Biology subjects

Zhu, Y.

Publications and source records attributed to Zhu, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

bioRxiv2018

Genomic characterization of additional cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

bioinformatics↗

bioRxiv2018

Proteomic Profile of TGF-β1 treated Lung Fibroblasts identifies Novel Markers of Activated Fibroblasts in the Silica Exposed Rat Lung

We performed liquid chromatography-tandem mass spectrometry (LC-MS/MS) on control and TGF-{beta}1-exposed rat lung fibroblasts to identify proteins differentially expressed between cell populations. A total of 1648 proteins were found to be differentially expressed in response to TGF-{beta}1 treatment and 196 proteins were expressed at [≥] 1.2 fold relative to control. Guided by these results, we next determined whether similar changes in protein expression were detectable in the rat lung after chronic exposure to silica dust. Of the five proteins selected for further analysis, we found that levels of all proteins were markedly increased in the silica-exposed rat lung, including the proteins for the very low density lipoprotein receptor (VLDLR) and the transmembrane (type I) heparin sulfate proteoglycan called syndecan 2 (SDC2). Because VLDLR and SDC2 have not, to our knowledge, been previously linked to the pathobiology of silicosis, we next examined whether knockdown of either gene altered responses to TGF-{beta}1 in MRC-5 lung fibroblasts. Interestingly, we found knockdown of either VLDLR or SDC2 dramatically reduced collagen production to TGF-{beta}1, suggesting that both proteins might play a novel role in myofibroblast biology and pathogenesis of silica-induced pulmonary fibrosis. In summary, our findings suggest that performing LC-MS/MS on TGF-{beta}1 stimulated lung fibroblasts can uncover novel molecular targets of activated myofibroblasts in silica-exposed lung.\n\nHighlightsWe identified 196 proteins differentially expressed between control and TGF-{beta}1 treated fibroblastsby LC-MS/MS.\n\nSeveral proteins identified by LC-MS/MS were also found to be differentially expressed in whole lung tissues and isolated fibroblasts after chronic exposure to silica dust, including the very low density lipoprotein receptor (VLDLR) and the transmembrane type I heparan sulfate proteoglycan called syndecan 2\n\nKnockdown of SDC2 or VLDLR markedly inhibited collagen production in MRC-5 fibroblasts, suggesting a novel pathogenic role for these proteins in myofibroblast biology.

pharmacology and toxicology↗

bioRxiv2018

5-Hydroxymethylcytosines from Circulating Cell-free DNA as Diagnostic and Prognostic Markers for Hepatocellular Carcinoma

The lack of highly sensitive and specific diagnostic biomarkers is a major contributor to the poor outcomes of patients with hepatocellular carcinoma (HCC), the second-most common cause of cancer deaths worldwide. We sought to develop a clinically convenient and minimally-invasive approach that can be deployed at scale for the sensitive, specific, and highly reliable diagnosis of HCC, and to evaluate the potential prognostic value of this approach. The study cohort comprised of 2,728 subjects, including HCC patients (n = 1,208), controls (n = 965) (572 healthy individuals and 393 patients with benign lesions), as well as patients with chronic hepatitis B infection (CHB) (n =291), liver cirrhosis (LC) (n = 110), and cholangiocarcinoma (CCC) (n = 154), was recruited from three major liver cancer hospitals in Shanghai, China, from July 2016 to November 2017. Circulating cell-free DNA (cfDNA) were collected from plasma samples from these individuals before surgery or any radical treatment. Applying our 5hmC-Seal technique, the summarized 5-hydroxymethylcytosine (5hmC) profiles in cfDNA were obtained. Molecular annotation analysis suggested that the profiled 5hmC loci in cfDNA were enriched with liver tissue-derived regulatory markers (e.g., H3K4me1). We showed that a weighted diagnostic score (wd-score) based on 117 genes detected using the summarized 5hmC profiles in cfDNA accurately distinguished HCC patients from controls (AUC = 95.1%; 95% CI, 93.6-96.5%) in the validation set, markedly outperformed -fetoprotein (AFP) with superior sensitivity. The wd-scores, which not only detected early BCLC stages (e.g., Stage 0: AUC = 96.2%; 95% CI,94.1-98.4%) and small tumors (e.g., < 2 cm: AUC = 95.7%; 95% CI: 93.6-97.7%), also showed high capacity for distinguishing HCC from non-cancer patients with CHB/LC (AUC = 80.2%; 95% CI, 75.8-84.6%). Moreover, the prognostic value of 5hmC markers in cfDNA was evaluated for HCC recurrence, showing that a weighted prognostic score (wp-score) based on 16 marker genes predicted the recurrence risk (HR = 6.67; 95% CI, 2.81-15.82, p < 0.0001) in 555 patients who have been followed up after surgery. In conclusion, we have developed and validated a robust 5hmC-based diagnostic model that can be applied routinely with clinically feasible amount of cfDNA (e.g., from ~2-5 mL of plasma). Applying this new approach in the clinic could significantly improve the clinical outcomes of HCC patients, for example by early detection of those patients with surgically resectable tumors or as a convenient disease surveillance tool for recurrence.

