Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram.

BACKGROUND: Malignant esophageal fistula (MEF), which occurs in 5% to 15% of esophageal cancer (EC) patients, has a poor prognosis. Accurate identification of esophageal cancer patients at high risk of MEF is challenging. The goal of this study was to build and validate a model to predict the occurrence of esophageal fistula in EC patients. METHODS: This study retrospectively enrolled 122 esophageal cancer patients treated by chemotherapy or chemoradiotherapy (53 with fistula, 69 without), and all patients were randomly assigned to a training (n = 86) and a validation (n = 36) cohort. Radiomic features were extracted from pre-treatment CTs, clinically predictors were identified by logistic regression analysis. Lasso regression model was used for feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the clinical nomogram, radiomics-clinical nomogram and radiomics prediction model. The models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. RESULTS: The radiomic signature consisting of ten selected features, was significantly associated with esophageal fistula (P = 0.001). Radiomics-clinical nomogram was created by two predictors including radiomics signature and stenosis, which was identified by logistic regression analysis. The model showed good discrimination with an AUC = 0.782 (95% CI 0.684-0.8796) in the training set and 0.867 (95% CI 0.7461-0.987) in the validation set, with an AIC = 101.1, and good calibration. When compared to the clinical prediction model, the radiomics-clinical nomogram improved NRI by 0.236 (95% CI 0.153, 0.614) and IDI by 0.125 (95% CI 0.040, 0.210), P = 0.004. CONCLUSION: We developed and validated the first radiomics-clinical nomogram for malignant esophageal fistula, which could assist clinicians in identifying patients at high risk of MEF.

Effect of intravenous vitamin C on adult septic patients: a systematic review and meta-analysis.

Background: An increasing number of studies indicate that vitamin C (VC) reduces the mortality of adult septic patients, while some articles suggest otherwise. We performed this systematic review and meta-analysis to resolve the discrepancies in reported results concerning the efficacy of VC in septic patients. Methods: We comprehensively searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled trials for randomized controlled trials (RCTs) evaluating the efficacy of intravenous VC (IVVC) on adult septic patients published from inception to November 28, 2022. The quality of outcomes for eligible studies was assessed using the Recommendations Assessment, Development, and Evaluation methodology. The results were analyzed using the pooled mean difference (MD) or risk ratio (RR) and 95% confidence intervals (CIs). Results: Twenty-two studies (3,570 adult septic patients) were included. IVVC treatment did not improve 28-day mortality compared to the control group (RR, 0.92; 95% CI, 0.81-1.04; I2 = 26%; evidence risk, moderate). IVVC monotherapy decreased mortality (RR, 0.69; 95% CI, 0.52-0.93; I2 = 57%), whereas combination therapy did not affect mortality (RR, 1.03; 95% CI, 0.90-1.17; I2 =0%). IVVC had a trend to decrease the mortality of septic patients (RR, 0.83; 95% CI, 0.69-1.00; I2 = 33%) but did not affect septic shock patients (RR, 1.01; 95% CI, 0.85-1.21; I2 = 18%). IVVC reduced the duration of vasopressor use (MD, -8.45; 95% CI, -15.43 to -1.47; evidence risk, very low) but did not influence the incidence of AKI, ICU length of stay, duration of mechanical ventilation. Conclusions: IVVC treatment did not improve the 28-day mortality in septic patients. Subgroup analysis indicated that VC had a trend to decrease the 28-day mortality in patients with sepsis but not septic shock. IVVC monotherapy, rather than combination therapy, decreased the 28-day mortality in septic patients. The findings imply that Hydrocortisone, Ascorbic acid, Thiamine (HAT) combination therapy is not superior to IVVC monotherapy for septic patients. These findings warrant further confirmation in future studies, which should also investigate the mechanisms underlying the enhanced efficacy of IVVC monotherapy in septic patients. Systematic review registration: https://inplasy.com/.

Effects of Gabexate Mesylate on the Gut Microbiota and Metabolomics in Rats with Sepsis.

