Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.

This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.

Clinically Approved Ferric Maltol: A Potent Nanozyme with Added Effect for High-Efficient Catalytic Disinfection.

Nanozyme has been proven to be an attractive and promising candidate to alleviate the current pressing medical problems. However, the unknown clinical safety and limited function beyond the catalysis of the most reported nanozymes cannot promise an ideal therapeutic outcome in further clinical application. Herein, we find that ferric maltol (FM), a clinically approved iron supplement synthesized through a facile scalable method, exhibits excellent peroxidase-like activity than natural horseradish peroxidase-like (HRP) and commonly reported Fe-based nanozymes, and also shows high antibacterial performance for methicillin-resistant Staphylococcus aureus (MRSA) elimination (100%) and wound disinfection. In addition, with added effects inherited from contained maltol, FM can accelerate skin barrier recovery. Therefore, the exploration of FM as a safe and desired nanozyme provides a timely alternative to current antibiotic therapy against drug-resistant bacteria.

Metal-phenolic networks derived CN-FeC hollow nanozyme with robust peroxidase-like activity for total antioxidant capacity detection.

A tannate-iron network-derived peroxidase-like catalyst loaded with Fe ions on carbon nitride (C3N4) was reported for detection of total antioxidant capacity (TAC) in food in this study. Metal-phenolic networks (MPNs) was employed to form a low coordination compound on C3N4, and calcined catalyst formed hollow structure with abundant and uniform Fe sites and surface folds. CN-FeC exhibited significant peroxidase-like activity and high substrate affinity. The homogeneous distribution of amorphous Fe elements on the C3N4 substrate provides more active sites, resulting in provided excellent catalytic activity to activate H2O2 to ·OH, 1O2 and O2·-. The established CN-FeC/TMB/H2O2 colorimetric system can detect AA in the concentration range of 5-40 µM, with the detection limits of 1.40 µM, respectively. It has good accuracy for the detection of vitamin C tablets, beverages. Taken together, this work shows that metal-phenolic networks can be an effective way to achieve efficient utilization of metal atoms and provides a promising idea for metal-phenolic networks in nanoparticle enzyme performance enhancement.

Transition Metal High-Entropy Nanozyme: Multi-Site Orbital Coupling Modulated High-Efficiency Peroxidase Mimics.

Strong substrate affinity and high catalytic efficiency are persistently pursued to generate high-performance nanozymes. Herein, with unique surface atomic configurations and distinct d-orbital coupling features of different metal components, a class of highly efficient MnFeCoNiCu transition metal high-entropy nanozymes (HEzymes) is prepared for the first time. Density functional theory calculations demonstrate that improved d-orbital coupling between different metals increases the electron density near the Fermi energy level (EF ) and shifts the position of the overall d-band center with respect to EF , thereby boosting the efficiency of site-to-site electron transfer while also enhancing the adsorption of oxygen intermediates during catalysis. As such, the proposed HEzymes exhibit superior substrate affinities and catalytic efficiencies comparable to that of natural horseradish peroxidase (HRP). Finally, HEzymes with superb peroxidase (POD)-like activity are used in biosensing and antibacterial applications. These results suggest that HEzymes have great potential as new-generation nanozymes.

Dual-Mechanism Tuned Engineered Polyphenols with Cascade Photocatalytic Self-Fenton Reaction for Sustainable Biocidal Coatings.

Traditional disposable personal protective equipment (PPE) only blocks pathogenic bacteria by mechanical filtration, with the risk of recontamination and transmission remaining. Herein, inspired by phenolic-enabled nanotechnology (PEN), we proposed engineered polyphenol coatings by plant-derived aromatic aldehydes and metal involvement, denoted as FQM, to obtain the desired photocatalysis-self-Fenton antibacterial performance. Experiments and theoretical analysis proved the dual mechanism of Fe-induced enhancement: (1) tuning of molecular structure realized improved optical properties; (2) Fe(III)/Fe(II) triggered photocatalytic cascade self-Fenton reaction. Mechanism study reveals FQM killing bacteria by direct-contact ROS attack and gene regulation. Further, the FQM was developed as the ideal antibacterial coating on different fabrics (cloth cotton, polyester, and N95 mask), killing more than 93% of bacteria after 5 cycles of use. Such photocatalysis-self-Fenton coatings based on engineered polyphenols endowed with desirable safety, sustainability, and efficient antibacterial features are promising solutions to meet the challenges of the currently available PPE.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.

