Defining specific in-vivo activities requires monitoring histone dynamics during regulator depletion, which has been technically challenging. We have recently generated histone-exchange sensors in Saccharomyces cerevisiae, which we now use to define the contributions of 15 regulators to histone dynamics genome-wide. While replication-independent exchange in unperturbed cells maps to promoters, regulator depletions primarily affected gene bodies. Depletion of Spt6, Spt16 or Chd1 sharply increased nucleosome replacement sequentially at the beginning, middle or end of highly expressed gene bodies. They further triggered re-localization of chaperones to affected gene body regions, which compensated for nucleosome loss during transcription complex passage, but concurred with extensive TF binding in gene bodies. We provide a unified quantitative screen highlighting regulator roles in retaining nucleosome binding during transcription and preserving genomic packaging.DNA packaging within chromatin depends on histone chaperones and remodelers that form and position nucleosomes. Cells express multiple such chromatin regulators with overlapping   Histones are small proteins that package eukaryotic genomes within chromatin. At the core of this packaging are nucleosomes, composed of histone octamers wrapped by ∼147 base pairs (bp) of DNA . Enzymes2 tetramer to DNA, followed by the association of two H2A–H2B dimers 2 tetramer exchange occurs at gene promoters, but is not evident at gene bodies where H2A–H2B exchange is widespread  and H2B   generated from the original AA strain  in fact corresponds to the tor2-1 S1971I mutation  were in YGY688)  using a . We notmutation  insertedmutation  to the hStrains for ChEC-seq analysis were generated by C-terminally fusing MNase to the indicated target in the desired AA strain. For Reb1, MNase was N-terminally fused. All strains were verified by Sanger sequencing and per experiment in the associated genomic data. See S. cerevisiae cultures with at least 6 exponential doublings, reaching an OD 0.4–0.6 in YPD media at 30°C at the start of the experiment. Cells were taken prior (time 0) and at the indicated time following Rapamycin (BarNaor BNR-5000) addition to a final concentration of 1ug/ml of culture. 50 ml samples were taken for ChIP experiments, crosslinked for 5 min at room temperature (RT) in 1% Formaldehyde , quenched by adding 0.125 M freshly prepared Glycine (Merck G7126) at RT for 5 min, washed twice in 50 ml ice-cold water, pelleted and snap frozen in liquid nitrogen. 15 ml samples were taken for ChEC experiments.All experiments were carried out on Exponentially growing cultures were brought to OD of 0.3 and serially diluted 4-fold. The cultures and five serial dilutions were then spotted onto YPD plates supplemented or not with 1 ug/ml of rapamycin and incubated at the indicated temperature for 2.5 days.2, 1 mM CaCl2 and 0.075% NP-40, freshly supplemented with 1 mM β-mercaptoethanol, 500 μM spermidine, and EDTA-free protease inhibitor cocktail), and warmed to 37°C in a heatblock for 5 min. 100 ul NP buffer supplemented with 40 units of Micrococcal Nuclease  were added and samples were incubated for 20 min at 37°C. The MNase reaction was stopped by adding 200 ul ice-cold stop buffer , vortexing and placing on ice. Following a 30-min ice incubation, the MNase-treated lysates were centrifuged at 17 000 × g for 10 min at 4°C. MNase digestion resulted in ∼85% mononucleosomes and ∼15% dinucleosomes, with the latter being filtered out in analysis. This degree of digestion was calibrated per MNase stock, and prevents over-digestion by MNase.ChIP was carried out as in . Cell peThe 400 ul supernatant was divided into two separate wells of a 96-well LoBind Eppendorf plate; 110 ul lysate for anti-HA IP (added 10 ul of 12CA5 hybridoma supernatant) or 110 ul lysate for anti-myc (added 10 ul of 9E10 hybridoma supernatant). The subsequent steps and sequencing libraries were prepared as in .2 was added to a final concentration of 2 mM for exactly 30 seconds, and 120 μl of stop buffer  were mixed with 120 μl of sample. Proteinase K treatment, nucleic acid extraction and library preparation were performed as previously described  using Bowtie 2  were aligned to the Bowtie 2  with theBowtie 2  with theSaccharomyces cerevisiae genome (R64-1-1) using Bowtie 2  from the library preparation. Filtered reads were then trimmed to a length of 40bp and aligned to the Bowtie 2  using thBowtie 2  with theSelected region was divided into 20-bp bins and the mean normalized coverage across the biological repeats in each bin calculated, smoothened along 7 bins and shown.n = 3, 4, 6, 251, 17, 188, 256, 23, 2444, 3947, 4776 and 4349 for Histone promoters, rRNA37, rRNA5, ARS, Centromere, tRNA, TY + LTR, telomere, promoter, 5′ mid and 3′ respectively). To determine the relative change during regulator depletion we first selected all features of one type, e.g. 5′ gene, log2-transformed their occupancy in all repeats of a certain depletion time course, e.g. spt6 depletion and normalized each to its mean coverage at the starting point (before depletion), i.e. relative occupancy. We then fitted a single line across all the relative occupancies of all selected features over all repeats against the time (in min) after depletion for each repeat. The slope of this line was then multiplied by 90 to estimate the mean relative change of this feature type after 90 min of regulator depletion (indicated as color), if not noted otherwise the Pearson correlation coefficient of this relation (rel. occupancy vs. time) is indicated by the size of the dots.Yeast genome annotation , TSS posAs above, but the features  are further subdivided into 5 groups according to the expression level, taken from ,  and the smoothed coverage at 300 regular intervals selected, i.e. 300 bp for each gene, to calculate the mean coverage in the middle region. This approach better preserves the nucleosome patterning in the start and the end of genes.These mean meta profiles of biological repeats were then log2-transformed (after adding a pseudo-count of 1) and normalized by the corresponding mean zero timepoint to calculate the relative change shown.For 2C, 4C and S4C, only highly expressed genes (Top200) and the 90-minute depletion timepoint are selected for the metagene.After annotating the features on the genome (see above), the mean occupancy of each regulator across all features of one type was calculated, i.e. 12 mean occupancies (one per type) per regulator. Then enrichment of a certain regulator at a feature type is then defined as its relative occupancy when compared to all other chaperones (in log2):2-mean)/ std, where EnrR,T is the enrichment of regulator R at the feature type T, OccR,T is the mean occupancy of regulator R across all features of type T and OccS,T is the mean occupancy at features of type T across all regulators (S).EnrR,T =  for each gene at a certain timepoint/regulator, they are sorted by and plotted against the expression level  of the cSteady-state ChEC-seq profiles along the Top200 expressed genes were normalized for length (see above), aligned by their TSS and the mean across those genes is shown. In Figure Similar to above, but for ChEC-seq profiles instead of ChIP-seq profiles. Terminator corresponds to the 100bp downstream of the TTS. Moreover, instead of calculating the linear fit across all regions kept separately, we first calculated the mean occupancy across all regions of the same type, e.g. 5′ and high expression level. As before, these mean occupancies were then log2-transformed and normalized against mean before fitting a line were the x-value is the time of depletion (0 or 60 min) and the y-value is the mean normalized occupancy. Each biological repeat is one point. The slope x 60 min is then used as the mean relative occupancy change across all repeats.2-transformed. The relative change of each segment in each sample was then calculated by comparing its myc level against the average myc level across all samples. For each sample, relative changes across all segments were z-score normalized and then used as an input for the principal component analysis. The mean values and standard error of the first and second PC across all biological repeats were used to plot Figure Each gene was divided into three segments , i.e. ∼15 000 segments in total, and the mean myc level of each segment in the corresponding samples  calculated and logn = 5424) of two TF samples are considered as the promoter preference similarity and shown in Figure Promoters of all genes were determined as described previously , i.e. frFor each TF  motif PWM was downloaded from CISBP  ID: M00. Based oR ≤ 0.06 and < 0.03). To normalize each spiked-in TF sample against the Aro80 spike-in, we thus first calculated the normalized number of reads falling into the unique Aro80 binding regions, i.e. spike in reads. Next, we calculated the number of unique TF reads as the mean occupancy around all promoter or gene body motifs (from −25 to −6 and +6 to +25 bp excluding the exact binding site). For each spiked-in sample, these mean occupancies (promoter and gene body) were then divided by the sum of spike in reads and shown as a function of regulator depletion time in Figure Based on its binding profile, the Aro80 transcription factor has only three target sites  and no o2-transformed ratio is shown.To determine the relative binding to gene body versus promoter motifs, the mean normalized occupancy across all promoter or gene body motifs was calculate and divided by each other. The logFor Motif occurrences were grouped according to their genic position, i.e. promoter, 5′, mid, 3′ or terminator, and the corresponding gene expression level (5 groups as above). For each group, the mean change across all time courses and time points was calculated using a linear fit as described above for myc and HA levels.Bound promoter and gene body motifs after regulator depletion were determined by comparing the mean TF occupancy around them after 90 min of regulator depletion against the mean occupancy at a promoter or gene body position. Motifs with a two-fold higher occupancy are considered as bound motifs, and were then grouped into four groups: promoter-bound, gene body-bound, promoter-unbound