Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

Clostridium butyricum upregulates GPR109A/AMPK/PGC-1α and ameliorates acute pancreatitis-associated intestinal barrier injury in mice.

Acute pancreatitis frequently causes intestinal barrier damage, which aggravates pancreatitis. Although Clostridium butyricum exerts anti-inflammatory and protective effects on the intestinal barrier during acute pancreatitis, the underlying mechanism is unclear. The G protein-coupled receptors 109 A (GPR109A) and adenosine monophosphate-activated protein kinase (AMPK)/ peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1α) signaling pathways can potentially influence the integrity of the intestinal barrier. Our study generated acute pancreatitis mouse models via intraperitoneal injection of cerulein and lipopolysaccharides. After intervention with Clostridium butyricum, the model mice showed reduced small intestinal and colonic intestinal barrier damage, dysbiosis amelioration, and increased GPR109A/AMPK/PGC-1α expression. In conclusion, Clostridium butyricum could improve pancreatic and intestinal inflammation and pancreatic injury, and relieve acute pancreatitis-induced intestinal barrier damage in the small intestine and colon, which may be associated with GPR109A/AMPK/PGC-1α.

A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy.

BACKGROUND: Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. RESULT: The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS-FD CASR-RF and MWS-FD chemical characteristic band RF, respectively. Their respective R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. CONCLUSIONS: This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.

Exploring the effect of Clostridium butyricum on lung injury associated with acute pancreatitis in mice by combined 16S rRNA and metabolomics analysis.

OBJECTIVES: Acute lung injury is a critical complication of severe acute pancreatitis (SAP). The gut microbiota and its metabolites play an important role in SAP development and may provide new targets for AP-associated lung injury. Based on the ability to reverse AP injury, we proposed that Clostridium butyricum may reduce the potential for AP-associated lung injury by modulating with intestinal microbiota and related metabolic pathways. METHODS: An AP disease model was established in mice and treated with C. butyricum. The structure and composition of the intestinal microbiota in mouse feces were analyzed by 16 S rRNA gene sequencing. Non-targeted metabolite analysis was used to quantify the microbiota derivatives. The histopathology of mouse pancreas and lung tissues was examined using hematoxylin-eosin staining. Pancreatic and lung tissues from mice were stained with immunohistochemistry and protein immunoblotting to detect inflammatory factors IL-6, IL-1ß, and MCP-1. RESULTS: C. butyricum ameliorated the dysregulation of microbiota diversity in a model of AP combined with lung injury and affected fatty acid metabolism by lowering triglyceride levels, which were closely related to the alteration in the relative abundance of Erysipelatoclostridium and Akkermansia. In addition, C. butyricum treatment attenuated pathological damage in the pancreatic and lung tissues and significantly suppressed the expression of inflammatory factors in mice. CONCLUSIONS: C. butyricum may alleviate lung injury associated with AP by interfering with the relevant intestinal microbiota and modulating relevant metabolic pathways.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

Design, synthesis, and evaluation of pirfenidone-NSAIDs conjugates for the treatment of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal, chronic and progressive lung disease that threaten public health like many cancers. In this study, targeting the significant driving factor, inflammatory response, of the IPF, several conjugates of pirfenidone (PFD) with non-steroidal anti-inflammatory drugs (NSAIDs), along with their derivatives, were designed and synthesized to enhance the anti-IPF potency of PFD. Among these compounds, the (S)-ibuprofen-PFD conjugate 5b exhibited the most potent anti-proliferation activity against NIH3T3 cells, demonstrating up to a 343-fold improvement compared to PFD (IC50 = 0.04 mM vs IC50 = 13.72 mM). Notably, 5b exhibited superior activity in inhibiting the migration of macrophages induced by TGF-ß compared to PFD. Additionally, 5b demonstrated significant suppression of TGF-ß-induced migration of NIH3T3 cells and induction of apoptosis in NIH3T3 cells. Mechanistic studies revealed that 5b reduced the expression of collagen I and α-SMA by inhibiting the TGF-ß/SMAD3 pathway. In a bleomycin-induced pulmonary fibrosis model, treatment with 5b (40 mg/kg/day, orally) exhibited a more pronounced effect on reducing the degree of histopathological changes in lung tissue and alleviating collagen deposition compared to PFD (100 mg/kg/day, orally). Moreover, 5b could block the expression of collagen I, α-SMA, fibronectin, and pro-inflammatory factors (IL-6, IFN-γ, and TNF-α) compared to PFD, while demonstrating low toxicity in vivo. These preliminary results indicated that the hybridization of PFD with NSAIDs represented an effective modification approach to improve the anti-IPF potency of PFD. Consequently, 5b emerged as a promising candidate for the further development of new anti-IPF agents.

