Deciphering the interplay between the promoter connectivity and activity of cis-regulatory elements during lineage commitment is crucial for understanding developmental transcriptional control. Here, we use Promoter Capture Hi-C to generate a high-resolution atlas of chromosomal interactions involving ~22,000 gene promoters in human pluripotent and lineage-committed cells, identifying putative target genes for known and predicted enhancer elements. We reveal extensive dynamics of cis-regulatory contacts upon lineage commitment, including the acquisition and loss of promoter interactions. This spatial rewiring occurs preferentially with predicted changes in the activity of cis-regulatory elements and is associated with changes in target gene expression. Our results provide a global and integrated view of promoter interactome dynamics during lineage commitment of human pluripotent cells.Long-range DOI:http://dx.doi.org/10.7554/eLife.21926.001 Virtually every cell in the body contains the same set of DNA, which encodes thousands of genes. The activities of these genes vary between different types of cells and at different points in time. As a result, our DNA contains a complex array of molecular switches that instruct genes to switch on and off at the right time and in the right cells. These molecular switches, termed regulatory elements, are often a long way away from the genes that they control, and this can make it difficult to find out which switch controls which genes.DNA is made up of several different building blocks known as bases and the order of these bases encodes specific information about the gene. Every human cell contains approximately two meters of DNA, which is highly folded in the cell nucleus. This three-dimensional folding allows regions that are far apart on the DNA thread to physically contact each other. To reach the genes they control, regulatory elements form loops on the DNA that are near-impossible to predict from looking at the sequence of bases alone. Mapping the locations of these loops can reveal the hidden circuitry within our DNA and help us to understand how unwanted changes (mutations) within regulatory elements may cause disease.Freire-Pritchett, Schoenfelder et al. set out to reveal how loops between genes and their regulatory elements change as the stem cells specialise into immature brain cells. The experiments show that the pattern of DNA loops is extensively altered after the stem cells specialise into brain cells, that is, some loops are lost and new ones form. Furthermore, the regulatory elements themselves were often toggled between “on” and “off” states. These two processes tend to occur together, so that new loops are formed at the same time as the switch is activated.Freire-Pritchett, Schoenfelder et al. also show that individual genes are often connected to many different regulatory elements. The next step is to understand how these multiple connections work together to coordinate gene activity, and whether this information could be used to control how stem cells specialise. This knowledge may lead to the development of stem cell-based therapies for stroke, Parkinson’s disease and other conditions.DOI:http://dx.doi.org/10.7554/eLife.21926.002 Cis-regulatory elements such as transcriptional enhancers play a key role in this process by integrating regulatory inputs from intrinsic and extracellular cues, and by mediating the recruitment of core activator and repressor complexes  and derived methods have enabled the biochemical mapping of looping interactions to offer new insights into their architecture across different cell types . In partcis-regulatory regions as the cells differentiate (cis-regulatory units (CRUs) that provide a view of multi-modular gene regulation. We show that CRUs undergo extensive reorganisation during lineage commitment that involves both the ‘rewiring’ (acquisition or loss) of promoter interactions, as well as chromatin state changes at pre-existing interactions. Importantly, we demonstrate that this reorganisation is associated with changes in target gene expression, thereby providing insights into the transcriptional control of early human development.Recently, we developed Promoter Capture Hi-C that uses sequence capture to enrich Hi-C libraries for interactions involving the promoters of most annotated genes, providing a global view on promoter interactions that is independent of the activity of interacting regions and identity of proteins recruited to them . Here, wrentiate . Our larcis-interactions between promoters and other genomic regions in ESCs and 75,624 in NECs. In addition, approximately 300 significant trans-interactions were detected in each cell type. As examples of this rich dataset, high-confidence interactions are shown for the SOX2 and PAX6 promoters (http://osf.io/sdbg4), and raw sequencing reads have been deposited to Gene Expression Omnibus (accession GSE86821).We used PCHi-C to profile the interactomes of 21,841 promoters in human ESCs and NECs . We generomoters . These eTo gain insight into the chromatin properties of the promoter-containing interactions, we integrated the PCHi-C data with published genome-wide histone modification profiles in ESCs and NECs . In bothWe used ChromHMM  to integPOU5F1, PRDM14 and CHD7 genes, together with each of their respective PIRs, are shown in The chromatin states of several example promoters, including those for the Taken together, these data provide a comprehensive resource that links many thousands of known and predicted regulatory elements with their putative target genes and will enable the investigation of regulatory contacts during human lineage commitment.POU3F2. Strikingly, four out of the five POU3F2 PIRs tested experimentally in VISTA transgenic assays showed reporter activity exclusively in neural tissues, and one PIR was inactive  in both cell types . We asseFor INs, we used the published genomic coordinates (available for ESCs only) that were defined on the basis of cohesin ChIA-PET and CTCF-binding data . Just unTo investigate the potential regulatory features of CRUs, we first characterised their chromatin properties by considering the proportion of PIRs in each chromatin state within a CRU. Applying hierarchical clustering based on this property, we obtained eight distinct clusters of CRUs in both ESCs  and NECsWe set out to investigate CRU chromatin state transitions on ESC to NEC differentiation. For this analysis, and in each cell type, we classified CRUs into either single-state active , single-state repressed , or background (containing only background-state PIRs). CRUs containing a combination of both active and repressed (Polycomb-associated/poised) PIRs were classified as dual-state. We found that 65% of the single-state CRUs in ESCs remained single-state CRUs in NECs, although approximately half of them switched their state . In addiRGMB and MAP2, which are transcriptionally upregulated in NECs  as sets of interactions connected to the same promoter. Taking the view of CRUs, we consider jointly the dynamics of chromatin states and interactions as ESCs differentiate, and assess the potential contribution of these processes to changes in gene expression during development. We observe that CRUs reconfigure extensively upon cell differentiation. These include the CRUs of ESC- and NEC-specific genes, for which CRU reconfiguration associates with transcriptional changes upon differentiation, as well as the CRUs of genes that are not expressed in either cell type, consistent with the model of progressive chromatin changes at lineage-inappropriate genes during lineage commitment  that is consistent with the ‘hit-and-run’ model of transcriptional regulation , and proDrosophila have identified exceptions to additive enhancer activity  to promote stronger transcriptional outputs . Our obsactivity  and haveactivity , which iactivity . These mPromoter – enhancer interactions are vitally important for gene regulation and their disruption may lead to pronounced developmental abnormalities . The higCVCL_9773) were cultured at 37°C in 5% CO2 in air in Pluripro media and matrix ). Authentication of ESCs was achieved by confirming the expression of pluripotency genes and protein markers, and by SNP analysis of sequencing data. ESCs were routinely verified as mycoplasma-free using a PCR-based assay ). The H9/WA09 line is not on the list of commonly misidentified cell lines . ESCs were differentiated into NECs using a previously described protocol ; RRID:protocol  and sampAB_756086; 5 µL per million cells) and CD56-PE , Cat# 345812, RRID:AB_2629216; 20 µL per million cells) antibodies in 100 µl PBS containing 2% FBS. After washing, DAPI was included at a final concentration of 5 µl/mL for live/dead cell discrimination, and flow cytometry analysis was performed using a BD LSRFortessa with subsequent data analysis using FlowJo V10.1.Following dissociation with accutase, ESCs and NECs were stained on ice for 45 min with CD326-AF647 , Cat# 324212, RRID:7 cells (ESCs or NECs) were fixed in 2% formaldehyde ) for 10 min, after which the reaction was quenched with ice-cold glycine . Cells were collected by centrifugation (400 x g for 10 min at 4°C), and washed once with PBS (50 ml). After another centrifugation step (400 x g for 10 min at 4°C), the supernatant was completely removed and the cell pellets were immediately frozen in liquid nitrogen and stored at −80°C. After thawing, the cell pellets were incubated in 50 ml ice-cold lysis buffer ) for 30 min on ice. After centrifugation to pellet the cell nuclei (650 x g for 5 min at 4°C), nuclei were washed once with 1.25 x NEBuffer 2. The nuclei were then resuspended in 1.25 x NEBuffer 2, SDS was added  and the nuclei were incubated at 37°C for 1 hr with agitation (950 rpm). Triton X-100 was added to a final concentration of 1.7% and the nuclei were incubated at 37°C for 1 hr with agitation (950 rpm). Restriction digest was performed overnight at 37°C with agitation (950 rpm) with HindIII . Using biotin-14-dATP ), dCTP, dGTP and dTTP , the HindIII restriction sites were then filled in with Klenow ) for 75 min at 37°C. After addition of SDS  and incubation at 65°C with agitation (950 rpm) for 20 min, ligation was performed for 4 hr at 16°C (50 units T4 DNA ligase (Life Technologies) per 7 million cells starting material) in a total volume of 8.2 ml ligation buffer  per 7 million cells starting material. After ligation, reverse crosslinking (65°C overnight in the presence of Proteinase K (Roche)) was followed by RNase A (Roche) treatment and two sequential phenol/chloroform extractions. After DNA precipitation (sodium acetate 3 M pH 5.2 (1/10 volume) and ethanol (2.5 x volumes)) overnight at −20°C, the DNA was spun down (centrifugation 3200 x g for 30 min at 4°C). The pellets were resuspended in 400 µl TLE , and transferred to 1.5 ml eppendorf tubes. After another phenol/chloroform extraction and DNA precipitation overnight at −20°C, the pellets were washed three times with 70% ethanol, and the DNA concentration was determined using Quant-iT Pico Green (Life Technologies). The efficiency of biotin incorporation was assayed by amplifying a 3C ligation product (primers available upon request), followed by digest with HindIII or NheI.Hi-C and Promoter CHi-C libraries were generated essentially as described , with miTo remove biotin from non-ligated fragment ends, 40 µg of Hi-C library DNA were incubated with T4 DNA polymerase (NEB) for 4 hr at 20°C, followed by phenol/chloroform purification and DNA precipitation overnight at −20°C. After a wash with 70% ethanol, sonication was carried out to generate DNA fragments with a size peak around 400 bp . After end repair  in the presence of dNTPs in ligation buffer (NEB)) for 30 min at room temperature, the DNA was purified  PCR purification kit). dATP was added with Klenow exo- (NEB) for 30 min at 37°C, after which the enzyme was