-012-D1SC04390K-s005SC-012-D1SC04390K-s006


Halobacterium salinarum has never been studied in Escherichia coli.Archaea form a third domain of life that is distinct from Bacteria and Eukarya. So far, many scholars have elucidated considerable details about the typical promoter architectures of the three domains of life. However, a functional promoter from the archaeon Halobacterium salinarum showed a promoter function in Escherichia coli. This Escherichia coli promoter structure contains − 10 box, -10 box extension and − 29 elements, however, no -35 box. The − 29 element is exercised by the TATA box in archaea. And we isolated the RM10 fragment that possessed the fusion characteristics of bacteria and archaea, which was overlapped with functionality of TATA box and − 29 elements.This paper found that the promoter of The − 29 element reflects the evolutionary relationship between the archaeal promoter and the bacterial promoter. The result possibly indicated that there may be a certain internal connection between archaea and bacteria. We hypothesized that it provided a new viewpoint of the evolutionary relationship of archaea and other organisms.The online version contains supplementary material available at 10.1186/s12866-022-02489-y. Archaea is a third domain of life different from bacteria and eukaryotes. Archaeal organisms have a unique position in the evolutionary tree of life, and share features in common with both bacteria and eukaryote. Therefore, comparative analyses of certain metabolic pathways (arginine metabolism and glycolysis ) or infoThe core promoter architecture is such a example. Considerable details about the typical promoter architectures of the three domains of life  have been elucidated. It has now been confirmed that the core promoter architecture of archaea is closely similar to eukaryotic RNA polymerase II promoters, while sharing few similarities with the bacterial paradigm. Multiple functional and statistical analysis have identified three basal promoter elements common to the three archaea groups:an AT-rich TATA box centered around − 26/−27, which is the major basal promoter element, (2) an adjacent purine-rich region around − 33/−34, which is designated as transcription factor TFB, recognition element (BRE), and (3) a weakly conserved initiator element (INR) around the transcription start site. From the perspective of the sequence and spatial requirements, the archaeal promoter elements are homologous to those of eukarya . During the transcription initiation in archaea, the TATA box and BRE function as the binding sites for archaeal homologues of the eukaryotic TATA box binding protein (TBP) and TFIIB, the latter is called as TFB in archaea. The formed TBP-TFB-promoter ternary complex recruits the RNA polymerase to specifically initiate transcription in a eukaryotic-like manner . Four diHaloarcula hispanica amyH gene promoter), which is inconsistent with the established view.However, our previous study  found suHaloarcula hispanica’s amyH gene was revealed to possess a combination of the typical characteristics  of archaeal and bacterial promoters. This suggested that the core promoters of some archaeal genes may share common features with their counterparts in bacteria [The core promoter architecture of the halophilic archaeon bacteria .Halobacterium salinarum was shown to possess promoter activity in haloarchaea (archaea) as well as in Escherichia coli (bacteria). The core functional promoters responsible for the promoter activity in haloarchaea and E. coli overlap with each other, which may play a physiological function of binding to RNA polymerase. Thus, we speculated that some evolutionary relationships may exist between basal promoter structures of halophilic archaea and bacteria. A functional promoter from Halobacterium salinarum is transcriptionally active in E. coli, which may expand our understanding of the evolutionary history of transcription.Our occurrent study provides a new evidence for this hypothesis. In the present study, a promoter of the halophilic archaeon Halobacterium salinarum R1 was constructed by using the E. coli promoter probe vector pKK232-8 . The genomic DNA fragments of H. salinarum R1 were generated by SalI partial digestion and inserted upstream of the promoterless cat (chloramphenicol acetyltransferase) gene in pKK232-8. After transforming into E. coli DH5α, the resulting transformants were selected, and then their chloramphenicol resistance was determined on LB plates containing gradient chloramphenicol. Among these putative promoter clones, two clones showed chloramphenicol resistance above 100 µg/mL chloramphenicol, while E. coli DH5α containing pKK232-8 was merely sensitive to 5 µg/mL chloramphenicol. These results demonstrated that the corresponding haloarchaea–origin inserts (a 492-bp fragment was named as RM07 and a 1848-bp fragment was named as RM10) exhibited considerable promoter activity in E. coli, confirming the RM07 fragment and the RM10 fragment (GenBank accession no. AY640305) were worth for further investigation. The