PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% ∼ 23.3% in the area under precision-recall curve (PRC) compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pre-trained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

Global hypermethylation of the N6-methyladenosine RNA modification associated with apple heterografting

Grafting can facilitate better scion performance and is widely used in plants. Numerous studies have studied the involvement of mRNAs, small RNAs, and epigenetic regulations in the grafting process. However, it remains unclear whether the mRNA N6-methyladenosine (m6A) modification participates in the apple (Malus x domestica Borkh.) grafting process. Here, we decoded the landscape of m6A modification profiles in 'Golden delicious' (a cultivar, Gd) and Malus prunifolia 'Fupingqiuzi' (a unique rootstock with resistance to environmental stresses, Mp), as well as their heterografted and self-grafted plants. Interestingly, global hypermethylation of m6A occurred in both heterografted scion and rootstock compared with their self-grafting controls. Gene Ontology (GO) term enrichment analysis showed that grafting-induced differentially m6A-modified genes were mainly involved in RNA processing, epigenetic regulation, stress response, and development. Differentially m6A-modified genes harboring expression alterations were mainly involved in various stress responses and fatty acid metabolism. Furthermore, grafting-induced mobile mRNAs with m6A and gene expression alterations mainly participated in ABA synthesis and transport (e.g. carotenoid cleavage dioxygenase 1 [CCD1] and ATP-binding cassette G22 [ABCG22]) and abiotic and biotic stress responses, which might contribute to the better performance of heterografted plants. Additionally, the DNA methylome analysis also demonstrated the DNA methylation alterations during grafting. Downregulated expression of m6A methyltransferase gene MdMTA (ortholog of METTL3) in apples induced the global m6A hypomethylation and distinctly activated the expression level of DNA demethylase gene MdROS1 (REPRESSOR OF SILENCING 1) showing the possible association between m6A and 5mC methylation in apples. Our results reveal the m6A modification profiles in the apple grafting process and enhance our understanding of the m6A regulatory mechanism in plant biological processes.

[Clinical characteristics and vaccination status of SARS-CoV-2 Omicron variant infected children]

Objective: To investigate the clinical characteristics and vaccination status of SARS-CoV-2 Omicron variant infected children. Methods: A total of 105 children infected with Omicron variant admitted to Tianjin Haihe Hospital (designated referral hospital for SARS-CoV-2 infection in Tianjin) from January 8, 2022 to February 3 were included for a retrospective study. The cases were divided into pneumonia group and non-pneumonia group according to chest imaging. Based on the doses of inactivated SARS-CoV-2 vaccine, the children who completed SARS-CoV-2 antibody test within 3 days after hospitalization were divided into 2 dose group and<2 dose group.Rank sum test and Chi-square test were used for the comparison between the groups. Results: The age of these 105 children was 10 (8, 11) years on admission, 53 children were males and 52 were females. Eighty-seven cases (82.9%) had mild symptoms, 13 cases (12.4%) had common symptoms and 5 cases (4.8%) were asymptomatic. Ninety-one cases (86.7%) completed 2 doses vaccination. The clinical symptoms were characterized by cough (74 cases, 70.5%), fever (58 cases, 55.2%), sore or dry throat (34 cases, 32.4%), nasal congestion (28 cases, 26.7%), rhinorrhea (23 cases, 21.9%). None of the children received antivirals, steroids, immunosuppressant or oxygen therapy. Seventy-six cases(72.4%) received traditional Chinese medicine treatment. The pneumonia group had a higher rate of positive SARS-CoV-2 IgG within 1 day after admission (13/13 vs. 87.0% (80/92), χ2=42.81, P<0.001) than the non-pneumonia group. Among the 62 children who had serial SARS-CoV-2 antibody tests within 3 days after hospitalization, Compared to the<2 dose group, the 2 dose group had a higher rate of nucleic acid conversion within 16 days after onset and a higher rate of positive SARS-CoV-2 IgG 1 day after admission and 3 days after hospitalization (96.4% (54/56) vs. 4/6, 100.0% (56/56) vs. 2/6, 100.0% (56/56) vs. 3/6, all P<0.05). Conclusions: Most children infected with Omicron variant have mild symptoms, mainly respiratory infection symptoms. The proportion of SARS-CoV-2 antibody IgG positive in children who have received 2 doses of inactivated SARS-CoV-2 vaccines is higher,and the time of whose nucleic acid conversion may be shortened.

Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA prediction tools. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users in selecting promising miRNA candidates in an interactive mode, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.

Relationship between the elastic modulus of the cage material and the biomechanical properties of transforaminal lumbar interbody fusion: A logarithmic regression analysis based on parametric finite element simulations

BACKGROUND AND OBJECTIVE

Conventional method for evaluating the biomechanical effects of a specific elastic modulus of cage (cage-E) on spinal fusions requires establishing a "one-on-one" biomechanical model, which seems laborious and inefficient when dealing with the emergence of numerous cage materials with various cage-Es. We aim to offer a much convenient method to instantly predicting the biomechanical effects of any targeted cage-E on transforaminal lumbar interbody fusion (TLIF) by using a parametric finite element (FE) analysis to determining the regression relationship between cage-E and biomechanical properties of TLIF.

