Introduction: Neuroinflammation in the central nervous system, particularly the prefrontal cortex (PFC), plays a role in the pathogenesis of schizophrenia, which has been found to be associated with maternal immune activation (MIA). Recent evidence suggests that epigenetic regulation involves in the MIA-induced neurodevelopmental disturbance. However, it is not well-understood how epigenetic modulation is involved in the neuroinflammation and pathogenesis of schizophrenia.Methods: This study explored the modulation of histone acetylation in both neuroinflammation and neurotransmission using an MIA rat model induced by prenatal polyriboinosinic-polyribocytidylic acid (Poly I:C) exposure, specifically examining those genes that were previously observed to be impacted by the exposure, including a subunit of nuclear factor kappa-B (Rela), Nod-Like-Receptor family Pyrin domain containing 3 (Nlrp3), NMDA receptor subunit 2A (Grin2a), 5-HT2A (Htr2a), and GABAA subunit β3 (Gabrb3).Results: Our results revealed global changes of histone acetylation on H3 (H3ace) and H4 (H4ace) in the PFC of offspring rats with prenatal Poly I:C exposure. In addition, it revealed enhancement of both H3ace and H4ace binding on the promoter region of Rela, as well as positive correlations between Rela and genes encoding histone acetyltransferases (HATs) including CREB-binding protein (CBP) and E1A-associated protein p300 (EP300). Although there was no change in H3ace or H4ace enrichment on the promoter region of Nlrp3, a significant enhancement of histone deacetylase 6 (HDAC6) binding on the promoter region of Nlrp3 and a positive correlation between Nlrp3 and Hdac6 were also observed. However, prenatal Poly I:C treatment did not lead to any specific changes of H3ace and H4ace on the promoter region of the target genes encoding neurotransmitter receptors in this study.Discussion: These findings demonstrated that epigenetic modulation contributes to NF-κB/NLRP3 mediated neuroinflammation induced by prenatal Poly I:C exposure via enhancement of histone acetylation of H3ace and H4ace on Rela and HDAC6-mediated NLRP3 transcriptional activation. This may further lead to deficits in neurotransmissions and schizophrenia-like behaviors observed in offspring. Accumulated epidemiological evidence has suggested that maternal immune activation (MIA) induced by pregnancy infection and other inflammatory processes is a particular environmental pathogenic factor of neuropsychiatric disorders, including schizophrenia and autism  and HTR2C  offspring before the onset of behavioral deficits, but without changes in acetylation of histone H4K8 at these promoter loci , the class II Hda1-like proteins , the class III Sir2-like proteins (SIRT1–7), and the class IV protein , N-methyl-D-aspartate (NMDAR) and GABAA (GABAAR) receptors that play a key role in the pathophysiology of mental illness , is involved in the pathogenesis of schizophrenia, in which the nuclear factor kappa-B (NF-κB) and related pathways play an important role . Each cage housed two rats from the same treatment group, and a divider separated the cage into two chambers of equal size, each with its own food hopper and water bottle for ad libitum access to water and standard laboratory chow diet. The two rats could see, hear, and smell each other through perforated holes in the divider.The methods for establishing a Poly I:C rat model were reported previously, which showed a phenotype with schizophrenia-like behavioral deficits in both adolescent and adult offspring . According to the rat brain atlas  having added 400 μl lysis buffer, which was made up of 0.3 M phenyl-methylsulfonyl fluoride , 50 mM β-Glycerophosphate , NP40 cell lysis buffer , and protease inhibitor cocktail . An 8%–12% sodium dodecyl sulfate-polyacrylamide gel  was applied for protein electrophoresis. After being trans-blotted onto a polyvinylidene difluoride membrane  and blocked with 5% skim milk in Tris-buffered saline with 0.1% Tween-20, the separated protein was incubated overnight with anti-H3ace , anti-H4ace  or mouse anti-actin polyclonal antibodies . The separated protein was then incubated with horseradish peroxidase (HRP)-labeled anti-rabbit antibody  for 1-h the next day. Western blot results were visualized using an Amersham GelImager 600 , quantified by a Fiji Image processing package, and normalized by both β-Actin and the control group.Rela, Nlrp3, NMDA receptor subunit 2A (Grin2a), 5-HT2A (Htr2a), and GABAA subunit β3 (Gabrb3), because their mRNA level in the PFC had been previously observed to be significantly impacted by prenatal Poly I:C exposure in the PFC of female juvenile offspring rats . Cell lysis was performed and sheared to a 200–1,000 bp fragment by a Branson 450 Digital Sonifier . The cross-linked DNA was then transferred to a strip well which had been binding with chip-grade Anti-Acetyl Histone H4, Anti-Acetyl Histone H3, HDAC6 , or anti-mouse IgG (negative control) for protein/DNA immunoprecipitation. The fragmented DNA binding with these specific antibodies was then reversed and purified for further analysis. qPCR was performed in duplicate for each sample with SYBR Green Master Mix  to determine the target gene that was bound with H3ace, H4ace, or HDAC6 on its promoter region . The primers were designed on the relatively higher peak regions of the histone markers in the database of ChIP-AtlasChIP-qPCR was used to evaluate the enrichment of H3ace, H4ace, or HDAC6 binding on the promoter region of target genes of both the NF-kB/NLRP3 pathway and neurotransmitter receptors. These included Nlrp3, and −765 to −651 bp in Rela  ΔCt [normalized ChIP] = (Ct [ChIP] − (Ct [Input] − Log2(Input Dilution Factor))).The ChIP-qPCR pre-experiments were conducted in three samples randomly taken from the control group, which revealed a relatively strong binding of: (1) both H3ace and H4ace on the promoter region of −1,097 to −1,007 bp in  in Rela ; (2) HDAa  of in Htr2a ; and (4) , Crebbp (CBP), Tip60, Hdac2, Hdac4, and Hdac6. In brief, total RNA was extracted using the PureLinkTM RNA Mini Kit , and quantified by NanoDrop 2000 . It was then converted to cDNA using a High-Capacity cDNA Reverse transcription kit . qRT-PCR was run on a Quant StudioTM qRT-PCR system  with the following conditions: 95°C 10 min, 40 cycles of 95°C 15 s, and 60°C 1 min. All samples were measured in duplicate using SYBRTM Green PCR Master Mix  for p300 , Tip60 , Crebbp , Pcaf , and Gcn5 . TaqMan® Gene Expression Assays  were used for Hdac2 (Rn01407870_g1), Hdac4 (Rn01427040_m1), Hdac6 (Rn01528284_g1), β-actin (Hs01060665_g1), and Gapdh (Rn01775763_g1). The relative mRNA expression of each target gene was normalized by both β-actin and Gapdh, then calculated according to the 2−ΔΔCT method.RT-qPCR was applied for measuring the mRNA level of genes encoding HATs and HDACs, including t-test was used to compare the difference between Poly I:C and saline-treated groups, and a Pearson correlation was used to determine the relationships between the various measurements. For those without normal distribution, a nonparametric Mann–Whitney U test and Spearman correlation were performed. Statistical significance was accepted when p < 0.05. The results were presented as the mean ± SEM.Data were analyzed and graphed using GraphPad Prism 7.04 . The outliers were identified and removed using Boxplot on SPSS 25.0 . Data distribution was examined by a Kolmogorov-Smirnov test. For all data with normal distribution, a student t = 2.162, df = 10, p = 0.028; t = 2.654, df = 10, p = 0.024; Compared with the control, the global level of H3ace was significantly decreased in the Poly I:C group  was observed between the Poly I:C (63.730 ± 12.180%) and saline groups  in the rats with prenatal Poly I:C exposure compared with the control .There were positive correlations between Rela was involved in the activation of the NF-κB mediated inflammatory pathway in the PFC induced by prenatal Poly I:C exposure. This histone acetylation event was modulated by the specific histone acetyltransferases EP300 (p300) and CBP (Crebbp), both of which were type A HATs that originated in the nucleus and showed a typical function of catalyzing the process of transcription-related histone acetylation  induced by a single IP injection of 20 mg/kg PolyI:C at GD12.5  exposure caused abnormal mRNA expression of neurotransmitter receptors Rela mRNA expression  and CBP (Crebbp) were also observed in this study. Since the critical mechanism of A- HATs of EP300 and CBP in activating gene expression were through acetylating specific lysine residues in histones in the context of chromatin  Project Grant (APP 1104184) to CD and JL. JL was also supported by an NHMRC Early Career Fellowship Award (APP1125937). YS was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (2020Y9144), and Natural Science Foundation of Fujian Province (2021J01412). The funding bodies had no further role in the study design, decision to publish or preparation of the manuscript.