RM07 fragment has firstly been investigated [H. volcanii WFD11. H. volcanii WFD11 possesses a genetic system very similar to other haloarchaea  and is widely used as the host organism in haloarchaea genetic analysis. Use a promoterless bgaH  gene [H. volcanii WFD11, respectively, and then the obtained transformants were independently measured for intracellular β-galactosidase activity. The β-galactosidase activity in H. volcanii (pSY-P1848) was determined to 98.2 mU/mg of total cellular protein, while no β-galactosidase activity was detected in H. volcanii (pSY2), demonstrating that the haloarchaea-origin RM10 fragment possesses promoter activity in haloarchaea (Archaea) as well as in E. coli (Bacteria) as expected.A plasmid genomic library of halophilic archaeon stigated  and revese) gene , 14 in pH. salinarum R1  and the E.coli ) 247 bp upstream of the ATG start codon of the rad25b gene as shown in Fig. H. salinarum R1 and this promoter is a gene of H. salinarum R1.In order to locate the functional promoter in RM10 responsible for its promoter activity in haloarchaea, the full-length (1848 bp) RM10 fragment was sequenced (GenBank accession no. AY640305) and positioned in the genome of H. salinarum R1’s transcriptome has been detected multiple new genes (including protein-coding genes and ncRNA-coding genes) that were previously missing from the original genome annotation of H. salinarum R1 [This speculation is quite conceivable, because an incomplete analysis of the narum R1 .E. coli was determined by primer extension analysis  245 bp upstream of the rad25b start codon  as shown in Fig. Next, the start site of RM10 fragment in E. coli promoter still needs to be further determined, but the 3’ boundary of the functional E. coli promoter was already set with the locating of + 1(E), which indicated that the core functional promoters responsible for the promoter activity in haloarchaea and E. coli overlap with each other.Within the scope of RM10, the precise region of the functional Halobacterium salinarum and Fig. H. volcanii. The results show that the 5’-most region, -1514(H) to -36(H), and 3’-partial region, + 50(H) to + 334(H), can be removed without substantially altering promoter activity  and + 3(H) was performed, using the wild-type promoter fragment − 35(H) to + 49(H) as a template  and + 47(E) is the major control responsible for the promoter function of RM10 in E. coli , which was similar to the promoter of H. hispanica amyH. The promoter structure of RM10 fragment existed the fusion characteristics of bacteria and archaea [In conclusion, we separated the RM10 fragment that is very particular. The RM10 possessed promoter activity in haloarchaea (Archaea) as well as in  archaea . All the archaea , 19. In  archaea . BesidesE. coli. Meanwhile, the promoter structure consisted of -10 boxes, -10 box extensions and − 29 elements, without − 35 boxes. The − 29 element overlapped with the TATA box required for the function of archaea and also performed the physiological function of binding with RNA polymerase. We hypothesized that it provided a new viewpoint of the evolutionary relationship of archaea and other organisms. A functional promoter from Halobacterium salinarum(archaea) was transcriptionally active in E. coli(bacteria), indicating that more investigations about archaea promoter activity are required in the future.In this research, We deduced that a Haloarchaea promoter (RM10 fragment) was shown promoter function in Halobacterium salinarum R1 was grown in modified growth medium (25% MGM) [Haloferax volcanii strain WFD11 was grown in modified growth medium  50 mL and H2O 1000 mL) [Escherichia coli strains DH5α and Escherichia coli strains JM110 were cultured in Luria-Bertani (LB) medium supplemented with antibiotics when necessary [25% MGM) . Haloferecessary .Halobacterium salinarum R1 was isolated from [E. coli promoter probe vector pKK232-8 [E. coli DH5α, and 17 colonies were obtained on selective plates containing 20 µg/mL chloramphenicol. Then the chloramphenicol resistance of all transformants was determined independently by the plate streak method. Two of these clones were resistant to chloramphenicol above 100 µg/mL chloramphenicol, and the corresponding inserted fragments (named as RM07 and RM10) were characterized in our previous study [The genomic DNA of ted from  and partpKK232-8  upstreamH. volcanii WFD11 and E. coli DH5α). According to the method described in [H. salinarum and H. volcanii containing plasmid pSY-P1848 . Total RNA was also isolated from E. coli containing plasmid pKK-P1848 by using the hot phenol method [H. salinarum); HVRT, 5’-AGTAGCCGGTCGAATAGAA-3’ (for the RNA sample from H. volcanii containing plasmid pSY-P1848); ERT, 5’-CGAGAATGCCACGAAGAG-3’ (for the RNA sample from E. coli containing plasmid pKK-P1848). The primer extension products were analyzed on 8% polyacrylamide/urea sequencing gels. The transcription start sites were identified by the sequencing ladders directly parallel to the run-off reverse transcripts.Primer extension analyses were performed to