MATERIALS AND METHODS

A L4/5 FE TLIF construct was modeled. Cage-E was linearly increased from 0.1 GPa (cancellous bone) to 110 GPa (titanium alloy). The function equations for assessing the influence of cage-E on the biomechanical indexes of TLIF were established using a logarithmic regression analysis.

EXPERIMENTAL RESULTS

As cage-E increased from 0.1 GPa to 110 GPa, all the biomechanical indexes initially increased or decayed rapidly, and then slowed over time. Logarithmic regression models and functional equations were successfully established between cage-E and these indexes (P<0.0001). Their determination coefficients ranged from 0.72 to 0.99. The range of motions decreased from 0.37-1.10° to 0.20-1.07°. The mean stresses of the central and peripheral grafts reduced from 0.10-0.41 and 0.25-0.42 MPa to 0.03-0.04 and 0.19-0.27 MPa, respectively. In addition, the maximum stresses of the screw-bone interface and posterior instrumentation reduced from 11.76-25.04 and 8.91-84.68 MPa to 9.71-18.92 and 6.99-70.59 MPa, respectively. Finally, the maximum stresses of the cage and endplate increased from 0.28-1.35 MPa and 3.90-8.63 MPa to 14.86-36.16 MPa and 11.01-36.55 MPa, respectively.

CONCLUSIONS

The decrease of cage-E reduces the risks of cage subsidence, cage breakage, and pseudarthrosis, while increasing the risk of instrumentation failure. The logarithmic regression models optimally demonstrate the relationship between cage-E and biomechanical properties of TLIF. The functional equations based on these models can be adopted to predict the biomechanical effects of any targeted cage-Es on TLIF, which effectively simplifies the procedures for the biomechanical assessments of cage materials.

Rare-Earth-Metal Complexes Bearing an Iminodibenzyl-PNP Pincer Ligand: Synthesis and Reactivity toward 3,4-Selective Polymerization of 1,3-Dienes

A PNP-pincer ligand provides a versatile ligation framework, which is highly useful in organometallic chemistry and catalytic chemistry. In this work, by a de novo strategy, a simple and efficient synthetic pathway, has been developed to prepare the new iminodibenzyl-based PNP pincer proligand ^imin-RPNP(Li or H) (R = isopropyl, phenyl). By employing salt metathesis or direct alkyl elimination, we successfully synthesized a series of iminodibenzyl-PNP rare-earth-metal (Ln = Sc, Y, Dy, Ho, Er, Tm, Lu) complexes and characterized them by NMR and X-ray diffraction analyses. Upon addition of a borate and triisobutylaluminum (TIBA), the rare-earth-metal complexes 2-Y, 2-Dy, 2-Ho, 2-Er, and 2-Tm bearing the ^imin-PhPNP ligand exhibited unexpectedly high 3,4-selectivity (up to 95%) for the polymerization of 1,3-dienes (isoprene and myrcene); in particular, the chosen yttrium complex 2-Y promoted the 1,3-diene polymerization in a living manner. A computational study suggested that the sterically congested configuration around the metal center imposed by the ^imin-RPNP ligand might be the main reason for this unusual selectivity.

Access to Derivatizable Octahydrofluorenyl Ligand: Half-Sandwich Rare-Earth Metal Complexes for Highly Syndiospecific (Co)Polymerization of Styrene

A simple and facile synthetic pathway for accessing new derivatizable bulky-demanding octahydrofluorenyl (OHF) ligands has been developed, and a series of half-sandwich rare-earth metal (Sc, Y, Lu) complexes bearing the OHF ancillary ligands have been synthesized. In conjunction with a borate, the OHF-ligated Sc complexes exhibited high catalytic activity for styrene (co)polymerization to afford polymers with highly syndiotactic polystyrene sequence (>99% rrrr).

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.

Generalized temperature dependence model for anammox process kinetics

Temperature is a key operational factor influencing the anammox process kinetics. In particular, at temperatures below 15 °C, the specific anammox activity (SAA) considerably decreases. This study aimed to describe the temperature dependence of the anammox process kinetics in the temperature range from 10 to 55 °C, including the specific characteristics of "cold anammox". The commonly used Arrhenius and extended and modified Ratkowsky equations were examined. The Ratkowsky equations yielded a strong correlation (coefficient of determination, R² = 0.93-0.96) between the measured and predicted data over the analyzed temperature range (10-55 °C). However, these equations could not correctly reflect the anammox temperature dependence at temperatures below 15 °C (R² = 0.36-0.48). Therefore, a new generalized temperature model was proposed. The generalized temperature equation (GTE) considered the division of the analyzed temperature range into three temperature ranges: 10-15 °C, 15-35 °C and 35-55 °C. The ranges correspond to "cold anammox", "(low) mesophilic anammox" and "thermophilic anammox". The applied approach yielded a strong correlation between the measured and predicted SAA (R² = 0.97) over the temperature range from 10 to 55 °C and over the low-temperature range from 10 to 15 °C (R² = 0.99). Overall, the GTE could enhance the predictions of the temperature dependence of the anammox process kinetics. The GTE can help examine anammox-based bioaugmentation systems operating at both high temperatures (sidestream reactors) and low temperatures (mainstream reactors).