AGL19 expression, whereas HOS15 and HDA9, together with the photoperiodic evening complex, regulate flowering time through repression of GI transcription. However, the role of the HOS15/PWR/HDA9 core repressor complex as a functional unit in the regulation of flowering time is yet to be explored. In this study, we reported that the loss-of-function hos15-2/pwr/hda9 triple mutant accumulates higher transcript levels of AGL19 and exhibits an early flowering phenotype similar to those of hos15, pwr, and hda9 single mutants. Interestingly, the accumulation of HOS15 in the nucleus was drastically reduced in pwr and hda9 mutants. As a result, HOS15 could not perform its role in histone deacetylation or interaction with H3 in the nucleus. Furthermore, HOS15 is also associated with the same region of the AGL19 promoter known for PWR-HDA9 binding. The acetylation level of the AGL19 promoter was increased in the hos15-2 mutant, similar to the pwr and hda9 mutants. Therefore, our findings reveal that the HOS15/PWR/HDA9 repressor complex deacetylates the promoter region of AGL19, thereby negatively regulating AGL19 transcription, which leads to early flowering in Arabidopsis.Arabidopsis HOS15/PWR/HDA9 repressor complex, which is similar to the TBL1/NcoR1/HDAC complex in animals, plays a well-known role in epigenetic regulation. PWR and HDA9 have been reported to interact with each other and modulate the flowering time by repressing  FT expression  subunits and these complexes also target AGL19 chromatin region and affect H3K27me3 enrichment  regulates the acetylation level of AGL19, which in turn results in repression of FT gene (AGAMOUS LIKE 19 (AGL19) was first discovered through phylogenetical identification of conserved MADS-box domain . Expressrichment . AGL19 f FT gene .Gene regulation in signaling pathways is determined genetically and epigenetically. Epigenetic regulation of genes is achieved by histone modification, chromatin remodeling, DNA methylation, non-coding RNA, and microRNA . HistoneII250 related (AGL19) by deacetylating its promoter region (SOC1 and AGL24 in Brassica juncea (Acetylation and deacetylation of histones are mediated mainly by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively. In plants, HDACs are classified into three groups: the RPD3/HDA1 superfamily, HD-tuin family, and SRT (sirtuin) family . Plant H related . HDACs ar region . HDA9 ala juncea . In addia juncea .The Arabidopsis protein HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 15 (HOS15) is a homolog of mammalian TBL1 and TBLR1, which are core components of the SMRT/NCoR1 co-repressor complex . FurtherGI , pwr-3 (SALK_006823), hda9-1 (GK_305G03), hda9-2 (SALK_007123), and hos15-2 (GK_785B10) mutants, and HOS15pro::HOS15-HA/hos15-2  and triple mutants (pwr-2 hda9-2 hos15-2) were generated by crossing. Seeds were surface-sterilized in a solution containing 3% (v/v) sodium hypochlorite solution  for 5 min and then rinsed five times with sterilized water. After stratification at 4°C for 3 days in the dark, the plants were grown on half-strength Murashinge and Skoog (MS) medium or soil at 23°C ± 1°C under 16-h light/8-h-dark conditions with 100 µM m-2s-1 light intensity.The /hos15-2 , HOS15pr/hos15-2  transgenFlowering time analysis of the WT and mutant plants was performed at 23°C ± 1°C in long-day conditions. Flowering time was measured by counting the number of days-to-flowering and the total number of rosette leaves from the primary meristems at bolting.Total RNA was extracted from two-week-old plants using the RNeasy Plant Mini Kit (Qiagen) and treated with DNase (Sigma-Aldrich). cDNA was synthesized using reverse transcriptase . The SYBR Green PCR Master Mix Kit (Bio-Rad) was used for quantitative PCR using the indicated gene-specific primers. Primer sequences are provided in -1 leupeptin, 1 μg mL-1 aprotinin, 1 μg mL-1 pepstatin, 5 μg mL-1 antipain, 5 μg mL-1 chymostatin, 2 mM Na2VO3, 2 mM NaF, and 50 mM MG132). The proteins were separated and detected using SDS-PAGE and immunoblottied with the appropriate antibodies, anti-HOS15 , 0.5% (v/v) NP-40, 1 mM EDTA, 3 mM DTT, and protease inhibitors , 10 mM β-mercaptoethanol, 100 mg/mL phenylmethylsulfonyl fluoride, 0.5 mg/mL antipain, and 0.5 mg/mL leupeptin) , and anti-H3 (Abcam) antibodies, and antigen proteins were visualized by chemiluminescence using ECL-detecting reagent (Bio-Rad).Nuclei were extracted from two-week-old plants using Honda’s buffer  . The samUBQ10 DNA fragment was used for normalization.The chromatin immunoprecipitation (ChIP) assay was performed as described by hos15-2 mutant and three complementation lines expressing the construct HOS15pro::HOS15-HA in the hos15-2 background  mutant plants and tested their flowering phenotypes. As expected, double and triple knockout mutants of the HOS15-PWR-HAD9 complex showed an early flowering phenotype statistically similar to single mutants of HOS15, PWR, and HDA9, signifying that these mutations were not additive  assay showed that HOS15 was indeed associated with the E region of the AGL19 promoter, in a region already known for HDA9 association  antibody. H3 acetylation of AGL19 chromatin was higher in hos15-2 mutant plants than in the WT plants (AGL19 chromatin region, thereby negatively regulating AGL19 expression.Previous reports have revealed that HOS15, PWR, and HDA9 repress target gene expression by regulating histone H3 acetylation status . To invent lines . As show mutants . In lineAGL19, a flowering-regulating gene (AGL19 expression through histone modification of AGL19 chromatin thereby negatively regulating flowering time in Arabidopsis (AGL19 was strongly upregulated in hos15, hda9 and pwr mutants, which highlights the involvement of HOS15-HDA9-PWR complex in floral transition. The diurnal expression pattern of AGL19 was altered in the triple and the double mutants hos15 hda9 and pwr hda9 with a decline at ZT12 (AGL19 transcripts enhancing the oscillations (Epigenetic regulation plays an essential role in various signaling pathways in plants. Our study revealed the role of the HOS15-PWR-HDA9 co-repressor complex in the epigenetic regulation of flowering time through the expression of ing gene . Althougbidopsis Figure 6llations .hda9 and pwr mutants both in nucleus and cytosol. As shown in hda9 and pwr mutants compared with WT. By contrast, HOS15 accumulation in the cytosol was almost similar among all tested lines (hda9 and pwr mutants (Previously we have shown that HDA9 and HOS15 regulate flowering time by interfering with GI transcription . We haveed lines Figure 3hos15, hda9 and pwr single, double, and triple mutants. In addition to this, we have also found that HOS15-HDA9-PWR complex regulates flowering time through regulation of AGL19 chromatin (AGL19 chromatin was hyperacetylated in loss of function mutants of HOS15-HDA9-PWR complex (via AGL19 regulation. In the WT plants, the HOS15-PWR-HDA9 co-repressor complex negatively regulated the expression of AGL19 by promoting histone deacetylation at the AGL19 promoter, which compacts chromatin. In the absence of PWR and HDA9, the HOS15 protein was unstable in the nucleus. The removal of either one of the components of the repressor complex prevented histone deacetylation, thereby relaxing AGL19 chromatin, the increase in the expression of AGL19 and accelerating the flowering time (AGL19 through increased histone deacetylation.Recent reports have shown that HOS15-HDA9-PWR is a co-repressor complex which modulates histone acetylation status thereby regulating physiological processes including senescence . Consisthromatin Figure 5The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.CL, KP, AA, and D-JY conceived and designed the research. D-JY supervised the experiments. CL, KP, MJ, and SR performed the experiments. MS, JP, HK, ZB, SZ, MB, JC and Z-YX provided technical assistance and advice to CL and KP CJL, KSP, AA, JMP, and D-JY wrote the manuscrip. All authors contributed to the article and approved the submitted version.