separately map the start sites of the transcriptions, which were derived by RM10 fragment in the two host organisms  as described previously [To facilitate promoter analysis in haloarchaea, the  on pSY1 . Use theeviously .E. coli by using the E. coli promoter probe vector pKK232-8. The 1848 bp (full-length) RM10 fragment was again used as a template to generate a series of deletion derivatives by PCR. The primers  are indicated in Fig. E. coli DH5α for promoter activity assay.The RM10 fragment was also subjected to deletion analysis in H. volcanii WFD11 as described in section .A series of scanning mutations spanned a 38 bp region of the RM10 fragment from − 35 to + 3, which were relative to the experimentally determined transcription start site in haloarchaea (designated + 1(H) as described in section E. coli (designated + 1(E) as described in section E. coli DH5α for promoter activity assay.A series of scanning mutations spanned a 38 bp region of the RM10 fragment from − 37 to + 1, which were relative to the experimentally determined transcription start site in H. volcanii transformants by the ONPG assay as described by [β-Galactosidase activity was measured in cell lysates of relevant ribed by . A certaE. coli transformants were grown in LB medium with an OD600 nm of 0.3 ~ 0.4. Samples were collected, after pelleting, the cells were resuspended in 50 mM Tris-HCl (pH = 7.5) and lysed by sonication. The cell extracts were centrifuged to remove intact cells and debris, and then the supernatants were used for CAT and total protein determinations. The CAT levels in cell extracts were determined by using the CAT ELISA kit (Roche) according to the manufacturer’s instructions. The total protein concentrations were determined by the Bradford method with BSA as a standard [In order to determine the expression of CAT, cultures of relevant standard . CAT conAdditional file 1.


Gliomas portray a large and heterogeneous group of CNS tumors, encompassing a wide range of low- to high-grade tumors, as defined by histological and molecular characteristics. The identification of signature mutations and other molecular abnormalities has largely impacted tumor classification, diagnosis, and therapy. Transcription factors (TFs) are master regulators of gene expression programs, which ultimately shape cell fate and homeostasis. A variety of TFs have been detected to be aberrantly expressed in brain tumors, being highly implicated in critical pathological aspects and progression of gliomas. Herein, we describe a selection of oncogenic  and tumor suppressor  TFs that are deregulated in gliomas and are subsequently associated with tumor development, progression, and migratory potential. We further discuss the current targeting options against these TFs, including chemical (Bortezomib) and natural (Plumbagin) compounds, small molecules, and inhibitors, and address their potential implications in glioma therapy. Gliomas represent the majority (80%) of all primary malignant central nervous system (CNS) neoplasms, affecting both the brain and spinal cord. Primary CNS tumors occur more frequently in adults 29 per 10,000) than in children and adolescents; however, they are the most common types of solid tumors among pediatric cancers. According to histological similarities and cell origins, gliomas are divided to glial  and non-glial (meningiomas and medulloblastomas) tumors. The distinct types of gliomas range from grade I to IV regarding their aggressiveness and proliferative potential, with grade IV corresponding to the most malignant phenotype 0,000 tha.cyclin-dependent kinase inhibitor 2A (p16) gene and complete chromosome 1p/19q deletion. They also involve mutations in tumor-suppressive genes such as Neurofibromatosis type 1(NF1), Phosphatase and Tensin homolog (PTEN), tumor protein p53 (p53), and Retinoblastoma (RB) and in genes that are associated with metabolism and telomere length maintenance, like Isocitrate Dehydrogenase (IDH) isozyme genes and Telomerase Reverse Transcriptase (TERT), alpha-thalassemia/mental retardation, X-linked(ATRX), and Death domain Associated protein (DAXX), respectively. The signaling axis RAS/RAF/MEK is commonly dysregulated in certain types of gliomas, with a mutation in serine/threonine protein kinase BRAF where valine is substituted with glutamic acid at amino acid 600, affecting cell growth and differentiation. In addition to genetic changes, epigenetic alterations involving DNA methylation, histone modifications, and miRNAs have emerged in the last few years as important contributors to neoplastic transformation and progression due to their interplay with gene expression pyridin-7-one 2 (ABBV-075), among other BRD4 nonspecific inhibitors that also exhibited Hh pathway restrictive potential. Consequently, they generated a derivative compound 25 by fusing a fluoro substituent at the C3 position of the pyrrole core and compound 35, with 4-methylcyclohexyl amino ousting the phenylether motif. Both molecules were shown to be efficient GLI inhibitors, while compound 25 was further shown