Detection of Neisseria gonorrhoeae and Chlamydia trachomatis infections in pregnant women by multiplex recombinase polymerase amplification

Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) are the main pathogenic microorganisms causing sexually transmitted infections. In this study, a multiplex thermostable recombinase polymerase amplification-lateral flow detection (RPA-LFD) assay was established, and the reaction conditions such as the ratio of primer concentration, magnesium ion concentration, amplification time and template DNA concentration in the multiplex RPA reaction were optimized. The optimized multiplex RPA-LFD method was used to detect both CT and NG positive control plasmids, and it was found that the LFD could be used to obtain visible results when the plasmid copy number was only 200. The sensitivity of the multiplex RPA-LFD method used for clinical samples was 85.62 (95% CI at 53.66–97.29) for NG detection and 90.90 (95% CI at 57.12–99.52) for CT detection.

Integrative sequencing discovers an ATF1-motif enriched molecular signature that differentiates hyalinizing clear cell carcinoma from mucoepidemoid carcinoma

OBJECTIVES

Salivary gland tumors are comprised of a diverse group of malignancies with widely varying prognoses. These cancers can be difficult to differentiate, especially in cases with limited potential for immunohistochemistry (IHC)-based characterization. Here, we sought to define the molecular profile of a rare salivary gland cancer called hyalinizing clear cell carcinoma (HCCC), and identify a molecular gene signature capable of distinguishing between HCCC and the histopathologically similar disease, mucoepidermoid carcinoma (MEC).

MATERIALS AND METHODS

We performed the first integrated full characterization of five independent HCCC cases.

RESULTS

We discovered insulin-like growth factor alterations and aberrant IGF2 and/or IGF1R expression in HCCC tumors, suggesting a potential dependence on this pathway. Further, we identified a 354 gene signature that differentiated HCCC from MEC, and was significantly enriched for genes with an ATF1 binding motif in their promoters, supporting a transcriptional pathogenic mechanism of the characteristic EWSR1-ATF1 fusion found in these tumors. Of the differentially expressed genes, IGF1R, SGK1 and SGK3 were found to be elevated in the HCCCs relative to MECs. Finally, analysis of immune checkpoints and subsequent IHC demonstrated that CXCR4 protein was elevated in several of the HCCC cases.

CONCLUSION

Collectively, our data identify an ATF1-motif enriched gene signature that may have clinical utility for molecular differentiation of HCCCs from other salivary gland tumors and discover potential actionable alterations that may benefit the clinical care of recurrent HCCC patients.

deepEA: a containerized web server for interactive analysis of epitranscriptome sequencing data

The containerized web server deepEA allows interactive, reproducible, and collaborative analysis of epitranscriptome sequencing data.

Rigid Acridane-Based Pincer Supported Rare-Earth Complexes for cis-1,4-Polymerization of 1,3-Conjugated Dienes

A convenient synthetic route has been developed for preparing the novel rigid 4,5-(PR₂)2-2,7,9,9-tetramethylacridane-based pincer ligands (^acri-RPNP; R = ⁱPr and Ph), and the first rare-earth (Ln = Y, Lu) alkyl complexes bearing the ^acri-RPNP ligands were synthesized by a salt metathesis reaction (for the isopropyl-substituent ^acri-iPrPNP complexes, 1-Ln) or direct alkylation (for the phenyl-substituent ^acri-PhPNP complexes, 2-Ln). For both 1-Ln and 2-Ln, the NMR spectroscopy and X-ray diffraction study confirmed the successful coordination of the ^acri-RPNP ligand to the central metal ion in a tridentate manner via the two phosphine and the nitrogen donors. In contrast to 1-Ln that are solvent-free complexes, the metal centers in 2-Ln are each coordinated with one tetrahydrofuran molecule. Upon activation by [Ph₃C][B(C₆F₅)₄], 1-Y and 2-Lu could catalyze the living polymerization of isoprene and β-myrcene with high catalytic activity and high cis-1,4-selectivity (up to 92.3% for isoprene and 98.5% for β-myrcene). Moreover, the 1-Y/[Ph₃C][B(C₆F₅)₄] catalytic system also could promote the polymerization of butadiene and its copolymerization with isoprene to produce copolymers with high cis-1,4-selectivity and narrow polydispersity.