Cellular functions depend on the dynamic assembly of protein regulator complexes at specific cellular locations. Single Molecule Tracking (SMT) is a method of choice for the biochemical characterization of protein dynamics in vitro and in vivo. SMT follows individual molecules in live cells and provides direct information about their behavior. SMT was successfully applied to mammalian models. However, mammalian cells provide a complex environment where protein mobility depends on numerous factors that are difficult to control experimentally. Therefore, yeast cells, which are unicellular and well-studied with a small and completely sequenced genome, provide an attractive alternative for SMT. The simplicity of organization, ease of genetic manipulation, and tolerance to gene fusions all make yeast a great model for quantifying the kinetics of major enzymes, membrane proteins, and nuclear and cellular bodies. However, very few researchers apply SMT techniques to yeast. Our goal is to promote SMT in yeast to a wider research community. Our review serves a dual purpose. We explain how SMT is conducted in yeast cells, and we discuss the latest insights from yeast SMT while putting them in perspective with SMT of higher eukaryotes. Molecular biology provides static snapshots of cellular processes, while quantitative fluorescence microscopy allows observation of their dynamics in live cells. Based on ChIP (Chromatin Immunoprecipitation), protein complexes appear to be built in an orderly, sequential, linear fashion, and the assembled complexes appear to persevere on the time scale of minutes until the task is completed  label molecules in live cells with a photostable dye, (b) ensure that each molecule of the protein is labeled by just a single organic dye molecule, (c) record molecules on an imaging setup that enhances the signal to noise ratio (SNR), (d) identify molecules and track them in the recorded time-lapse movies, and (e) convert tracks of the molecules into biophysical parameters.PDR5 [pdr5 homozygous Knock Out (KO) diploid cells in comparison to wild type  were developed at HHMI (Janelia) . New and present C.Single molecules emit weak signals, which present a challenge for the detection and tracking of live cells above the auto-fluorescence background. Long-term imaging of single molecules with wide-field illumination may present a problem; for single molecules to become visible, it requires pre-bleaching of the field-of-view to reduce the background noise ,16. StabHILO and LLS substantially reduce out-of-focus fluorescence by using a thin sheet of light for excitation. To increase SNR and thus enable SMT, a laser sheet with a thickness of ~5 μm is produced by tilting and flattening a laser beam on a custom microscope with HILO illumination . The tilA commercially available LLS microscope relies on using separate objective lenses for illumination and image collection. The illumination in LLS is produced by a Bessel-shaped, rather than Gaussian, laser beam formed into a lattice by the use of a spatial light modulator. This pattern is dithered perpendicular to the detection objective, which results in a thin sheet of illumination beyond which the areas are not irradiated . The thiGAL10 mRNA and TF Gal4 [Common 2D SMT imaging technology is limited by photobleaching and sites going out of focus. Thus, it is not well suited for simultaneous observation of two processes occurring on different time scales requiring long-time imaging in 3D and fast time sampling in two channels. Orbital Tracking allows two-channel observation of fluorescent protein binding in 3D to moving fluorescent target sites . In the  TF Gal4 . Several TF Gal4 , and comMINFLUX is a new method of SMT that promises unprecedented localization precision down to 2 nm and a temporal resolution of 1 ms . On a MIIn addition, 2D or 3D SMT with super-resolution in live mammalian cells was performed on custom-built systems, such as Target-locked 3D STED , a multiEfficient tracking requires a small number of labeled molecules per cell. A low concentration of fluorescent particles (1–2 per nucleus) may be achieved in two ways. First, HaloTag and SNAP-Tag fusions may be labeled sparsely (using very low concentration), as shown in Live yeast cells may be mounted onto LabTek coverglass chambers under agarose pads with CSM; yeast cells are immobilized and slightly flattened by the agarose pad. Nonfluorescent media instead of CSM may reduce autofluorescence . HoweverImages are collected sequentially with defined time intervals. The brightness of the molecules defines the minimal exposure allowing visualization of the molecule above the image noise. Fast (~10–12 ms) acquisition with continuous illumination with a high-power laser is applied if the focus is mainly on diffusing and short binding events. Slow acquisition with long exposures (≥200 ms) interspersed with dark periods is applied if the focus is on long-binding events. In most of the studies performed in mammalian or yeast cells, molecules are tracked in the whole cell or nucleus. For most proteins, multiple binding sites with a range of affinities exist in the cell. Therefore, the binding parameters are an average of multiple activities on multiple sites for the same protein. More information may be obtained if SMT is performed at a specific location in the cell, where a protein of interest is known to perform a specific function. Specific locations may be labeled by fluorescent markers in a different channel. This label should be readily distinguished from individual marker molecules, moving by diffusion, or bound ectopically; thus, clusters of fluorescent molecules are usually utilized for the unambiguous detection of a specific site.646 and Ace1p-3xGFP, the former was used for SMT, and the latter for visualization of the target sites at a specific CUP1 promoter [CUP1 promoter contains four binding motifs for the TF Ace1p. Four molecules of Ace1p-3xGFP bound to a single promoter can be detected at high exposure but cannot be imaged repeatedly. However, the CUP1 locus consisting of ~10 tandem copies of the same gene provides a cluster of ~40 binding motifs, which may be observed for a prolonged time by binding of the 3xGFP-tagged Ace1p. Tracking of RNA Polymerase II (Pol II) can be performed at CUP1 promoters visualized by the Ace1p-3XGFP  .t0. The survival time distribution is traditionally fit to exponential decay. The first point of the fitted survival curve supplies the information on the ratio of bound molecules to total (bound and diffusing) molecules, eqC. The simulated survival distribution of CUP1 and at the array of lacO and demonstrated that the long-residence fraction practically disappears at the lacO sites [2+ ions. In the absence of copper, TF Ace1p may bind only nonspecifically to targets in the nucleoplasm [The survival distribution is normalized to the bound fraction calculated as (number of bound particles) × (number of time frames the particles are bound)/ B. NormalcO sites . As a becO sites . Howeverleoplasm . NSτ and Sτ, which characterize the average time that nuclear factors remain bound to non-specific and specific targets on chromatin, respectively. The fitting also reveals the fractions of molecules in each state . From the fractions in each bound state, one may estimate the average number of chromatin sites sampled by a molecule before it finds a specific motif, trialsN,Bi-exponential distributions provide estimates for the residence times, resτ is the average of the non-specific and specific binding,The search time (the time nuclear factors spend in search of the specific sites) may be estimated B, Left: eqC may result from: (a) a change in the availability of the TF, freeC, (b) a change in the TF dissociation rate, offk, (c) a change in the availability of the specific motifs, eqS, or (d) a change in the TF association rate, onk. eqS indicates how many specific motifs in the promoter are free from the nucleosomes and accessible to TFs, and onk is dependent on the size of the area for the search [Changes in e search . Directlonk, the availability of the specific motifs, eq,S is required. eqS may be available for some TFs from previously published estimates. Alternatively, eqS may be estimated from the fraction of non-activated specific loci observed by RNA smFISH (single molecule Fluorescence In Situ Hybridization) [To extract ization) .The survival curve may follow not a bi-exponential, but a Power Law distribution B, Right.The length of time that the observed molecules appear to remain stationary depends on binding kinetics as well as on photobleaching, and therefore the raw survival distribution . The distribution of the logarithm of extracted diffusion coefficients for the individual tracks can then be fit by Gaussian, bi-Gaussian, or tri-Gaussian models to extract the sub-fractions of bound, intermediate, and unbound particles . Figure α) to determine the type of motion encountered. Brownian motion  is characterized by α = 1, while α > 1 indicates facilitated motion (super-diffusion), and α < 1 indicates confined motion or sub-diffusive processes [cR [The MSD may also be approximated by a Power Law  = trocesses . For parsses [Rc ,MSD(t)=cR = 1 μm for diploid nucleus). For all tracks obtained from yeast RNA Pol II, we observed α = 0.35, indicating sub-diffusion, and cR = 0.3 μm, which reveals that Pol II is confined to a smaller area of the nucleus  residence time analysis to extract binding times for stationary molecules, (b) fitting of diffusion coefficient distributions to identify various types of motion, and (c) kinetic models that determine different states of motion occupied by the molecule under investigation. The latter group of methods includes sophisticated machine learning techniques.MatTrack ,42 