and gene body-unbound. For each time point the mean HA (or myc) level of the H3 sensor around (+/− 75 bases) all motifs of each of these 4 groups was calculated. Shown is the level for each time point the mean HA (or myc) level on bound (or unbound) gene body motifs against the HA (or myc) of the corresponding promoter motifs.Mean ChEC and ChIP profiles were smoothed using a running average of 141 or 250 bp, respectively. The similarity was calculated as the Pearson's correlation across all promoter and gene body positions between two smoothened profiles.Normalized reads along unscaled gene body (500 bp upstream to 1000 bp downstream) of the respective groups (mid and high expressing) are aligned by their TSS, and their mean HA and myc level at the corresponding sample calculated. After smoothing this meta gene profile with a moving average running window of 51 bp, the enrichment at each position is calculated as the log2-transformed ratio between the smoothened, mean H3K79me3 level at this position and the corresponding HA level (after adding a pseudo count of 0.5).2 fold change at each time point compared to the steady state condition calculated. Shown is the moving average over three time points of this median.Exponentially growing cells were supplemented with 1 ug/ml of rapamycin and harvested every 10 min at the indicated times for total mRNA extraction. RNA was extracted and libraries were prepared and processed as previously described in . For theTo provide a unified view on the contribution of chromatin regulators to nucleosome dynamics, we screened nine representative chaperones and an additional six remodeler-complexes to detect candidates for deeper exploration Figure . The selRecently, we introduced a new method—histone exchange timers—for mapping exchange along the genome . This mein vivo, providing rapid binding sampling with high sensitivity, reproducibility and spatial resolution . Briefly, AA depletes the nucleus of a tagged target protein by conditional tethering it to a tagged abundant cytoplasmic anchor protein using rapamycin-dependent heterodimerization . Depletisolution ,53.Overall, we defined the localization and exchange phenotypes of 15 chromatin regulators, representing all major conserved histone chaperones and remodeler complexes except for the H2A.Z-specialized SWR1C Figure . In wildTo further verify the ability of the sensor to describe differences between the tested regulators, we examined additional genomic features, comparing also to an equivalently modified, grown and treated control with no regulator depleted Figure . SimilarFocusing on all genes aligned by their Transcription Start Site (TSS) revealed a subset of chaperones whose depletion led to H3 exchange phenotypes at distinct genic regions Figure . We deteIncreased H3 replacement in highly expressed genes emerged as the most prominent effect of regulator depletion in gene bodies Figure . Three cOf note, many previous studies examining the perturbation of Spt6, FACT ,43,85–88In our experiments, depletion led most prominently to the expression correlated displacement of ‘old’ H3 histones, which are retained in wild-type cells, with new histones incorporated from the free myc-enriched pool. For Spt6 and FACT, these results are consistent with observations for select genes ,32, and It is notable that the increase in histone replacement caused by chaperone depletion was limited to the H3 histone. In fact, the rapid H2B replacement characterizing highly expressed genes in wild-type cells  decreaseOur results point at increased H3 replacement along the gene body in highly expressed genes as the prominent and most immediate effect of regulator depletion. To replace H3–H4 histones, nucleosomes must first be evicted, a process that could result from the progressing polymerase, followed by the incorporation of the detected new myc-tagged histones. The fact that no chaperone in our screen led to reduced H3 replacement in gene bodies further supports highly efficient retention in unperturbed cells , indicatTo identify the chaperones that incorporate new histones when H3 retention is lost, we first examined for chaperones that change their localization upon depletion of H3-retaining regulators. We reasoned that to compensate, a chaperone would need to re-localize to the affected locations, previously served by the depleted regulator. Re-localization of Asf1 and Hir1 was previously observed upon depletion of FACT and Spt6, and Asf1 and Hir1 were further suggested to act in re-distributing nucleosomes ejected during transcription . We expaDepletion of the promoter-serving Ino80 Figure  had no dregulators , Reb1  and the RNA polymerase II-associated TATA Binding Protein (TBP), following the depletion of Ino80, Chd1, Spt6, Spt16 or their combination Figure . None ofin-vitro motifs residing in the genes acted on by those regulators, i.e. highly expressed ones . Lower occupancy is expected if incorporation is unlikely when nucleosomes are evicted, but not retained, during gene transcription or TF binding. Our interpretation is that nucleosome eviction was readily compensated through increased incorporation, leading to H3 replacement rather than loss. Depleting Spt6, Spt16 or Chd1 led not only to the loss of H3 retention in gene bodies of highly expressed genes, but also to the re-localization of promoter-residing factors to the affected regions Figure ,38,85,96The increased accessibility of gene bodies to the binding of promoter residing factors provides a dual effect. On the one hand, it allows promoter residing chaperones to compensate for the loss of nucleosomes at highly transcribed genes, thereby guarding genome stability. On the other hand, it allows binding of other factors, including sequence-specific TFs Figure . Of noteAs nucleosome occupancy and the ensuing chromatin ‘openness’ remain largely unaltered in our measurements, three possibilities come to mind that could make these regions more accessible. First, the nucleosomes might be less organized, although we do not see strong evidence for this in our analysis: we detect loss of organization in RSC, Ino80 or Chd1 depletion , the latgkad615_Supplemental_FilesClick here for additional data file.


Multiciliated cells (MCCs) project dozens to hundreds of motile cilia from their apical surface to promote the movement of fluids or gametes in the mammalian brain, airway or reproductive organs. Differentiation of MCCs requires the sequential action of the Geminin family transcriptional activators, GEMC1 and MCIDAS, that both interact with E2F4/5-DP1. How these factors activate transcription and the extent to which they play redundant functions remains poorly understood. Here, we demonstrate that the transcriptional targets and proximal proteomes of GEMC1 and MCIDAS are highly similar. However, we identified distinct interactions with SWI/SNF subcomplexes; GEMC1 interacts primarily with the ARID1A containing BAF complex while MCIDAS interacts primarily with BRD9 containing ncBAF complexes. Treatment with a BRD9 inhibitor impaired MCIDAS-mediated activation of several target genes and compromised the MCC differentiation program in multiple cell based models. Our data suggest that the differential engagement of distinct SWI/SNF subcomplexes by GEMC1 and MCIDAS is required for MCC-specific transcriptional regulation and mediated by their distinct C-terminal domains. GMNC) and MCIDAS/Multicilin (encoded by MCIDAS) and shown to be critical for their functions , an, an13], CCNO expression and MCC differentiation at some developmental stages, and its associated proteins ARNT and AIP, as well as ELMSAN1/MIDEAS and DNTTIP1, defining components of the MiDAC complex, were identified as proximal interactors highly enriched with GEMC1 compared to MCIDAS , that is required for DAS Fig.  39, 40], 40CCNO DAS Fig.  41, 42], 42CCNO While many other proteins involved in coactivator and corepressor complexes, as well as basal transcription factors, splicing regulators and RNA binding proteins, were identified  or SMARCA4 (BRG1). While GEMC1 and MCIDAS identified similar core subunits, we observed differential labeling of subunit-specific complexes and ATPase domains. In the case of GEMC1, we found primarily BAF, defined by the ARID1A/B subunits, and to a lesser extent PBAF subunits and higher labeling of BRG1, a known interactor of Geminin Fig.  45]. In. In45]. To further validate some of these results, we performed small scale BioID affinity purifications (BioID-AP) followed by western blotting in transfected AD-293 cells. We observed that expression of either GEMC1 or MCIDAS fused to BirA* led to similar, robust levels of YAP1 biotinylation that was not observed in controls , within the region derived from MCIDAS  to examine the subcellular localization of GEMC1, MCIDAS or the hybrid proteins, all tagged with FLAG, relative to BRD9 Fig. . We obseARID1A-KO cells, neither protein could activate FOXJ1 or CCNO following transfections  and the effects of dBRD9 on this interaction. At Day 3, PLA with FLAG (MCIDAS) and BRD9 antibodies resulted in nuclear foci representing their proximal interaction Fig. . This stFoxj1 and Trp73 at the mRNA and protein level in a dose-dependent manner  complex . This cohttp://www.proteinatlas.org). This may explain why overexpression of GEMC1 and MCIDAS is incapable of driving efficient multiciliation in this cell type [www.depmap.org). This new cell line system may help facilitate future work in defining the machinery involved in multiciliation, as it can potentially be more easily scaled up and genetically manipulated than systems relying on primary cells. A drawback of this system is that the multiple cilia do not appear to be motile, limiting its use in studying cilia function (Supplementary Movies SWe identified distinct SWI/SNF subcomplexes that influence the transcriptional outputs of GEMC1 and MCIDAS and inhibition of BRD9 impaired multiciliogenesis in 2 distinct systems. This observation may suggest direct effects on ependymal MCC differentiation in mice or human patients with SWI/SNF mutations and hydrocephaly , 74. Desell type –80. ConsSupplementary Table S1 and Figures S1-S7Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Movie S1Supplementary Movie S2Reporting checklist.