[The value of conventional magnetic resonance imaging based radiomic model in predicting the texture of pituitary macroadenoma]

Objective: To investigate the value of conventional magnetic resonance imaging (MRI) based radiomic model in predicting the texture of pituitary macroadenoma. Methods: The complete data of 101 patients with pituitary macroadenoma confirmed by surgery and pathology in Yijishan Hospital of Wannan Medical College from December 2014 to December 2019 were retrospectively analyzed. According to the texture of the intraoperative pituitary tumor, patients were divided into soft group (n=58) and hard group (n=43). They were randomly divided into training group (n=72) and validation group (n=29) at a ratio of 7∶3. All patients underwent conventional MRI scan of the pituitary gland. Itk-snap software was used to manually outline the T(1)-weighted image (T(1)WI), T(2)-weighted image (T(2)WI) and enhanced T(1)WI image section by section on tumor area of interest (ROI) and perform three-dimensional fusion. Then AK software was imported to extract texture features. The regression analysis methods of minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection and radiomic signature establishment. The reliability of the model was verified by 100 leave-group-out cross validation (LGOCV), and the predictive ability of the model was evaluated by drawing the receiver operating characteristic (ROC) curve. The decision curve analysis (DCA) was used to evaluate the clinical application value of the model. Results: The AUC (Area Under the ROC Curve) (95%CI) values of T1WI, T2WI, enhanced T1WI, and the combined sequence model to predict the texture of pituitary macroadenomas in the training and validation groups were 0.91 (0.84-0.98) and 0.90 (0.78-1.00), 0.86 (0.78-0.95) and 0.83 (0.64-1.00), 0.90 (0.83-0.97) and 0.89 (0.77-1.00),0.92 (0.85-0.98) and 0.91 (0.79-1.00), respectively. DCA demonstrated that T(1)WI, T(2)WI, enhanced T(1)WI, and combined sequence model all had good net benefits in clinical practice. Conclusions: T(1)WI, T(2)WI, enhanced T(1)WI, and combined sequence model of conventional MRI all had high efficacy in predicting the texture of pituitary macroadenoma, which provided a new quantitative method for predicting the texture of pituitary macroadenoma.

Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS

Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.

Rationale for the advancement of PI3K pathway inhibitors for personalized chordoma therapy

PURPOSE

Chordomas are rare and serious tumors with few effective treatments outside of aggressive surgery and radiation. Targeted therapies may present a more effective option for a subset of patients with lesions possessing certain genetic biomarkers.

METHODS

A small molecule inhibitor library was tested in patient-derived UM-Chor1 cells to identify targeted therapies with potential efficacy. Targeted exome sequencing of UM-Chor1 and UM-Chor2 cells was performed to investigate genetic aberrations in relevant pathways. Chordoma cell lines were treated with inhibitors of the phosphotidylinositol 3-kinase (PI3K), epidermal growth factor receptor (EGFR), and cyclin dependent kinase (CDK) pathways, and responses were determined using resazurin cell viability assays, Annexin V apoptosis assays, and western blotting. Pan-PI3K inhibitor BKM120 was also tested in five chordoma xenograft models.

RESULTS

Unbiased small molecule profiling nominated PI3K-AKT-mTOR pathway inhibitors as a promising therapy in chordoma, and genetic analyses of UM-Chor1 and UM-Chor2 cell lines revealed aberrations in PTEN, EGFR, and CDKN2A. Treatment of UM-Chor1 and UM-Chor2 with targeted PI3K, EGFR, and CDK inhibitors inhibited growth and proliferation and induced apoptosis more robustly than imatinib, a currently used chordoma therapy. Furthermore, BKM120 significantly inhibited tumor growth in a subset of the xenograft models tested.

CONCLUSION

Targeted therapies, especially those inhibiting PI3K, display promising effects in multiple chordoma cell line and xenograft models. Nevertheless, the limited effects of PI3K, EGFR, and CDK targeting agents in other models reveal the presence of resistance mechanisms, which motivates future research to both identify biomarkers of response and develop combination therapies.

[Diagnostic value of optical imaging combined with indocyanine green-guided sentinel lymph node biopsy in gastric cancer: a meta-analysis]