G-quadruplexes associated with R-loops promote CTCF binding.

CTCF is a critical regulator of genome architecture and gene expression that binds thousands of sites on chromatin. CTCF genomic localization is controlled by the recognition of a DNA sequence motif and regulated by DNA modifications. However, CTCF does not bind to all its potential sites in all cell types, raising the question of whether the underlying chromatin structure can regulate CTCF occupancy. Here, we report that R-loops facilitate CTCF binding through the formation of associated G-quadruplex (G4) structures. R-loops and G4s co-localize with CTCF at many genomic regions in mouse embryonic stem cells and promote CTCF binding to its cognate DNA motif in vitro. R-loop attenuation reduces CTCF binding in vivo. Deletion of a specific G4-forming motif in a gene reduces CTCF binding and alters gene expression. Conversely, chemical stabilization of G4s results in CTCF gains and accompanying alterations in chromatin organization, suggesting a pivotal role for G4 structures in reinforcing long-range genome interactions through CTCF.

Sex-Based Differences in the Presentation and Outcomes of Acute Pulmonary Embolism: A Systematic Review and Meta-Analysis.

BACKGROUND: The study aimed to review differences in the presentation and outcomes of acute pulmonary embolism (PE) between men and women. METHODS: PubMed, CENTRAL, Web of Science, and Embase were searched for studies comparing clinical features or outcomes of PE between men and women. Baseline comorbidities, risk factors, clinical features, and mortality rates were also compared between men and women. RESULTS: Fourteen studies were included. It was noted that men presented with PE at a statistically significantly younger age than women (P < .001). Smoking history (P < .001), lung disease (P = .004), malignancy (P = .02), and unprovoked PE (P = .004) were significantly more frequent among men than among women. There was no difference between the sexes for hypertension, diabetes, and a history of recent immobilization. A significantly higher proportion of men presented with chest pain (P = .02) and hemoptysis (P < .001), whereas syncope (P = .005) was more frequent in women. Compared with men, women had a higher proportion of high-risk PE (P = .003). There was no difference in the use of thrombolytic therapy or inferior vena cava filter. Neither crude nor adjusted mortality rates were significantly different between men and women. CONCLUSION: This review found that the age at presentation, comorbidities, and symptoms of PE differed between men and women. Limited data also suggest that women more frequently had high-risk PE compared with men, but the use of thrombolytic therapy did not differ between the 2 sexes. Importantly, both crude and adjusted data show that the mortality rate did not differ between men and women.

Controlled Synthesis of Flower-like Hierarchical NiCo-Layered Double Hydroxide Integrated with Metal-Organic Framework-Derived Co@C for Supercapacitors.