OBJECTIVES: To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms. METHODS: A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated. RESULTS: 92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870-0.6178, all p < 0.001). CONCLUSIONS: The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

OBJECTIVES: To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. METHODS: A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. RESULTS: DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. CONCLUSIONS: DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. KEY POINTS: • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.

Hydrogel loading 2D montmorillonite exfoliated by anti-inflammatory Lycium barbarum L. polysaccharides for advanced wound dressing.

Designing wound dressing materials with hemocompatibility, suitable mechanical properties, outstanding hemostatic effects and anti-inflammatory activity is of great practical significance for wound management. Herein, a hemostatic hydrogel loaded with Lycium barbarum L. polysaccharide (LBP)-functionalized ultrathin MMT nanosheets (L-MMT NSs) was fabricated for efficient hemostasis and wound healing. Loading the L-MMT NSs into polyvinyl alcohol (PVA), the obtained P-L-MMT hydrogel exhibited a 3D porous structure with good swelling properties, cytocompatibility, hemocompatibility, and anti-inflammatory activity. Importantly, in vivo investigations demonstrated that the P-L-MMT hydrogel exerts outstanding hemostasis activity in the hemorrhaging mouse liver model and reduces tissue damage caused by inflammation to shorten wound healing time. Altogether, the convenient exfoliation and functionalization of bulk MMT using LBPs make this inexpensive and rising nanostructure more attractive in the application of nanomedicine. Moreover, due to the synergy between hemostasis and anti-inflammation, this newly developed multifunctional P-L-MMT hydrogel represents a promising material in biomedical fields.

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT.

OBJECTIVES: To evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT). METHODS: A standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS: The test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively. CONCLUSIONS: The majority of radiomics features were non-reproducible within and between SECT and DECT. KEY POINTS: • Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process.

Lycium Barbarum Polysaccharide-Iron (III) Chelate as Peroxidase Mimics for Total Antioxidant Capacity Assay of Fruit and Vegetable Food.

Quantitative evaluation of the antioxidant capacity of foods is of great significance for estimating food's nutritional value and preventing oxidative changes in food. Herein, we demonstrated an easy and selective colorimetric method for the total antioxidant capacity (TAC) assay based on 3,3',5,5'-tetramethyl-benzidine (TMB), hydrogen peroxide (H2O2) and synthetic Lycium barbarum polysaccharide-iron (III) chelate (LBPIC) with high peroxidase (POD)-like activity. The results of steady-state kinetics study showed that the Km values of LBPIC toward H2O2 and TMB were 5.54 mM and 0.16 mM, respectively. The detection parameters were optimized, and the linear interval and limit of detection (LOD) were determined to be 2-100 µM and 1.51 µM, respectively. Additionally, a subsequent study of the determination of TAC in six commercial fruit and vegetable beverages using the established method was successfully carried out. The results implied an expanded application of polysaccharide-iron (III) chelates with enzymatic activity in food antioxidant analysis and other biosensing fields.

Gentiana straminea Maxim. polysaccharide decolored via high-throughput graphene-based column and its anti-inflammatory activity.

Gentiana straminea Maxim. exhibits various biological activities. However, the purification and functions of polysaccharides in Gentiana straminea Maxim. have never been reported. Herein, by proposing a flexible 3D graphene-based decoloration column (3DD column), Gentiana straminea Maxim. polysaccharide (GMP) was high-throughput obtained and its anti-inflammatory activity was investigated. Benefiting from the large macroporous network of 3D NH2-graphene oxide hydrogel with selective adsorption towards pigments, the 3DD column exhibits high decoloration ratio (96.41%). In addition, the 3DD column provides superior practical functionality as compared to the traditional approaches, which are time-consuming and need toxic solvents, and exhibiting widespread-application for the purification of polysaccharide from other common plant species. More importantly, the decolored GMP as a natural product has promising anti-inflammatory activity on RAW264.7 cells without negative impact on cell viability. Overall, this work reveals a new functional polysaccharides and provides a flexible approach for polysaccharide decoloration, exhibiting a promising prospect for natural polysaccharides in practical application of pharmaceutical.