Objective: To systematically evaluate the diagnostic value of optical imaging combined with indocyanine green (ICG)-guided sentinel lymph node (SLN) biopsy in gastric cancer, and to identify potential factors that would influence diagnostic accuracy. Methods: Study was carried out by searching the electronic database of PubMed, Embase, Medline, Web of Science, and the Cochrane Library with keywords as "gastric/stomach" and "cancer/carcinoma/tumor/tumour/adenocarcinoma/neoplasm" and "sentinel lymph node" and "near-infrared/NIR or fluorescent imaging" and "indocyanine green/ICG" . Literature inclusion criteria: (1) gastric cancer clinical stage was cT0-3; (2) clinical stage determined by at least 2 kinds of imaging modalities; (3) optical imaging (near-infrared or fluorescence imaging) combined with ICG-guided SLN biopsy; (4) prospective study to predict lymph node metastasis; (5) intraoperative or postoperative pathology for all lymph nodes removed; (6) patients number in the literature >10 cases. Exclusion criteria: (1) patients with a history of ICG allergy or chemoradiotherapy; (2) previous history of endoscopic mucosal resection or endoscopic submucosal dissection; (3) patients with a variety of gastrointestinal tumor; (4) case reports, conference abstracts, clinical guidelines, editorials, reviews, meta-analysis and correspondence letters; (5) in vitro or animal experiments; (6) insufficient diagnostic efficacy data. The meta-analysis was performed in the Stata12.0 software using the "bivariate mixed-effects model" combined with the "midas" command to pool the data. Information such as true positive value, false positive value, false negative value, and true negative value of each included articles were extracted. The literature quality assessment map was drawn to describe the overall quality of the articles; the heterogeneity analysis was performed with the forest map, with P<0.01 considered as statistical significance; the funnel plot was used to describe publication bias, with P<0.1 considered as statistically significant. Area under curve (AUC) of summary receiver operator characteristic (SROC) was used to describe the diagnostic accuracy and the AUC closer to 1 indicated higher diagnostic accuracy. If there was heterogeneity (I(2)>50%) among studies, regression analysis and subgroup analysis were performed. P<0.05 was considered as statistically significant. Results: A total of 15 studies (1020 patients) were included. The optical imaging contained near-infrared (NIR) and fluorescent imaging (FI). The diagnostic value of optical imaging combined with ICG-guided SLN biopsy in gastric cancer was as follows: the pooled sensitivity (Sen) was 0.95 (95% CI: 0.82 to 0.99), specificity (Spe) was 1.00 (95% CI: 0.92 to 1.00), positive likelihood ratio (PLR) was 30.39 (95% CI: 9.14 to 101.06), negative likelihood ratio (NLR) was 0.05 (95% CI:0.01 to 0.20), diagnostic odds ratio (DOR) was 225.54 (95% CI: 88.81 to 572.77), AUC was 1.00 (95% CI: 0.99 to 1.00), threshold value was sensitivity=0.95 (95% CI: 0.82 to 0.99) and specificity=1.00 (95% CI: 0.92 to 1.00). Deeks method revealed DOR funnel plot of SLN biopsy was not asymmetrical obviously with significant difference (P=0.01), which indicated remarkable publishing bias. Meta-subgroup analysis showed that compared to FI, NIR imaging had higher sensitivity (0.98 vs. 0.73); compared to 0 minutes, optical imaging performed 20 minutes after ICG injection had higher sensitivity (0.98 vs. 0.70); compared to mean detected number of SLN of 4, mean detected number≥4 had higher sensitivity (0.96 vs. 0.68); compared to HE stain, immunohistochemistry + HE had higher sensitivity (0.99 vs. 0.84); compared to subserous injection of ICG, submucosa injection of ICG had higher sensitivity (0.98 vs. 0.40); compared to injection of 5 g/L ICG, 0.5 g/L and 0.05 g/L had higher sensitivity (0.98 vs. 0.83); compared to cT2-3 tumor, early stage (cT1) tumor had higher sensitivity (0.96 vs. 0.72); compared to ≤ enrolled 26 cases in the study, > 26 cases had higher sensitivity (0.96 vs. 0.65); compared to papers before 2010, papers after 2010 had higher sensitivity (0.97 vs. 0.81); whose differences were all significant. Sensitivity differences between mean tumor diameter of ≤30 cm and >30 cm, open surgery and laparoscopic surgery, lymph node regional dissection and retrieved dissection were not significant (all P>0.05). Conclusions: Optical imaging combined with ICG-guided SLN biopsy is clinically feasible, and especially suitable for early gastric cancer. However, the ICG being used in current studies may be overdosed. Higher sensitivity may be achieved from NIR imaging when compared with FI method.

PEA: an integrated R toolkit for plant epitranscriptome analysis

Motivation

The epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.

Results

Here, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery and gene functional enrichment analysis. PEA also takes advantage of machine learning (ML) technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction and annotation and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics.

Availability and implementation

PEA Docker image is available at https://hub.docker.com/r/malab/pea, source codes and user manual are available at https://github.com/cma2015/PEA.

Supplementary information

Supplementary data are available at Bioinformatics online.

P6499Genetic screening in 109 adult Chinese patients with thoracic aortic aneurysm and dissection

Background

Thoracic aortic aneurysm and dissection (TAAD) comprises a heterogenous group of cardiovascular urgencies, which could be further categorized into syndromic and non-syndromic entities. The accurate and timely identification of culprit genetic variants is of grave importance for TAAD patients, since different genetic defects have been associated with different risks for aortic dissection, thus different thresholds for preventive aortic intervention.

Purpose

With the advent of next-generation sequencing (NGS) techniques, accumulating records of rare variants have been found in TAAD patients, while inadequate functional validation also makes it difficult to give proper counsel for individual TAAD patients. Therefore, it is necessary for us to start re-evaluating clinical applications of genetic screening strategies in specific patient populations.