to abrogate tumor growth in vivo ..82].Hypoxic conditions are considered as a common outcome of tumor progression and development among different cancer types, because cancer cells proliferate rapidly outgrowing the tumor’s blood supply. GBM tumors, probably because of their aggressive nature, are very keen to develop perivascular hypoxia. This is supported by immunohistochemistry that identifies HIF-2α expression in GBMs .HIF-2α in glioma xenograft models increased survival and stalled the appearance of neurological impairment. Concomitantly, upon CD44 intracellular domain inhibition, a downregulation of HIF-2α and a containment of hypoxia-induced glioma stemness were observed [HIF-2α protein is closely linked to the stem phenotype of glioma cells, which is essential for tumor recurrence and resistance to therapy. The transcription factor is selectively upregulated in GSCs but absent in normal progenitor cells ,85. Althobserved ,87,88. Mobserved .LAT1 upregulation. In particular, HIF-1α solely mediates BCAT1 transcription in GBM cells, notwithstanding that both proteins are able to bind directly to the HRE at the first intron of the BCAT1 human gene. Additional evidence of HIF-mediated reprogramming of BCAA metabolism relies on the fact that knockout of HIF1A and HIF2A significantly reduced glutamate labeling of BCAAs in GBM cells in hypoxic conditions. Altogether, HIF family is important for cell homeostasis and its members have been risen as possible mediators of tumor progression [HIFs also seem to drive the metabolic reprogramming of branched-chain amino acids (BCAAs) in GBM in response to hypoxia. BCAAs, including leucine, isoleucine, and valine, are transported to the cytosol by members of the L-type amino acid transporters family (LAT1-4) and catabolized by branched-chain aminotransferases BCAT1 and BCAT2. In GBM cells, HIF-1 and HIF-2 induce gression .HIF-1α and its target genes (PGK-1 and VEGF) expression, suggesting that HIF pathway inhibition drives ACF-mediated glioma cell death. These findings are of immense importance, since ACF results in almost 100% long-term survival, as confirmed by MRI and histological analysis [HIF-1α expression, inhibits the transcription of HIF-1α target genes and prevents angiogenesis and metabolic adaptation in gliomas [HIF-1α expression. This flavonoid has been shown to comply with Hyperbaric oxygen (HBO) in increasing the sensitivity of glioma tumors to radiotherapy in mice [HIF1α expression [Regarding the effects of hypoxic conditions in transcription of certain genes that promote the malignant properties of gliomas and neoangiogenesis, inhibition of HIF TFs and their signaling pathway components has caught the attention of the research community as a possible molecular therapeutic target. Specifically, Acriflavine (ACF), an FDA-approved small molecule, can be administered locally in the brain by penetrating the blood–brain barrier via biodegradable polymers and drive the apoptosis of glioma cells. The pathway leading to apoptosis involves the reduction of analysis . In a coanalysis . Additioanalysis . In accoanalysis . Another gliomas . Moreove in mice . In addipression .FOXM1 gene contains ten exons. To date, four isoforms of FOXM1 have been established, which rise from alternative splicing between the Vα and VIIα exons. Despite their functional differences, all isoforms recognize and bind to the consensus sequence 5′-A-C/T-AAA-C/T-AA-3′ of their target genes, but only the latter three are transcriptionally active (Forkhead Box M1 (FOXM1), also known as Trident, is a proliferation-specific factor that resides in the Forkhead box superfamily of proteins that share a preserved DNA-binding region. FOXM1 protein consists of three crucial for its function domains: the conserved winged helix DNA binding domain 1 (DBD), an N-terminal repressor domain (NRD), and a C-terminal transactivation domain (TAD) ,99. Regay active  [100,101FOXM1, in turn, is supervised at the transcriptional level by other transcription factors, which interact with cis-elements, E-boxes, and other regulatory elements contained, mainly, in its core promoter region. Transcription factors, including GLI1, CTCF, CREB, STAT3, E2F, and HIF-1a, act as activators, whereas LXRa and p53 function as repressors by direct binding to activating or repressing cis-elements of FOXM1, respectively. Additional elements, like Estrogen-Responsive Element (ERE) and E-box, can potentially bind both activating and repressive TFs, exhibiting a dual role in the regulation of FOXM1 transcription. It is also worth noting that the FOXM1 protein can bind to the FOXM1 promoter region during an autoregulatory loop. In regard to the posttranscriptional regulation of FOXM1 mRNA, several microRNAs, namely miRNA-214 and miRNA-149, exhibit inhibitory properties, in contrast with long-noncoding RNAs, like lncRNA-H19 and CCAT2, which upregulates its expression. Following the translation, the FOXM1 protein may undergo several modifications, including phosphorylation, ubiquitination, SUMOylation, acetylation, and methylation [hylation .FOXM1 and Abnormal Spindle-like Microcephaly (ASPM) expression, a protein essential for normal mitotic spindle function in embryonic neuroblasts associated with poor outcome of glioma patients. ChIP assay and luciferase reporter analysis showed that FOXM1 wields ASPM expression via the direct binding to its promoter at −236 to −230 bp and −1354 to −1348 bp [FOXM1 and MYBL2 expression are linked in gliomas. The downregulation of MYBL2 and FOXM1 by siRNAs resulted in cell cycle arrest, apoptosis, and concomitantly abrogated the expression of certain EMT and invasion markers, such as N-cadherin and MMP-2. Taken together with inhibition studies aiming the Akt/FOXM1 signaling, these results propose that transcription factor MYBL2 functions as a key downstream component of the Akt/FOXM1 axis, promoting the progression of gliomas [MRE11 and RAD51) and inhibited the Homologous Recombination (HR) pathway, an essential DNA double-stranded break (DSB) repair mechanism. In the same study, a physical interaction of FOXM1 with the phosphorylated state of STAT3 transcription factor was demonstrated under radiation, leading to the hypothesis that the two factors cooperate to establish radioresistance in GBM cells [A Disintegrin And Mettaloproteinase 17 (ADAM17) promoter upregulates its expression and maintains the ADAM17/EGFR feedback loop that promotes mesenchymal transition in GBM [FOXM1 and recruits histone acetyltransferase CBP in order to activate its transcription. This mechanism that leads to the induction of FOXM1 expression is present mostly in GSCs and is implicated in GBM progression [FOXM1 is predominantly detected in progenitor cells and regenerating tissues. Nevertheless, it is also detected in malignancies promoting aberrant cell proliferation, migration, and genomic instability, the known hallmarks of cancer . FOXM1 h−1348 bp . Further gliomas . MoreoveBM cells . SenfterBM cells . Regardin in GBM . Tao et gression .Plumbago zeylanica L., was found to cause reduction of glioma tumor growth and cell proliferation, in vivo and in vitro via apoptotic pathways. Treatment with Plumbagin downregulated the expression of FoxM1 and its downstream targets, cyclin D1 and Cdc25B, while elevated the expression of p21 and p27. These findings suggested that this natural compound may function against glioma progression through inactivation of FoxM1 [The role of proteasome inhibitor (PI) Bortezomib in gliomas has been investigated by several research groups, although the mechanism behind its antitumor effects has not been fully understood ,112,113.of FoxM1 .ATF4 is located at the 22q13.1 locus of chromosome 22. The three open reading frames (uORFs) observed in human ATF4 mRNA reside in the 5’ UTR foregoing its coding sequence are vital for the factor’s response under stressful and hypoxic conditions. At the protein level, ATF4 encompasses certain crucial motifs for its dimerization, stability, and binding to genes of interest. These motifs include an ODDD, the betaTrCP degradation recognition domain, an N-terminal TAD, and a DBD within the basic region at the C-terminal. Enclosed in the basic region, there is a sequence described as KKLKK that extends from amino acids 280 to 284 and plays an important role in nuclear targeting. Furthermore, ATF proteins mediate transcriptional regulation by recognizing the TGACGTCA consensus sequence at the promoter of their target genes [Activating transcription factor 4 (ATF4) belongs to a group of basic-region leucine zipper (bZIP) transcription regulators, which embody the CREB/ATF family. et genes ,117.ATF4 is mostly known as a stress responsive gene whose expression gets upregulated during oxygen deprivation (hypoxia/anoxia), endoplasmic reticulum stress (UPR pathway), oxidative stress, and amino acid or nutrient destitution but still possesses a part in skeletal and eye development, autophagy, and hematopoiesis. ATF4 holds a dual role in cell homeostasis due to its ability to target either adaptive or stressful condition genes that promote long-term cell survival or proapoptotic genes. The outcome of ATF4 activation is context-dependent and associates with the protein partners that it interacts with or dimerizes. For instance, heterodimers of ATF4 with C/EBPβ or C/EBPγ provoke adaptation, whereas its dimerization with CHOP results in proapoptotic signaling by regulating BCL2 and BIM. Noteworthy, ATF4 also forms heterodimers with members of AP-1 (FOS and JUN), C/EBP bZIP subfamilies, including its own family, and has the ability to homodimerize, although the homodimers do not represent a stable complex even when bound to DNA.ATF4 and some other mRNAs. Noteworthy, hypoxia has the same effect in ATF4 translation, since, during such conditions, eIF2a is phosphorylated by an indirect mechanism that implicates UPR and PERK. However, ATF4 stability is also regulated via its ODDD independently of the peIF2a presence. The posttranslational control of ATF4 is coordinated by interactions with βTrCP and hypoxia inducible PHD3 proteins. Specifically, casein kinase-dependent