Clock proteins and their collaborating transcription factors often act as distal enhancers to regulate the rhythmic transcription of gene promoters. Those transcription factors need to interact with the mediator complex and general transcription factors near the transcription start site to finally control transcription. Pol II pausing, which is determined by Pol II recruitment, pause release, and premature transcription termination near the transcription start site, plays a critical role in influencing the final transcription output. However, the way Pol II pausing is regulated during daily transcription and its role in shaping transcription rhythms have not been systematically investigated. We recently carried out a quantitative ChIP-seq study to characterize Pol II pausing across the day in mouse liver. Our analyses suggest that Pol II recruitment, pause release, and premature transcription termination activities exhibit genome-wide changes that can peak at distinct clock phases. Such complexity of Pol II pausing regulation during daily transcription provides new perspectives on the transcription regulation of circadian rhythms and warrants future studies to dissect the regulatory mechanisms of Pol II pausing and their roles in shaping the daily rhythms of gene transcription.Cell autonomous circadian oscillation is present in central and various peripheral tissues. The intrinsic tissue clock and various extrinsic cues drive gene expression rhythms. Transcription regulation is thought to be the main driving force for gene rhythms. However, how transcription rhythms arise remains to be fully characterized due to the fact that transcription is regulated at multiple steps. In particular, Pol II recruitment, pause release, and premature transcription termination are critical regulatory steps that determine the status of Pol II pausing and transcription output near the transcription start site (TSS) of the promoter. Recently, we showed that Pol II pausing exhibits genome-wide changes during daily transcription in mouse liver. In this article, we review historical as well as recent findings on the regulation of transcription rhythms by the circadian clock and other transcription factors, and the potential limitations of those results in explaining rhythmic transcription at the TSS. We then discuss our results on the genome-wide characteristics of daily changes in Pol II pausing, the possible regulatory mechanisms involved, and their relevance to future research on circadian transcription regulation. Per1/2 and Cry1/2, whose protein products are repressors that inhibit CLOCK/BMAL1 action through negative feedback TSS) signals within the TSS region were quantitated to measure the paused Pol II, while the Pol II signals in the gene body ([Pol II]GB) were quantitated to measure the gene transcription rate. The Pol II traveling ratios (TR: [Pol II]TSS:[Pol II]GB), which are quantitative measures of Pol II pausing TSS exhibits a great rebound at ZT2. Most genes also have higher [Pol II]TSS near ZT2. Moreover, numerous genes’ transcription rates, as measured via [Pol II]GB, are higher at ZT2, which is concurrent with the increase in [Pol II]TSS and [Tbp]TSS  GB for numerous genes GB) of those genes peak at different phases. For example, the [Tbp]TSS and [Pol II]GB values of Bmal1 are the highest at ZT2, but several genes’ transcription rates peak near ZT14, when their [Tbp]TSS values are low compared to other time points. Such results clearly indicate that Pol II recruitment does not directly determine the transcription rate. This appears to be at odds with the current view of coordinated regulation of Pol II recruitment and transcription bursting us genes . Six exabursting . Nonethebursting . Upon thbursting . Howeverbursting . Such reTSS, [Pol II]TSS, and [Pol II]GB at ZT2 have high burst frequencies but low burst sizes at this time point. By contrast, the upregulation of some genes’ transcription near ZT14 may be due to increased burst sizesassociated with high pause release activity. Such a scenario is supported by other evidence, such as the genome-wide increase in H3K4me3 in mouse liver in the early night TSS as the index of Pol II stability within the TSS region. As evident in TSS (thus Pol II TR) in a manner independent of P-TEFb, it leads to an inaccurate estimate of the pause release. For example, the Pol II TRs of most genes are low at ZT14, which is most probably due to a global increase in the pause release. However, some genes’ TRs peak at ZT14  to bind the m7G cap . ImportaTSS as the measure of paused Pol II. However, [Pol II]TSS actually contains signals from multiple Pol II forms TSS (and TR) independent of pause release. Those confounding factors can lead to inaccuracies in the TR results. Ideally, only signals of paused Pol II should be used for TR calculation to measure Pol II pausing and pause release. However, it is not feasible in practice to find an antibody that exclusively recognizes paused Pol II. Even monoclonal antibodies against a specific Pol II CTD form  show various cross-reactivities to other forms, and different antibodies against the same epitope can yield dramatically different results II forms , includi results . We sugg results  as surro


A, is responsible for the transcription of most housekeeping genes, while the others are alternative. A large number of σ factors induced in most bacteria by various stresses belong to group 4 of sigma factors, called extracytoplasmic function sigma factors (ECF σ). All four articles in this Research Topic are dedicated to this type of σ. Two articles describe original research, and two others are reviews. Three contributions deal with gram-positive bacteria  and one with a gram-negative bacterium (Burkholderia sp.).Gene expression in bacteria is controlled by three main types of protein regulators: DNA-binding transcriptional regulators, two-component systems, and sigma (σ) factors of RNA polymerase. Since RNA polymerase is essential for transcription, sigma factors, as its subunits are involved in the control of each gene. In many cases, the main regulators control the expression of a gene cooperatively. Bacteria contain between two and more than 60 different σ factors, depending on the species. One of these, usually called σC. glutamicum is the only exclusively nonpathogenic species in the selection. Busche et al. identified 12 genes belonging to the σE regulon. These genes were found to be involved in protein quality control, regulation of proteases, and maintenance of membrane integrity. Thus, σE was shown to regulate the cell surface response, as previously suggested. Interestingly, all identified σE-controlled genes were also active with σH. It has been shown in earlier studies that σH controls genes activated by heat and oxidative stress. The discovered overlap in the recognition specificity of σE and σH is based on very similar consensus sequences of σE- and σH-dependent promoters. It is evident that some proteins  that are essential under most stresses that lead to protein damage are alternatively induced by multiple sigma factors. Moreover, σH and σE drive the transcription of sigB, which also controls a variety of functions. Thus, σB-σE-σH, which can be termed the “Big Three,” maintain the viability of C. glutamicum cells during the transition and stationary growth phases and under various types of stress conditions.Mycobacterium tuberculosis, as a member of the Mycolata group, is related to C. glutamicum, but it is pathogenic. Therefore, σE may perform different functions in this bacterium exposed to host defense mechanisms. In their review, Manganelli et al. described σE as a master regulator in M. tuberculosis. It is involved in a regulatory network that controls responses to various environmental stresses  and functions connected to virulence. The expression of the sigE gene itself is controlled by three different sigma factors  and a two-component system. In addition, the activity of σE is inhibited by the anti-sigma factor (similar to C. glutamicum), which is regulated by intracellular redox potential and proteolysis resulting from cell surface stress. Among several regulators, σEinitiates the transcription of σB, which induces genes during the stationary growth phase. Thus, the activities and control mechanisms of σA, σE, and σH in M. tuberculosis are interwoven into a complex regulatory network similar to the tangle of C. glutamicum, although with different functions. Recently, this network was also found to be involved in the development of persister cells capable of surviving the action of various antimicrobials. In conclusion, σE is considered a master regulator in M. tuberculosis, whereas σH is considered a global regulator in the related C. glutamicum.Burkholderia, which includes several human pathogens. The very specific sigma factors described in the review by Grove have evolved in these bacteria as part of an “arms race” in which the pathogen and the host attempt to outwit the opponent by developing more sophisticated defense mechanisms. In the “battle for iron,” vertebrate hosts sequester iron in ferritin or the heme complex or reduce its levels by other mechanisms. As a result, the amount of free iron available to invading bacteria is greatly reduced, hindering pathogen replication and virulence. Several pathogenic Burkholderia species produce and secrete siderophores that efficiently chelate iron and allow its uptake into the cell. The production of siderophores and iron transporters is induced by iron limitation and controlled by specific ECF sigma factors. In some Burkholderia, the first genes in the gene clusters that determine siderophore synthesis and transport encode the ECF sigma factors MbaS  and OrbS (ornibactin sigma). These genes are repressed by the global transcriptional regulator ferric uptake regulator (Fur) or other global regulators when iron is available. Thus, the cooperation of sigma factors and global regulators ensures that iron is accessible to the pathogen when its availability is limited.The importance of sigma factors in pathogenesis has also been documented in the genus Listeria monocytogenes is an intracellular foodborne pathogen that can infect both humans and animals. It can survive and grow under food production conditions, such as low and high temperatures, low and high pH, high salt concentrations, ultraviolet radiation, and in the presence of biocides. Alternative sigma factors have been found to be involved in the infection and replication cycles of this pathogen. In a comprehensive study of 15 mutants carrying different sigma factor deletions, Rukit et al. elucidated the role of each sigma factor in L. monocytogenes virulence. While σA is sufficient for the bacterium to invade and multiply in human cells, σB is essential for its invasion. Although all five L. monocytogenes sigma factors  are involved in invasion and intracellular growth, interestingly, σB and σL can play a negative role. Deletion of σL can even enhance host invasion. In conclusion, the intricate regulatory network formed by sigma factors in L. monocytogenes requires further studies to unravel the mechanisms of host cell infections and replication within them. These findings may help improve the development of anti-Listeria agents needed to eliminate deadly listeriosis outbreaks.MP wrote the draft. RM and KT edited the text. All authors contributed to the article and approved the submitted version.