Methods

From June 2016 to July 2017, genetic screening using an NGS-based panel of 18 candidate genes (FBN1, FBN2, TGFBR1, TGFBR2, TGFB2, TGFB3, SMAD3, COL1A1, COL3A1, COL5A2, COL5A1, PLOD1, ACTA2, MYH11, MYLK, PRKG1, MFAP5, and SKI) was applied in 109 adult TAAD patients from our institution. Patients with bicuspid aortic valve disease, complex congenital cardiac defect, aortic root infection, aortitis, pregnancy, and an age older than 70 years were excluded from the present study.

Results

Among 109 TAAD patients, 36 harboured an FBN1 variant, including 2 splicing site, 6 frame shift, 5 non-sense, and 23 mis-sense variants. The pathogenicity of mis-sense variants was further categorized into 10 disease-causing variants via database survey, 5 disease-causing variants via family survey, and 8 variants of uncertain significance (VUS). On the other hand, 25 patients harboured a non-FBN1 variant, including 3 established pathogenic variants on TGFBR1, TGFB2, and ACTA2 genes, as well as 22 VUS. Patients with an FBN1 variant displayed younger age, lower rate of hypertension, higher rate of aortic root aneurysm, and more frequent mitral valve prolapse, while an extreme male predominance (24/25) was observed in patients with a non-FBN1 variant.

Conclusion

In an adult Chinese TAAD cohort, disease-causing genetic variants were found in 28.4% (31/109) of patients, with FBN1 mutations still being the single leading cause of disease. The present study advocated a genetic screening strategy emphasizing the detection of FBN1 mutations in adult Chinese TAAD patients, and further studies should address the pathogenicity and clinical relevance of non-FBN1 VUS in TAAD patients.

Risk factors and a predictive nomogram for non-sentinel lymph node metastases in Chinese breast cancer patients with one or two sentinel lymph node macrometastases and mastectomy

Background

Two ongoing prospective randomized trials are evaluating whether omitting axillary lymph node dissection (alnd) in patients with breast cancer (bca) and sentinel lymph node (sln) macrometastases undergoing mastectomy is safe. Determining predictive risk factors for non-sln metastases and developing a model to predict the probability of those patients having non-sln metastases is also important.

Methods

This retrospective study enrolled 396 patients with bca and 1-2 slns with macrometastases who underwent alnd and mastectomy between January 2012 and December 2016. Factors influencing the non-sln metastases were determined, and a predictive nomogram was formulated. Performance of the nomogram was evaluated by its area under the curve (auc).

Results

We developed a predictive nomogram with an auc of 0.81 (cross-validation 95% confidence interval: 0.75 to 0.86) that included 4 factors (tumour size, histologic grade, and number of negative slns and axillary lymph nodes on imaging).

Conclusions

Our predictive nomogram assesses the risk of non-sln metastases in patients with bca and 1-2 sln macrometastases undergoing mastectomy.

Muse cells and Neurorestoratology

Multilineage-differentiating stress-enduring (Muse) cells were discovered in 2010 as a subpopulation of mesenchymal stroma cells (MSCs). Muse cells can self-renew and tolerate severe culturing conditions. These cells can differentiate into three lineage cells spontaneously or in induced medium but do not form teratoma in vitro or in vivo. Central nervous system (CNS) diseases, such as intracerebral hemorrhage (ICH), cerebral infarction, and spinal cord injury are normally disastrous. Despite numerous therapy strategies, CNS diseases are difficult to recover. As a novel kind of pluripotent stem cells, Muse cells have shown great regeneration capacity in many animal models, including acute myocardial infarction, hepatectomy, and acute cerebral ischemia (ACI). After injection into injury sites, Muse cells survived, migrated, and differentiated into functional neurons with synaptic junctions to local neurons and contributed to recovery of function. Furthermore, Muse cell differentiation did not need to be induced pre-transplantation and no tumors were observed post- transplantation. The Muse cell population is promising and may lead to a revolution in regenerative medicine. This review focuses on recent advances regarding the Muse cells therapies in Neurorestoratology and discusses future perspectives in this field.

miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences

microRNAs (miRNAs) are short, noncoding regulatory RNAs derived from hairpin precursors (pre-miRNAs). In synergy with experimental approaches, computational approaches have become an invaluable tool for identifying miRNAs at the genome scale. We have recently reported a method called miRLocator, which applies machine learning algorithms to accurately predict the localization of most likely miRNAs within their pre-miRNAs. One major strength of miRLocator is the fact that the machine learning-based miRNA prediction model can be automatically trained using a set of miRNAs of particular interest, with informative features extracted from miRNA-miRNA* duplexes and the optimized ratio between positive and negative samples. Here, we present a detailed protocol for miRLocator that performs the training and prediction processes using a python implementation and web interface. The source codes, web interface, and manual documents are freely available to academic users at https://github.com/cma2015/miRLocator .

Corrigendum: Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning

[This corrects the article DOI: 10.3389/fpls.2018.00519.].