Layered double hydroxides (LDHs) have come to the foreground recently, considering their unique layered structure and short ion channels when they act as electrode materials for supercapacitors (SCs). However, due to their poor rate and cycle performance, they are not highly sought after in the market. Therefore, a flower-like hierarchical NiCo-LDH@C nanostructure with flake NiCo-LDH anchored on the carbon skeleton has emerged here, which is constructed by calcination and hydrothermal reaction and applying flake ZIF-67 as a precursor. In this structure, NiCo-LDH grows outward with abundant and homogeneously distributed Co nanoparticles on Co@C as nucleation sites, forming a hierarchical structure that is combined tightly with the carbon skeleton. The flower-like hierarchical nanostructures formed by the composite of metal-organic frameworks (MOFs) and LDHs have successfully enhanced the cycle and rate performance of LDH materials on the strength of strong structural stability, large specific surface area, and unique cooperative effect. The NiCo-LDH@C electrode displays superb electrochemical performance, with a specific capacitance of 2210.6 F g-1 at 1 A g-1 and 88.8% capacitance retention at 10 A g-1. Furthermore, the asymmetric supercapacitor (ASC) constructed with NiCo-LDH@C//RGO reveals a remarkable energy density of 45.02 W h kg-1 with a power density of 799.96 W kg-1. This project aims to propose a novel avenue to exploit NiCo-LDH electrode materials and provide theory and methodological guidance for deriving complex structures from MOF derivatives.

A squarate-pillared titanium oxide quantum sieve towards practical hydrogen isotope separation.

Separating deuterium from hydrogen isotope mixtures is of vital importance to develop nuclear energy industry, as well as other isotope-related advanced technologies. As one of the most promising alternatives to conventional techniques for deuterium purification, kinetic quantum sieving using porous materials has shown a great potential to address this challenging objective. From the knowledge gained in this field; it becomes clear that a quantum sieve encompassing a wide range of practical features in addition to its separation performance is highly demanded to approach the industrial level. Here, the rational design of an ultra-microporous squarate pillared titanium oxide hybrid framework has been achieved, of which we report the comprehensive assessment towards practical deuterium separation. The material not only displays a good performance combining high selectivity and volumetric uptake, reversible adsorption-desorption cycles, and facile regeneration in adsorptive sieving of deuterium, but also features a cost-effective green scalable synthesis using chemical feedstock, and a good stability (thermal, chemical, mechanical and radiolytic) under various working conditions. Our findings provide an overall assessment of the material for hydrogen isotope purification and the results represent a step forward towards next generation practical materials for quantum sieving of important gas isotopes.

Evaluation of breast cancer malignancy, prognostic factors and molecular subtypes using a continuous-time random-walk MR diffusion model.

PURPOSE: To assess the continuous-time random-walk (CTRW) model's diagnostic value in breast lesions and to explore the associations between the CTRW parameters and breast cancer pathologic factors. METHOD: This retrospective study included 85 patients (70 malignant and 18 benign lesions) who underwent 3.0T MRI examinations. Diffusion-weighted images (DWI) were acquired with 16b-values to fit the CTRW model. Three parameters (Dm, α, and ß) derived from CTRW and apparent diffusion coefficient (ADC) from DWI were compared among the benign/malignant lesions, molecular prognostic factors, and molecular subtypes by Mann-Whitney U test. Spearman correlation was used to evaluate the associations between the parameters and prognostic factors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) based on the diffusion parameters. RESULTS: All parameters, ADC, Dm, α, and ß were significantly lower in the malignant than benign lesions (P < 0.05). The combination of all the CTRW parameters (Dm, α, and ß) provided the highest AUC (0.833) and the best sensitivity (94.3%) in differentiating malignant status. And the positive status of estrogen receptor (ER) and progesterone receptor (PR) showed significantly lower ß compared with the negative counterparts (P < 0.05). The high Ki-67 expression produced significantly lower Dm and ADC values (P < 0.05). Additionally, combining multiple CTRW parameters improved the performance of diagnosing molecular subtypes of breast cancer. Moreover, Spearman correlations analysis showed that ß produced significant correlations with ER, PR and Ki-67 expression (P < 0.05). CONCLUSIONS: The CTRW parameters could be used as non-invasive quantitative imaging markers to evaluate breast lesions.

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio.

Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

Corrigendum: Clostridium butyricum protects against pancreatic and intestinal injury after severe acute pancreatitis via downregulation of MMP9.

[This corrects the article DOI: 10.3389/fphar.2022.919010.].