cancer biology↗

bioRxiv2018

Visualizing ATP-dependent substrate-processing dynamics of the human 26S proteasome at near-atomic resolution

The proteasome is an ATP-dependent 2.5-megadalton machine responsible for ubiquitylated protein degradation in all eukaryotic cells. Here we present cryo-EM structures of the substrate-engaged human 26S proteasome in seven conformational states at 2.8-3.6 [A] resolution, captured during polyubiquitylated protein degradation. These structures visualize a continuum of dynamic substrate-proteasome interactions from ubiquitin recognition to processive substrate translocation, during which ATP hydrolysis sequentially navigate through all six ATPase subunits. Three principle modes of coordinated ATP hydrolysis are observed, featuring hydrolytic events in two oppositely positioned ATPases, in two consecutive ATPases, and in one ATPase at a time. They regulate deubiquitylation, translocation initiation and processive unfolding of substrates, respectively. A collective power stroke in the ATPase motor is generated by synchronized ATP binding and ADP release in the substrate-engaging and disengaging ATPases, respectively. It is amplified largely in the substrate-disengaging ATPase, and propagated unidirectionally by coordinated ATP hydrolysis in the third consecutive ATPase.

biochemistry↗

bioRxiv2018

High-throughput single-cell transcriptome profiling of plant cell types

Single-cell transcriptome analysis of heterogeneous tissues can provide high-resolution windows into the genomic basis and spatiotemporal dynamics of developmental processes. Here we demonstrate the feasibility of high-throughput single-cell RNA sequencing of plant tissue using the Drop-seq approach. Profiling of >4,000 individual cells from the Arabidopsis root provides transcriptomes and marker genes for a diversity of cell types and illuminates the gene expression changes that occur across endodermis development.

plant biology↗

bioRxiv2018

Integrated modeling of peptide digestion and detection for the prediction of proteotypic peptides in targeted proteomics

MotivationThe selection of proteotypic peptides, i.e., detectable unique representatives of proteins of interest, is a key step in targeted shotgun proteomics. To date, much effort has been made to predict proteotypic peptides in the absence of mass spectrometry data. However, the performance of existing tools is still unsatisfactory. One crucial reason is their neglect of the close relationship between protein proteolytic digestion and peptide detection.\n\nResultsWe present an algorithm (named AP3) that firstly considers peptide digestion probability as a feature for proteotypic peptide prediction and demonstrated peptide digestion probability is the most important feature for accurate prediction of proteotypic peptides. AP3 showed higher accuracy than existing tools and accurately predicted the proteotypic peptides for a targeted proteomics assay, showing its great potential for assisting the design of targeted proteomics experiments.\n\nAvailability and ImplementationFreely available at http://fugroup.amss.ac.cn/software/AP3/AP3.html.\n\nContactyfu@amss.ac.cn or zhuyunping@gmail.com\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Genome-wide identification and functional analysis of circRNAs in Zea mays

Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs, which increasingly drawn researchers attention in recent years as their importance in regulating gene expression at the transcriptional and post-transcriptional levels. With the development of high-throughput sequencing and bioinformatics, circRNAs have been widely analysed in animals, but the understanding of characteristics and function of circRNAs is limited in plants, especially in maize. Here, 3715 unique circRNAs were predicted in Zea mays systematically, and 8 of 12 circRNAs were validated by experiments. By analysing circRNA sequence, the events of alternative circularization phenomenon were found prevailed in maize. By comparing circRNAs in different species, it showed that part circRNAs are conserved across species, for example, there are 273 circRNAs conserved between maize and rice. Although most of the circRNAs have low expression levels, we found 213 differential expressed circRNAs responding to heat, cold, or drought, and 1782 tissue-specific expressed circRNAs. The results showed that those circRNAs may have potential biological functions in specific situations. Finally, two different methods were used to search circRNA functions, which were based on circRNAs originated from protein-coding genes and circRNAs as miRNA decoys. 346 circRNAs could act as miRNA decoys, which might modulate the effects of multiple molecular functions, including binding, catalytic activity, oxidoreductase activity, and transmembrane transporter activity. Maize circRNAs were identified, classified and characterized systematically. We also explored circRNA functions, suggesting that circRNAs are involved in multiple molecular processes and play important roles in regulating of gene expression. Our results provide a rich resource for further study of maize circRNAs.

genetics↗

bioRxiv2018

Comparative genomic analysis revealed rapid differentiation in the pathogenicity-related gene repertoires between Pyricularia oryzae and Pyricularia penniseti isolated from a Pennisetum grass

BackgroundsPyricularia is a multispecies complex that could infect and cause severe blast disease on diverse hosts, including rice, wheat and many other grasses. Although the genome size of this fungal complex is small [~40 Mbp for Pyricularia oryzae (syn. Magnaporthe oryzae), and ~45 Mbp for P. grisea], the genome plasticity allows the fungus to jump and adapt to new hosts. Therefore, deciphering the genome basis of individual species could facilitate the evolutionary and genetic study of this fungus. However, except for the P. oryzae subgroup, many other species isolated from diverse hosts, such as the Pennisetum grasses, remain largely uncovered genetically.\n\nResultsHere, we report the genome sequence of a pyriform-shaped fungal strain P. penniseti P1609 isolated from a Pennisetum grass (JUJUNCAO) using PacBio SMRT sequencing technology. We performed a phylogenomic analysis of 28 Magnaporthales species and 5 non-Magnaporthales species and addressed P1609 into a Pyricularia subclade that is distant from P. oryzae. Comparative genomic analysis revealed that the pathogenicity-related gene repertoires were fairly different between P1609 and the P. oryzae strain 70-15, including the cloned avirulence genes, other putative secreted proteins, as well as some other predicted Pathogen-Host Interaction (PHI) genes. Genomic sequence comparison also identified many genomic rearrangements.\n\nConclusionTaken together, our results suggested that the genomic sequence of the P. penniseti P1609 could be a useful resource for the genetic study of the Pennisetum-infecting Pyricularia species.

genomics↗

bioRxiv2018

A Novel AuNPs-based Glucose Oxidase Mimic with Enhanced Activity and Selectivity Constructed by Molecular Imprinting and O2-Containing Nanoemulsion Embedding

In spite of the competitive advantages, inorganic nanoparticle mimic enzymes exhibit inherent disadvantages of limited catalytic efficiency and lacking selectivity. Here a AuNPs based mimic enzyme with significantly enhanced glucose selectivity and catalytic activity was constructed and demonstrated for the first time. Aminophenylboronic acid was employed to increase the affinity to glucose, as well as build molecular imprinted polymer shells to realize the selectivity for template molecules of glucose. Besides that, heptadecafluoro-n-octyl bromide nanoemulsion with the function of providing oxygen was introduced to gain a further improvement in catalytic activity, which successfully enhanced the catalytic efficiency (kcat/Km) up to about 270-fold. Based on the demonstrated catalytic properties, AuNPs based glucose oxidase mimics have been successfully applied in practical glucose detection of drinks and blood glucose.