This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington’s disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue , and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj  HTT) gene, producing a toxic protein that adversely affects the brain’s cells , CUB And Sushi Multiple Domains 3 [CSMD3], 5-Methyltetrahydrofolate-Homocysteine Methyltransferase Reductase [MTRR], Pecanex 1 [PCNX], PC-Esterase Domain Containing 1B [PCED1B], Glucose-6-Phosphatase Catalytic Subunit 2 [G6PC2], Protocadherin 7 [PCDH7], Glypican 6 [GPC6], Ribosomal Protein S25 [RPS25], and Casein Kinase 1 γ 3 [CSNK1G3]) that are correlated with the metabolites, phenylalanine, and methionine. Further details are available in This study found correlations between DNA methylation and the metabolome, specifically identifying several genes  interacted with cg17068512 , HMDB0000159 interacted with cg14837557 , and HMDB0000883  interacted with cg17068512 . These CpGs were located on different transcripts of the SEPSECS gene body. Interestingly, both metabolites were found to be negatively correlated with the methylation of sites belonging to the SEPSECS gene  tRNA:Sec (Selenocysteine) tRNA Synthase enzyme, is a focal point. This enzyme facilitates selenocysteine synthesis, a rare amino acid with antioxidant properties [Moreover, recent evidence suggests that alterations in the expression of the is of HD ,66,67. Sis of HD . A studyis of HD . In summrecision ,55. A prrecision . Cytosolrecision ,57,58. Ooperties ,61. Giveoperties ,63,64. OIn conclusion, we have used an epimetabolomics approach to highlight the significance of the aminoacyl-tRNA biosynthesis pathway in the development of HD. However, this study was performed on a limited number of samples, and this is due to the difficulty of obtaining PM brain tissue. However, as a proof-of-concept study, we aimed to demonstrate the feasibility of the proposed methodology combining metabolomics and DNA methylation, which is the first of its kind in studying the pathogenesis of HD. For the analysis of methylome, we have considered known, potentially confounding, factors, such as sample age, gender, estimated proportion of neurons, and postmortem delay. The effects of nutrition or stress are assumed to be equally distributed between sample groups, and they should not have a significant impact on the outcome. We have also experimented with the use of RUV , the met


MP/ARF5 and point out the known mechanism of MP/ARF5-dependent regulation of gene expression during various developmental processes, namely embryogenesis, leaf formation, vascularization, and shoot and root meristem formation. However, many questions remain about the auxin dose-dependent regulation of gene transcription by MP/ARF5 and its isoforms in plant cells, the composition of the MP/ARF5 protein complex, and, finally, all the genes under its direct control. In addition, information on post-translational modifications of MP/ARF5 protein is marginal, and knowledge about their consequences on MP/ARF5 function is limited. Moreover, the epigenetic factors and other regulators that act upstream of MP/ARF5 are poorly understood. Their identification will be a challenge in the coming years.For many years, research has been carried out with the aim of understanding the mechanism of auxin action, its biosynthesis, catabolism, perception, and transport. One central interest is the auxin-dependent gene expression regulation mechanism involving AUXIN RESPONSE FACTOR (ARF) transcription factors and their repressors, the AUXIN/INDOLE-3-ACETIC ACID (Aux/IAA) proteins. Numerous studies have been focused on MONOPTEROS (MP)/ARF5, an activator of auxin-dependent gene expression with a crucial impact on plant development. This review summarizes over 30 years of research on MP/ARF5. We indicate the available analytical tools to study  A review of the action of MP/ARF5 in auxin-dependent regulation of gene expression, its role in the plant developmental process, and available tools for molecular analysis. Avena sativa seedlings lacking a shoot apical meristem . The l-Trp-independent pathway is poorly understood, in contrast to the l-Trp-dependent pathway, for which many enzymes have already been identified. The l-Trp-mediated IAA biosynthesis pathway can be further divided into four routes. In the first route, tryptophan aminotransferases [TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1 (TAA1) and TAA1-RELATED 1-2 (TAR1-TAR2)] synthesize indolyl-3-pyruvic acid (IPyA) from l-Trp which is converted into IAA by the flavin monooxygenases YUCCA (YUC1–YUC11)  into IAA  . This mo1–YUC11) . AnotherProteins facilitating the transport of IAA between and within cells are well known and well characterized. Among them, plasma membrane-localized AUX1, LIKE-AUX1 (LAX), and NITRATE TRANSPORTER 1.1 (NRT1.1) proteins import IAA into the cell. IAA moves inside the cell, in and out the endoplasmic reticulum, and exits the cell mainly via the activity of PIN-FORMED (PIN) and PILS (PIN-LIKE) auxin exporters. Members of the ARABIDOPSIS THALIANA ATP-BINDING CASSETTE B (ABCB) family can mediate auxin influx and efflux (reviewed by ARF genes and 29 Aux/IAA repressors were identified (monopteros (mp) mutant was discovered after screening a population of Landsberg erecta (Ler) Arabidopsis thaliana mutants obtained by ethyl methanesulfonate (EMS) chemical mutagenesis  together with its plasma membrane-localized partner TRANSMEMBRANE KINASE (TMK1–TMK4), TRANSPORT INHIBITOR RESPONSE 1 (TIR1), and its homologs AUXIN SIGNALING F-boxes (AFB1–AFB5) localized in the nucleus and cytosol, and nuclear-located S-PHASE KINASE-ASSOCIATED PROTEIN 2a (SKP2a), and ETTIN/AUXIN RESPONSE FACTOR 3 (ETT/ARF3)  and MP11ir    . The MP1.94 kDa) .in vivo (The DBD consists of three subdomains: the B3 DNA-binding motif flanked by two dimerization domains (DDs) and, at the C-terminus of the DBD domain, an ancillary subdomain (AC), related to the Tudor domain (a five-stranded β-barrel-like structure). The AC subdomain interacts tightly with the DD subdomains. However, its exact function remains unknown  of MP/ARin vivo . MP/ARF5The sequence of the amino acid residues of the MR domain is specific for each ARF TF and determines whether the TF acts as a transcriptional activator or repressor . In the in vitro analyses confirmed the interaction between MP/ARF5 and IAA1, IAA3, IAA13–IAA14, IAA17, and IAA19 ] are continually being discovered. What other post-transcriptional modifications does the MP/ARF5 protein undergo, and how does it affect its stability and activity? These questions remain open. Additionally, the role of the MP/ARF5 isoforms in plant developmental processes is puzzling.Summing up, despite the structure of the MP/ARF5 protein being known, details on how MP/ARF5 functions The AuxRE motif recognized by MP/ARF5 is found in the promoter of 11 092 genes and MP/ARF5 protein directly regulates the expression of 4585 target genes in an auxin-dependent way  Fig. 2AFig. 2A.TIR1/AFB complex, ubiquitinated, and degraded by the 26S proteasome. In the absence of BDL/IAA12, MP/ARF5 recruits chromatin-remodeling complexes containing the BRAHMA (BRM) or SPLAYED (SYD) SWI/SNF ATPases via its MR domain, and this unlocks chromatin through nucleosome eviction and histone acetylation (cis-element motif in the vicinity of AuxREs. The bZIP11 TF can recruit the multiprotein SAGA complex (SPT–ADA–GCN5–ACETYLTRANSFERASE), which acetylates nearby histones to open up the chromatin and allow gene transcription by RNA polymerase II (Pol II)  .in vitro , it would be interesting to investigate how the lack of the PB1 domain in MPΔ affects cis-element recognition in the promoter of controlled genes. Does MP/ARF5, like ETT/ARF3, possess an IAA-binding domain and can non-canonically regulate gene expression? Answering those questions would require further studies.To elucidate the complex action of MP/ARF5 and its MP11ir isoform, lines expressing truncated MPΔ proteins  may be a species . While M species . This Chin vivo and in vitro, mainly in the model plant Arabidopsis, with the help of numerous tools and transgenic lines  , as shown by the r embryo . MP/ARF5r tissue  (Cole ettyledon)  (Mayer etyledon) . The embfication . In the SHORT ROOT (SHR) gene expressed in the provascular cells encodes a mobile TF promoting asymmetric division within the ground tissue, giving rise to root endodermis and cortex  bdl/iaa12 embryos. Additionally, the morphological defects observed in the wrky23 mutant are very similar to those seen in mp/arf5 and bdl/iaa12 mutants (AP2 domain-containing TFs PLETHORA 1 (PLT1) and PLT2 are redundantly required for correct quiescent center specification, leading to the maintenance of the stem cells in the root meristem  and ERF115 activate auxin signaling via induction of MP/ARF5 and promote lateral root development  gene, encoding an AP2 transcription factor involved in embryonic patterning, auxin perception, and transport  and PRESSED FLOWER (PRS) in the middle domain, leading to an outgrowth of the leaf  through binding to the AuxREs in its promoter. ATHB8 is involved in leaf pre-procambial cell fate acquisition  and LOG4, encoding cytokinin biosynthetic enzymes. Cytokinin is required for the periclinal cell divisions within procambial cells. MP/ARF5 can also induce the expression of the Dof5.8 gene in the pre-procambial stage (ARABIDOPSIS RESPONSE REGULATOR (ARR) genes, ARR7 and ARR715. ARR7 and ARR15 are negative regulators of the cytokinin response, leading to inhibition of cambial activity , play a pivotal role in vascular development in roots. Both genes are expressed in an MP/ARF5-dependent manner in the xylem precursor cells of the root and the vasculature of the globular embryo , PHAVOLUTA (PHV), and REVOLUTA (REV). In axils of young leaves, MP/ARF5 activates BRASSINAZOLE-RESISTANT 1 (BZR1) and PHYTOCHROME-INTERACTING FACTOR 4 (PIF4), which directly repress AGO10 transcription and prevent axillary meristem initiation. In axils of older leaves, MP/ARF5 up-regulates AGO10 expression to promote axillary meristem initiation  abolish floral meristem formation, resulting in the naked inflorescence stem phenotype (LEAFY (LFY), AINTEGUMENTA (ANT), AINTEGUMENTA-LIKE 6/PLETHORA 3 (AIL6/PLT3), FILAMENTOUS FLOWER (FIL), TMO3/CYTOKININ RESPONSE FACTOR 2 (CRF2), SCARECROW (SCR), and SHR  and BREVIPEDICELLUS (BP), key pluripotency genes stimulating meristematic fate, is crucial for reproductive primordium initiation. MP/ARF5 contributes indirectly to STM repression by up-regulating FIL. In parallel, MP/ARF5 acts with ETT/ARF3 and ARF4 repressors expressed in incipient reproductive primordia and promoter of flower initiation through histone acetylation (ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 6 (AHP6)  physically interacts with WUSCHEL (WUS), a key regulator of stem cell maintenance. SCL23 and WUS expression is subject to negative feedback regulation from stem cells through the CLAVATA (CLV) signaling pathway , SCHLAFMUTZE (SMZ), Dof1.8, and LOB DOMAIN-CONTAINING PROTEIN 3 (LBD3). In particular, Dof1.8 may repress MP/ARF5 expression, as MP/ARF5 and DOF1.8 genes show complementary expression patterns  pathway. The levels of translation reinitiation of MP/ARF5 can be increased several fold without affecting mRNA stability  activity, at the tability .de novo shoot organogenesis, and its expression determines the acquisition of shoot progenitor cell identity  genes in Arabidopsis involved in the zygotic embryogenic process, whose mutation induces defects during embryogenesis (in vivo. However, numerous studies are still needed to identify the function of all TF genes, especially those acting redundantly. Until then, we can conclude that MP/ARF5 reigns supreme over the other members of the ARFs and most of the genes encoding TFs.Among the members of the ARF family, MP/ARF5 is at the center of scientific interest. MP/ARF5 is the key TF regulating almost every aspect of plant development. Research on it contributes knowledge about the regulation of gene expression, particularly in response to auxin. Analytical tools allow the study of the interactions between MP/ARF5 and DNA, as well as MP/ARF5 and other proteins, revealing an extremely complex interaction network. Many genes that may be under the direct control of MP/ARF5 have been recognized. The challenge of the next few years will be to learn how the level of MP/ARF5 as an oligomer, dimer, or monomer affects its function. It will also be essential to discover in detail the activator and repressor regulatory elements acting upstream of MP/ARF5. It is interesting to study epigenetic factors affecting the activity of the ogenesis . 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Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress . Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress. coli, measured every 10 minutes, defining three primary stages of promoter activation in response to heavy metal stress.Understanding how cells dynamically adapt to their environment is important, but temporal information about cellular behaviour is often limited. Here, Miano et al. apply unsupervised machine learning to a dataset describing the activity of over 1,800 promoters in E.  