heat-inactivated (20 min at 65°C). A double size selection using AMPure XP beads  was performed: first, the ratio of AMPure XP beads solution volume to DNA sample volume was adjusted to 0.6:1. After incubation for 15 min at room temperature, the sample was transferred to a magnetic separator , and the supernatant was transferred to a new eppendorf tube, while the beads were discarded. The ratio of AMPure XP beads solution volume to DNA sample volume was then adjusted to 0.9:1 final. After incubation for 15 min at room temperature, the sample was transferred to a magnet . Following two washes with 70% ethanol, the DNA was eluted in 100 µl of TLE . Biotinylated ligation products were isolated using MyOne Streptavidin C1 Dynabeads (Life Technologies) on a DynaMag-2 magnet (Life Technologies) in binding buffer  for 30 min at room temperature. After two washes in binding buffer and one wash in ligation buffer (NEB), PE adapters  were ligated onto Hi-C ligation products bound to streptavidin beads for 2 hr at room temperature . After washing twice with wash buffer  and then once with binding buffer, the DNA-bound beads were resuspended in a final volume of 90 µl NEBuffer 2. Bead-bound Hi-C DNA was amplified with seven PCR amplification cycles using PE PCR 1.0 and PE PCR 2.0 primers (Illumina). After PCR amplification, the Hi-C libraries were purified with AMPure XP beads (Beckman Coulter). The concentration of the Hi-C libraries was determined by Bioanalyzer profiles  and qPCR ), and the Hi-C libraries were paired-end sequenced  at the Babraham Institute Sequencing Facility.2O, and custom hybridization blockers (Agilent Technologies) were added to the Hi-C DNA. Hybridization buffers and the custom-made RNA capture bait system  were prepared according to the manufacturer’s instructions . The Hi-C library DNA was denatured for 5 min at 95°C, and then incubated with hybridization buffer and the RNA capture bait system at 65°C. After 24 hr incubation at 65°C, biotin/streptavidin pulldown  and washes were performed according to the SureSelect Target enrichment protocol (Agilent Technologies). After the final wash, the beads were resuspended in 30 µl NEBuffer 2. After a post-capture PCR (four amplification cycles using Illumina PE PCR 1.0 and PE PCR 2.0 primers), the Promoter CHi-C libraries were purified with AMPure XP beads (Beckman Coulter). The concentration of the Promoter CHi-C libraries was determined by Bioanalyzer profiles (Agilent Technologies) and qPCR (Kapa Biosystems), and the Promoter CHi-C libraries were paired-end sequenced  at the Babraham Institute Sequencing Facility.For Promoter Capture Hi-C, 500 ng of Hi-C library DNA was resuspended in 3.6 µl Hescribed : 37,608 http://homer.salk.edu/homer/)  . Coverag/homer/) . TADs we/homer/)  of Hi-C A/B compartments were called by computing the principal components of the distance- and coverage-corrected interaction profile correlation matrix at 250 kb resolution . PositivHindIII fragments using version 0.1.4 of the CHiCAGO pipeline  with default parameters to remove the standard Illumina adapter sequence. Reads were mapped to the GRCh37 assembly using tophat (http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). Raw read counts per transcript were calculated using the RNA-seq quantitation pipeline on the Ensembl v70 gene set using non-directional counts. Differential analysis of gene expression was performed using the default settings in DESeq2 . Indexed mRNA-seq libraries were constructed from 500 ng total RNA using the Tru-Seq RNA Library Prep Kit v2 (Illumina). Library fragment size and concentration was determined using an Agilent Bioanalyzer 2100 and KAPA Library Quantification Kit (KAPA Biosystems). Samples were sequenced on an Illumina HiSeq as single-end libraries at the Babraham Institute Sequencing Facility. Reads were trimmed using trim galore  for ESCs and NECs were from HindIII fragments in the human genome (including baits and PIRs) were then classified according to the chromatin states detected within them. When more than one chromatin state was present, classification was resolved in the following manner: (i) any functional state  was prioritised above background; (ii) active, poised and Polycomb-associated states were prioritised above intermediate; (iii) poised state was prioritised above the Polycomb-associated state; (iv) active state together with any inactive state (i.e. poised or Polycomb-associated) was labelled as mixed. Based on these heuristics, we assigned a single chromatin state (including the background state) to 81% of PIRs in both cell types.Chromatin segmentations were performed on the basis of multiple histone modification ChIP datasets using a Hidden Markov Model-based method implemented in ChromHMM (version 1.10)  with defTransgenic reporter assays for enhancer activity are described within the VISTA Enhancer Browser . The enhHindIII fragments were mapped to TADs defined as described above and INs obtained from dist function in R) with the average agglomeration method (method='average’ in hclust function in R). PIRs assigned intermediate or mixed chromatin states did not contribute to the clustering procedure, in the latter case because the states of the regulatory elements interacting with target promoters within these PIRs are not identifiable. CRUs containing only mixed or intermediate PIRs were not included in the analysis.Each CRU was categorised according to the fraction of PIRs in the active, poised and Polycomb-associated state. These fractions were used for hierarchical clustering based on Euclidian distances .Sequencing data have been deposited in Gene Expression Omnibus (GEO) with accession number GSE86821. Processed data including interaction peak calls in the WashU Genome Browser text format and RNA-seq raw read counts were deposited in the same GEO repository. CHiCAGO objects containing all detected interactions, ChromHMM segmentation data, DESeq2-processed RNA-seq data and the defitions of TADs have been made available through the Open Science Framework , is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Fiona Watt as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Frank Alber (Reviewer #3).Thank you for submitting your article "Global reorganisation of cis-regulatory units upon lineage commitment of human embryonic stem cells" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. In the course of discussion the reviewers and editors decided that this work would be more appropriately reconsidered as a Tools and Resources (TR) paper rather than a Research Article (RA). The work appears to be a valuable resource but the primary research results are not considered sufficiently novel to warrant publication as an RA. Nonetheless, we hope you are willing to go forward with this in the category of a TR paper.Summary:Freire-Pritchett et al. employ Capture Hi-C to detect chromatin interactions between promoters and distal elements in ES cells and in ESC derived neuroectodermal cells (NECs). The authors report significant interactions between many promoters and different distal elements in ESCs and NECs. Analysis of the chromatin state of these elements indicates that these are functional elements including (poised) enhancers. Further, the connectivity between promoters and distal elements differs between ESCs and NECs and this relates to the transcriptional status of the genes and chromatin status of the distal elements. The authors then define cis-regulatory units (CRUs) as promoters and their associated distal elements. Although many of the CRUs are contained within TADs and INs, as had been expected based on earlier studies, the authors report that many can extend beyond TAD and IN boundaries.The main impact of this paper is to announce the availability of a large data set of enhancer-promoter contacts identified via capture Hi-C. Many of the experimental technologies and statistical analysis methods are established and rely on previously published methodologies. That said the reviewers raise concerns about specific aspects of the analysis. Further, many of the reported correlations are already known and the results confirm earlier studies . The main new aspect of this work is the fact that a genome-wide promoter-anchored interaction atlas is described, and this resource could be of interest to the community given that many study these cells.Essential revisions:Please address these main criticisms (more details are in the minor points section):1) Many of the correlations between looping, gene expression and chromatin state are not novel. Focus on the new things, and present the data as a large dataset or resource for the community.2) The reviewers raised issues related to the statistical analysis and computational methods to determine PIRs , TADs, and CRUs (below). Please address all of them.3) Related to the previous point, the reviewers raised concerns about the claim that CRUs are distinct from INs, TADs. The main point to address is how the methods to determine these features compare and whether lack of overlap can be due to sensitivity of the computational methods.Minor points:1) One concern is that the CHiCAGO method identifies significant interactions using only the general distance decay of interactions into account. It does not take into account that any pair of interactions between loci located within a TAD or IN, or between loci located in a similar compartment  are generally higher than loci located in different TADs/INs/Compartments. As a result, a number of significant interactions are not necessarily "loops" (specific point-to-point interactions). This is not to say that these loci do not interact more frequently than expected given their genomic distance, but these interactions can reflect general higher order structures such as domains etc. This can explain why many interacting loci appear to not have chromatin marks: these can be "bystander" interactions that are the result of a nearby structural feature. I recommend that the authors repeat their analysis using a background model that takes domains into consideration.2) The TAD calling procedure is quite simple. Is there any evidence that this procedure produces TAD calls of comparable quality to those produced by, for example, the HMM of Dixon et al. Nature 2012?3) The TAD and IN comparison is unsatisfying. The main conclusion seems to be that CRUs are not particularly aligned with TADs and INs. This could be because the two phenomena are more or less independent of each other, or because the TADs, INs and CRUs are each defined in a fairly arbitrary fashion. If, for example, the simple TAD calling scheme employed here is not working well, then it would lead to the observed discordance.4) The finding that CRUs can extend beyond TAD or IN boundaries is interesting, but the data is not sufficient to claim a new "feature of genome architecture". Further analyses are needed such as: when a CRU extends beyond a TAD boundary, is this a weak TAD boundary? It is well known that TADs are nested structures. Do CRUs remain within the larger nested domains? This seems to be the case for POUF2. Quantification of boundary strength is required, as it seems not inconsistent with the TAD/IN models of enhancer action if the authors find interactions across weak boundaries, but not strong boundaries. Also, are these interactions beyond the boundaries equally strong, or are they much lower in contact frequency5) The main text claims that the promoter capture Hi-C data was processed to identify "significant cis-interactions." Looking at Methods, we learn that these contacts correspond to a threshold of 12, on a "log-transformed, weighted p-value" scale. This vague description makes it impossible to actually interpret the confidence associated with a threshold of 12, especially since we are told that the threshold corresponds to "a threshold of 5 in Chicago v1.0.0+ due to a soft-thresholding procedure