incluhttps://github.com/Reyes-LamotheLab/Bound2Learn, accessed on 10 December 2022).Bound2Learn is a promising new approach that incorporates a machine learning algorithm to classify molecules as bound or diffusing . Gaussiahttps://spoton.berkeley.edu/SPTGUI/, accessed on 10 December 2022) and works by calculating the distributions of displacements at each available time lag. This ensemble of displacement distributions is then jointly fit to kinetic models with one, two, or three states. The application of kinetic models to a collection of tracks or track segments offers the possibility of obtaining multiple states with higher fidelity than is possible with curve fitting. Spot-On is one tool that will return the fractions of tracks within each of up to three states . Spot-Onhttp://vbspt.sourceforge.net, accessed on 10 December 2022) uses a variational Bayesian treatment of a Hidden Markov Model (HMM) to obtain a model that can contain any number of states, with each state characterized by a unique diffusion coefficient. In addition to the presence of the state, vbSPT also estimates the transition rates between states. A major limitation of vbSPT is that it does not take into account localization precision, meaning it may be better suited for the analysis of fast-moving states rather than slow chromatin-bound states. An early technique that was developed for the estimation of diffusional states from SMT data is vbSPT . Availabhttps://github.com/p-koo/pEMv2, accessed on 10 December 2022), is a machine learning framework that utilizes maximum-likelihood estimation and Bayesian information criteria (BIC) to obtain the diffusion parameters for each track. A global model that explains all track diffusion parameters is found using a perturbation expectation maximum technique. In pEMv2, each track is classified as a single state; therefore, it is necessary to split tracks into lengths that minimize the probability of transitions between states.Another tool used for calculating an unknown number of diffusion states and transitions from SMT data is perturbation Expectation Maximum version 2 or pEMv2 . pEMv2, https://github.com/hatzakislab/Diffusional-Fingerprinting, accessed on 10 December 2022), diffusional fingerprinting calculates 17 features of each track, and a logistic regression classifier is used to determine which state each track belongs to. Diffusional fingerprinting is another machine learning approach for calculating up to four states and transitions from tracking data . Availabhttps://github.com/alecheckert/saspt, accessed on 10 December 2022) and has the potential to estimate an unlimited number of underlying states within the SMT data, but it cannot estimate transitions between states. A new method uses a finite state approximation of a Dirichlet process mixture model to simultaneously estimate localization error and diffusion coefficients for each track . This teSimulations are often used in SMT to validate analysis methods or test hypotheses arising from parameters extracted from SMT data. For example, Koo and Mochrie simulated populations of molecules with various diffusion characteristics to show that pEMv2 successfully classified the tracks . PresmanYeast provides many advantages for SMT. Yeast has a small genome; therefore, many genes are presented only as a single copy, which simplifies genetic manipulations, such as gene tagging with fluorescence markers, controllable protein degradation, introduction of specific mutations in specific genes, the ability to use mutants that will delay specific cell cycle stages, the availability of conditional alleles for numerous proteins to determine their functional impact, and the usage of synthetic genetic systems. Yeast has practically no silent DNA, and all regulatory pathways in yeast are simple and easy to manipulate. Yeast is tolerant to xenobiotic DNA; therefore, yeast is a simplified model for in vivo SMT of the proteins of higher eukaryotes. However, conclusions from yeast may not be completely extrapolated to higher eukaryotes. As discussed below, we already know from SMT that the initiation of transcription and perhaps some other cellular processes are partially different in yeast and mammalian cells. This might be predicted for gene expression, as yeast has a compact genome with practically no satellite DNA and no repetitive sequences. Most yeast genes are constitutively expressed, and there are fewer histone variants and histone modifications in yeast than in multicellular eukaryotes. Furthermore, yeast has a low level of DNA cytosine modifications and lacks enhancers. In general, there are fewer gene copies for yeast cellular proteins, which may be indicative of a simplified genetic regulation for many cellular processes.In addition, there are certain technical disadvantages to using yeast for SMT. The yeast nucleus is much more mobile than in mammalian systems, and this may impose technical problems on long-term observations. In addition, due to its small size, the presence of a cell wall, and the accumulation of autofluorescent biological by-products in a sizeable vacuole, yeast cells have high autofluorescence and light scattering. This results in a reduced SNR and, thus, forces the usage of higher laser power and, consequently, reduces the length of observation time and the number of usable tracks. The small size of yeast cells also affects the size of intracellular protein pools and limits protein traveling distance, thus affecting the specifics of protein kinetics.In this section, we summarize the current state of the field. Many SMT experiments were performed in insect or mammalian cells; however, our goal is not to give a comprehensive coverage of all SMT research, but to use only a few relevant publications to highlight the overall meaning of the yeast data. Thus, some valuable research performed in higher eukaryotes was left unmentioned in this review. For further reading, we recommend recent reviews ,56,57,58Please note, most of the research discussed below is based on thorough studies of transcription. Unfortunately, there are very few SMT studies beyond this field. However, studies of transcription point to a general behavior that should be relevant in some way to other protein complexes, processive or non-processive. All cellular proteins must search and find specific locations in the cell where molecular complexes assemble and function; dynamics of all protein complexes must be optimized; we may expect intricate regulation of complexes that incorporate subunits with different scales of dynamics; mechanisms must exist for dynamic complexes to regulate stepwise continuous processes. Findings in transcription provide a roadmap for interrogating other protein functions by SMT. However, it is expected that the expansion of SMT to other cellular processes will reveal new ways of molecule mobility and dynamics in live cells. By ChIP, the main events of transcription activation were defined as orderly and sequential: (1) a pioneer factor binds to regulatory sites covered by nucleosomes, (2) chromatin remodelers are recruited and clear the sites from nucleosomes, making them accessible to Mediator complex and multiple General Transcription Factors (GTFs), (3) GTFs bind to the promoter which facilitates RNA Pol II binding, (4) RNA Pol II binds to the promoter, (5) RNA Pol II recruits more GTFs to form the PIC (Preinitiation Complex). RNA Pol II then escapes from the PIC to transcribe the open reading frame (ORF) A. How will transcription activation look if observed by SMT? In yeast, SMT was applied to the components of the PIC  and replA priori, mammalian and yeast cells might display a difference in PIC lifetime and component dynamics as yeast genes are shorter in length and require less time to be transcribed; the whole yeast genome, as opposed to higher eukaryotes, is much more active transcriptionally, and there are much fewer stalled RNA Pol II complexes in yeast. Indeed, dynamics of TBP and Pol II measured by FRAP are much faster in yeast than in mammalian cells ,60,61. BWhat dynamics are displayed by RNA Pol II complex engaged in the processive elongation ? Is it stable during a single elongation cycle? What are the dynamics of its components through the single elongation cycle? Direct measurements of the progress of the processive enzymes are beyond the resolution of currently available imaging technology. To detect any directional movement, long tracks with fast temporal sampling are required; however, photobleaching and loss of focus limit either the rate of sampling or the number of timepoints; fast sampling will lead to short tracks, and a low sampling rate will create problems in linking the correct molecules in sequential frames. Moreover, while detecting binding, we cannot distinguish between true binding or site sampling due to the inadequate spatial resolution of the typical SMT experiments. As an example, if the average speed of the replication fork is 1.6 kbp/min, and the size of a half-replicon replicated by a single replisome is 18.5 kb, then assuming for simplicity that the DNA template is a straight filament, and assuming a typical SMT resolution of 30–40 nm, it will take ~3.5 s for the replisome progress to be detected. However, if, as expected, replicated DNA is in a loop or a more complex spatial configuration, the progress of the replisome will be completely undetectable with the current spatial resolution of the SMT technique. Similar considerations apply to RNA Pol II during elongation if the average elongation rate is 3.9 kbp/min . TherefoIt is hard to estimate the residence time of the RNA Pol II elongation complex in vivo by SMT in ensemble studies. A residence time of 23 s was reported for the biggest subunit of RNA Pol II , but thiKapadia et al. explored the intranuclear dynamics of DNA polymerases