Currently, RNA sequencing (RNAseq) is the most widely used method to determine the relative abundance of each mRNA transcript in a cellular population3. However, RNAseq requires the destruction of cells to harvest RNA, making it challenging to obtain the necessary time resolution to study the dynamics of transcription. Although sampling RNA from parallel cultures at different times can partially overcome this limitation, many protocols only sample mRNA levels at a few time points resulting in a lack of information regarding transcriptional regulatory network (TRN) dynamics5.Over the past few decades, various high-throughput technologies have emerged to investigate how cellular transcription changes in response to environmental perturbationsEscherichia coli promoter activity in real-time using fluorescence time lapse microscopy6. This technology allows us to grow more than 2000 unique bacterial strains continuously in small cell “traps" in the presence of various nutrients and stressors. To monitor the dynamics of the E. coli TRN, we combined a pre-existing E. coli promoter library with Dynomics. The promoter library, created by Zaslaver et al., contains approximately 2000 unique E. coli strains where each strain has a different, native promoter driving the expression of green fluorescent protein (GFP) on a low-copy number plasmid7. This library enables highly accurate dynamic measurements of each promoter in the genome as initially demonstrated in an experiment looking at the response of E. coli during a diauxic growth shift7. By combining this library with our microfluidic device and a custom imaging platform, we were able to record transcriptional cellular responses to environmental perturbations every 10 minutes for up to 14 days. Using this technology, we observed the dynamics of all E. coli promoters with unprecedented temporal resolution.To overcome these limitations, we previously introduced Dynomics, a microfluidic platform for monitoring E. coli responds to heavy metal exposure. Heavy metal contamination, due to industrial wastewater discharge, has become a significant threat to the environment8. In response to the presence of these ions, many microorganisms have evolved strategies to cope with high concentrations of toxic heavy metals. For instance, some bacteria express enzymes that can reduce heavy metal ions, lowering their toxicity9. The breadth of metal-responsive promoters and genes found throughout the microbial world have made bacteria a popular choice for engineering heavy metal biosensors6 and bioremediation strategies10. While the potential applications of studying bacterial responses to heavy metals have received significant attention, we are interested in the fundamental biological implications of the timing of transcriptional responses to different types of heavy metals. This analysis has the potential to shed light on TRN structure and to help hypothesize on the cellular responses to specific metals based on the temporal patterns of gene activations observed.In the original Dynomics study, Graham et al. explored how 11. Independent component analysis (ICA), an unsupervised machine learning technique developed to deconvolute mixed signals into individual sources12, has proven particularly successful at extracting biologically relevant transcriptional modules across the phylogenetic tree from a variety of transcriptomic data sets17. In fact, ICA outperformed 42 similar methods in reproducing known transcriptional modules18. Using ICA, we can identify groups of genes, called iModulons, which are co-regulated throughout the data and are associated with promoter activity levels in each sample. Generating iModulons has been useful for understanding TRNs and low-resolution dynamics in RNAseq and microarray data19. For instance, ICA was able to quantitatively summarize the major steps in Bacillus subtillis sporulation by grouping relevant genes into specific iModulons13. Moreover, ICA consistently identifies similar gene groupings, even between RNAseq and microarray data sets19.Transcriptomic methods such as RNAseq and Dynomics generate complex, high dimensional data sets that require sophisticated analysis techniques to interpretE. coli20. Our results confirm previously known patterns of promoter activation while also identifying additional gene associations. Importantly, to our knowledge, this is the first time ICA has been applied to a time-series transcriptomic data set with measurements taken at regular short time intervals across multiple days. Combining ICA with high temporal-resolution data enabled us to generate iModulons that reveal the response patterns of different gene groups as a function of time. This approach generates interesting hypotheses regarding transcriptional dynamics.In this study, we apply the ICA workflow to the gene expression dataset originally generated by Graham et al., thereby leveraging pre-existing data to derive insights into the temporal patterns of activation of bacterial promoters in response to external heavy metal stress. The data was previously generated using environmentally relevant concentrations of heavy metals, typically several orders of magnitude smaller than the minimum inhibitory concentration (MIC) known for 6. In brief, each strain was induced with a heavy metal ion for four hours and then left to recover in minimal media for 20 h  to each iModulon. We named each iModulon according to its shape and the predominant environmental condition associated with it Fig. b. From tFor lead, iron, cadmium, and chromium, a single iModulon per metal was sufficient to capture the observed response, and these iModulons represent promoters that became steadily activated over the course of the induction. Zinc, on the other hand, had four separate iModulons that captured unique activation patterns. We classified these patterns as “Steady Activation" for promoters whose activity steadily increased over the induction window, “Fast Activation" for promoters that activated quickly after the beginning of the induction, “Intermediate Activation" for promoters that were mostly active in the middle of the activation window, and lastly “Partial Steady Activation" for promoters that were steadily activated at the beginning of the induction window and then suddenly repressed in the last part of the induction window. A summary of the different activation patterns is included in Supplementary Fig. 22. After applying the calculated cutoffs, we extracted and analyzed the promoters with the highest coefficients for each iModulon. To better understand the overall bacterial response to each heavy metal, we classified the promoters by phenotype  and activation profile   Fig. . In the yfeY, bacA, ropE) and to peptidoglycan stress that were present in both fast and steady activation profiles, consistent with previous literature25. Notably, we also found that two genes associated with steady activation  are directly involved in peptidoglycan synthesis. This iModulon also includes the activation of the glnW promoter, which transcribes glutamine tRNA. Given that glutamine is important for fatty acid synthesis, we speculate that the upregulation of glnW may help restore membrane damage caused by zinc exposure. Our ICA analysis also revealed multiple genes involved in the oxidative stress response, consistent with former studies26, and distinguished between fast activation  and steady activation . Interestingly, the promoters for yedY and yfcG specifically regulate genes that combat oxidative damage28, indicating a targeted response against cellular damage. However, the genes associated with steady activation are involved in cellular processes unrelated to defense response. These include lipoate biosynthesis29, nucleoside transport30, and hydrogen peroxide detoxification31.We observed that the zinc iModulons were associated with several functions, including envelope stress response, oxidative stress response, zinc resistance, the dissimilatory nitrate reduction to ammonium pathway, and protein metabolism Fig. a. WithinzntA gene which is associated with zinc export as a detoxification mechanism when excess levels of zinc are detected32. Interestingly, our analysis is also able to detect the activation of two genes (narZ and nrfE) belonging to the dissimilatory nitrate reduction to ammonium pathway (DNRA)32. In particular, we were able to observe the temporal dynamics of the pathway activation since narZ (nitrate reductase) belonged to the fast activation iModulon and nrfE (nitrite reductase) was associated with the intermediate activation profile. The order of activation matches the order required for nitrogen respiration, with nitrate first being reduced to nitrite by narZ, then nitrite being reduced to ammonium by nrfE33. This mechanism follows the “just-in-time” transcription program in metabolic pathways that was previously suggested in the literature34. To our knowledge, this is the first time that the activation of DNRA pathway in E. Coli is linked to the presence of elevated zinc concentrations. We hypothesize that in this context, DNRA activation is beneficial to the cells due to the generation of an electron sink that can be used for NADH re-oxidation into NAD+ which is a key metabolite in counteracting DNA damage and oxidative stress36. Future experiments will be needed to validate this claim. Finally, we also detect a “Partial Steady Activation” iModulon, characterized by an initial steady response which is followed by a sudden repression in the last phase of the induction window. Interestingly, we find that the promoter which transcribes for the gene potA is enriched. This gene is involved in the spermidine and putrescine transport system. Previous studies found that putrescine and spermidine added to the culture medium significantly increased expression of oxidative-stress related genes oxyR and katG37. In line with these findings, we observe that zinc exposure leads to the activation of both potA and katE promoters  which are both related to protein metabolism. The hisS gene encodes histidyl-tRNA synthetase, an essential enzyme in protein synthesis that charges tRNA with the amino acid histidine38. The pepB gene encodes endopeptidase PepB or leucyl aminopeptidase, involved in intracellular protein degradation and amino acid recycling39.Our results also confirm the activation of the 32, we found that the zntA gene was up-regulated, which confers cadmium tolerance as well as zinc tolerance as previously described. Furthermore, we detected the activation of the sodB promoter, which encodes a superoxide dismutase that is a key factor in defending the cell against oxidative stress through decomposition of superoxide radicals. This finding agrees with previous results reported in the literature, which showed that superoxide dismutases (SODs) can protect E. coli from heavy metal toxicity, particularly from cadmium exposure40.The cadmium iModulon was characterized by a steady increase in promoter activation and included promoters that are mainly associated with cadmium resistance and oxidative stress Fig. b. As expmipA, which encodes a scaffolding protein for murein synthesizing machinery41, and the promoter for exfK , which is involved in the assembly and insertion of beta-barrel proteins in the outer membrane42. Additionally, we identify three promoters which are known to be involved in nucleotide catabolism repression (deoR43), DNA replication (yejK44), and DNA recombination and repair (yhcG45) respectively. Finally, we detect the activation of the phoA promoter, which is responsible for the breakdown of organic phosphate esters46.The iModulon also captures the cell’s response to two of the main effects of oxidative stress: lipid peroxidation and DNA damage. In particular, we report the activation of the promoter for cueO promoter which encodes for a multicopper oxidase involved in copper tolerance under aerobic conditions47. Furthermore, we detect the activation of promoters known to be linked to the oxidative stress response  and to the envelope stress response (yoaE50). Interestingly, we also detect the activation of promoter mgsA which is known to be activated during phosphate starvation and glycolysis restriction51. Similarly, we report the activation of promoter prpR which is part of the Pho regulon which we also detected in the iron iModulon52.The copper iModulon was characterized by a “Steady” activation profile and a “Late” activation profile Fig. c. As expmcrB, involved in a restriction-modification system for defense against foreign DNA53; intB, encoding an integrase enzyme for site-specific recombination54; prpR, a transcriptional regulator controlling propionate catabolism55; and nfrB, part of the N4 phage outer membrane transport system involved in phage DNA entry during infection56. It is interesting to note that three of these genes  appear to be related to phage defense and interaction, suggesting a potential coordinated response to phage infection or other stressors that could impact the bacterial cell’s interaction with foreign DNA. We hypothesize that the activation of genes related to phage defense and interaction might reflect a broader stress response aimed at protecting the cell from additional threats, such as foreign DNA or phage infection, under conditions where cellular processes and genomic integrity are already compromised by copper-induced stress.The Late Activation iModulon is characterized by a late response with increased promoter activity starting roughly halfway through the induction