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. However, there is still no single drug that could reduce septic mortality. Previous studies have reported gabexate mesylate (GM) significantly reduced serum inflammatory factors, alleviated sepsis-induced lung injury and improved clinical outcomes. This study aimed to combine with microbiome sequencing and metabolomics analysis to explore the effects of GM administration in septic rats. Methods: Sixty SD rats were randomly divided into the sham control (SC), cecal ligation and puncture (CLP), and GM injection (GM) groups. The mortality was measured and colonic feces were collected to examine the gut microbiota and metabolism 24 h after the procedure. The lung tissues were collected for hematoxylin-eosin staining. Results: We observed the relative abundance of Pygmaiobacter, which contributed to short-chain fatty acids (SCFAs) promotion, Lactobacillus and Erysipelotrichaceae UCG-003 increased in the GM-treated rats, while Escherichia-Shigella and Akkermansia decreased compared to the sepsis-induced lung injury group. Furthermore, these 3 metabolites including Palmitoylethanolamide, Deoxycholic acid and Chenodeoxycholic acid correlated significantly to CLP- and GM-rich genus (P < 0.05). Besides, the lung tissues of CLP group showed more severe inflammatory infiltration and edema, and the mortality rate in the CLP group (10/20) was significantly higher than in the SC group (0/20) (P < 0.001) and GM group (4/20) (P < 0.05). Conclusion: Our findings showed that GM attenuated sepsis-induced lung injury rats and regulated metabolites related to gut microbiota, which may provide an effective treatment for sepsis patients.

Discriminative Suprasphere Embedding for Fine-Grained Visual Categorization.

Despite the great success of the existing work in fine-grained visual categorization (FGVC), there are still several unsolved challenges, e.g., poor interpretation and vagueness contribution. To circumvent this drawback, motivated by the hypersphere embedding method, we propose a discriminative suprasphere embedding (DSE) framework, which can provide intuitive geometric interpretation and effectively extract discriminative features. Specifically, DSE consists of three modules. The first module is a suprasphere embedding (SE) block, which learns discriminative information by emphasizing weight and phase. The second module is a phase activation map (PAM) used to analyze the contribution of local descriptors to the suprasphere feature representation, which uniformly highlights the object region and exhibits remarkable object localization capability. The last module is a class contribution map (CCM), which quantitatively analyzes the network classification decision and provides insight into the domain knowledge about classified objects. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed method in comparison with state-of-the-art methods.

The highly conserved RNA-binding specificity of nucleocapsid protein facilitates the identification of drugs with broad anti-coronavirus activity.

The binding of SARS-CoV-2 nucleocapsid (N) protein to both the 5'- and 3'-ends of genomic RNA has different implications arising from its binding to the central region during virion assembly. However, the mechanism underlying selective binding remains unknown. Herein, we performed the high-throughput RNA-SELEX (HTR-SELEX) to determine the RNA-binding specificity of the N proteins of various SARS-CoV-2 variants as well as other ß-coronaviruses and showed that N proteins could bind two unrelated sequences, both of which were highly conserved across all variants and species. Interestingly, both sequences are virtually absent from the human transcriptome; however, they exhibit a highly enriched, mutually complementary distribution in the coronavirus genome, highlighting their varied functions in genome packaging. Our results provide mechanistic insights into viral genome packaging, thereby increasing the feasibility of developing drugs with broad-spectrum anti-coronavirus activity by targeting RNA binding by N proteins.

An atlas of the binding specificities of transcription factors in Pseudomonas aeruginosa directs prediction of novel regulators in virulence.