at the S stage of the cell cycle when polymerases are predominantly assembled into replisomes . This moThe third polymerase, Pol α, is involved in RNA primer synthesis and extension, and therefore, must bind to DNA cyclically. One may expect that Pol α falls off the template after the synthesis of each short primer. However, the long residence time of Pol α (64 s) indicated that the same molecule may be involved in several cycles of priming activity; although, in the same replisome, individual molecules of Pol α are interchangeable. Thus, processive complexes appear to be more stable than regulatory complexes. At the same time, within the same complex, different subunits still may have different kinetics. Biologically, this means that the complex must hold together somehow while proceeding along the DNA, even when some components are temporarily disassociated from DNA. One possibility for such behavior is tethering the processive complex to the DNA, while some of the subunits temporarily separate from the DNA template but retain a connection with the replisome. Recently, a mechanism of tethered diffusion was described for kinesin in in vitro experiments. The authors observed the mobility of fluorescently labeled kinesin along microtubules attached to the coverslip. They discovered that the kinesin cofactor MAP7 was tethering kinesin to the microtubule and allowed it to sidestep obstacles by temporarily dissociating from the microtubule but still retaining connection with the same microtubule via MAP7 . TethereRegulatory complexes display fast dynamics; however, they may regulate processes with much slower dynamics. A good example is transcription, which coincidentally is a process extensively studied for molecule dynamics. The dynamics of individual TS observed in live cells by stem-loop labeling of nascent mRNA have demonstrated that for many genes in different organisms, transcription occurs not continuously but in periods of transcriptional activity (bursts) interspersed with periods of transcriptional silence , burst and inter-burst duration, burst amplitude , burst size , and burst frequency , and assuming that the switching of the CUP1 promoter to the ON state is triggered by binding of at least one Ace1p molecule. They inputted two parameters into this model: mRNA degradation rates, which were estimated separately, and the offk of Ace1p as the gene’s OFFk. The gene’s ONk, which was obtained from Monte-Carlo simulations during the fitting process, was very similar to the TF onk calculated from the SMT data. If the TF onk and offk would not be related to gene ONk and OFFk, then the returned ONk value would be different from TF onk. This indicated that the short binding events of Ace1p are the drivers switching the promoter to the ON state. It was demonstrated for the yeast or Ace1p . The autGAL10 were observed as short spikes of Halo Tag/JF646 fluorescence (τ = 35 s); they were followed by longer spikes in PP7-GFP fluorescence (τ = 152 s), which indicated that Gal4 binding precedes RNA appearance at the TS. Therefore, Gal4, like Ace1p, switches the promoter to an ON state.This conclusion was further supported by direct observation of TF binding and subsequent transcription bursts at the same promoter. Two groups took advantage of the uncommon technology of Orbital Tracking in mammalian cells  and yeasGAL10 TS and the appearance of nascent mRNA. This indicates that TF in yeast may be required for the gene to finalize PIC formation and RNA Pol II promoter escape. Furthermore, when a TF leaves the promoter, initiation of transcription stops, as demonstrated in a synthetic system in yeast where the binding of synthetic TF to the synthetic reporter was controlled by a photoactivable domain. Without photoactivation by blue light (405 nm), the TF was deactivated and left the promoter within a minute, and the initiation of transcription was abolished [Moreover, Gal4 is present at the promoter when transcription starts, as an overlap of 14 s was detected between the residence of Gal4 at bolished .Taken together, observations from these three papers ,13,69 leThe relationship between TF binding and transcription bursts in mammalian cells is more complex than in yeast, and it cannot be described by a simple two-step ON/OFF random telegraph model ,71. For These conclusions were confirmed in another mammalian system . The autTherefore, higher eukaryotes are different from yeast cells. In higher eukaryotes, TFs do not need to be present for the whole process of initiation. An interesting hypothesis links this feature with the evolution of enhancers . EnhanceRegulatory factors must search for their target sites in a complex nuclear landscape, which may obstruct or facilitate their search process due to changes in local protein concentration and the presence of sizeable nuclear bodies. Searching proteins must compete with multiple factors binding to the same targets, specifically or nonspecifically. This applies to any protein and any target site, such as DNA, RNA, protein aggregates, nuclear bodies, or cytoskeletal components. Thus far, the search process has been studied only for proteins binding to DNA, partially by convenience, as this type of binding occurs on a well-characterized target, DNA, with known consensus motifs, and furthermore, the binding of proteins to DNA can be reproduced in vitro to some extent. Mechanisms of nonspecific binding were assayed in bacteria, yeast, and mammalian cells (see review ). For most DNA-binding regulatory factors, multiple binding sites with different affinities are present in the genome, some of which match the consensus sequence. The latter are typically located in regulatory elements and perform specific regulatory functions. The others with different levels of mismatch to the consensus, are located randomly and they do not play any regulatory role and have no specific function. In eukaryotes, it is unclear how many of the putative binding sites are available for binding, as access to many of those may be obstructed by nucleosomes. 2+ for binding to a few specific sites in the promoters of only five genes; therefore, any binding detected in the absence of Cu2+ is nonspecific. In the nucleoplasm, Ace1p displayed much slower mobility than the free GFP molecules. Modeling of this process indicated that Ace1p moved by effective diffusion, i.e., it spent more time bound than diffusing [According to the common observations, the search for binding sites by TFs occurs by the sampling of DNA, i.e., by transient nonspecific binding alternated with diffusion. In yeast, the existence of nonspecific binding was first demonstrated by FRAP for the TF Ace1p. Ace1p requires Cuiffusing . Later, iffusing . What is known about the modes of protein mobility during the search for specific target sites? Most of the information comes from in vitro studies. TFs are expected to interact with DNA by facilitated diffusion, consisting of 3D translocation between DNA segments and 1D hopping and sliding along the DNA (reviewed in ) FigureE. The plA priori, sliding along the DNA in eukaryotic cells might be affected by histones. However, as demonstrated in vitro, 1D diffusion may occur even on a target covered by nucleosomes . SupposeE. coli, LacI spends ~90% of the time bound and gliding along the DNA and only 10% in the diffusing state [Bacterial regulatory proteins spend most of the time bound to DNA, but the regulatory proteins in eukaryotes spend more time unbound. This may result from multiple factors, including the rarity of naked DNA and the much bigger size of the eukaryotic genome meaning more sites to search. As observed by SMT, in ng state . In mammng state . The autInterestingly, some proteins do not search for DNA but instead search for the proteins associated with DNA, or alternatively, they may bind to DNA only if bound to another protein. Proteins that do not bind to DNA on their own should be incapable of 1D diffusion. Indeed, bacterial RNA polymerase, as opposed to bacterial regulatory protein LacI, is incapable of 1D diffusion; it searches for a binding site via 3D diffusion, as observed in vitro . In yeas2/s, and cR = 0.4 µm) indicate that they sample areas smaller than the whole nucleus. Small GTFs  in diffusing state ( D > 2 µm2/s) display cR = 0.75 µm, that corresponds to the radius of the nucleus, indicating that they may explore the whole nucleus. In sub-diffusive states with D = 0.4–0.6 µm2/s, small GTFs are confined to smaller areas with cR = 0.4 µm, similar in size to those of Mediator and TFIID, indicating that this subfraction of small GTFs associates with larger GTFs. Indeed, the sub-diffusive state of small GTFs disappears if Mediator, RNA Pol II, and TFIID are depleted. Thus, smaller GTFs may search for target sites as individual molecules (diffusive sate) or in association with bigger GTFs (sub-diffusive state). The chromatin-bound state of PIC components and histone H2B displayed tight spatial confinement (cR = 0.13 µm). SMT in yeast showed that the space exploration by the GTFs may be constrained to areas smaller than the volume of the whole yeast nucleus . In fasti. Deletion of this domain abolished the trapping of CTCF and led to exploration of the whole nucleus. Thus, different proteins may rely on different searching strategies in the same molecular environment. Moreover, the same protein may behave differently in different cell types. In a separate study, in non-cancerous HBEC3-KT cells, a greater fraction of c-Myc was specifically bound as compared to c-Myc in the human osteosarcoma U2OS cells (98% versus 46%) [Different modes of space exploration were described by SMT in more detail for the regulatory proteins in mammalian cells. Two proteins overexpressed in human osteosarcoma U2OS cells, the general transcriptional activator, c-Myc, and