introduced in this version." The choice of threshold must be fully justified and described. The text claims that Chicago uses an FDR control procedure, so the interactions that are presented here should be reported along with their FDR threshold.6) The manuscript contains no details on precisely how ChromHMM was run. Details like software version numbers and any parameter settings should be given. Importantly, the text should specify whether the segmentation was carried out independently on each cell type, or jointly, and if the latter, whether the data tracks were "stacked"  or "concatenated" .7) Details of the "curation" of the 16 inferred states should also be provided. When the 16 states were collapsed to four states, what was the basis for this collapsing? It seems strange that some states were eliminated at this stage because they contained multiple marks.8) How are PIRs defined: at the level of individual restriction fragments, or are adjacent fragments that both score significant merged into a single PIR?9) The authors do not show many tracks of real data . Throughout they only show arcs to indicate significant contacts. This makes it difficult to assess the quality of the data.10) Promoters often interact with CTCF sites. Did the authors find this in their dataset as well?11) The authors focus on cis-interactions. Can the authors describe trans interactions as well? Given that trans-regulation is considered rare, this analysis will provide context for interpretation of the cis data.12) Did the authors detect any known enhancer-promoter pairs? Also, the authors imply that the distal elements that touch promoters are regulating these promoters, but beside correlations between interactions, transcription and chromatin state of the distal elements, no evidence for direct regulation is provided. This would require deletion of the elements, e.g. by CRISPR. The Vista analysis of the POUF2 interactions suggests that many PIRs are enhancers. That is similar in information as the observation that PIRs overlap loci with histone modifications associated with enhancers. The key thing to validate is that these enhancers indeed regulate the interacting promoter. The fact that the pattern of activity of these enhancers resembles POU3F2 expression is encouraging but not definitive. The authors should either provide direct evidence for functional relationships or clearly discuss the extent to which the current data predicts such relationships even though the data currently is correlative.13) The authors examine the dynamic rewiring and recoloring of interactions as cells differentiate from ESCs to NECs. They report gain and loss of interactions and these are related to gain or loss of chromatin marks. This is interesting, but what needs to be tested is whether this is related compartment changes (see above). This is not to say these interactions are not relevant, but it would be important to determine how they relate to larger scale compartment changes. One interesting implication could be that in fact cell type-specific compartment changes are driven by altered interactions between functional elements that are active in a cell type-specific manner.14) How do the authors interpret interactions that involve non-expressed genes?15) The hierarchical clustering of PIRs by the prevalence of different chromatin state labels is fairly uninformative. The fact that these different categories exist in the data is not surprising, and the claim that these eight labels somehow have "potential implications for understanding the logic of signal integration at promoters" is not well supported.16) In the Abstract, the sentence "Here, we generate[…]" should mention what assay was employed.17).In the Introduction: "The extreme combinatorial complexity of Hi-C[…]" The complexity is really only quadratic, not combinatorial.18). In the Results section: The URL for data availability should be included at the end of the sentence, "This data resource[…]"19) Also in the Results section: "NEC PIRs were strongly enriched" This claim requires statistical support.20) New paragraph at "We next sought[…]"21) Two of the observations offered in the Discussion do not seem to be supported by direct evidence in Results: "we find extensive promoter connectivity to regions associated with Polycomb-associated repression and poising, and "we[…] detect large numbers of promoter interactions with regions devoid of chromatin features." These observations seem to be offered for the first time in Discussion.22) The work does not show whether all the interactions are present simultaneously in a cell, or if the promoter state may vary between cells, depending on the probability with which the promoter is interacting with any of the potential alternative enhancers of a given chromatin state. Please discuss this issue in the main text.23) Out of the ~21000 promoters, the authors focused their CRU analysis on only 16,000 protein-coding genes. Would the authors expect differences in the outcome if the other remaining promoters would be considered?24) Out of the 16,000 protein-coding promoters about 9000 were defined as CRU. It may be good to know the selection criteria (i.e. is it based on a minimum number of PIRs per promoter?).25)  Essential revisions:Please address these main criticisms (more details are in the minor points section):1) Many of the correlations between looping, gene expression and chromatin state are not novel. Focus on the new things, and present the data as a large dataset or resource for the community.We thank the editors and reviewers for their constructive and thorough feedback on our study. We are happy for our work to be reconsidered as a Tools and Resources paper and we have taken on board the advice to focus more on the resource aspect of our study. We have achieved this, in part, by describing the large dataset more fully, adding further methodological details and support, and adopting a more cautious posture regarding the correlations between interactions and chromatin state.2) The reviewers raised issues related to the statistical analysis and computational methods to determine PIRs , TADs, and CRUs (below). Please address all of them.3) Related to the previous point, the reviewers raised concerns about the claim that CRUs are distinct from INs, TADs. The main point to address is how the methods to determine these features compare and whether lack of overlap can be due to sensitivity of the computational methods.We thank the editors and reviewers for their helpful feedback on these two points. We address these criticisms in our responses below.Minor points:1) One concern is that the ChICAGO method identifies significant interactions using only the general distance decay of interactions into account. It does not take into account that any pair of interactions between loci located within a TAD or IN, or between loci located in a similar compartment  are generally higher than loci located in different TADs/INs/Compartments. As a result, a number of significant interactions are not necessarily "loops" (specific point-to-point interactions). This is not to say that these loci do not interact more frequently than expected given their genomic distance, but these interactions can reflect general higher order structures such as domains etc. This can explain why many interacting loci appear to not have chromatin marks: these can be "bystander" interactions that are the result of a nearby structural feature. I recommend that the authors repeat their analysis using a background model that takes domains into consideration.The reviewers are correct to point out that CHiCAGO’s domain-unaware model somewhat overestimates the background read count (i.e. that expected at random) for TAD-crossing interactions . As a reIn the future, it would indeed be worthwhile to incorporate domain information into the CHiCAGO background model. However, this is a complex and challenging task. One of the challenges that need to be addressed, for example, is that the background depends not just on whether an interaction crosses a TAD boundary, but also on TAD boundary stringency, and does so in a way that scales non-linearly with distance . It is aSince the identified TAD-crossing interactions are at least no weaker than the within-TAD interactions, and since implementing TAD-aware interaction calling is likely a challenging project of its own, we feel that addressing this point in full goes outside the scope of this study.2) The TAD calling procedure is quite simple. Is there any evidence that this procedure produces TAD calls of comparable quality to those produced by, for example, the HMM of Dixon et al. Nature 2012?We agree that a comparison with Dixon et al., 2015 data is worthwhile and have now performed this analysis. This has revealed that the cutoff on HOMER boundary stringency scores that we initially used (∆Z>1) likely results in a more lenient TAD definition than the Dixon et al., 2015 HMM-based method, as evidenced by the higher numbers of detected TADs (4491 vs 3062) and smaller TAD sizes . NotablyIn the absence of a gold standard for TAD detection, it is difficult to judge which of the two methods is “more correct”. However, for consistency we now use a more stringent HOMER threshold (∆Z>2) that results in fewer TADs (2761 vs 4491 with ∆Z>1) and a comparable distribution of TAD lengths with the Dixon et al., 2015 TADs . More thThe figures and the main text of the manuscript have been updated based on the new set of TAD calls. Using the more stringent set of TADs has resulted in fewer CRUs identified as crossing TAD boundaries . However, this is primarily due to the larger sizes of the updated TADs rather than their higher ‘insulation power’. The ratio of observed-to-expected numbers of CRUs fully contained within TAD boundaries has in fact gone slightly down in the updated set (from 1.5-fold to 1.3-fold). We have also added the results for Dixon et al., 2015 TADs to 3) The TAD and IN comparison is unsatisfying. The main conclusion seems to be that CRUs are not particularly aligned with TADs and INs. This could be because the two phenomena are more or less independent of each other, or because the TADs, INs and CRUs are each defined in a fairly arbitrary fashion. If, for example, the simple TAD calling scheme employed here is not working well, then it would lead to the observed discordance.We have now modified the TAD analysis as described above, which we believe addresses this point as far as TADs are concerned.The IN calls have been taken verbatim from the study that introduced this concept . We noted, however, that the reported INs were often nested, so we have repeated the analysis using only the largest span of each overlapping set. As expected from the larger IN sizes, the numbers of IN-crossing CRUs went down somewhat, but still less than half of them (~45%) were fully contained within the extremities of IN boundaries . Therefo4) The finding that CRUs can extend beyond TAD or IN boundaries is interesting, but the data is not sufficient to claim a new "feature of genome architecture". Further analyses are needed such as: when a CRU extends beyond a TAD boundary, is this a weak TAD boundary? It is well known that TADs are nested structures. Do CRUs remain within the larger nested domains? This seems to be the case for POUF2. Quantification of boundary strength is required, as it seems not inconsistent with the TAD/IN models of enhancer action if the authors find interactions across weak boundaries, but not strong boundaries. Also, are these interactions beyond the boundaries equally strong, or are they much lower in contact frequencyTo address this question, we plotted the distributions of HOMER strength scores for TAD boundaries that are crossed and not crossed by promoter interactions . As can As for the contact frequency of TAD-crossing and within-TAD interactions, it can be seen from