[A meta-analysis of prognosis after bipolar umbilical cord coagulation or radiofrequency ablation to reduce complicated monochorionic multiple pregnancies]

Objective: To evaluate the prognosis and complications after reduction of monochorionic multifetal pregnancies using bipolar umbilical cord coagulation (BCC) or radiofrequency ablation (RFA). Methods: A literature search were performed by using online databases including PubMed, Embase, and COCHRANE.The publications that described prognosis and complications after selective reduction of monochorionic twin pregnancies using either BCC or RFA for studies with clear outcome data were identified. Results: We identified five retrospective cohort studies for the meta-analysis and 231 cases of BCC and 174 cases of RFA.There was no statistical difference in overall survival after reduction between BCC group (79.2%) and RFA groups (76.4%) (RR=0.96; 95%CI: 0.86~1.08; P=0.48). Neonatal mortality was similar in both groups (8.2% vs 11.1%, respectively; RR=1.34; 95%CI: 0.60~2.99; P=0.48). However, intrauterine fetal death (IUFD) in the RFA group was 13.6%, and it significantly higher than that in the BCC group 7.7% (RR=2.15; 95%CI: 1.10~4.21; P=0.03). In contrast, after reduction, those in the RFA group had less preterm premature rupture of membranes (PPROM) compared with the BCC group (17.1% vs 27.5%, RR=0.58; 95%CI: 0.39~0.86; P=0.007). Conclusions: RFA and BCC groups have similar overall survival for complicated monochromic multiple pregnancies, but RFA was more prone to IUFD, and BCC tended to have more PPROM.Thus, procedures should be chosen according to specific clinical situations and pregnancy conditions.

PEA: an integrated R toolkit for plant epitranscriptome analysis

MOTIVATION

The epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.

RESULTS

Here, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery and gene functional enrichment analysis. PEA also takes advantage of machine learning (ML) technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction and annotation and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics.

AVAILABILITY AND IMPLEMENTATION

PEA Docker image is available at https://hub.docker.com/r/malab/pea, source codes and user manual are available at https://github.com/cma2015/PEA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A deep convolutional neural network approach for predicting phenotypes from genotypes

Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted based on genome-wide markers of genotypes. In this study, we present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypes when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional genotypic data. We used a large GS dataset to train DeepGS and compared its performance with other methods. The experimental results indicate that DeepGS can be used as a complement to the commonly used RR-BLUP in the prediction of phenotypes from genotypes. The complementarity between DeepGS and RR-BLUP can be utilized using an ensemble learning approach for more accurately selecting individuals with high phenotypic values, even for the absence of outlier individuals and subsets of genotypic markers. The source codes of DeepGS and the ensemble learning approach have been packaged into Docker images for facilitating their applications in different GS programs.

Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning

The emergence of epitranscriptome opened a new chapter in gene regulation. 5-methylcytosine (m⁵C), as an important post-transcriptional modification, has been identified to be involved in a variety of biological processes such as subcellular localization and translational fidelity. Though high-throughput experimental technologies have been developed and applied to profile m⁵C modifications under certain conditions, transcriptome-wide studies of m⁵C modifications are still hindered by the dynamic and reversible nature of m⁵C and the lack of computational prediction methods. In this study, we introduced PEA-m5C, a machine learning-based m⁵C predictor trained with features extracted from the flanking sequence of m⁵C modifications. PEA-m5C yielded an average AUC (area under the receiver operating characteristic) of 0.939 in 10-fold cross-validation experiments based on known Arabidopsis m⁵C modifications. A rigorous independent testing showed that PEA-m5C (Accuracy [Acc] = 0.835, Matthews correlation coefficient [MCC] = 0.688) is remarkably superior to the recently developed m⁵C predictor iRNAm5C-PseDNC (Acc = 0.665, MCC = 0.332). PEA-m5C has been applied to predict candidate m⁵C modifications in annotated Arabidopsis transcripts. Further analysis of these m⁵C candidates showed that 4nt downstream of the translational start site is the most frequently methylated position. PEA-m5C is freely available to academic users at: https://github.com/cma2015/PEA-m5C.

[Systematic evaluation of the influence of endometrial stimulation on pregnancy outcome in the patients with artificial insemination]

Objective: The association between endometrial stimulation and the intrauterine insemination outcome remains to be a controversial issue, for there being all kinds of conflicting data in the existing literatures. We conducted a Meta-analysis case to verify the relations between endometrial stimulation and intrauterine insemination (IUI). Methods: The articles had been searched in the following online databases: MEDLINE, CNKI, VIP, Wan Fang, EMBASE and the Cochrane Library. All of the statistics quoted for our studies have been published in English or Chinese from Jan, 2000 to May, 2016. Among the 6 studies included in this Meta-analysis, all of them used a case - control design. All the 6 studies evaluated a total of 1349 participants, including 734 cases of endometrial injury. Results: The results of the Meta-analysis showed that patients with endometrial stimulation were more likely to receive clinical pregnancy than those who did not. Compared with the control group, the clinical pregnancy rate of experiment group was higher (RR=0.88, 95% CI: 0.82-0.95, P=0.001), while the differences between their abortion rate (RR=0.72, 95% CI: 0.37-1.4, P=0.33) and multiple pregnancies rate (RR=0.78, 95% CI: 0.3-2.01, P=0.60) were insignificant. Conclusions: Endometrial stimulation could help increase the pregnancy rate in IUI cycles, while the abortion rate and multiple pregnancies rate would not be raised.