the transcription elongation factor P-TEFb, sampled the space in a different way. Modeling of the distribution of the angles between consecutive translocations indicated that c-Myc is a non-compact explorer, while P-TEFb is a compact explorer. Non-compact explorers search the space by isotropic diffusion, but compact explorers search the space by anomalous diffusion, with obstructed mobility . The autsus 46%) .offk of TF) or frequent binding (high onk of TF)—which is more important for gene regulation? High time-averaged promoter occupancy by transcription activators may occur due to long residence  for a specific TF, the number of TF molecules, and the number of binding sites. From these estimates, one may calculate how long it may take for the single protein to find a single target site in a cell of an appropriate volume. It will take 30 min for a single LacI in a very small E. coli cell [An increase in the binding frequency of a TF may improve the transcription rate, as demonstrated in ,68,69,71oli cell , 5 hoursoli cell , 31 daysoli cell , or 35 doli cell . TherefoGAL3 in yeast contains only one binding site for Gal4, but GAL10 has four binding sites, and transcription from GAL10 is much more efficient [Amplification of target motifs should increase the probability of binding of the TF and thus increase the time-averaged occupancy of the promoter by TF. Many promoters contain multiple binding sites for the same TF, and it was shown that amplification of target sites in a promoter increases the transcriptional output, probably due to higher occupancy of the promoter by TF. For instance, fficient . Howeverfficient . Different modes of search may have implications for the regulation of gene expression. Non-compact search is affected by the size of the target of the search and does not dependent on travel distance; compact search depends on the travel distance and does not depend on the size of the target. For example, from modeling of SMT data in mammalian cells, the estimated time needed to find a 10 nm target located at 250 nm distance was 506 s for c-Myc (non-compact searcher) and 7.4 s for P-TEFb (compact searcher), but if the target was located 5 μm away the search time was approximately the same (525 s) for c-Myc but substantially longer (64.6 s) for P-TEFb . A non-cSeveral papers provide direct or indirect evidence that cells may maintain optimal local concentrations of the TFs. Indeed, although an increase in the molecular concentration of the TF reduces the search time and leads to an increase in transcriptional output ,68,69,71Not much is known yet about the mechanisms controlling variability in protein concentration and the formation of protein clusters. Proteins may be sequestered by binding to bigger protein complexes, as demonstrated in yeast, where Mediator and TFIID associate with smaller GTFs and sequester them in small subnuclear areas . RegulatDrosophila melanogaster embryos, there is a gradient in the concentration of transcription factor Bicoid. Factor Zelda compensates for the drop in Bicoid concentration and maintains the Bicoid ON rate by promoting hubs of Bicoid on specific target sites [Recently, it was discovered that proteins may assemble into temporary clusters via liquid–liquid phase separation (LLPS), facilitated by Intrinsically Disordered Domains (IDR), which include the subcategory of Low-Complexity Domains (LDRs) (see review ). Supposet sites . Howeveret sites . There mGAL3 promoter containing appropriate UAS sequences [An increase in the residence time of transcription factors in mammalian cells may improve transcription ,89,90. Iequences . The lowoffk of TF on the same consensus sequences may be modulated.Similarly, a reduction in the binding affinity at active transcription sites was observed in a synthetic system in yeast . The autoffk rate, the longer the molecule will be bound to the site. However, external factors besides affinity may affect the residence time of TFs. Mostly, those factors are yet unknown, except for the chromatin remodelers. FRAP data for the nucleoplasmic mobility of Ace1p indicate that chromatin remodeler RSC actively removes Ace1p stuck at nonspecific sites [The residence time of a single molecule at the promoter is determined by its affinity to the binding site; the lower the ic sites . Thus, tCUP1 promoter: residence time of Ace1p is reduced by RSC, but the occupancy of the promoter (onk) by Ace1p is increased [CUP1-activated promoter, indicating rapid changes in the accessibility of the target motifs to Ace1p. RSC depletion leads to moderate changes in CUP1 transcription output from the cell population; however, this translates to detectable phenotypic effects, such as higher sensitivity to copper and elevated variability in transcription response between individual cells. This indicates that optimal, rather than long, residence time may be beneficial for cell survival.As observed by SMT, RSC plays two seemingly opposing roles on specific motifs at the ncreased . This maKim et al., 2021 carefully studied the dynamics of all yeast remodeler complexes and discovered that, as opposed to RNA Pol II and large subunits of PIC, all chromatin remodelers display very short residence times . Fast diMOT1, which encodes TBP-associated ATPase. In the absence of Mot1p, the residence time of TBP and TAF1 on DNA is increased, indicating that Mot1p is responsible for the removal of non-functional PIC and restoration of the available TBP pool [A couple of studies indicate that other proteins, besides chromatin remodelers, may curtail the residence time of TFs at promoters. FRAP data demonstrate that the kinetics of TBP and TAF1 are regulated in yeast by TBP pool . In mammTBP pool . CUP1, where for shorter residence times (τ = 2 s), only one out of three binding events of Ace1p are productive, but for longer residence times (τ = 5 s), each binding of Ace1p is productive [GAL10 [Alternatively, there appears to be a lower limit on residence time, below which TF binding to the promoter is futile. In mammalian cells, this was discovered in a synthetic system, which is discussed in oductive   and low residence time , due to a short search time and high frequency of binding. This mode of achieving high time-averaged occupancy is tailored to the mode of action of chromatin remodelers, allowing simultaneous binding of different remodelers to the same promoter and ensuring rapid dynamics in the accessibility of the target motifs in the promoter .Drosophila melanogaster embryos, the residence time of the TF Bicoid (BCD) is extremely short, ~1 s, and thus, promoter occupancy is dominated by onk and maintained by local enrichment for TFs at specific locations [In ocations   hypothesis, which predicts that enhancers may generate gradients of local effective concentrations for TFs . The autSo, what is more important—regulation by frequent binding or by prolonged residence time? Most probably, it is determined by the requirements of a particular system; to confirm that, we need to study multiple genetic models, encompassing housekeeping and inducible genes. Rapid dynamics at promoters may provide more efficient regulation. It has been demonstrated that regulation by the frequency of bursts is more efficient than regulation by burst amplitude, and the former may lead to more stable and higher transcriptional output . MoreoveSingle molecule tracking was mostly applied to transcription factors and factors of replication. Many lessons learned from these experiments may be applicable to other protein factors in the cell. In the future, one may anticipate SMT to be applied to factors of translation, molecular motors, etc. We still have a lot to learn about the behavior of individual proteins, and current imaging technology may still deliver a lot of answers. However, whatever we know is limited by spatio-temporal resolution and throughput. Currently, we cannot observe the behavior of two or more subunits in the same molecular complex, such as the replisome and the PIC; thus, we cannot observe subunit interactions directly, and we must deduce the behavior of the complex from the dynamics of its components observed individually. We cannot directly observe and quantify the work of processive enzymes due to the spatio-temporal resolution limitations of the current SMT technique. We need to improve our ability to track single molecules in 3D and to track several proteins simultaneously, we need to achieve higher spatio-temporal resolution without loss of the signal due to photobleaching, and to achieve high-throughput data acquisition and high throughput automation of the analysis. New and improved technology can help to solve these problems. Suppose we have those new tools. What will we be able to do with this new technology? Eventually, we want to study a cell in its entirety. The big questions are how the coordination of different processes occurs in the nucleus and what role does the microenvironment play? Many things happen at the same location at the same time. How do different proteins go about their business, how do their independent functions fit together, and what prevents unrelated processes from interfering with each other? Following multiple proteins at a time in their local environment provides information about the ensemble work of unrelated processes. We will observe the formation of the complexes and different protein dynamics in different cellular compartments in real time. We will understand how the different steps of gene expression  work together and overlap with replication, recombination, and repair. We want to be able to do in vivo all that we can currently do only in vitro, and more. We started with individual proteins, but eventually, we want to know and understand how all the separate pieces fit together in a living cell.