Finally, we agree that empirically, TADs indeed represent nested structures, and tools for TAD calling in a hierarchical fashion are beginning to emerge . However, the conceptual disparity between the different levels of TAD “nesting” is still unclear. For example, from what level of the hierarchy should a sub-TAD be a considered a TAD, or a TAD be considered a “super-TAD”, etc.? It is possible to devise criteria that seem intuitively acceptable, but it will take time for the underlying assumptions of such criteria to acquire a solid structural and biophysical underpinning.5) The main text claims that the promoter capture Hi-C data was processed to identify "significant cis-interactions." Looking at Methods, we learn that these contacts correspond to a threshold of 12, on a "log-transformed, weighted p-value" scale. This vague description makes it impossible to actually interpret the confidence associated with a threshold of 12, especially since we are told that the threshold corresponds to "a threshold of 5 in Chicago v1.0.0+ due to a soft-thresholding procedure introduced in this version." The choice of threshold must be fully justified and described. The text claims that Chicago uses an FDR control procedure, so the interactions that are presented here should be reported along with their FDR threshold.We believe that, in part, this question has arisen because we made a confusing statement in the text. CHiCAGO’s weighted multiple testing treatment is not, strictly speaking, “a false discovery control procedure”, and we have now altered the main text to clarify this.More generally, multiple testing correction when calling peaks in genomic data is a challenging problem, leading to the development of alternatives to standard approaches such as Bonferroni and FDR . In PCHi-C data, an additional challenge is presented by undersampling, which leads to the violation of the underlying assumptions of both the IDR and conventional FDR approaches. In the case of FDR, this is because the majority of fragment pairs have only one or zero reads, leading to highly non-uniform p-value distributions. Furthermore, in PCHi-C data false discovery rates strongly vary with linear distance, partly due to the decreasing statistical power as distance increases. To account for this property, CHiCAGO adjusts the p-values using a weighting procedure, in which the weights trained on the reproducibility of interaction calls with distance . Soft-thresholding was further introduced in CHiCAGO v1.0.0+ to shift the weighted -log-p-values (scores) such that the score of zero corresponds to the probability of a true interaction in the very short range given zero reads.CHiCAGO scores are conceptually similar, albeit clearly not identical, to posterior probabilities resulting from an empirical Bayesian treatment. We view them primarily as a ranking measure, and use a heuristic approach based on integration with other types of genomic data to choose a threshold. Specifically, for a range of threshold settings, we compute the observed overlap of the identified PIRs with genomic regulatory features (such as specific histone marks) and the same overlap expected at random (estimated with random regions that are promoter distance-matched to the detected PIRs). In choosing a threshold, we balance the ‘unexpected’ overlap of PIRs with regulatory features with the total number of interactions detected at a given setting. As can be seen in We now explicitly mention the rationale for choosing the threshold in the Methods section. However, we agree that it is a crude and potentially subjective method. While varying the threshold within the reasonable range is unlikely to affect the principal conclusions of our study, we release the scores for each fragment pair as part of the Chicago objects on Open Science Framework, so the users can employ other methods of tuning the threshold, as well as apply non-threshold-based approaches to analysing our data.6) The manuscript contains no details on precisely how ChromHMM was run. Details like software version numbers and any parameter settings should be given. Importantly, the text should specify whether the segmentation was carried out independently on each cell type, or jointly, and if the latter, whether the data tracks were "stacked"  or "concatenated" .We apologise for this omission and have now added full details on the chromHMM procedure to the Methods section. Briefly, the segmentation was carried out jointly, and the data tracks were concatenated.7) Details of the "curation" of the 16 inferred states should also be provided. When the 16 states were collapsed to four states, what was the basis for this collapsing? It seems strange that some states were eliminated at this stage because they contained multiple marks.We have added full details on how the chromHMM states were curated to the Methods section. Briefly, the states were curated into “Active”, “Poised”, “Polycomb-associated” and “Background” based on the presence of active (H3K4me1/H3K27ac) and repressive histone (H3K27me3) marks. Overall, we were able to unambiguously assign one of these chromatin states to 81% of PIRs in both cell types. Two more curated states were defined, but not considered further:1) “intermediate enhancers” characterised by H3K4me1 alone; the fraction of PIRs bearing this signature (~1%) was too small to analyse them as an individual category, and yet we felt that merging them with any other category was not biologically appropriate;2) the ambiguous (“mixed”) state that showed enrichment for both H3K27ac and H3K27me3; we felt that this conflicting pattern likely arose from a technical issue such as heterogeneity within the samples.8) How are PIRs defined: at the level of individual restriction fragments, or are adjacent fragments that both score significant merged into a single PIR?a priori assumptions about the “causal” regions driving the signals, which we feel still require further mechanistic support at this stage.PIRs are defined at the level of individual restriction fragments, with no signal smoothing or pooling across multiple sites. We have now clarified this in the Methods section. We have opted against further peak processing, because at present there is no reliable way to “fine-map” interaction signals without making 9) The authors do not show many tracks of real data . Throughout they only show arcs to indicate significant contacts. This makes it difficult to assess the quality of the data.We now provide two additional figures , in whic10) Promoters often interact with CTCF sites. Did the authors find this in their dataset as well?We have now incorporated this analysis. Indeed, PIRs are highly enriched for CTCF binding sites, as we now show using ENCODE CTCF ChIP-seq data for hESCs in 11) The authors focus on cis-interactions. Can the authors describe trans interactions as well? Given that trans-regulation is considered rare, this analysis will provide context for interpretation of the cis data.We now describe in the main text that we detect 338 trans-interactions in ESCs and 266 in NECs . Consistent with our previous observations in other PCHi-C datasets  reflect large-scale compartment signal that is detectable in conventional Hi-C data at 1-Mb resolution for the same cell types (data not shown). Given the small numbers of detected trans-interactions, we felt we should focus on cis-interactions in the manuscript. However, we agree that trans-interactions may be useful for future reanalyses of our data, and report them in the data files released on Open Science Framework.12) Did the authors detect any known enhancer-promoter pairs? Also, the authors imply that the distal elements that touch promoters are regulating these promoters, but beside correlations between interactions, transcription and chromatin state of the distal elements, no evidence for direct regulation is provided. This would require deletion of the elements, e.g. by CRISPR. The Vista analysis of the POUF2 interactions suggests that many PIRs are enhancers. That is similar in information as the observation that PIRs overlap loci with histone modifications associated with enhancers. The key thing to validate is that these enhancers indeed regulate the interacting promoter. The fact that the pattern of activity of these enhancers resembles POU3F2 expression is encouraging but not definitive. The authors should either provide direct evidence for functional relationships or clearly discuss the extent to which the current data predicts such relationships even though the data currently is correlative.This is an important point. We have added new text in the Discussion to highlight that our data set is predictive of regulatory relationships, but that extensive functional validation is required to infer direct regulation. In the absence of a comprehensive “VISTA-style” catalog of functionally validated enhancer-promoter relationships, we hope that large-scale resources such as ours will provide a strong impetus for future validation studies.13) The authors examine the dynamic rewiring and recoloring of interactions as cells differentiate from ESCs to NECs. They report gain and loss of interactions and these are related to gain or loss of chromatin marks. This is interesting, but what needs to be tested is whether this is related compartment changes (see above). This is not to say these interactions are not relevant, but it would be important to determine how they relate to larger scale compartment changes. One interesting implication could be that in fact cell type-specific compartment changes are driven by altered interactions between functional elements that are active in a cell type-specific manner.We have now done this analysis. While ~2% of 1-Mb regions transition between A and B compartments upon differentiation (in either direction), nearly all high-confidence rewired interactions detected in our data (>99%) map outside of these “dynamic” regions. We now mention this observation in the main text.14) How do the authors interpret interactions that involve non-expressed genes?This is an interesting point, which we now present more extensively in the Discussion. There are several possibilities to interpret interactions with non-expressed gene promoters. For example, it is known that at least in mouse ESCs, inactive genes and their regulatory regions form extensive topological networks . However, we do not intend to imply that all promoter interactions have a gene regulatory nature: some of them are potentially structural. Furthermore, additional and not yet fully understood mechanisms of topologically-mediated gene repression and priming may also be at play, particularly given the fact that a number of regions unmarked by conventional chromatin signatures currently emerge as having roles in gene regulation .15) The hierarchical clustering of PIRs by the prevalence of different chromatin state labels is fairly uninformative. The fact that these different categories exist in the data is not surprising, and the claim that these eight labels somehow have "potential implications for understanding the logic of signal integration at promoters" is not well supported.We agree that the majority of CRUs having consistent chromatin marks between PIRs and the promoter is relatively unsurprising. However, we feel that the classification we provide will be helpful for the users of our resource to make the most out of our data, and in particular to select targets for candidate-based studies. Additionally, our hierarchical clustering approach has been instrumental for detecting the relatively large numbers of ‘inconsistent’ CRUs, leading to the observation that the chromatin states at their promoters generally follow the ‘majority rule’ with respect to the chromatin states of the PIRs. We believe that these findings lead to specific and testable mechanistic hypotheses, and are therefore also interesting in this respect. Nevertheless, we concede that our claim about “potential implications[…]” was perhaps too far-fetched, so we have removed this sentence from the manuscript.16) In the Abstract, the sentence "Here, we generate[…]" should mention what assay was employed.This has now been done.17) In the Introduction: "The extreme combinatorial complexity of Hi-C[…]" The complexity is really only quadratic, not combinatorial.We have removed the words “extreme combinatorial” from this sentence to avoid confusion.18) In the Results section: The URL for data availability should be included at the end of the sentence, "This data resource[…]"We have now inserted a new sentence with details on the availability of raw and processed data.19) Also in the Results section: "NEC PIRs were strongly enriched" This claim requires statistical support.In the initial submission, the enrichment z-scores were given in 20) New paragraph at "We next sought.