window. Interestingly, we find that this iModulon was enriched for multiple genes related to the cell’s response against phages including ugpB, phnC and phoB) which all belong to the Pho regulon, a group of genes in E. coli that are involved in the regulation of phosphate metabolism57. In particular, phoB is the response regulator in a two component regulatory system with phoR (or creC) and regulates inorganic phosphate (Pi) uptake. The gene phnC is part of the ABC transporter complex PhnCDE involved in phosphonates, phosphate esters, phosphite and phosphate import. Finally, gene ugpB codes for a binding protein-dependent sn-glycerol-3-phosphate transport system which is under the control of the Pho regulon. In particular, ugp-dependent G3P transport activity is present only after growth at limiting concentrations of Pi58. Previous literature indicates that excess iron can cause the formation of toxic reactive oxygen species (ROS) through Fenton chemistry59. Both iron stress and oxidative DNA damage are successfully prevented by polyphosphates which are polymers formed by covalently linked inorganic phosphates59. Therefore, we hypothesize that the genes enriched in the Iron iModulon reflect the need for the cell to import additional phosphate in order to form polyphosphates which can be used as a defense mechanism against iron. When excess iron is present, E. colican transport it into the cell and store it within the polyphosphate granules, which act as a sink for the iron60. Based on the lack of specific iron toxicity genes in the iModulon, we propose that the concentration of iron used was too low to induce specific responses. Nonetheless, it is still informative that phosphate chemistry accounts for the ROS-related effects of an increase in iron.The iron iModulon is also characterized by a steady state activation profile Fig. d. IntereThe primary advantage of the Dynomics technology is the ability to track promoter activity over an extended period of time. This enabled us to analyze not only the effect of the heavy metal exposure during the induction window, but also to explore how the bacteria responded during the recovery period post-induction. In this case, we calculated the log2 of the fold change with respect to the end of the induction window Fig. a. We toorecX), peptoglycan stress  and oxidative stress (katE). We also report the activation of promoters which are related to transcription and translation processes such us RNA processing and decay (rne), purine metabolism (yneF), proline tRNAs (proL), methionine synthesis (metF), and DNA ligation (ligB). Additionally, we find that one of the zinc-related iModulons (iModulon 16) transcribes mRNA for monothiol glutaredoxin  which is involved in the biogenesis of iron-sulfur clusters61. This confirms previous studies that have shown that excess zinc is associated with the disruption of iron–sulfur clusters in E. coli62. Interestingly, the same iModulon contains the edd promoter which encodes the enzyme phosphogluconate dehydratase which is the first reaction in the Entner-Doudoroff pathway and uses the [4Fe–4S] iron-sulfur cluster as a cofactor63.We report four iModulons whose activity profiles show activation following zinc exposure. In line with the induction response, we identify a few promoters associated with stress responses such as the SOS response  and ydcL (uncharacterized lipoprotein).Furthermore, we report two iModulons containing promoters that activate following iron induction. We find that one of the promoters enriched in these iModulons is metF) and DNA replication/transcription processes (helD). We categorized the promoters according to their function in three main groups which are identified with dots of different colors in Fig. Finally, we report a copper recovery iModulon linked to the activation of five genes, two of which are involved in amino acid biosynthesis  to promoters associated with transcription, translation and synthesis functions (18% during induction versus 42% post induction) from a wide range of datasets, but these datasets lacked information on the time-dependent activation of genes. Instead, most studies have relied on static data obtained from RNA-seq, proteomics, and metabolomics experiments69. The Dynomics microfluidic platform enabled sampling of promoter activity every 10 min for multiple days, which is unrivaled by any transcriptome-scale analysis method to our knowledge.This study presents the integration of a genome-scale platform for monitoring temporal gene expression with the analytical power of ICAnarZ as an early responder and promoter nrfE as a late responder which are involved in the first and second step of dissimilatory nitrate reduction to ammonium metabolic pathway. This result is a clear example of the power of this platform when used for metabolic pathway reconstruction which is a topic of great interest in systems biology70.By applying ICA to this data set, we demonstrated the importance of time-dependent analysis in providing insights into the dynamic nature of gene expression in response to environmental stressors. By splitting different heavy metal inductions into separate 40 minute time windows, we were able to apply ICA to time-series transcriptomic data for the first time. We observed the richest response for the zinc inductions. Specifically, we found four different iModulons which differentiate fast responders (genes that are activated at the start of the induction window) from intermediate responders , steady responders (genes whose expression steadily increases throughout the induction window) and partially steady responders (genes that steadily increase over time until they are repressed in the last window). The zinc data demonstrates the ability of this analysis method to resolve the activation sequence of promoters involved in the same metabolic pathway. We detect the activation of promoter E. coli71. This allowed us to study the bacterial response to elevated, but non-toxic, levels of iron in the environment. Our ICA analysis suggests that in this scenario the bacteria activate several genes in the Pho regulon (which regulates phosphate uptake and metabolism) in response to elevated concentrations of iron. We hypothesize that bacteria accumulate phosphate as an early response to excess iron in preparation for polyphosphate synthesis. Several studies have shown that polyphosphates can sequester iron in E. coli and other microorganisms, limiting its bioavailability and protecting cells from iron toxicity72. Further studies need to be conducted to experimentally verify this hypothesis.Additionally, the data in this study were generated using environmentally relevant heavy metal concentrations which are significantly lower than the concentration range known to be toxic for E. coli following the removal of stressors such as heavy metals. Our findings indicate a marked shift in the cellular functions of enriched promoters during and after heavy metal induction. We quantified a transition from the activation of promoters associated with stress defense mechanisms and detoxification processes to the activation of promoters involved in ribosome biosynthesis, tRNA synthesis, mRNA processing and decay, amino acid biosynthesis, and replication.Lastly, this study expands our understanding of the recovery process E. coli promoters employed in this study represents the most comprehensive collection currently available and allows for substantial insights into gene expression dynamics, we acknowledge that it encompasses less than half of the total known promoters in E. coli. This limitation signifies that our analysis might not fully capture all the potential regulatory mechanisms at play, and we advocate for future studies to incorporate a more expansive promoter library to offer a more encompassing view of E. coli gene expression.While the library of 1805 E. coli led us to the observe distinct gene expression patterns compared to those reported in previous studies74. We speculate that by utilizing lower heavy metal concentrations, we are better able to reveal the subtle and nuanced responses of bacteria to these stressors, which might otherwise be overshadowed by the more pronounced effects at higher concentrations. This, in turn, contributes to a more comprehensive understanding of bacterial behavior and adaptation mechanisms under more environmentally relevant conditions, expanding our knowledge of how bacteria cope with heavy metal exposure in real-world scenarios. Overall, we believe that combination of Dynomics promoter activity data with our approach to applying ICA to time-series data will continue to serve as a valuable tool for generating hypotheses on how cells respond to various external stimuli, uncovering fundamental principles of transcriptional regulation.Using concentrations that are several orders of magnitude lower than the minimum inhibitory concentrations (MICs) for 6. In brief, data was obtained from fluorescence values extraction from flat-field corrected images gathered using a custom optical set up. The first step of the experimental set up is arraying the cells using a Singer ROTOR robot so that they could be spotted onto the microfluidic device before glass bonding. Once the device was ready, it was set up inside a custom box kept at 37 °C for imaging. For media flow, the inlet and outlet were connected to 140 mL syringes. The concentrations of the heavy metals tested in this study can be found in Fig. Detailed description of the Dynomics experimental set up can be found in the original study6. In summary, the data was first processed by subtracting the local background signal and then dividing the result by the background signal again in order to create a measure of the amplification of the signal over the background. Specifically, fluorescence values measured at a location outside the cell trap were subtracted from those obtained within the cell-containing regions. This method effectively removes signal noise not associated with the cells, thereby providing a more reliable representation of the cellular fluorescence signals. Then, the data were passed through a median filter  and normalized by subtracting and dividing the average expression values of the promoterless strains. The code used to process data from the original study is available on GitHub. The original data files post-processing for each heavy metal are also available on GitHub. The data were further processed by calculating the log base 2 of the fold change of the ratio of six time points (spaced 40 min apart) with respect to the start of the induction window for the data plotted in Figs. Detailed information on how the raw data was processed can be found in the original study22. In brief, we run the Scikit-learn70 (v0.19.2) implementation of the FastICA algorithm 100 times with random seeds, a convergence tolerance of 10−6. The number of components in each iteration was set to the number of components that reconstruct 99% of the variance as calculated by principal component analysis. The resulting source components (M) from all runs were clustered using the Scikit-learn implementation of the DBSCAN algorithm which does not require predetermination of the number of clusters. In our DBSCAN analysis, we used the following parameters: DISTANCE parameter, analogous to the epsilon parameter in traditional DBSCAN applications, was set to its default value of 0.1, determining the maximum distance between two points to be considered as in the same cluster. Meanwhile, the MIN_FRAC parameter, which specifies the minimum fraction of total data points required to form a valid cluster, was maintained at its default setting of 0.5. This implies that each cluster in our analysis contains at least 50% of the total data points present in the dataset. The final independent components were defined as the centroid of each cluster in M, and the weightings were defined as the centroid of their corresponding weighting vectors in A. To ensure that the final components were consistent across multiple runs, we computed the clustered components 100 times, and selected the components that were identified in every run. The previously published code used to compute robust independent components is publicly available at github.com/SBRG/precise-db. We also added the scripts specifically used in this paper in the “precise_db" folder within the “ICA_dynomics" GitHub repository.We used the pipeline for ICA implementation that has been described in previous studies2 test statistic which is a measure of the skew and kurtosis of a sample distribution. Genes with the largest absolute value were iteratively removed and the D’Agostino K2 test statistic was computed for the resulting distribution14. The statistic cutoff was kept fixed at 800 for the entire analysis. We only considered positively correlated genes in this analysis. The code to calculate the threshold was originally developed by the Palsson lab at UCSD and can be found at the GitHub repository pymodulon. We also included the scripts used specifically for this paper in the pymodulon folder at the ICA_dynomics repository.The dataset M contains all the genes coefficients associated with each iModulon. Most of these coefficients have values close to zero which indicates they are not significantly enriched for that iModulon. In order to extract the genes that belong to each iModulon we computed the D’Agostino KWe used a simple computational strategy that involved the analysis of the activation matrix data of each iModulon to associate them with the heavy metal that induced the highest aggregate expression over the six time points within the corresponding induction window. This approach allowed us to pinpoint the condition under which each iModulon exhibited the most pronounced change, thereby facilitating a data-driven classification. The script used to implement this classification can be found in the ICA_dynomics GitHub repository.In our study, we employed a simple computational method to classify the iModulons based on the shape of their activity coefficients within the induction window of heavy metal enrichment. This method involved calculating the center of mass (CM) and identifying the highest peak (HP) within the specified window for each iModulon, wherein the center of mass represents the mean position of a given function. The iModulons were categorized into three groups based on these calculations: Steady (CM ≤ 5 and HP ≥ 4), Intermediate (CM ≤ 4 and HP ≤ 4), and Fast (CM ≤ 4 and HP ≤ 3). iModulons that did not conform to these parameters were labeled as “other,” with plans for further characterization on a case-by-case basis. We have made the corresponding code block available in the “iModulon_identification” script within our GitHub repository, and illustrated the calculated values and resultant classifications in Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review File NEWDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Reporting Summary