Arabidopsis histone variant H3.3 in endowing seeds with post-embryonic developmental potentials. H3.3 is not essential for seed formation, but loss of H3.3 results in severely impaired germination and post-embryonic development. H3.3 exhibits a seed-specific 5′ gene end distribution and facilitates chromatin opening at regulatory regions in seeds. During germination, H3.3 is essential for proper gene transcriptional regulation. Moreover, H3.3 is constantly loaded at the 3′ gene end, correlating with gene body DNA methylation and the restriction of chromatin accessibility and cryptic transcription at this region. Our results suggest a fundamental role of H3.3 in initiating chromatin accessibility at regulatory regions in seed and licensing the embryonic to post-embryonic transition.The acquisition of germination and post-embryonic developmental ability during seed maturation is vital for seed vigor, an important trait for plant propagation and crop production. How seed vigor is established in seeds is still poorly understood. Here, we report the crucial function of  Producing vigorous seeds that can germinate and establish seedlings is vital for plant propagation. Here, the authors report the critical function of histone variant H3.3 in establishing chromatin accessibility and seed vigour in Arabidopsis. Producing highly vigorous seeds is thus key for plant reproduction and agriculture. Embryo obtains its germination capacity during the late seed maturation stage2. However, the underlying mechanisms establishing germination and post-embryonic developmental competence in seeds remain poorly understood.The formation of seeds is one of the most important innovations that contribute to the success of flowering plants. By seed formation, the embryo is protected in a desiccated and quiescent state, and, meanwhile, ready to swiftly initiate germination and post-embryonic development when optimal growth condition arisescis-regulatory elements such as promoters and enhancers, and thus affecting gene transcription4. During cellular differentiation and developmental transitions, epigenetic reprogramming induces chromatin structure changes, thereby modifying the accessibility of regulatory elements to transcription machinery6. The establishment of chromatin accessibility is thus a pioneer event in developmental transition and lineage-specific transcriptional regulation. Several mechanisms have been identified to control chromatin structure dynamics, including ATP-dependent chromatin remodeling, post-translational modifications on histones, and the exchange of histone variants8. Besides their sequence divergence, histone variants often possess different expression patterns and in some cases unique modifications11. The deposition of histones variants can modify nucleosome stability, which differentiates chromatin into specialized property and cellular function12.The eukaryotic genome is tightly packed in the form of chromatin, which consists of DNA and histone-formed nucleosomes as basic subunits. Chromatin compaction restricts the access of transcription factors to 13. Unlike H3.1, which is DNA replication-dependently expressed and incorporated into the chromatin, H3.3 is expressed and incorporated in a DNA replication-independent manner18. H3.3 is associated with transcribed regions and gene regulatory elements20. In Xenopus, H3.3 is essential for gastrulation, and it is proposed that H3.3 creates a permissive chromatin state critical for embryo development22. Knockout one of the two H3.3-coding genes in mouse causes reduced viability and infertility25, and complete deletion of H3.3 results in early embryonic lethality26. H3.3 is required to maintain a decondensed chromatin state and active histone modifications in mouse embryo27. Nevertheless, loss of H3.3 seems to have a limited impact on gene transcription, rather it causes abnormal chromosome segregation and impaired genome integrity27.In animals, the histone H3 variant H3.3 differs from the replicative H3.1/H3.2 with only 4–5 amino acids30. For instance, in Arabidopsis H3.3 differs from H3.1 with four amino acids31, and H3.3 and H3.1 are DNA replication-independently and dependently expressed respectively32. Moreover, Arabidopsis H3.3 is mostly enriched at the body of transcribed genes33. Three genes HTR4, HTR5 and HTR8 in Arabidopsis encode H3.3. Knockdown of Arabidopsis H3.3 (h3.3kd) results in mild developmental defects, including leaf serration, early flowering and slightly reduced fertility35. H3.3 promotes permissive histone modifications at the floral repressor FLOWERING LOCUS C (FLC), leading to its activation to repress flowering35. Compared with the broad distribution of H3.3 on transcribed genes, only hundreds of genes are misexpressed in h3.3kd, indicating that H3.3 may not regulate transcription directly32. Hence, the role of H3.3 in plants remains to be investigated.Although evolved independently in separate kingdoms, H3.1 and H3.3 in higher plants acquired comparable features as their counterparts in animals through convergent evolutionArabidopsis H3.3 in the establishment of chromatin accessibility and endowing seeds with post-embryonic developmental competence. Complete deletion of H3.3 shows no obvious impact on embryogenesis, but results in severe defects in germination and vegetative growth. H3.3 is enriched around both 5′ and 3′ gene ends in mature seeds. The 5′ enrichment of H3.3 is seed-specific, and it facilitates chromatin opening, particularly at the regulatory regions of responsive and post-embryonic developmental genes, and H3.3 is essential for their transcriptional regulation during germination. On the other hand, 3′ localization of H3.3 represents a canonical H3.3 deposition pattern that correlates with gene body DNA methylation and the restriction of chromatin accessibility and cryptic transcription. Our results support a fundamental role of H3.3 in the establishment of chromatin accessibility and gene transcriptional regulation important for post-embryonic development.Here, we report the critical function of htr4;htr5 double mutant with an htr8 mutant carrying a T-DNA insertion at HTR8. At that point, we did not obtain triple homozygous plants when screening the htr4/htr4;htr5/htr5;htr8/+ (h3.3ko/+) progenies, and thus we speculated that the h3.3ko mutant was embryonic lethal34. To analyze h3.3ko mutant phenotypes in detail, we examined the embryo development on h3.3ko/+ plants. Surprisingly, all seeds developed without abortion  Columbia (Col) mother, the transmission of h3.3ko was reduced to around 16% instead of the expected 50%34. These ungerminated seeds were then subjected to prolonged imbibition (up to two months), a portion of these seeds eventually germinated and produced small seedlings, and genotyping analysis confirmed that these seedlings were h3.3ko  under the HTR5 promoter  are critical tissues in dormancy maintenance, and they also impose a physical constraint on the radicle protrusion39. To examine which tissues are responsible for the h3.3ko germination defects, we isolated h3.3ko embryos by removing endosperm and testa. The isolated h3.3ko embryos showed much slower growth compared with WT . Moreover, seeds were stratified for two days during germination, further excluding the possibility that the  WT Fig. , indicath3.3ko, we profiled transcriptomes of WT and h3.3ko using RNA-seq. Seeds stored for three months after harvesting (hereinafter referred to as mature seeds) were subjected to imbibition and RNA was extracted from seeds collected at various time points  identified from each time point during imbibition and performed gene ontology (GO) analysis, and found that responsive genes were especially enriched , which induces a strong uniform gene expression pattern similar to that of HTR543. Seeds collected from h3.3ko/+;pER8::HTR5 were subjected to imbibition with or without estrogen, and HTR5 transcription was successfully induced by estrogen  or H2B.S (a seed-specific H2B variant gene), both of which are expressed specifically at the late stages of seed development46. Expressing HTR5 under either promoter increased the germination rate of h3.3ko, with the H2B.S promoter showing stronger capability  during seed maturation would help the identification of the earliest stage when h3.3ko starts to show defects. However, the h3.3ko seeds were morphologically identical to WT  in mature seeds . Overall, accessible regions were mainly enriched at euchromatin in both WT and h3.3ko . 3278 and 3436 regions significantly lost or gained accessibility respectively , we confirmed a strong reduction of gene body CG methylation in mature h3.3ko seeds. In addition, CHG and CHH methylation levels at the gene body were also slightly reduced . At these regions, H3.3 enrichment levels were drastically reduced during germination and in seedlings, yet this was only coupled with a moderate reduction in chromatin accessibility , H3.3 remained deposited in WT during imbibition and in seedlings and is required to maintain their low chromatin accessibility and to repress cryptic transcription at these regions Fig. , indicatArabidopsis, both paternal and maternal-derived H3.3 is removed from the zygote genome a few hours after fertilization, followed by de novo synthesis of H3.3 before the zygote divides. This process was speculated to prevent the transmission of epigenetic memory from parents and meanwhile establish the zygotic epigenome critical for embryogenesis60. Interestingly, our genetic evidence suggests that H3.3 is not essential for early plant embryonic development. It is likely that H3.1 or other H3 proteins largely compensate for the loss of H3.3 at this stage, especially considering that H3.1 is abundantly produced during cell division, a dominant event in early embryogenesis61. After pattern formation at the torpedo stage, the embryo gradually ceases cell division and initiates maturation2. This is coupled with a strong reduction in H3.1 expression and an increase in H3.3 expression , it is unlikely that H3.3 impacts on other physiological processes that would in turn affect chromatin accessibility. Therefore, we propose the following model  at 22 °C. For seed germination, after-ripened mature seeds (seeds harvested and stored at room temperature for three months) were sown on 1/2 MS medium. After being kept at 4 °C for two days (stratification), seeds were subjected for germination in long days at ~22 °C. Three