biochemistry↗

bioRxiv2018

Machine learning techniques for classifying the mutagenic origins of point mutations

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a unique relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g. CpG hypermutability. We have directly evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted mutations originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation classes. A logistic regression classifier proved to be substantially more powerful at discriminating between the different mutation classes than alternatives. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under the BSD 3-clause license.

bioinformatics↗

bioRxiv2018

PANDA: A comprehensive and flexible tool for proteomics data quantitative analysis

SummaryAs the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on two complex da-tasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop ap-plication tool with user-friendly interfaces.\n\nAvailabilityPANDA is freely available for download at https://sourceforge.net/projects/panda-tools/.\n\nContact1987ccpacer@163.com and zhuyunping@gmail.com

bioinformatics↗

bioRxiv2018

LFAQ: towards unbiased label-free absolute protein quantification by predicting peptide quantitative factors

Mass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.

bioinformatics↗

bioRxiv2018

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high -fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe.

systems biology↗

bioRxiv2018

Molecular determinants of enterotoxigenic Escherichia coli heat-stable toxin secretion and delivery

Enterotoxigenic Escherichia coli (ETEC), a heterogeneous diarrheal pathovar defined by production of heat-labile (LT) and/or heat-stable (ST) toxins, remain major causes of mortality among children in developing regions, and cause substantial morbidity in individuals living in or traveling to endemic areas. Studies demonstrating a major burden of ST-producing ETEC have focused interest on ST toxoids for ETEC vaccines. We therefore examined fundamental aspects of ETEC ST biology using ETEC H10407, which carries estH and estP genes encoding ST-H and ST-P, respectively, in addition to eltAB genes responsible for LT. In this background, we found that deletion of estH significantly diminished cGMP activation in target epithelia, while deletion of estP had a surprisingly modest impact, and a dual estH/estP mutant was not appreciably different than the estH mutant. Nevertheless, either ST-H or ST-P recombinant peptides stimulated cGMP production. We also found that the TolC efflux protein was essential for both toxin secretion and delivery, providing a potential avenue for efflux inhibitors in treatment of acute diarrheal illness. In addition, we demonstrated that the EtpA adhesin is required for optimal delivery of ST and that antibodies against either the adhesin or ST-H significantly impaired toxin delivery and cGMP activation in target T84 cells. Finally, we used FLAG epitope fusions to demonstrate that the ST-H pro-peptide sequence is secreted by the bacteria, potentially providing additional targets for antibody neutralization. These studies collectively extend our understanding of ETEC pathogenesis and potentially inform additional avenues to mitigate disease by these common diarrheal pathogens.

microbiology↗

bioRxiv2018

A naturally-occurring dominant-negative competitor of Keap1 against its inhibition of Nrf2

Transcription factor Nrf2 is a master regulator of antioxidant and/or electrophile response elements (AREs/EpREs)driven genes involved in homeostasis, detoxification and adaptation to various stresses. The cytoprotective activity of Nrf2, though being oppositely involved in both cancer prevention and progression, is critically controlled by Keap1 (Kelch-like ECH-associated protein 1) as an adaptor subunit of Cullin 3-based E3 ubiquitin ligase, that is a key sensor for oxidative and electrophilic stresses. Now, we first report a novel naturally-occurring mutant of Keap1, designated Keap1{Delta}C, which lacks most of its C-terminal Nrf2-interacting domain essential for inhibition of the CNC-bZIP factor. This mutant Keap1{Delta}C is yielded by translation from an alternatively mRNA-spliced variant lacking the fourth and fifth exons, but their coding sequences are retained in the wild-type Keap1 locus (with no genomic deletions). Although this variant was found primarily in the human highly-metastatic hepatoma (MHCC97H) cells, it was widely expressed at very lower levels in all other cell lines examined. No matter whether Keap1{Delta}C retains less or no ability to inhibit Nrf2, it functions as a dominant-negative competitor of Keap1 against its inhibition of Nrf2-target genes. This is due to its antagonist effect on Keap1-mediated turnover of Nrf2 protein.