Photothermal-boosted effect of binary CuFe bimetallic magnetic MOF heterojunction for high-performance photo-Fenton degradation of organic pollutants.

Overcoming the relatively low catalytic activity and strict acid pH condition of common photo-Fenton reaction is the key to alleviate the serious global burden caused by common organic pollutants. Herein, a binary homologous bimetallic heterojunction of magnetic CuFe2O4@MIL-100(Fe, Cu) metal-organic frameworks (MCuFe MOF) with photothermal-boosted photo-Fenton activity is constructed as an ideal practical photo-Fenton catalyst for the degradation of organic pollutants. Through an in-situ derivation strategy, the formed homologous bimetallic heterojunction with binary redox couples can simultaneously improve the visible light harvesting capacity and expedite the separation and transfer of photogenerated electrons/holes pairs, leading to the continuous and rapid circulation of both FeIII/FeII and CuII/CuI redox couples. Notably, the heterojunction shows intrinsic photo-thermal conversion effect, which is found to be beneficial to boost the photo-Fenton activity. Impressively, MCuFe MOF shows remarkable catalytic performance towards the degradation of various organic pollutants by comprehensively increasing H2O2 decomposition efficiency and decreasing the required dosage of MCuFe MOF (0.05 g L-1) with a wide pH range (3.0-10.0). As such, a photo-Fenton catalyst consisting of binary homologous bimetallic heterojunction is first disclosed, as well as its photothermal-enhanced effect, which is expected to drive great advance in the degradation of organic pollutants for practical applications.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia.

BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). METHODS: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. RESULTS: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. CONCLUSIONS: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation.

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.

Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.

MOTIVATION: Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. RESULTS: We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. AVAILABILITY AND IMPLEMENTATION: Code can be downloaded from https://github.com/Zoesgithub/MtBNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[New Model for Intelligent Imaging Screening of Pulmonary Nodules].

The artificial intelligence based on medical aid diagnosis has been in full swing in these years. How to better and more safely utilize this new technology to improve the diagnostic efficiency and quality of doctors poses new challenges for our hospital management. This paper aims to explore relevant management problems and corresponding solutions from seven aspects:data security, system integration, technical parameters, risks, workflows and diagnosis results by introducing a new intelligent image screening system. After these management problems have been better solved, we found that the intelligent image screening system can improve the diagnostic efficiency and quality of doctors.

Imputation for transcription factor binding predictions based on deep learning.

Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.

JARID1B is a luminal lineage-driving oncogene in breast cancer.

Recurrent mutations in histone-modifying enzymes imply key roles in tumorigenesis, yet their functional relevance is largely unknown. Here, we show that JARID1B, encoding a histone H3 lysine 4 (H3K4) demethylase, is frequently amplified and overexpressed in luminal breast tumors and a somatic mutation in a basal-like breast cancer results in the gain of unique chromatin binding and luminal expression and splicing patterns. Downregulation of JARID1B in luminal cells induces basal genes expression and growth arrest, which is rescued by TGFß pathway inhibitors. Integrated JARID1B chromatin binding, H3K4 methylation, and expression profiles suggest a key function for JARID1B in luminal cell-specific expression programs. High luminal JARID1B activity is associated with poor outcome in patients with hormone receptor-positive breast tumors.

Canonical nucleosome organization at promoters forms during genome activation.

The organization of nucleosomes influences transcriptional activity by controlling accessibility of DNA binding proteins to the genome. Genome-wide nucleosome binding profiles have identified a canonical nucleosome organization at gene promoters, where arrays of well-positioned nucleosomes emanate from nucleosome-depleted regions. The mechanisms of formation and the function of canonical promoter nucleosome organization remain unclear. Here we analyze the genome-wide location of nucleosomes during zebrafish embryogenesis and show that well-positioned nucleosome arrays appear on thousands of promoters during the activation of the zygotic genome. The formation of canonical promoter nucleosome organization is independent of DNA sequence preference, transcriptional elongation, and robust RNA polymerase II (Pol II) binding. Instead, canonical promoter nucleosome organization correlates with the presence of histone H3 lysine 4 trimethylation (H3K4me3) and affects future transcriptional activation. These findings reveal that genome activation is central to the organization of nucleosome arrays during early embryogenesis.

Identifying ChIP-seq enrichment using MACS.

Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ∼3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.