phosphorylation of nuclear ATF4 on Ser219 at its βTrCP recognition motif leads to binding of the βTrCP protein and, ultimately, ubiquination and proteosomal degradation of the factor. This interaction can be enhanced by the accumulation of the negative charge in proximity with the recognition motif’s region caused by phosphorylation on Thr213, Ser224, Ser231, Ser235, and Ser248. On the contrary, the binding of PHD3 protein stabilizes the ATF4 structure, probably due to proline hydroxylation at the ODDD [A dominant downstream event of many stress-induced signaling cascades is the phosphorylation of eukaryotic initiation factor 2 (eIF2a) on Ser51 of its α-subunit. A range of kinases involved in ER stress; amino acid limitation and UV exposure; viral infection; heme deprivation; and oxidative stress responses like PERK, GCN2, PKR, and HRI, respectively, are responsible for this modification. Although this alteration imposes a global suppression on protein synthesis, it heightens the translation of the ODDD ,117.ATF4 under such circumstances to survive the consequences of stressful conditions. ATF4 expression was found elevated in the malignant types of gliomas and in the high-grade tumors correlated with poor overall patient survival [ATF4 overexpressing glioma cells being bigger and displaying a polyplastic phenotype, while ATF4 knockdown cells are smaller and display a spindle-like phenotype, with a maximal two membrane extensions compared to the controls [ATF4 expression promotes glioma cell proliferation and migration. The same study showed that the ATF4 factor is responsible for the regulation of glutamate antiporter xCT (SLC7a11) expression, which is a critical tumor-induced intoxication of the brain’s microenvironment, and glutamate secretion in human malignant glioma specimens. ATF4 overexpressing tumor cells release an excess of glutamate in the microenvironment, contributing to neurodegeneration and brain swelling. In addition, glutamate secretion and ATF4-mediated function of xCT represent a candidate mechanism for the promotion of angiogenesis in ATF4 overexpressing gliomas, besides the induction of VEGF and HIF-1α expression. The xCT pathway mediated by ATF4, lastly, confers glioma tumor resistance towards chemotherapy with TMZ [Tumor growth induces stress and nutrient deficiency, which mostly affect the cells at the center of the mass. Despite the risk of being driven to apoptosis, cancer cells frequently activate the upregulation of survival ,119. ATFcontrols ,119. In with TMZ .Artemisia annua L., exhibits anticancer properties through a not-so-typical form of cell death caused by the iron-dependent production of reactive oxygen species (ROS). Experimental evidence indicates that DHA results in glioma cell death. However, this mechanism activates PERK/ATF4 as a response to ER stress, which, in turn, activates the genes that make glioma cells resistant to DHA treatment. A promising approach to overcome this problem seems to be the concomitant treatment with DHA and PERK inhibitor I (GSK2606414) or siRNA-mediated silencing of ATF4, shown to enhance the cytotoxic effects of DHA [ATF4 and other related genes were shown to switch the status of FKB-induced senescence to FKB-induced apoptosis in glioma cells [As previously mentioned, ER stress can often be triggered by anticancer agents and lead to a UPR response, which bestows tumor cells greater tumorigenic abilities and drug-resistance. Dihydroartemisinin (DHA), an active byproduct of Artemisin (ART) that derives from the Chinese medicinal herb s of DHA . Anotherma cells .Several transcription factors have also been detected to be involved in tumor suppression by modulating gene expression. Among them, members of NFI, T-box, and NZF families have demonstrated a tumor-suppressive role in gliomas with targeting potential.The Nuclear Factor I (NFI or CTF) family encompasses proteins that participate both in viral DNA replication and in gene expression regulation as transcription factors. The four components of the family, NFIA, NFIB, NIFC, and NIFX, play a decisive role in CNS development, specifically in axon guidance and outgrowth and glial and neuronal cell differentiation, as well as neuronal migration. It is of foremost importance that the expression of NFIA and NFIB persists in mature astrocytes. NFI factors are components of several hormonal and signal transduction pathways orchestrated by insulin, TGF-β, cAMP, steroid hormones, vitamin B6, TNF-α, FSH, thyrotropin, etc. ,124.At the transcriptional level, up to nine unique variants per gene have been identified, produced by alternative splicing. The longest mRNA transcript of each member contains 11 to 12 exons. The splice variants are conserved at a 90% degree among species, while it is impressive that different variants have been established in the brain with unknown significance and functional role. Thereinafter, at the protein level, NFI factors contain a DNA-binding and dimerization domain at the N-terminal and a transcription modulation domain, which is implicated in the activation