[…]"We have amended this.21) Two of the observations offered in the Discussion do not seem to be supported by direct evidence in Results: "we find extensive promoter connectivity to regions associated with Polycomb-associated repression and poising, and "we.… detect large numbers of promoter interactions with regions devoid of chromatin features." These observations seem to be offered for the first time in Discussion.Thank you for drawing our attention to this. We have now added details on these two points into the second section of Results (“Identification of putative regulatory elements and their associated gene promoters”).22) The work does not show whether all the interactions are present simultaneously in a cell, or if the promoter state may vary between cells, depending on the probability with which the promoter is interacting with any of the potential alternative enhancers of a given chromatin state. Please discuss this issue in the main text.This is a very good point. Indeed, Hi-C-based methods cannot distinguish between these two possibilities, and there is currently evidence in support of both the “hit-and-run” and the “chromatin hub” models. We discuss these topics in the section “Implications for developmental gene regulation by multiple enhancers”, which we have now expanded to explicitly outline these points.23) Out of the ~21000 promoters, the authors focused their CRU analysis on only 16,000 protein-coding genes. Would the authors expect differences in the outcome if the other remaining promoters would be considered?We now show the results for all captured promoters in 24) Out of the 16,000 protein-coding promoters about 9000 were defined as CRU. It may be good to know the selection criteria (i.e. is it based on a minimum number of PIRs per promoter?).We apologise for not explaining this clearly. The ~9,000 figure includes all CRUs that have at least one PIR per promoter. We have updated the main text to explicitly state this fact.25) We agree that this is a good idea and have amended the legend of


Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene expression. Initially, we collected the B. subtilis genome sequence from the NCBI database, and promoters were identified by their sigma factors in the DBTBS database. We then grouped the promoters according to 15 factors in 2 domains, corresponding to sigma 54 and sigma 70 of Gram-negative bacteria. Based on these data we developed a script in Python to search for promoters in the B. subtilis genome. After processing the data, we obtained 767 promoter sequences for B. subtilis, most of which were recognized by sigma SigA. To validate the data we found, we developed a software package called BacSVM+, which receives promoters as input and returns the best combination of parameters in a LibSVM library to predict promoter regions in the bacteria used in the simulation. All data gathered as well as the BacSVM+ software is available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip.This paper presents a prediction of  Specifications TableValue of the data•The data obtained can be used in further studies on gene regulation expression. The regulation of gene expression is essential for bacterial metabolic adaptation to environmental changes, allowing bacterial survival and multiplication.•E. coli. The promoters of B. subtilis described in this paper allow further research in this area.Most related papers on bacterial promoters are restricted to Gram-negative bacteria, particularly •Data on Gram-positive bacteria promoters in the literature are scarce. The process described here can be used by researchers to validate promoters in other bacteria of this type.1Transcription at a coding region starts when the RNA polymerase (RNAp) enzyme recognizes the promoter region. Promoter regions are conserved DNA sequences that signal and direct the transcription of an adjacent gene or group of genes. Promoters are considered key factors for transcription as they are the initial step in gene expression and part of transcriptional regulation Bacillus subtilis bacteria and to make a promoter data set available. This bacteria is considered a model organism in laboratory research due to its easy genetic manipulation B. subtilis with a length of 80 nucleotides.A typical bacterial promoter is located approximately 70 bp upstream from the starting point of gene transcription. A comparative analysis of several sigma 70 promoters (Gram-negative bacteria) allowed the identification of two consensus sequences: (A) one localized at − 10 bp (5′-TATAAT-3′) from the transcription start point; and (B) another located at − 35 bp (5′-TTGAC-3′). These conserved regions define the affinity of the RNA polymerase complex for a promoter and the accuracy of gene expression. The aim of this paper was to study the promoter regions of 2B. subtilis from the NCBI  database and promoters recognized by their sigma factors from the DBTBS  database operons. SigA stands out due to its high number of operons and promoters identified (46.07%). Initially, we collected the fasta file containing the genome of The data obtained in DBTBS database had the following information: (1) Operon; (2) Regulated Gene; (3) Absolute Position; (4) Location; and (5) Link Sequence. Due to space restrictions, we only present the data obtained for sigma SigL operons in Concerning the experimental evidence for sigma SigL, acoABCL was demonstrated by the mapping of the 5′ extremities of the mRNA by primer extension for the acoA gene and by homology analysis B. subtilis, mostly related to sigma SigA. All data obtained are available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip. B. subtilis genome.The FASTA genome file and the promoters obtained were used as input for a program written in Python To validate the data we found, we developed a software package called BacSVM+ that uses LibSVM library The lack of a user-friendly database could make this first step demanding for users. In this context, the major innovation of BacSVM+ is its data preparation step. If the user does not have the promoters, the program searches (with the python script described earlier) the whole genome for promoters of the respective bacteria. Based on the promoters gathered during the first step, it is possible to define LibSVM parameters and simulate promoter classification.C) and the gamma (G) parameters, where C indicates how much the support vectors are penalized when the prediction is wrong. In other words, this is the penalty when points are placed outside the range of correct classification in the hyperplane. On the other hand, the G parameter is a way to configure the kernel. In the case of a Gaussian function, this parameter controls the standard deviation function. BacSVM+ allows an extensive search of C and G parameters by setting a range of possible values.LibSVM library allows setting a wide range of parameters, as shown in A), specificity (S) and sensitivity (SN) values, using the following formulas TP = promoter sequences classified as promoters (true positives); TN = promoter sequences classified as non-promoters (true negatives); FP = promoter sequences not classified as promoter ; FN = promoter sequences classified as non-promoter .Finally, in the last step, the user can predict promoter regions and the results can be exported to a text file or a spreadsheet. The architectures performance was evaluated for its accuracy  and kernels  available were made. The cost parameter was set between 0.00390625 and 65,536, with a multiplicative factor of 16. In addition, the gamma parameter was set between 1.52587890625E−5 and 256, with a multiplicative factor of 16. The initial and final values were defined through brute-force tests. The other parameters were chosen according to the default values of the LibSVM library.The results obtained in simulations with 767 promoters from B. subtilis are consistent with related works found in the literature, thus validating the data gathered. The best combination found was the NU-SVC and C-SVC algorithms with an RBF kernel, leading to a 93.20% and a 95.63% prediction accuracy, respectively. The main innovation of BacSVM+ is in the feature of promoter searching during the data preparation step, allowing the user to use the software even if they do not have promoters and non-promoters examples for running the simulation. Our results can be seen in B. subtilis promoter regions with Support Vector Machines were found in the literature. Monteiro et al. B. subtilis were used in their research. The accuracy they obtained was lower than the accuracy obtained with BacsVM+, at 76%. Another group developed PePPER as a webserver-based promoter prediction tool , but they did not show results B. subtilis.Related works that predict


Fungi play a major role in the global carbon cycle because of their ability to utilize plant biomass  as carbon source. Due to the complexity and heterogenic composition of plant biomass, fungi need to produce a broad range of degrading enzymes, matching the composition of (part of) the prevalent substrate. This process is dependent on a network of regulators that not only control the extracellular enzymes that degrade the biomass, but also the metabolic pathways needed to metabolize the resulting monomers. This review will summarize the current knowledge on regulation of plant biomass utilization in fungi and compare the differences between fungal species, focusing in particular on the presence or absence of the regulators involved in this process.The online version of this article (doi:10.1186/s13068-017-0841-x) contains supplementary material, which is available to authorized users. Plant biomass is the most abundant carbon source on Earth and therefore is of major importance for ecology and the global carbon cycle. Fungi are highly efficient degraders of plant biomass. Due to the complexity and heterogeneity of plant biomass see “” Table , fungi hHowever, plant biomass is also the source of many food and industrial products that are used in our society. This has resulted in a broad interest from researchers and industry in the enzymatic degradation of plant biomass, starting with the first application of these degrading enzymes in the beginning of the last century . Since Aspergillus spp. and Trichoderma reesei) have been developed for industrial applications [http://www.novozymes.com), DSM (http://www.dsm.com), Roal Oy , AB Enzymes (http://www.abenzymes.com), and DuPont (http://www.dupont.com), have selected these fungi, due to their good fermentation properties and high level of protein production. However, enzymatic hydrolysis  is still one of the major bottlenecks in the biorefinery process, due to the recalcitrance of plant cell wall and the insufficient efficiency of current enzymatic cocktails to convert all the biomass into the desired products. Strategic improvements are required, and recent developments in fungal research  have provided a much deeper insight into the mechanisms/strategies