However, the regulatory determinants of domain co‐activity remain unclear. Here, we leverage human individual variation in gene expression to characterize the co‐regulatory processes underlying domain co‐activity and systematically quantify their effect sizes. We employ transcriptional decomposition to extract from RNA expression data an expression component related to co‐activity revealed by genomic positioning. This strategy reveals close to 1,500 co‐activity domains, covering most expressed genes, of which the large majority are invariable across individuals. Focusing specifically on domains with high variability in co‐activity reveals that contained genes have a higher sharing of eQTLs, a higher variability in enhancer interactions, and an enrichment of binding by variably expressed transcription factors, compared to genes within non‐variable domains. Through careful quantification of the relative contributions of regulatory processes underlying co‐activity, we find transcription factor expression levels to be the main determinant of gene co‐activity. Our results indicate that distal  Human individual variation is used to characterize the co‐regulatory processes underlying domain co‐activity. Systematic quantification of their effect sizes reveals transcription factor expression as the main determinant of co‐activity. These results are further supported by a higher expression correlation between neighboring active genes within co‐activity domains compared to gene pairs outside of domains .We reasoned that the enrichment in physical contacts within TADs should be reflected by the observed co‐activity domains. In agreement with previous observations  modeling . Overall, the expression of a gene across individuals showed no or only weak correlation with their corresponding number of ABC‐connections  of shared eQTLs among eGene pairs in co‐activity domains even when only considering pairs at least 100 kb apart  on genes within the same domain. We focused on genes associated with at least one eQTL (eGenes). Overall, we found that 38% of neighboring eGene pairs contained within the same co‐activity domain shared at least one eQTL, compared to only 7% of eGene pairs located outside of co‐activity domains Fig . Furtherpart Fig .Taken together, these results indicate that co‐activity domains are capturing local neighborhoods of genes, whose collective output is influenced by an environment enriched in regulatory interactions, permissive chromatin, and the co‐operative effects of local sequence variants.UGT2B15 and UGT2B17 and pseudogene UGT2B29P  gene regulatory inputs: histone modifications, TF binding, enhancer‐gene interactions, and genetic variants.P = 8 × 10−10; Mann–Whitney U‐test), as well as a 20% higher interaction variability  and more variable  chromatin interaction frequencies, as inferred from Hi‐C data . In fact, VCMs accounted for as much as 85% coverage of variable co‐activity domains, indicating that variable chromatin domains are capturing the same variability, although through smaller domains. This further demonstrates that variation in co‐activity can be derived from orthogonal assays, including those measuring histone modifications. Thus, it appears that chromatin state reflects co‐activity domain variability.Domains of co‐varying histone modifications have previously been described as variable chromatin modules (VCMs) . Thus, the association between genotype and co‐activity score was stronger but not unique to variable domains. We speculate that this is due to the fact that there is some inter‐individual variability also in matched domains, although to a lower degree . We generally excluded the influence of histone modifications on gene co‐activity, since histone PTMs can likely both influence and be influenced by transcription . We further calculated, for each individual, a polygenic risk score‐inspired measurement activity of genes might mean a reduced complexity in the TF binding grammar at their promoters. Indeed, differences in variability have been associated with differences in core promoter architecture . VCMs were previously identified also in LCLs in a similar population of healthy individuals and, similar to our co‐activity domains, are also enriched in chromatin contacts and genetic variants  were excluded from all analyses.et al, et al, et al, For transcriptional decomposition, GEUVADIS . We assume that these read counts are distributed as negative binomial, such that yi∼NBs−1μiθ where s is the library depth offset (number of millions of reads), μi is the mean RPM for bin i and θ is a hyperparameter representing the overdispersion. We model the log of the mean expression in a given bin i as a combination of two latent components and an intercept, in other words logμi=logs+α+PDi+PIi, where α is the intercept and PDi and PIi are the positionally dependent (co‐activity) and independent components respectively.Briefly, the transcriptional decomposition approach is as follows: let PDi−PDi−1 having a normal distribution with mean zero and variance 1/τPD, and where for identifiability purposes the component is scaled to ∑i=1NPDi−PDi−1=0.The co‐activity component was modeled as a first‐order random walk, dependent on neighboring bins and assuming normally distributed differences. This equates to the difference between neighboring bins, PIi⊥PIi−1) where PIi is Gaussian with variance 1/τPI. The model was fit as a hierarchical model in INLA, whereby priors for the hyperparameter θ was given a Gaussian prior and τPD and τPI were fixed according to the scheme described below. For each of the co‐activity and independent components, the mean and variance based on samples from modeled posterior was taken for each bin and used in subsequent analyses.The positionally independent component was modeled assuming bins to be independent and identically distributed (IID) . While efforts were made to make the results as generalizable as possible, we cannot rule out small batch differences impacting our results. Furthermore, we did not investigate the impact of different resolutions (bin sizes) and/or parameterizations of the model itself on the overall results. These aspects could potentially be addressed in future studies.In order to achieve good convergence and maximum comparability across individuals, chromosomes, and groups, the hyperparameters of the model were fixed using the following strategy: A series of random walk and IID precision hyperparameters was used to run the model, and the combination most closely matching the CAGE‐derived transcriptional components of GM12878  were identified. These regions were merged if the gap between them was 100 kb or less. Finally, regions containing at least two genes with a minimum expression of 0.1 TPM were considered for further analyses Dataset .et al, et al, Compartment locations were obtained . Peak regions were defined as ± 300 base pairs from the peak summit. In the case of overlapping peak regions (when two summits are closer than 300 bp), only the region with most CAGE‐derived  were considered. For analysis of eQTL sharing of neighboring eGene pairs over distance Fig , all neiR2 = (t_statistic / sqrt(degrees_of_freedom + t_statistic^2))^2 where t_statistic is the t statistic for each SNP‐gene linear model and degrees_of_freedom denotes the number of degrees of freedom for the full model estimated by MatrixEQTL.Proportions of variance explained were calculated from MatrixEQTL results as follows: https://github.com/broadinstitute/ABC‐Enhancer‐Gene‐Prediction) across 68 individuals using DNAse‐seq and H3K27ac ChIP‐seq bigwigs mapped to GRCh37  in co‐activity domains was assessed using 83 ENCODE TF ChIP‐seq peaks. Contingency tables were constructed with counts reflecting the whether the OCR was in a variable co‐activity domain (versus a non‐variable co‐activity domain), combined with whether or not it had at least one binding site for the TF in question. Using these contingency tables, Fisher's exact tests were performed. The same was repeated using matched non‐variable co‐activity domains instead of variable co‐activity domains, against the background of positive co‐activity domains.et al, JASPAR . For the domain‐wide analysis, variable and matched non‐variable co‐activity domains were separately supplied as foreground, with all co‐activity domains as background. For the promoter‐specific analysis, promoters of genes with an average log(TPM + 1) expression of more than 0.01 were extracted from variable and matched non‐variable co‐activity domains and supplied separately as foreground using all co‐activity domains as background.The JASPAR enrichment tool  of > 0.8 were removed. Then, all QTLs which had a genotype correlation of > 0.8 with the QTL with the second lowest To summarize QTLs into a single score, all QTLs were first polarized with regards to the sign of their effect size, by inverting all genotypes for QTLs with a negative effect size, and taking the absolute effect size. Next, the effect sizes were used to weigh the contribution of risk alleles (now defined as leading to an increase in effect size). Thus, the mean of genotypes was computed, weighted by the effect size. A QSS closer to two indicates the individual has a combined cis genotype that is more likely to lead to a higher co‐activity score.r‐squared) was noted. The whole process was repeated 3 times, and the most frequently occurring 10 TFs over all top 10 TFs or the most frequently occuring top single TF (according to R2) over all runs was used in the following modeling.In order to robustly select transcription factors most closely associated with domain co‐activity variability, a Random Forest  was constructed, using the following predictors over the set of 29 individuals with overlapping data availability: the total number of ABC connections, the QSS of cis co‐activity QTLs, and the expression values of top associated TFs (10 most predictive or single most predictive TF). The dependent variable was the average co‐activity score in the domain. Using analysis of variance (ANOVA), we calculated the proportions of the type II sum of squares attributed to each of the predictors, including the TFs as a single predictor group in the case where the top 10 was included. To make sure no overfitting was occurring due to the limited set of considered individuals, two additional models were constructed, involving as predictors the QSS and TF expression (top 10 or top single TF) for 343 individuals.et al, For further comparison and assessment of the robustness of the TF expression predictability, the laboratory from where each of the individual samples was handled (Lappalainen For further comparison and assessment of the robustness of the QSS measure, models were constructed using the genotype of the most significant co‐activity QTL per region, instead of the QSS. In order to calculate the significance of predictors on the given domain, adjusting for the other terms in the model, each predictor was held out one by one, and the linear model without that predictor was compared to the full model containing all of the predictions using ANOVA.Lucas van Duin: Conceptualization; software; formal analysis; investigation; visualization; methodology; writing – original draft; writing – review and editing. Robert Krautz: Methodology; writing – review and editing. Sarah Rennie: Conceptualization; software; formal analysis; supervision; methodology; writing – original draft; writing – review and editing. Robin Andersson: Conceptualization; supervision; funding acquisition; methodology; project administration; writing – review and editing.The authors declare that they have no conflict of interest.Appendix S1Click here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.PDF+Click here for additional data file.