replicates were performed to assess the germination and growth of h3.3ko, at least 110 seeds were used for each replicate. h3.3ko seeds germinated within two months after sowing were considered as germinated, and the rest seeds were discarded. The germination date of the first 12 germinated seeds was scored.h3.3ko seeds were isolated by manually removing endosperm and testa. The isolated embryos were cultivated on 1/2 MS medium under long-day conditions.For dissected embryo growth analysis, embryos from after-ripened Col and h3.3ko complementation constructs, HTR5 or HTR13 coding sequences were placed under the control of HTR5 or HTR13 promoter in the binary vector pBlligatorR43, which contains a mature seed stage-specific mCherry fluorescence selection marker36. For the estradiol induction, HTR5 coding sequences were placed under the pER8 promoter in the binary vector pMDC775. To express HTR5 with at2S3 or H2B.S promoter, the at2S3 or H2B.S promoter sequences were first cloned into pGWB51076, followed by the insertion of HTR5 coding sequences.For h3.3ko;pER8::HTR5 seeds with or without estrogen (10 μM) treatment during imbibition, 50 seeds were assessed in each replicate and seeds were transferred onto a new 1/2 MS plate supplemented with or without estrogen every 5 days in case of estrogen degradation. The germination rates were scored after 2 month of imbibition. For the germination test of h3.3ko;pat2S3::HTR5 and h3.3ko;pH2B.S::HTR5 seeds, germination rates were scored after 1 month and 10 days imbibition respectively. At least 38 seeds were assessed in each replicate.For the germination test of Complete dried mature seeds were spread on a white background. After the image taken, seed size was measured with ImageJ. At least 47 seeds were scored for each genotype.h3.3ko mature seeds were homogenized in protein extraction buffer . Extracted proteins were resolved on 15% SDS-PAGE gel and stained with Coomassie Blue.One hundred and fifty Col or h3.3ko mature seeds were subjected to library preparation with MGIEasy RNA directional library preparation kit. Prepared libraries were sequenced on a DNBSEQ-T7 platform and paired-end 150 bp reads were generated.Total RNA was extracted from mature seeds (seeds stored for three months after harvesting) or imbibed seeds using Minibest plant RNA extraction kit (Takara). Three independent biological replicates were performed. Sequencing libraries were prepared with BGISEQ-500 RNA-seq library preparation kit according to the manufacturer’s instruction. For strand-specific RNA-seq, total RNA extracted from Col and 77. Reads were mapped to the Arabidopsis genome (TAIR10) using Hisat2 version 2.1.1078. The numbers of mapped reads were determined with SAMtools version 1.979, and are listed in Supplementary Table 80. Transcripts per million (TPM) values were generated using R. Differential gene expression analysis was performed using DESeq2 version 1.26.081. Genes that displayed a more than two-fold expression change and had a P adjust value <0.05 were considered as differentially expressed. For data visualization, bigwig coverage files were generated using deepTools utility bamCoverage82 with a bin size of 10 bp and normalized with CPM. The PCA plot was generated with plotPCA in DESeq2 version 1.26.0. The expression of HTR4, HTR5 and HTR8 during embryogenesis was extracted from the published datasets45. Gene ontology analysis was performed with DAVID (https://david.ncifcrf.gov)83. Clustering of genes associated with accessibility decreased regions in mature h3.3ko seeds compared with Col was performed based on their expression changes during germination in Col using K-Means clustering in R.Adapter trimming was performed and low-quality reads were filtered with fastp version 0.20.1PP2A was used as an endogenous control for normalization. Three independent biological replicates were performed for each line and condition. Primers used to amplify HTR5, HTR13, GA3OX1, GA3OX2, GA20OX1, GA20OX2, CYP707A2, and PP2A are listed in Supplementary Table HTR5 amplification primers were designed to only amplify the WT but not Crispr-mutated HTR5. Primers amplifying HTR5 and HTR13 were tested to have similar amplification efficiency.Total RNA was extracted from mature seeds (seeds stored for three months after harvesting), imbibed seeds or 7-day-old seedlings using Minibest plant RNA extraction kit (Takara). Reverse transcription was performed using HiScript III 1st Strand cDNA Synthesis Kit . Real-time quantitative PCR was conducted on an Applied Biosystems QuantStudio 6 Flex Real-Time PCR System using ChamQ Universal SYBR qPCR Master Mix . 52. Nuclei were extracted and mononucleosomes were generated with micrococcal nuclease (MNase)  digestion as previously described52. Immunoprecipitation was conducted with anti-GFP  or anti-H2A.Z51 antibody. After antibody incubation, Protein A Dynabeads  were added to collect the immunocomplexes. After washing, elution, reverse cross-linking and DNA purification, recovered DNA was subjected to library preparation with VAHTS universal DNA library prep kit for Illumina  according to the manufacturer’s instruction and sequenced with Illumina NovaSeq 6000 to generate paired-end 150 bp reads. ChIP-seq experiments were performed with two independent biological replicates.ChIP was performed with mature HTR5-GFP seeds (seeds stored for three months after harvesting) or seeds imbibed for different times. Materials were ground with liquid nitrogen into fine powder and immediately fixed with 1% formaldehyde77. Reads were mapped to the Arabidopsis genome (TAIR10) with Bowtie2 version 2.4.284, and filtered for duplicated reads by using Picard version 2.24.0 MarkDuplicates (https://github.com/broadinstitute/picard). The numbers of mapped reads were determined with SAMtools version 1.979, and are listed in Supplementary Table 82 with a bin size of 10 bp and normalized to input or sequencing depth using RPKM. Visualization was performed with IGV version 2.7.285. Heatmaps and average normalized ChIP-seq profiles were generated using deepTools utilities plotHeatmap and plotProfile. Published HTR5-GFP ChIP-seq data in seedlings were processed in the same manner35.Adapter trimming was performed and low-quality reads were filtered with fastp version 0.20.1Mature seeds were ground with liquid nitrogen into fine powder and immediately fixed with 1% formaldehyde. Chromatin was sheared by sonication and immunoprecipitation was performed with anti-RNA Pol II antibody abcam, ab26721). The amounts of immunoprecipitated DNA were quantified by real-time quantitative PCR. Three independent biological replicates were performed. Primers used for PCR amplification are listed in Supplementary Table 6721. TheMature seeds (seeds stored for three months after harvesting), seeds imbibed for different times or 10-day-old seedlings were chopped in lysis buffer . After being filtered with a 30μm filter (CellTrics), nuclei were stained with DAPI and subjected to fluorescence-activated cell sorting (FACS). 50,000 nuclei per sample were collected and mixed with Tn5 transposase , after 20 min of incubation at 37 °C, tagmented DNA was recovered with ChIP DNA Clean & Concentrator kit . After PCR amplification, sequencing libraries were purified with VAHTS DNA Clean Beads (Vazyme N411) and sequenced with Illumina NovaSeq 6000 to generate paired-end 150 bp reads. ATAC-seq experiments were performed with two independent biological replicates.77. Reads were mapped to the Arabidopsis genome (TAIR10) with Bowtie2 version 2.4.284. Reads mapped to chloroplast and mitochondria genome were removed and duplicated reads were filtered with Picard version 2.24.0 MarkDuplicates (https://github.com/broadinstitute/picard). The numbers of mapped reads were determined with SAMtools version 1.979, and are listed in Supplementary Table 86. The q-value cutoff for peak calling was 0.05. Only peaks identified from both biological replicates were kept. Peak distributions were analyzed with ChIPseeker version 1.22.187. Promoter refers to accessible regions located <2 kb upstream of the transcription start sites (TSS), downstream refers to accessible regions located <1 kb downstream of the transcription end sites (TES), Intergenic region refers to accessible regions located >2 kb upstream of TSS and more than 1 kb downstream of TES. To identify differentially enriched peaks, a common peak set was created by merging peaks in Col and h3.3ko, scores for open chromatin peak regions were calculated with deepTools utility multiBigwigSummary82, and differential peaks were called by requiring more than two-fold difference and P adjust <0.05. Genes were assigned to the peaks when the genic and 1 kb upstream regions overlapped with the peak regions for at least one base pair. For data visualization, bigwig coverage files were generated using deepTools utility bamCoverage82 with a bin size of 10 bp and normalized with RPKM. DNA motif analysis was performed with HOMER (version 4.11) using “findMotifs.pl“88.Adapter trimming was performed and low-quality reads were filtered with fastp version 0.20.1Genomic DNA was extracted from mature seeds (seeds stored for three months after harvesting) with Quick-DNA Plant/Seed Miniprep Kit . 200 ng fragmented genomic DNA (200 bp–500 bp) was used for library preparation with VAHTS universal Pro DNA library prep kit for Illumina . NEBNext Multiplex Oligos for Illumina  were used for adapter ligation. After purification with VAHTS DNA Clean Beads (Vazyme N411), bisulfite conversion was performed with EZ DNA Methylation-Gold Kit , followed by DNA purification and PCR amplification. Libraries were sequenced with Illumina NovaSeq 6000 to generate paired-end 150 bp reads. BS-seq experiments were performed with two independent biological replicates.77. Reads were mapped to the Arabidopsis genome (TAIR10) with BS-Seeker2 version 2.1.8 using default parameters89. Duplicated reads were filtered with Picard version 2.24.0 MarkDuplicates. The numbers of mapped reads are listed in Supplementary Table 90. Methylation data were visualized with ggplot2 in R (version 1.1.1106). CG DMRs were called with CGmaptools using dmr function with default parameters.Adapter trimming was performed and low-quality reads were filtered with fastp version 0.20.1Statistical tests performed are indicated in the figure captions.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesDataset 1Dataset 2Dataset 3Dataset 4Reporting Summary