of fungi related to plant biomass utilization.Currently, only a small number of Ascomycetes  involved in plant biomass have been characterized from the fungal kingdom, mainly belonging to zinc binuclear cluster family  see “Tr” Table . HoweverAspergillus spp. [T. reesei [Neurospora crassa [Penicillium oxalicum [Regulation of gene expression directly affects the composition of the resulting enzyme mixtures and is therefore highly relevant for applications , resultilus spp. –19, T. r. reesei –23, Neura crassa , and Penoxalicum , 26. ThiPlant biomass consists mainly of polysaccharides, but also contains proteins and the aromatic polymer lignin. Its precise composition is highly complex and varies depending on plant species and tissue, season, and geographic location. Plant polysaccharides Table  can be dd-glucose residues, organized in bundles called microfibrils [d-xylose), xyloglucan , and mannan . These backbones are interrupted, branched, or decorated with several different monomers or chains, resulting in different variants of these polymers [Cellulose is the most abundant plant cell wall polysaccharide, consisting of a linear chain of β-1,4-linked ofibrils . Hemicelpolymers , 29. Pecd-glucose residues which can be branched at α-1,6-linked points (amylopectin) [d-fructose with a terminal d-glucose residue [Starch is one of the main storage polysaccharides, consisting of an α-1,4-linked polymer (amylose) of opectin) , 31. Ano residue , 33. Gum residue , 34.Lignin is the most complex and recalcitrant heteropolymer, consisting of aromatic alcohols known as monolignols, built from three phenylpropanoid precursors: ρ-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol. The exact structure of lignin varies depending on plant species or tissue , 35.In order to release the monomers present in those complex structures, the simultaneous action of several plant biomass-degrading enzymes are required.http://www.cazy.org), based on their amino acid sequences. CAZy contains six main groups: glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), auxiliary activities (AA). and carbohydrate-binding modules (CBMs). In each group, there are several families, some containing only a single known enzyme activity , while others contain several activities  and are therefore not an immediate indication of enzyme function [Those enzymes have been classified into families in the Carbohydrate-Active enZymes database (CAZy)  cellulases have been described in fungi: A. niger , cellobis oryzae , gentioburogenum , and sop. reesei , 49. Amo. reesei  have sumAspergillus spp., the (hemi)-cellulolytic systems are strictly coregulated via the monomeric inducer d-xylose [T. reesei and N. crassa are more distinctively regulated. It has been shown that l-arabitol is the inducer of the arabinanolytic and xylanolytic system in Aspergilli [T. reesei, only l-arabinose and not l-arabitol can trigger these effects [A. niger [T. reesei [d-xylose induction is concentration-dependent: at very low concentrations, it acts as an inducer for xylanases, while at higher concentrations as a repressor through CreA/Cre1.Commonly, it is believed that in d-xylose –53 and/od-xylose , 53, whipergilli –56, whil effects . It has A. niger , 59 and . reesei , 60 thatT. reesei, pure saccharides such as d-xylose, xylobiose, sophorose, lactose, and galactose can act as inducers for (hemi)-cellulase production, but each one can activate only part of these systems [T. reesei appears different and more diverse than in other fungi [N. crassa or A. niger, and a general model for the substrates recognition and the induction of (hemi)-cellulolytic systems in T. reesei has been suggested [d-xylose in Aspergillus, sophorose in T. reesei [N. crassa [d-galactose in N. crassa and cellobiose and lactose in T. reesei [In  systems , 61, 62.er fungi , such asuggested . While e. reesei  or cello. crassa , other c. reesei , 67.T. reesei [At the industrial level, lactose is a promising inducer for (hemi)-cellulase production in . reesei , becausexyr1 is involved in this process [Although the exact mechanism of this induction is not known yet,  process , 69. In  process , 70, 71.Trichoderma reesei [Neurospora crassa [Myceliophthora thermophila [The degradation of crude plant biomass is more complex than that of simple polysaccharides, requiring a network of TFs which respond to different inducers. These regulators need to act together in a coordinated manner to express the right enzymes over time, depending on the substrate and the fungal species. In the literature, only few studies have covered these aspects, but recently, expression profiles of a “large” time course of CAZy and pathway genes have been reported in Aspergilli –74, Trica reesei , Neurospa crassa , and Mycrmophila  or mixedrmophila .A. niger during growth on steam-exploded sugarcane bagasse (SEB) prefers to use first d-glucose, followed by d-xylose, and finally l-arabinose, affecting enzyme production over time. In this system, the transcription factor XlnR , agdA/B (α-glucosidases), eglA (endoglucanase), lacA , aglB , axhA (arabinoxylan arabinofuranohydrolase). and xlnD (β-endoxylanase) [As de Souza et al.  have shoylanase) . Interesylanase)  showed tylanase)  showed tAll these data suggest that the presence of a fine-tuned and complex crosstalk between inducers and regulatory systems involved in plant biomass utilization in filamentous fungi still remains to be fully understood.Many fungal transcription factors have been described to be directly involved in the regulation of plant biomass utilization  in their binding domains, and it is classified in “fold groups” based on the overall shape of the protein backbone in the folded domain, which depends and on the amounts of cysteine and histidine residues. The majority of positive regulators appear to belong to the ZnNeurospora crassa and Trichoderma reesei. This schema is a general overview and needs to be adapted to every fungal species regarding the presence/absence, function(s). and possible interaction of each regulator.Interestingly, several TFs are involved in cellulose degradation , suggesting overlapping function and/or fine tune regulation, depending on the species. Table −3, percentage match cutoff 60% as for identification of distant homologs [2Cys6, Cys2His2, MADS box, and basic helix-loop-helix (bHLH). Manual curation of the groups was performed by aligning the amino acid sequences with a suitable outgroup using MAFFT [Phylogenomic analysis was performed to study TF distribution along fungal kingdom following the pipeline of Todd et al. . In summhomologs . Ortholong MAFFT , and falng MAFFT  with 100This approach allowed us to identify a new putative TF, named ClbR3  see “”  [T. reesei and Trichoderma atroviride (xyr1) [N. crassa (xlr-1) [F. graminearum (xyr1) [Fusarium oxysporum (xlnR) [Magnaporthe oryzae (xlr1) [Penicillium canescens (xlnR) [Talaromyces cellulolyticus (xlnR) [d-xylose catabolism and the xylanolytic system [M. oryzae [P. canescens [M. oryzae, xlr1 controls mainly d-xylose catabolism, while the xylanolytic system appears to be controlled not only by this regulator, but also by another unknown regulatory network [P. canascens, where XlnR is involved only in xylan degradation and not in d-xylose catabolism [l factor  involvedl factor , 85. Thi. (xlnR) , 84, 86,e (xyr1) , 87, N.  (xlr-1) , F. gramm (xyr1) , Fusarium (xlnR) , Magnapoe (xlr1) , Penicils (xlnR) , and Tals (xlnR) , but itsc system , 88–90, . oryzae  and P. canescens . In M. otabolism .Aspergillus aculeatus [T. reesei [T. reesei is the only fungus with a clear XYR1 dependent regulation of the cellulolytic system [N. crassa, xlr-1 can only modulate the expression levels of a few cellulases, suggesting that other TFs are more important [clr-1 and clr-2 [In Aspergilli , 94, 95,culeatus , and T. . reesei , 97 thisc system . In N. cmportant  such as nd clr-2   with the loss of cellulolytic function or in Trichoderma spp. [T. atroviride and the “most recent” T. reesei [Talaromyces cellulolyticus is a surprising exception, without an apparent biological explanation that deserves deeper investigation. Despite their taxonomic distance and different lifestyles, the functions of XlnR in Fusarium genus and T. cellulolyticus appear similar in that it regulates xylanase but not cellulase production [Although XlnR orthologs are present in almost all filamentous Ascomycota, the set of genes controlled by this regulator is highly species specific  and apperma spp.  with the. reesei , 111. Cooduction .Aspergillus spp., the arabinanolytic system is under control of AraR (homolog of XlnR), present only in the family Trichocomaceae. It appears to have originated from a gene duplication event from XlnR after this family split from the other filamentous Ascomycetes [In omycetes .d-xylose and l-arabinose, respectively, but also interact with each other [Aspergillus genus, both in expression and growth profiles, suggesting evolutionary changes in Aspergilli regarding pentose utilization [l-arabinose-responsive regulator, ARA1, has been described in M. oryzae. Despite having no significant sequence similarity to AraR, ARA1 appears to be the functional analog of AraR, controlling the arabinanolytic system as well as l-arabinose catabolism. A preliminary phylogenetic analysis shows that it is present only in the Sordariomycetes and Leotiomycetes  and partially in A. nidulans (ClrA/B) [A. niger [A. oryzae (ManR) [Talaromyces cellulolyticus (tCLB2) [Penicillium oxalicum (CLR2) [Homologs of these regulators are present in almost all genomes of filamentous Ascomycetes  , 115, A.A. niger , and A. e (ManR) , 117, bu (tCLB2)  and Penim (CLR2) , showingN. crassa and A. nidulans [clrB/2. Then ClrB/2 directly induces expression of cellulases in both fungi, while only a few selected hemicellulases are induced in N. crassa [ClrA/1 appears to have a conserved role in cellulose-sensing, at least in nidulans . The pre. crassa , 119.N. crassa. Particularly in Aspergillus spp. ClrB (ManR) appears to have different functions compared to N. crassa CLR-2, and ClrA is not required for the induction of cellulases or other GH-encoding genes [T. reesei lacks a clear homolog of CLR-1, indicating a different strategy for cellulase regulation/sensing in these species [The roles of these two TFs in other fungi seem to differ from what has been shown in ng genes . T. rees species .A. oryzae, the CLR-2 ortholog was characterized as a regulator of mannan-degrading enzymes, and consequently named ManR [A. oryzae genome and therefore not included in Additional files In med ManR . Only lamed ManR . SurprisN. crassa, A. nidulans [A. niger [A. oryzae [P. oxalicum [T. reesei [T. cellulolyticus [T. cellulolyticus, cellulase and xylanase production appears to be under control of the Zn2Cys6 TF TctA  [N. crassa CLR-1/2 regulates the expression of other main TF-encoding genes, necessary for the lignocellulose utilization, such as xlr-1, vib-1, col-26 cellulase production and cpc-1 and the homolog of tamA involved in amino acid/nitrogen metabolism [CLR-2/B is absolutely necessary for cellulolytic activity in nidulans , A. nigeA. niger , A. oryz. oryzae , and P. oxalicum , but not. reesei  and T. colyticus . In factduction) , confirmtabolism . The opptabolism .N. crassa, as well as their mechanism. They act as a homocomplex and not as a heterocomplex as previously supposed [clr-2/B mutants showed that only a relatively small group of cellulolytic genes have a strictly conserved dependence on CLR-2/B. This conservation between Aspergillus and Neurospora, which are only distantly related, suggests that this cellulase set is of major importance for cellulose utilization strategies in ascomycete filamentous fungi, but with a different regulatory mechanism [The binding sites of CLR-1 and CLR-2 -cellulase production, compared to other species [ACE2 is acting as activator of cellulases and