LRRC6 is an assembly factor for dynein arms in the cytoplasm of motile ciliated cells, and when mutated, dynein arm components remained in the cytoplasm. Here, we demonstrate the role of LRRC6 in the active nuclear translocation of FOXJ1, a master regulator for cilia-associated gene transcription.We generated Lrrc6 knockout (KO) mice, and we investigated the role of LRRC6 on ciliopathy development by using proteomic, transcriptomic, and immunofluorescence analysis. Experiments on mouse basal cell organoids confirmed the biological relevance of our findings.The absence of LRRC6 in multi-ciliated cells hinders the assembly of ODA and IDA components of cilia; in this study, we showed that the overall expression of proteins related to cilia decreased as well. Expression of cilia-related transcripts, specifically ODA and IDA components, dynein axonemal assembly factors, radial spokes, and central apparatus was lower in Lrrc6 KO mice than in wild-type mice. We demonstrated that FOXJ1 was present in the cytoplasm and translocated into the nucleus when LRRC6 was expressed and that this process was blocked by INI-43, an importin α inhibitor. Taken together, these results hinted at the LRRC6 transcriptional regulation of cilia-related genes via the nuclear translocation of FOXJ1.Video AbstractThe online version contains supplementary material available at 10.1186/s12964-023-01135-y. Multiple motile cilia perform beating function in a single to produce a synchronized wave-like motion, and defects in these organelles cause disorders in developmental stages and in several organs –4. DurinSo far, approximately 57 genes have been identified as causing motile ciliopathies when mutated, accounting for 70% of cases. Most of these genes encode subunits of axonemal structures of motile cilia , 9. The ODA is a large multi-subunit protein complex comprising light, intermediate, and heavy chains . ODA is LRRC6, also known as Seahorse, was first identified as a candidate gene for cystic kidney phenotype in zebrafish and was found to be required for establishing left–right symmetry. LRRC6 encodes a protein that contains leucine-rich repeats (LRRs), an LRR cap domain, a coiled-coil region, and a CS-like domain near the C-terminus -X2–5-PY. Sequences with boundaries represent residues essential for composing syntax. D, disordered; DBD, DNA binding domain. Cytoplasmic localization of LRRC6 and FOXJ1  was observed in HeLa cells transfected with LRRC6, FOXJ1  plasmid. Scale bars, 50 nm. Percentage of cells in which FOXJ1 translocated to the nucleus as a result of the transfection conditions. Experiments were repeated more than three times independently.Additional file 2: Table S1. Quantification results of basal feet angle variations.Additional file 3: Video S1. Videomicroscopy of brain ventricles from a P14 wild-type mouse.Additional file 4: Video S2. Videomicroscopy of brain ventricles from a P14 Lrrc6-/- mouse.Additional file 5: Video S3. Videomicroscopy of basal cell organoid from a wild-type mouse.Additional file 6: Video S4. Videomicroscopy of basal cell organoid from a Lrrc6-/- mouse.


In many cases, additional coactivators or regulators, including mediator complexes, connect the gene-specific transcription factors and transcription machinery , thereby transducing signals from the cis-acting regulatory sequences to the core promoter to modulate target gene expression.Plants have their roots fixed in the soil, so they are unable to escape from adverse environments. Thus, they must deal with various biotic and abiotic stresses by regulating gene expression to induce cellular tolerance responses. To initiate gene transcription correctly, RNA polymerase II must come together with several general transcription factors on a core promoter to form a large multiprotein–DNA preinitiation complex. In contrast, gene-specific transcription factors recognize specific upstream In eukaryotic cells, genomic DNA is packed into the nucleus by wrapping it around histones to form nucleosomes, the basic unit of chromatin structure. To initiate the transcription of a gene, the DNA–histone interaction in the surrounding chromatin must be relaxed to enable access by transcriptional activators and RNA polymerase. Gene expression is accompanied by chromatin remodeling . In addition, small and long non-coding RNAs modulate gene expression at the transcriptional and posttranscriptional levels. The epigenetic changes can be transmitted to newly developed cells during vegetative growth and even inherited by the progeny. Thus, plants pre-exposed to stressful conditions often grow better under subsequent stressful conditions in the same generation and in the next generation of the plants.International Journal of Molecular Sciences includes a total of five contributions. Three original articles describe the discovery and characterization of gene-specific transcription factors from cotton, strawberry, and iris [This Special Issue entitled ‘Transcription control mechanisms for plant stress responses’ in the and iris ,2,3. In and iris  providesand iris  reviewedVerticillium dahliae. In that study, a cis-acting element-FC-rich motif was found to be responsible for the defense response to V. dahliae. Upon inoculation with the fungus, the GhHSFB2a gene transcripts accumulated in the roots, stems, and leaves. Ectopic expression of GhHSFB2a in Arabidopsis enhanced resistance to V. dahlia, while virus-induced gene silencing of the gene significantly impaired cotton resistance. Transcriptome analyses revealed that GhHSFB2a participates in cotton disease resistance by regulating abscisic acid (ABA) and ethylene signaling, linoleic acid metabolism, and the phenylpropanoid pathway.Liu et al.  identifiLi et al.  identifiIris japonica Thunb and the deciduous Iris tectorum Maxim, by evaluating their morphological and physiological characteristics to ensure their developmental status. Under artificial dormancy-inducing conditions, the deciduous iris entered into dormancy earlier than the evergreen iris. The expression patterns of the dormancy marker genes involved in key pathways of winter dormancy suggested important roles for phytohormones and carbohydrates in coordinating growth and stress responses during dormancy induction in both iris species. In particular, it appears that dormancy-related MADS-box genes and Snf1-related protein kinases (SnRKs) represent a bridge between carbohydrate and phytohormone interactions during dormancy. These findings allude to a mechanistic model for investigating the key factors contributing to later dormancy in the evergreen iris relative to the deciduous iris. This study provides a basis for future research into the molecular mechanisms in perennials that exclusively exhibit eco-dormancy in the transition zone between temperate and subtropical climates, together with a reference for postharvest storage, shipping, and breeding.Xu et al.  investigMediator complexes transmit signals delivered by gene-specific transcription factors to the transcription machinery assembled at promoters to control transcription initiation. Chen et al.  present Drosophila and Caenorhabditis elegans) and the process of flowering in plants, the authors propose a hypothetical model for the mechanism of mitotic transmission in transcriptional memory. In this model, epigenetic marks, including modified histones  and chromatin regulators  protein complexes and trithorax group (TrxG) complexes) remain bound to chromatin loops formed in response to stress and are transmitted through mitosis. Certain transcription factors and chromatin remodeling complexes remain in close proximity to the chromatin of a memory locus, establishing a subsequent transcriptional status . However, there is little evidence to support the notion that chromatin architecture formation contributes to the memory of drought-stress tolerance in plants. Therefore, this speculative model should be verified in future studies of the mechanisms of drought-stress memory.Nguyen et al.  reviewedFor transgenerational inheritance, possible roles for DNA methylation are discussed , with reThe identification of specific transcription factors in a wide range of agronomically important crops, and elucidation of the molecular mechanisms underlying environmental stress tolerance, will facilitate the breeding of crops that are able to withstand global climate change more effectively. In particular, the principles and mechanisms underlying cellular memory should be further exploited to improve agronomic technology and crop breeding because epimutations can be artificially induced more rapidly than through traditional genetic manipulation.