molecular biology↗

bioRxiv2018

Nach is a novel ancestral subfamily of the CNC-bZIP transcription factors selected during evolution from the marine bacteria to human

All living organisms have undergone the evolutionary selection under the changing natural environments to survive as diverse life forms. All life processes including normal homeostatic development and growth into organismic bodies with distinct cellular identifications, as well as their adaptive responses to various intracellular and environmental stresses, are tightly controlled by signaling of transcriptional networks towards regulation of cognate genes by many different transcription factors. Amongst them, one of the most conserved is the basic-region leucine zipper (bZIP) family. They play vital roles essential for cell proliferation, differentiation and maintenance in complex multicellular organisms. Notably, an unresolved divergence on the evolution of bZIP proteins is addressed here. By a combination of bioinformatics with genomics and molecular biology, we have demonstrated that two of the most ancestral family members classified into BATF and Jun subgroups are originated from viruses, albeit expansion and diversification of the bZIP superfamily occur in different vertebrates. Interestingly, a specific ancestral subfamily of bZIP proteins is identified and also designated Nach (Nrf and CNC homology) on account of their highly conservativity with NF-E2 p45 subunit-related factors Nrf1/2. Further experimental evidence reveals that Nach1/2 from the marine bacteria exerts distinctive functions from Nrf1/2 in the transcriptional ability to regulate antioxidant response element (ARE)-driven cytoprotective genes. Collectively, an insight into Nach/CNC-bZIP proteins provides a better understanding of distinct biological functions between these factors selected during evolution from the marine bacteria to human.\n\nSignificanceWe identified the novel ancestral subfamily (i.e. Nach) of CNC-bZIP transcription factors with highly conservativity from marine bacteria to human. Combination of bioinformatics with genomics and molecular biology demonstrated that two of the most ancestral family members classified into BATF and Jun subgroups are originated from viruses. The Jun and CNC subfamilies also share a common origin of these bZIP proteins. Further experimental evidence reveals that Nach1/2 from the marine bacteria exerts nuance functions from human Nrf1/2 in the transcriptional ability to regulate antioxidant response element (ARE)-driven genes, responsible for the host cytoprotection against inflammation and cancer. Overall, this study is of multidisciplinary interests to provide a better understanding of distinct biological functions between Nach/CNC-bZIPs selected during evolution.

ecology↗

bioRxiv2018

Arsenite exposure inhibits histone acetyltransferase p300 for attenuating H3K27ac at enhancers in low-dose exposed mouse embryonic fibroblast cells

Epidemiological investigations have validated the positive relationships between arsenic in drinking water and several cancers, including skin, liver and lung cancers. Besides genotoxicity, arsenic exposure-related pathogenesis of disease is widely considered through epigenetic mechanisms; however, the underlying mechanistic insight remains elusive. Herein we explore the initial epigenetic changes via acute low-dose arsenite exposures of mouse embryonic fibroblast (MEF) cells and Dot1L knockout MEF (Dot1L-/- for abbreviation) cells. Our RNA-seq and Western blot data demonstrated that, in both cell lines, acute low-dose arsenite exposure abolished histone acetyltransferase p300 at the RNA level and subsequent protein level. Consequently, p300-specific main target histone H3K27ac, a marker separating active from poised enhancers, decreased dramatically as validated by both Western blot and ChIP-seq analyses. Concomitantly, H3K4me1 as another well-known marker for enhancers also showed significant decreases, suggesting an underappreciated crosstalk between H3K4me1 and H3K27ac involved in arsenite exposure. Significantly, arsenite exposure-reduced H3K27ac and H3K4me1 inhibit the expression of genes including EP300 itself and Kruppel Like Factor 4(Klf4), a tumor suppressor gene. Collectively, our investigations identify p300 as an internal bridging factor within cells to sense external environmental arsenite exposure to alter chromatin, thereby changing gene transcription for disease pathogenesis.

pharmacology and toxicology↗