or repression of transcription at the C-terminal. Of importance, the parts of the protein’s structure that are conserved among the family members include the 200–220-amino acid-long DNA-binding domain and four cysteine residues within it. Three of the cysteine residues are important for the DNA-binding process, while the fourth accounts for the susceptibility of NFI factors to oxidative inactivation and redox control. The C-terminal domain is proline-rich and, as foretold, is responsible either for transactivation or repression of target genes transcription, depending on the promoter type, the cellular context, and interaction with coactivator proteins.Although the mechanism determining whether a NFI factor is going to repress or activate transcription is not fully understood, one thing is certain, that all members need to form homo- or heterodimers in order to bind successfully to the common recognition sequence 5’-TTGGCXXXXXGCCAA-3’. The factors are also able to bind to the consensus half sites (TTGGC or GCCAA) at a lower affinity. The binding affinity is modulated by sequences close to the consensus and the configuration of the 5-nucleotide spacer region. Several posttranslational modifications of NFI proteins have been observed, including phosphorylation by cell division cycle 2 (CDC2) and JAK kinases, as well as N- or O-linked glycosylation ,125.The role of NFI factors in glial differentiation has led to investigation of their impact in the pathogenesis and progression of gliomas. In this area, the existing evidence is very contradicting, as NFI factors have been reported to promote glioma progression in some studies and suppress it in some others. Chen et al. demonstrated that NFIA and NFIB are co-expressed mainly in the same cells of GBM tumors and that their expression decreases as the tumor grade rises. This finding is expected, since these factors promote cell differentiation and high-grade tumors contain mostly undifferentiated cells. Furthermore, NFIA’s and NFIB’s presence were correlated with genes representing the mature astrocytic state. Co-staining with astrocytic (GFAP) and proliferation (Ki67) markers in GBM samples and cell lines showed that these TFs are associated to a nonproliferating and differentiated profile of cells expressing them. Overexpression of these factors was shown to be adequate for the switch of proliferative cells towards astrocytic differentiation in xenografts . AnotherOn the other hand, Yu et al. reported an enriched expression of NFIA in GBM that conferred to TMZ resistance and was associated with adverse patient outcome. Furthermore, they evaluated the functional role of NFIA in TMZ-resistant GBM. A concomitant increase in NFIA and NF-κΒ levels was observed in a TMZ-resistant cell line, in which suppression of NFIA resulted in NF-κΒ downregulation and re-sensitization in TMZ. They also demonstrated that NFIA expression was positively correlated to NF-κΒ promoter’s activity and that NFIA mediated the phosphorylation of NF-κΒ p65 unit on Ser536. IKKβ overexpression increased the levels of phosphorylated NF-κΒ, although this effect could be reversed, partially, by NFIA knockdown. This evidence indicates that NFIA promotes the resistance of GBM cells to TMZ through NF-κΒ phosphorylation .Regarding NFI role in glioma therapy, NFIA has been identified to undergo regulation from microRNA miR-302b, which decreases glioma cell survival. Specifically, miR-302b suppresses NFIA expression, which, in turn, disrupts the dose-dependent binding of NFIA to IGFBP2 promoter and the subsequent enhancement of IGFBP2 downstream signaling. Hence, this particular miRNA seems to function through a regulatory loop that involves NFIA/IGFBP2 inhibition in order to induce death of glioma cells . AdditioT-Box Transcription Factor T (TBXT), also known as Brachyury, is the founding member of the T-box protein family. The TBXT protein is located in the nucleus of notochord-derived cells, where it exerts its function as a transcription regulator of genes required in mesoderm formation and differentiation. With respect to development, Brachyury’s misexpression has been associated with several congenital defects, mainly neural tube defects, and the fact that homozygous embryos die after a few days of gestation.T-box protein sizes range from 50 to 78 kDa and consist of two significant domains: the DNA-binding and a transcription modulator, whose position varies among the family members. The DNA-binding domain is often referred to as T-box, a relatively large region that occupies the one third of the entire protein. In general, the homology ratio of the T-box varies between proteins, but some specific residues within it remain 100% conserved. Despite the sequence variations of the DNA-binding domain, all T-box proteins bind to a specific DNA element, the palindromic T-site (TCACACCT). T-box proteins are able to activate or repress the transcription of their target genes and this regulation is guided by sequences at the C-terminal part ,133.Brachyury mostly functions as a transcriptional