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 patients with solitary brain tumor (104 glioblastoma and 98 BM) were retrospectively obtained between February 2016 and September 2022. The data were divided into training and validation sets in a 7:3 ratio. An additional 32 patients (19 glioblastoma and 13 BM) from a different hospital were considered testing set. Single-MRI-sequence DL models were developed using the 3D residual network-18 architecture in tumoral (T model) and tumoral + peritumoral regions (T&P model). Furthermore, the combination model based on conventional MRI and DWI was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. The attention area of the model was visualized as a heatmap by gradient-weighted class activation mapping technique. For the single-MRI-sequence DL model, the T2WI sequence achieved the highest AUC in the validation set with either T models (0.889) or T&P models (0.934). In the combination models of the T&P model, the model of DWI combined with T2WI and contrast-enhanced T1WI showed increased AUC of 0.949 and 0.930 compared with that of single-MRI sequences in the validation set, respectively. And the highest AUC (0.956) was achieved by combined contrast-enhanced T1WI, T2WI, and DWI. In the heatmap, the central region of the tumoral was hotter and received more attention than other areas and was more important for differentiating glioblastoma from BM. A conventional MRI-based DL model could differentiate glioblastoma from solitary BM, and the combination models improved classification performance.

NDNet: Spacewise Multiscale Representation Learning via Neighbor Decoupling for Real-Time Driving Scene Parsing.

As a safety-critical application, autonomous driving requires high-quality semantic segmentation and real-time performance for deployment. Existing method commonly suffers from information loss and massive computational burden due to high-resolution input-output and multiscale learning scheme, which runs counter to the real-time requirements. In contrast to channelwise information modeling commonly adopted by modern networks, in this article, we propose a novel real-time driving scene parsing framework named NDNet from a novel perspective of spacewise neighbor decoupling (ND) and neighbor coupling (NC). We first define and implement the reversible operations called ND and NC, which realize lossless resolution conversion for complementary thumbnails sampling and collation to facilitate spatial modeling. Based on ND and NC, we further propose three modules, namely, local capturer and global dependence builder (LCGB), spacewise multiscale feature extractor (SMFE), and high-resolution semantic generator (HSG), which form the whole pipeline of NDNet. The LCGB serves as a stem block to preprocess the large-scale input for fast but lossless resolution reduction and extract initial features with global context. Then the SMFE is used for dense feature extraction and can obtain rich multiscale features in spatial dimension with less computational overhead. As for high-resolution semantic output, the HSG is designed for fast resolution reconstruction and adaptive semantic confusion amending. Experiments show the superiority of the proposed method. NDNet achieves the state-of-the-art performance on the Cityscapes dataset which reports 76.47% mIoU at 240 + frames/s and 78.8% mIoU at 150 + frames/s on the benchmark. Codes are available at https://github.com/LiShuTJ/NDNet.

Clostridium butyricum Protects Against Pancreatic and Intestinal Injury After Severe Acute Pancreatitis via Downregulation of MMP9.

Background: Evidence have shown that gut microbiota plays an important role in the development of severe acute pancreatitis (SAP). In addition, matrix metalloproteinase-9 (MMP9) plays an important role in intestinal injury in SAP. Thus, we aimed to determine whether gut microbiota could regulate the intestinal injury during SAP via modulating MMP9. Methods: In this study, the fecal samples of patients with SAP (n = 72) and healthy controls (n = 32) were analyzed by 16S rRNA gene sequencing. In addition, to investigate the association between gut microbiota and MMP9 in intestinal injury during SAP, we established MMP9 stable knockdown Caco2 and HT29 cells in vitro and generated a MMP9 knockout (MMP9-/-) mouse model of SAP in vivo. Results: We found that the abundance of Clostridium butyricum (C. butyricum) was significantly decreased in the SAP group. In addition, overexpression of MMP9 notably downregulated the expressions of tight junction proteins and upregulated the expressions of p-p38 and p-ERK in Caco2 and HT29 cells (p < 0.05). However, C. butyricum or butyrate treatment remarkably upregulated the expressions of tight junction proteins and downregulated the expressions of MMP9, p-p38 and p-ERK in MMP9-overexpressed Caco2 and HT29 cells (p < 0.05). Importantly, C. butyricum or butyrate could not affect the expressions of tight junction proteins, and MMP9, p-p38 and p-ERK proteins in MMP9-knockdown cells compared with MMP9-knockdown group. Consistently, C. butyricum or butyrate could not attenuate pancreatic and intestinal injury during SAP in MMP9-/- mice compared with the SAP group. Conclusion: Collectively, C. butyricum could protect against pancreatic and intestinal injury after SAP via downregulation of MMP9 in vitro and in vivo.