Opposing actions of the synapse-associated protein of 97-kDa molecular weight (SAP97) and Disrupted in Schizophrenia 1 (DISC1) on Wnt/β-catenin signaling

It has been suggested that synapse-associated protein of 97-kDa molecular weight (SAP97) is a susceptibility factor for childhood and adult neuropsychiatric disorders. SAP97 is a scaffolding protein that shares direct and indirect binding partners with the Disrupted in Schizophrenia 1 (DISC1) gene product, a gene with strong association with neuropsychiatric disorders. Here we investigated the possibility that these two proteins converge upon a common molecular pathway. Since DISC1 modifies Wnt/β-catenin signaling via changes in glycogen synthase kinase 3 beta (GSK3β) phosphorylation, we asked if SAP97 impacts Wnt/β-catenin signaling and GSK3β phosphorylation. We find that SAP97 acts as inhibitor of Wnt signaling activity and can suppress the stimulatory effects of DISC1 on β-catenin transcriptional activity. Reductions in SAP97 abundance also decrease GSK3β phosphorylation. In addition, we find that over expression of DISC1 leads to an increase in the abundance of SAP97, by inhibiting its proteasomal degradation. Our findings suggest that SAP97 and DISC1 contribute to maintaining Wnt/β-catenin signaling activity within a homeostatic range by regulating GSK3β phosphorylation.

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus, a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Toward this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization), in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The "leave-one-out" cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2). Moreover, RAP ranked 53.68% (204/380) flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.

Cloning and characterisation of JAZ gene family in Hevea brasiliensis

Mechanical wounding or treatment with exogenous jasmonates (JA) induces differentiation of the laticifer in Hevea brasiliensis. JA is a key signal for latex biosynthesis and wounding response in the rubber tree. Identification of JAZ (jasmonate ZIM-domain) family of proteins that repress JA responses has facilitated rapid progress in understanding how this lipid-derived hormone controls gene expression and related physiological processes in plants. In this work, the full-length cDNAs of six JAZ genes were cloned from H. brasiliensis (termed HbJAZ). These HbJAZ have different lengths and sequence diversity, but all of them contain Jas and ZIM domains, and two of them contain an ERF-associated amphiphilic repression (EAR) motif in the N-terminal. Real-time RT-PCR analyses revealed that HbJAZ have different expression patterns and tissue specificity. Four HbJAZ were up-regulated, one was down-regulated, while two were less effected by rubber tapping treatment, suggesting that they might play distinct roles in the wounding response. A yeast two-hybrid assay revealed that HbJAZ proteins interact with each other to form homologous or heterogeneous dimer complexes, indicating that the HbJAZ proteins may expand their function through diverse JAZ-JAZ interactions. This work lays a foundation for identification of the JA signalling pathway and molecular mechanisms of latex biosynthesis in rubber trees.

Structure-function analysis of SAP97, a modular scaffolding protein that drives dendrite growth

Activation of AMPA receptors assembled with the GluA1 subunit can promote dendrite growth in a manner that depends on its direct binding partner, SAP97. SAP97 is a modular scaffolding protein that has at least seven recognizable protein-protein interaction domains. Several complementary approaches were employed to show that the dendrite branching promoting action of full length SAP97 depends on ligand(s) that bind to the PDZ3 domain. Ligand(s) to PDZ1, PDZ2 and I3 domains also contribute to dendrite growth. The ability of PDZ3 ligand(s) to promote dendrite growth depends on localization at the plasma membrane along with GluA1 and SAP97. These results suggest that the assembly of a multi-protein complex at or near synapses is vital for the translation of AMPA-R activity into dendrite growth.

Efficient transformation and artificial miRNA gene silencing in Lemna minor

Despite rapid doubling time, simple architecture and ease of metabolic labelling, a lack of genetic tools in the Lemnaceae (duckweed) has impeded the full implementation of this organism as a model for biological research. Here, we present technologies to facilitate high-throughput genetic studies in duckweed. We developed a fast and efficient method for producing Lemna minor stable transgenic fronds via Agrobacterium-mediated transformation and regeneration from tissue culture. Additionally, we engineered an artificial microRNA (amiRNA) gene silencing system. We identified a Lemna gibba endogenous miR166 precursor and used it as a backbone to produce amiRNAs. As a proof of concept we induced the silencing of CH42, a magnesium chelatase subunit, using our amiRNA platform. Expression of CH42 in transgenic L. minor fronds was significantly reduced, which resulted in reduction of chlorophyll pigmentation. The techniques presented here will enable tackling future challenges in the biology and biotechnology of Lemnaceae.