activator and has been found upregulated in several types of cancers, including breast, lung, colorectal, prostate, testicular, and gastrointestinal stromal tumors. In addition, supporting evidence has revealed its role in the promotion of tumor cell migration, invasion, and metastasis through EMT. Pinto et al. investigated the TBXT role in gliomas in two different studies. At first, they observed a differentiation of the mRNA levels in normal brain samples of both adults and children and glioma cell lines. TBXT expression was present in normal brains while absent or at low levels in gliomas and was inversely correlated with tumor grade, and TBXT loss was linked to the mesenchymal subtype of GBM. The latter is also associated with poor prognosis and indicates the tumor suppressive role of the protein in gliomas. They further investigated the reduced expression of TBXT in gliomas using RNA-sequencing, which revealed that, within the different anatomical structures of the tumor, TBXT is preferentially expressed in sections with a higher concentration of normal cells. Moreover, they demonstrated that Brachyury was able to increase the expression of several pro-apoptotic proteins and autophagy, which was confirmed by a decrease in cell viability in vitro and, consequently, in the tumor growth observed in vivo. In their second study, they demonstrated via gene-editing methods for the overexpression of the factor in glioma cells that TBXT-positive cells exhibit reduced invasive and migratory capability and stem cell features. Additionally, the same cells displayed a higher expression of differentiation markers. Furthermore, they used TMZ-resistant and TMZ-responsive cell lines and induced TBXT exogenous and endogenous activation through overexpression and retinoic acid treatment, respectively. This activation drove the TMZ sensitization of glioma-resistant cell lines. In conclusion, these novel findings highlighted the tumor suppressive nature of Brachyury in brain cancer, impairing gliomas’ aggressive features and progression ,134.The neural zinc-finger (NZF) protein family represents a small group of specific DNA-binding proteins, which includes the Myelin Transcription factor 1 (Myt1/NZF2), Myt1-like (Myt1l/NZF1), and Suppressor of Tumorigenicity 18 (ST18/NZF3) . OverallThe components of the NZF family contain two bundles (clusters) of C2H2 zinc fingers. The first cluster is spotted close to the middle of the protein and is composed of one pair of zinc fingers, while the other one consists of three pairs in Myt1l and four in Myt1 and ST18 at the C-terminal. In addition, Myt1 and Myt1l contain one more pair within the N-terminal. Hence, the Myt1 and Myt1l structure encompasses seven and six pairs of zinc fingers, respectively. The zinc fingers are notably conserved among the three NZF members but also exhibit high levels of similarity between them within each protein. The transcription factors recognize the consensus sequence AAAGTTT through interactions with their DNA-binding domain Cys-X4-Cys-X4-His-X7-His-X5-Cys  ,142. Of Due to their participation in the evolvement of CNS and evidence of gene suppression, several studies have tried to decipher their role in gliomas. Myt1 has been shown to limit the growth of glioblastoma in a xenograft model by regulating the expression of RNA-binding protein Rbfox1 . MelhuisAt last, JLK1486, an 8-hydroxyquinoline–substituted benzylamine, was shown to induce anticancer activity in vivo through intravenous and oral administrative routes in a xenograft model and exhibited the same beneficial effects with those of TMZ. The benefits of JLK1486-treatment derive from its ability to activate various transcription factors, such as Myt1, STAT1, and peroxisome proliferator-activated receptor γ, in glioma cells. The activation of these TFs by JLK1486 had a cytostatic rather than a cytotoxic outcome of glioma cells .Overall, gliomagenesis and tumor progression rely on deregulation, among others, of the transcription factors emphasized in this review. These master regulators are responsible for glial differentiation, adaptation to stressful conditions, cell cycle control, and angiogenesis contributing to the aggressive nature and recurrence of the disease. They have also emerged as therapeutic targets and tools with prognostic values . Current approaches for the investigation of their functional role are based mostly on glioma cell lines that are considered sometimes as an inadequate model for the understanding of the disease. Thus, patient-derived cells and orthotopic xenografts (PDX) may prove as more suitable models, since they resemble the original tumors and patient characteristics . Since cFuture therapeutic approaches could be directed towards utilizing miRNAs against upregulated transcription effectors and antisense oligonucleotides as the means for the degradation of miRNAs that promote tumor development. Furthermore, it is important in this matter to increase the stability and optimize their delivery systems in order to decrease nonspecific target effects . Moreove