hemicellulases (mainly xylanases) in  species , 62.4 as XYR1 [xyn2 promoter. It has been proposed that phosphorylation and dimerization are prerequisites for the binding of ACE2 to its target promoters [Interestingly, ACE2 binds to the same promoter motif GGC(T/A) as XYR1 , but a sromoters .ace2 in T. reesei resulted in the reduction of expression of all main cellulase-encoding genes  and the xylanase-encoding gene xyn2, but not xyn1. This indicates that ACE2 acts as an activator of these genes, even though other factors also play a role in their induction. Sophorose induction is not affected by deletion of ace2, which suggests that the sophorose and cellulose inductions are at least partially mediated by different mechanisms [xyn2 expression in xylobiose-induced cultures, acting through the XAE sequence in the gene’s promoter.When cellulose is the only carbon source, deletion of chanisms . Würleitchanisms  showed tace2 appears to be necessary for the formation of high levels of cellulase. ace2 is induced by lactose and appears to not be affected by CCR, but similarly to XYR1, requires CRE1 for full induction during growth on lactose [Expression of  lactose .T. reesei: the activator of cellulase expression 3 (ACE3) [2Cys6 transcription factor and acts as positive regulator of cellulases and  xylanases, both in direct and indirect manners, by regulating xyr1 transcription. This suggests that ace3 overexpression can both directly and indirectly through xyr1 improve cellulolytic and xylanolytic gene expressions. For these reasons, it is considered a master regulator of cellulolytic and a modulator of xylanolytic genes [Recently, a novel regulator of lignocellulose degradation has been described in 3 (ACE3) . Like ACic genes .Interestingly, our phylogenetic analysis  cluster genes, respectively . AmyR is. oryzae , while M. oryzae . AmyR isA. niger –131. Itsd-glucose. A. nidulans AmyR shows 72 and 75% structural homology with its orthologs from A. oryzae and A. niger, respectively [8(C/A)GG with the A or C depending on the species. Most likely AmyR binds as a dimer with strong affinity to a motif with the two CGG triplets [A. oryzae [A. nidulans and A. oryzae [A. niger AmyR appears to be involved in the utilization of a broader range of oligo- and polysaccharides compared to other Aspergilli [myR in A. nidulans results in no growth on maltose or starch, while in A. oryzae it only reduced growth on starch. The genes under control of AmyR appear to differ in these two strains [A. oryzae [malR) [malP), and a maltase  [A. fumigatus, A. flavus, A. clavatus, and A. fischeri [Deletion of this regulator results in reduced or impaired growth on starch and/or maltose, due to insufficient production of the enzymes to convert these saccharides to ectively , and thetriplets . In addi. oryzae . Two Amy. oryzae . AmyR re. oryzae . In fact. oryzae . Howeverpergilli . Deletio strains , 136, ma. oryzae . This coe [malR) , 137, a e  , 137. Thfischeri .malR in A. oryzae resulted not only in a growth defect on maltose and reduced malP and malT expression [amyR is induced by starch, maltos). and isomaltose (strongest), while malR in induced only by maltose and earlier in time than AmyR.Deletion of pression , but alspression . ExpressIn contrast to AmyR, MalR is constitutively localized in the nucleus, probably due to the absence of the MH4 domain in its sequence . All of T. reesei as a regulator only of β-glucosidases genes with the exception of bgl1, which is seemingly under control of xyr1 [BglR was initially identified in  of xyr1 . It is pbglR increased cellulase production during cellobiose growth, possibly due the inability to produce a glucose signal for CCR. In N. crassa, the homolog of BglR, COL-26, regulates glucose sensing and metabolism, separately from CRE1-mediated CCR [Fusarium spp., Magnaporthe spp., Verticillium spp., Botrytis spp., Alternaria spp., Septoria spp., etc. This suggests that BglR could be a main TF for those fungi which require a functional and accurate cellulose/glucose sensing for survival and/or virulence [Deletion of ated CCR . BglR isirulence .bglR homolog in T. cellulolyticus (tbgA) does not control the cellulolytic system, but only affects part of the xylanolytic system. However, this needs to be confirmed by further investigation [The tigation .Much remains to be learned about BglR/COL-26 function, but it is likely to play a key role in cellulase overproduction in cellobiose cultures , mainly A. aculeatus, which is involved in the early phase of cellobiose and cellulose induction through both XlnR-independent and XlnR-dependent signaling pathways [cbhI, cmc2, xynIa (XlnR independent), cmc1, and xynIb (XlnR-dependent). However, all these genes are still induced in a ΔclbR strain, suggesting that others regulators, such as ClrA/B, are also involved [Recently, the TF ClbR has been described in pathways . It reguinvolved   orthologs are also found in other fungal orders and classes, including Sordariomycetes. Preliminary results in A. aculeatus, showed that ClbR2 regulates only cbhI, cmc2, and manR, and appears to compete with ClbR for the same DNA-binding region in the promoter of its target genes. ClbR2 is currently under investigation, in order to clarify its function, particularly in relation to ClbR [ClbR orthologs are only present in the Eurotiales, including  to ClbR .Trichoderma spp.) genomes contain a third paralog with unknown function, named ClbR3  utilization is controlled by the novel activator GaaR [In ose see “”, RhaR, rhamnose , 42, 144alactose   [A. niger (GaaR) [d-galacturonic acid (GA), polygalacturonic acid (PGA). and  pectins, regulating most of the pectinolytic genes, GA-transporters, as well as d-galacturonic acid catabolism. The residual growth reported in A. niger ΔgaaR during growth on pectin, can be easily explained by the presence of alternative regulation mechanisms independent of GaaR and/or by growth on other sugars from pectin, such as l-arabinose, d-galactose, d-xylose or l-rhamnose, which are regulate by different TFs  [A. niger and T. reesei   and A. nr (GaaR) . It is eaR etc.) . This TFsei see “”. In add of GaaR .Aspergillus spp. and Trichoderma spp.  seems to be identical to the DNA-binding site of AmyR [inuR and amyR are deleted growth is abolished on inulin in A. niger, suggesting coregulation by these regulators of inulin conversion [inuA and inuE) and sucrose metabolism (sucA and sucB), as well as the (putative) transporters related to these sugars. Our phylogenetic analysis  and yeasts, e.g., Saccharomyces cerevisiae  [d-galactose utilization have been observed among the Aspergilli and even more between aspergilli and S. cerevisiae [So far, nd Rtg3) . Those TGal4, Rtg and Rtg3nd Rtg3) . GalX annd Rtg3) . In agrerevisiae –150.d-galactose catabolic pathway [Nidulantes  is not clear yet and currently under investigation, particularly in T. reesei [d-galactose response has been reported that is mediated by XYR1 and other(s) unidentified TF(s), possibly including CLR2 [Phylogenetic analysis . This suggests that GalR , may be required for expression of genes encoding d-galactose transporters during germination [Differences in growth on mination , while Amination .2–His2 double zinc finger TF. So far this TF is the only one which is conserved throughout the fungal kingdom  is a universal regulatory system which prevents wasting energy on the production of extracellular enzymes, as well as metabolic routes that are not needed. In relation to plant biomass conversion, this means that when there is sufficient monosaccharide already present, there is no need to produce enzymes to release more monomers from the polysaccharides, and therefore CreA/CRE1 represses the expression of the genes encoding these enzymes , 154–156cbh2 promoter in T. reesei [So far all binding sites of CreA/1 described are two closely spaced SYGGRG, and repression occurs only through this double-binding sites. However, data from in vitro studies suggested that only one site is required, as has been shown in . reesei .creA/1 leads to derepression of transcription of (hemi)-cellulolytic genes and other genes involved in polymeric carbon sources, under both repressing and inducing conditions [In general, deletion of nditions , 160.N. crassa, CreA/1 directly represses mainly expression of the genes encoding enzymes involved in plant cell wall degradation, such as (hemi)-cellulases [T. reesei. In this fungus, CRE1 appears to mainly “switch on/off” the transport of inducers/repressor depending on the growth conditions [In aspergilli and llulases , 161 or llulases , 162, whnditions , 164 andxlnR/xyr1/xlr1, appear to be under control of CCR and have CreA/CRE1 and/or ACEI (see “xlnR/xyr1 in A. nidulans [A. niger [T. reesei [T. koningii (208), with ace2 and ace1 in T. reesei [amyR in A. nidulans [N. crassa [Although all main TFs, including nidulans , A. nigeA. niger , T. rees. reesei , 163 and. reesei , and witnidulans , 165 and. crassa .T. reesei [T. koningii [N. crassa XLR-1 appears to be regulated mainly by other CCR mechanisms [T. reesei CRE1 acts together with other regulatory proteins in a coordinated manner to promote CCR and optimize C-source utilization. This ensures a good adaptation to different growth condition [However, during repressing condition in . reesei  and T. kkoningii , the maichanisms . Moreoveondition .Trichoderma reesei appears to be the only fungus in which full induction of xyr1 and ace2 requires the positive action of CRE1, at least during growth on lactose [T. reesei RUT-C30, partial truncation of CRE1  leads to an increase of cellulase production, while a full deletion has a less pronounced effect [cre1-96 [ lactose . In T. rd effect . CRE1-96[cre1-96 . RUT-C30[cre1-96 , and is A. nidulans and T. reesei CreA/1 directly affects chromatin structure  during repression condition. Particularly, T. reesei CRE1 is involved in organizing the local chromatin structure (packaging) or nucleosome positioning in xyr1 promoter and cellulases cbh1 and cbh2 during repressing conditions, and its loss leads to a less dense chromatin structure during CCR [A. nidulans CreA is involved in chromatin remodeling, through histone deacetylation [Beside transcriptional regulation, CCR appears to act also through chromatin remodeling. In ring CCR –170. Simtylation .F. oxysporum, P. chrysogenum and M. oryzae, suggesting that this mutation is lethal in these species [Alternaria citri and Alternaria brassicola, suggesting that this genus regulates expression of cell wall-degrading enzymes in a novel manner [A CreA/1 deletion also results in impaired colony morphology in almost all analyzed fungi , 172–174 species . The onll manner , 177.P. chrysogenum [T. harzianum [A. nidulans and T. reesei [CreA/1 is also involved in many other processes, such as penicillin production in ysogenum , mycopararzianum , nitroge. reesei , and env. reesei . All theS. cerevisiae, reflecting the differences in lifestyle and the ability of filamentous fungi to utilize a broad range of C-sources, such as pentoses.The mechanism of CCR is more complex in filamentous fungi compared with S. cerevisiae, CCR in filamentous fungi is not only regulated by glucose, but also by high concentrations of other monosaccharides and by nuclear localization [Sclerotinia sclerotiorum [T. reesei [A. nidulans, this is not the case [cre1/A differs between species. In T. reesei and A. nidulans, cre1/A are negatively autoregulated and transcribed at lower level in repressing conditions [Acremonium chrysogenum, cre1 transcription is dependent on glucose concentration [B. cinerea and Gibberella fujikuroi, creA expression is C-source independent [In contrast to lization . In Sclerotiorum  and T. r. reesei , nuclearthe case . Also thnditions , 183, whntration . In B. cependent .S. cerevisiae. In addition to d-glucose, high concentrations of other simple C-sources trigger CCR through nuclear localization