Inactivation of MrSir2 in Monascus ruber Influenced the Developmental Process and the Production of Monascus Azaphilone Pigments.

Monascus species are the producers of Monascus azaphilone pigments (MonAzPs) and lipid-lowering component Monacolin K, which have been widely used as food colorant and health products. In this study, silent information regulator 2 (Sir2) homolog (MrSir2) was characterized, and its impacts on the development and MonAzPs production of Monascus ruber were evaluated. Enzyme activity test in vitro showed that MrSir2 was an NAD+-dependent histone deacetylase. Compared to WT, Δmrsir2 strain accumulated more acetylated lysine residues of histone H3 subunit during its vegetative growth phase, and it exhibited accelerated mycelial aging, more spores, increased resistance to oxidative stress, and more MonAzPs production. RNA-Seq-based transcriptome analysis revealed that MrSir2 mainly regulated the gene expression in macromolecular metabolism such as carbohydrates, proteins, and nucleotides, as well as genes encoding cell wall synthesis and cell membrane component, indicating that MrSir2 probably facilitates the metabolic transition from the primary growth phase to the mycelial aging. Taken together, MrSir2 mainly targets H3 subunit at the vegetative growth phase and affects the development of M. ruber and MonAzPs production.

Targeted Nuclease Approaches for Mapping Native R-Loops.

R-loops are three-stranded, DNA:RNA hybrid-containing structures that form naturally throughout the genome as a consequence of transcription. Accurately determining the genomic locations and strand of origin of R-loops is critical to understanding their roles in gene regulation and disease. Here, we describe a nuclease-based protocol for genome-wide and strand-specific R-loop detection, which we term MapR. This method targets native R-loops for cleavage and release using a modified RNase H enzyme, followed by deep sequencing. An extension of the protocol, BisMapR, can additionally introduce strand specificity via non-denaturing bisulfite conversion of the R-loop's single-stranded DNA component. MapR and BisMapR identify R-loops with high resolution and low background, can be performed with low cell input, and require short experimental time.

Proximity labeling identifies a repertoire of site-specific R-loop modulators.

R-loops are three-stranded nucleic acid structures that accumulate on chromatin in neurological diseases and cancers and contribute to genome instability. Using a proximity-dependent labeling system, we identified distinct classes of proteins that regulate R-loops in vivo through different mechanisms. We show that ATRX suppresses R-loops by interacting with RNAs and preventing R-loop formation. Our proteomics screen also discovered an unexpected enrichment for proteins containing zinc fingers and homeodomains. One of the most consistently enriched proteins was activity-dependent neuroprotective protein (ADNP), which is frequently mutated in ASD and causal in ADNP syndrome. We find that ADNP resolves R-loops in vitro and that it is necessary to suppress R-loops in vivo at its genomic targets. Furthermore, deletion of the ADNP homeodomain severely diminishes R-loop resolution activity in vitro, results in R-loop accumulation at ADNP targets, and compromises neuronal differentiation. Notably, patient-derived human induced pluripotent stem cells that contain an ADNP syndrome-causing mutation exhibit R-loop and CTCF accumulation at ADNP targets. Our findings point to a specific role for ADNP-mediated R-loop resolution in physiological and pathological neuronal function and, more broadly, to a role for zinc finger and homeodomain proteins in R-loop regulation, with important implications for developmental disorders and cancers.