of CreA/1 in filamentous fungi, such as cellobiose and xylose in A. nidulans, or ethanol in F. oxysporum [Moreover, in filamentous fungi, carbon catabolite derepression occurs only in the presence of inducers and during carbon limitation or metabolic stress , while ixysporum .S. cerevisiae and filamentous fungi, phosphorylation of CreA/1, mediated by SnfA/1, is a key for its re-localization during derepression conditions. In A. nidulans SnfA is essential for removal of CreA from the nucleus [S. sclerotiorum, although it is still necessary for Cre1 derepression [T. reesei, CRE1 is positively regulated by phosphorylation under repressing conditions [T. reesei, while it is able to phosphorylate Mig1 when heterologously expressed in yeast. These differences, suggests that CCR has evolved differently in T. reesei compared to other fungal species [In both  nucleus , but notpression . In contnditions . Moreove species .A. nidulans, A. oryzae, and T. reesei, CreA/1 forms an essential complex for CCR with the deubiquitinating enzyme CreB/2 [A. nidulans [F. oxysporum [In e CreB/2 –191, thee CreB/2  and HECTe CreB/2 , 193. Thnidulans  and F. oxysporum , F-box pxysporum .T. reesei and T. koningii, ACE1 acts as a repressor for both cellulase and xylanase production [Talaromyces cellulolyticus, its homologous gene tacA appears to act as an inducer of cellulases and xylanases, as well as cutinases. However, the TacA protein shows a low similarity to ACE1 except for the zinc finger domain , suggesting that it is a novel transcriptional regulator protein [In contrast to its initially assigned name, in oduction , 197. In protein . Homolog2His2-type zinc fingers and was shown to bind in vitro to eight sites in the cbh1 promoter, all of which contain the core AGGCA sequence [Trichoderma spp., but their functions are not completely clear. In the xyn1 promoter, ACE1 binds to two GGCTAA motifs, competing with the positive regulator XYR1 [The ACE1 protein contains three Cyssequence . This cotor XYR1 .T. reesei, the ace1 deletion resulted in an increase in the expression of genes encoding all the main cellulases  and hemicellulases (such as xyn1 and xyn2) [ace1 transcription is induced by lactose, and repressed by CRE1-mediated CCR [In sophorose- and cellulose-induced cultures of nd xyn2) . Despiteated CCR .ace1 deletion strains show strong impaired growth on d-sorbitol, suggesting additional targets and a more general regulatory role than expression of cellulase- and hemicellulose-encoding genes [A. nidulans, a gene (stzA) encoding a highly similar protein to ACE1 is involved in abiotic stress response, such as sensitivity to salt and DNA damaging agents [All ng genes , 197. Ing agents . AccordiN. crassa a novel Cys2His2-type zinc fingers TF involved in hemicellulase regulation, HCR-1, has been characterized [l-arabinose or xylan. It appears to be conserved across several cellulolytic fungi but not characterized yet in other species, suggesting a conserved role in lignocellulose degradation. Unfortunately, very little is currently known about its function(s), and deeper investigations are required, particularly regarding its mechanism and role in the regulatory network [Recently in cterized . This re network .MADS-box proteins are a well conserved family of transcription factors in eukaryotic organisms, controlling a broad range a cellular function through interaction with their cofactors, such as primary metabolism, cell cycle, and cell identity. Based on the amino acid sequence of the conserved MADS-box domain, these protein are classified into two types: SRF-like (Serum Response Factor) (type I) and MEF2 (Myocyte Enhancer Factor2) (type II). Only few MADS-box proteins have been reported in fungi, mainly in the SRF-like subfamily .6GG (the CArG box) on the target genes [The MADS-box motif, of SRF proteins, generally binds to the consensus sequence CC(A/T)et genes , 202. Thet genes , 203.A. nidulans, the MADS-box protein McmA (similar to S. cerevisiae Mcm1) has been shown to positively control cellulase expression, probably through interaction with ClrB/ManR [eglA promoter in a cooperative manner [T. cellulolyticus, the mcmA homologous tmcA appears to have only small effects on cellulase production [In lrB/ManR , 124. Mce manner , 205 thaoduction .Additional factors affect plant biomass utilization by fungi, such as temperature, pH, light, nitrogen sources, and the access to heterochromatin. These phenomena are poorly studied in relation to plant biomass utilization, but are crucial to fully understand the regulation of this process. In addition, their signal pathways are not clarified yet, and most likely, they are involved in the transduction of many different processes. Here we present a short overview of these factors.T. reesei, named xylanase promoter-binding protein (XPP1) [xyn1, xyn2, and bxl2) during growth on d-glucose or high d-xylose concentration at later cultivation stages, while it has no effect on cellulase or d-xylose catabolism [Recently a novel transcription factor has been characterized in n (XPP1) . It actstabolism . In additabolism .XPP1 is a basic helix-loop-helix TF (bHLH) with E-box domain, which typically would bind to a hexameric palindrome 5′-CANNTG-3′ . ExperimAccess to heterochromatin is involved in the control of gene expression and therefore also in plant biomass utilization. It is mainly organized through the CCAAT box (Hap complex), methylation (mainly through LaeA/1 –212). anSaccharomyces cerevisiae, and it consists of the Hap2, Hap3, Hap4, and Hap5 proteins. Homologs have been identified in several organisms, such as HAP2-3-5 in T. reesei, Hap5 in N. crassa and AnCF from A. nidulans [CCAAT sequences are present in the 5′ regions of approximately 30% of all eukaryotic genes. The Hap complex is the first CCAAT-binding complex described in nidulans .cbh2 in T. reesei [CCAAT sequences have been found not only in the promoters of respiratory genes , but als. reesei . This re. reesei .T. reesei xyn2 promoter was mutated, resulting in a slight increase in xyn2 transcription in glycerol and xylan grown cultures [T. reesei the HAP complex, CRE1 and an unknown GTAATA-binding protein affect nucleosome positioning, influencing the accessibility to the TATA box for transcription initiation of cbh2 [A. nidulans and T. reesei   [T. reesei [N. crassa [N. crassa, it has been shown that WWC controls several TF depending on circadian rhythms (Dawn- and dusk-phases), including the glucose-dependent repressor CSP1, important for (hemi)-cellulase production [T. reesei and T. atroviride LAE1 (LaeA ortholog) and VEL1 (VeA ortholog) are essential for the expression of (hemi)-cellulase-encoding genes [Two light-responsive complexes have been described so far in fungi. The White collar complex (WCC) , 241 tog reesei) , 235. Th reesei)  in T. re. reesei  and N. coduction . The othoduction , 224, cooduction –246. Intng genes , 247, whng genes , 249.Aspergillus spp. [These light-responsive systems are in general highly conserved in Ascomycetes, but some species do not have VVD orthologs, such as lus spp. , indicatlus spp. .These data confirm the crosstalk between nutrition, circadian clock and light response in filamentous fungi , 240.Plant biomass utilization is also affected by nitrogen sources, environmental pH and Reactive Oxygen Species (ROS).A. nidulans [Trichoderma spp. [N. crassa [Fusarium spp. [A. nidulans and N. crassa, and occurs through 3 key TFs: the activator AreA/NIT-2  [Similarly to CCR, Nitrogen Metabolite Repression (NMR) enables preferential utilization of easily assimilated N-sources (ammonium or glutamine) instead of energetically less-favored ones , preventing wasting energy , 251. Thnidulans , 253, Trrma spp. , 254, an. crassa , 255–257ium spp. . This efctively)  and NmrActively) , 260.2Cys2-binding-domain [AreA belongs to GATA family with Cysg-domain , and it g-domain . AreA acg-domain  in the pg-domain , and remg-domain  and incrg-domain . The actg-domain , 266.Another GATA repressor is AreB, coding for three distinct proteins . It moduA. nidulans. In this system, NmrA appears to modulate AREA and AREB activities in response to the carbon status of the cell [Macios et al.  showed tthe cell , confirmthe cell .N-starvation on hydrolytic enzyme (CAZy) production observed and described above. Those regulators mediate chromatin remodeling, in particular during nitrogen starvation, affecting the CAZyme production in A. nidulans [A. nidulans contain potential binding sites for the global carbon and nitrogen regulatory TFs, such as CreA, XlnR and AreA, in their promoter [This crosstalk can explain the negative effects of nidulans , 252. Papromoter . This supromoter .Fungi also need to adapt to ambient pH changes , mediateA. nidulans [Saccharomyces cerevisiae and reviewed previously [2His2 type), both highly conserved in fungi [27) induces the transcription of alkaline-expressed genes (including pacC itself) and represses transcription of acid-expressed genes [T. reesei [T. reesei PAC1 (homolog of PacC) increases cellulase production by induction of xyr1 and ace2 expression [The pH regulation has been well studied in nidulans , 278 andeviously . In summin fungi . The acted genes , 281, ined genes . This ised genes  and ClrBed genes  or/and u. reesei . Particupression . This inDue to the complexity of plant biomass, fungi have evolved different strategies to utilize all or some of its components as carbon source. They need a broad set of degrading enzymes as well as diversified metabolic pathways, which are under control of several regulators. An additional level of complexity is the crosstalk between regulatory systems. Moreover, regulatory systems are poorly conserved in fungi. This is likely caused by the need of different fungal species to adapt to their specific biotope such as forest, crops, herbivore dung, and gut etc. On top of that, the lifestyle of every fungus  adds a second level of adaptation, affecting gene regulation. Example of this complex adaptation is the transcriptional rewiring, where a single TF has different functions depending on the species.With the availability of an ever-increasing number of fungal genomes, transcriptomes, and proteomes, the differences between regulatory systems in fungi related to in plant biomass utilization have become more evident. This study demonstrated both similarities and differences between fungal species, as well as the much higher level of complexity of and interaction between the regulatory systems than was previously assumed. With the addition of new regulators, our understanding of plant biomass strategies of fungi increases, enabling a more fine-tuned manipulation of enzyme production in industrial settings. This will not only benefit the development of better enzyme cocktails for the production of biofuel and biochemical, but also many other industrial sectors, such as pulp and paper, food and feed, and textiles.Additional file 1. Prevalence of transcription factors in Fungi.Additional file 2. Maximum likelihood tree of Zn2Cys6 transcription factors (TFs). In bold are the species for which the TF has been characterized, while in bold and larger font are the species in which the TF was first discovered.Additional file 3. Maximum likelihood tree of Cys2Hys2 transcription factors (TFs). In bold are the species for which the TF has been characterized, while in bold and larger font are the species in which the TF was first discovered.Additional file 4. Maximum likelihood tree of MADS BOX (McmA) transcription factors (TFs). In bold are the species for which the TF has been characterized, while in bold and larger font are the species in which the TF was first discovered.Additional file 5. Maximum likelihood tree of bHLH (XPP1) transcription factors (TFs). In are the species for which the TF has been characterized, while in bold and larger font are the species in which the TF was first discovered.