A high-throughput systematic evolution of ligands by exponential enrichment assay was applied to 371 putative TFs in Pseudomonas aeruginosa, which resulted in the robust enrichment of 199 unique sequence motifs describing the binding specificities of 182 TFs. By scanning the genome, we predicted in total 33,709 significant interactions between TFs and their target loci, which were more than 11-fold enriched in the intergenic regions but depleted in the gene body regions. To further explore and delineate the physiological and pathogenic roles of TFs in P. aeruginosa, we constructed regulatory networks for nine major virulence-associated pathways and found that 51 TFs were potentially significantly associated with these virulence pathways, 32 of which had not been characterized before, and some were even involved in multiple pathways. These results will significantly facilitate future studies on transcriptional regulation in P. aeruginosa and other relevant pathogens, and accelerate to discover effective treatment and prevention strategies for the associated infectious diseases.

A compendium of DNA-binding specificities of transcription factors in Pseudomonas syringae.

Pseudomonas syringae is a Gram-negative and model pathogenic bacterium that causes plant diseases worldwide. Here, we set out to identify binding motifs for all 301 annotated transcription factors (TFs) of P. syringae using HT-SELEX. We successfully identify binding motifs for 100 TFs. We map functional interactions between the TFs and their targets in virulence-associated pathways, and validate many of these interactions and functions using additional methods such as ChIP-seq, electrophoretic mobility shift assay (EMSA), RT-qPCR, and reporter assays. Our work identifies 25 virulence-associated master regulators, 14 of which had not been characterized as TFs before.

Publisher Correction: Recognition of RNA N6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

CRISPR-assisted detection of RNA-protein interactions in living cells.

We have developed CRISPR-assisted RNA-protein interaction detection method (CARPID), which leverages CRISPR-CasRx-based RNA targeting and proximity labeling to identify binding proteins of specific long non-coding RNAs (lncRNAs) in the native cellular context. We applied CARPID to the nuclear lncRNA XIST, and it captured a list of known interacting proteins and multiple previously uncharacterized binding proteins. We generalized CARPID to explore binders of the lncRNAs DANCR and MALAT1, revealing the method's wide applicability in identifying RNA-binding proteins.

Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally.

DNA and histone modifications have notable effects on gene expression1. Being the most prevalent internal modification in mRNA, the N6-methyladenosine (m6A) mRNA modification is as an important post-transcriptional mechanism of gene regulation2-4 and has crucial roles in various normal and pathological processes5-12. However, it is unclear how m6A is specifically and dynamically deposited in the transcriptome. Here we report that histone H3 trimethylation at Lys36 (H3K36me3), a marker for transcription elongation, guides m6A deposition globally. We show that m6A modifications are enriched in the vicinity of H3K36me3 peaks, and are reduced globally when cellular H3K36me3 is depleted. Mechanistically, H3K36me3 is recognized and bound directly by METTL14, a crucial component of the m6A methyltransferase complex (MTC), which in turn facilitates the binding of the m6A MTC to adjacent RNA polymerase II, thereby delivering the m6A MTC to actively transcribed nascent RNAs to deposit m6A co-transcriptionally. In mouse embryonic stem cells, phenocopying METTL14 knockdown, H3K36me3 depletion also markedly reduces m6A abundance transcriptome-wide and in pluripotency transcripts, resulting in increased cell stemness. Collectively, our studies reveal the important roles of H3K36me3 and METTL14 in determining specific and dynamic deposition of m6A in mRNA, and uncover another layer of gene expression regulation that involves crosstalk between histone modification and RNA methylation.

Author Correction: Recognition of RNA N6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation.

In the version of this Article originally published, the authors incorrectly listed an accession code as GES90642. The correct code is GSE90642 . This has now been amended in all online versions of the Article.

Recognition of RNA N6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation.

N6-methyladenosine (m6A) is the most prevalent modification in eukaryotic messenger RNAs (mRNAs) and is interpreted by its readers, such as YTH domain-containing proteins, to regulate mRNA fate. Here, we report the insulin-like growth factor 2 mRNA-binding proteins (IGF2BPs; including IGF2BP1/2/3) as a distinct family of m6A readers that target thousands of mRNA transcripts through recognizing the consensus GG(m6A)C sequence. In contrast to the mRNA-decay-promoting function of YTH domain-containing family protein 2, IGF2BPs promote the stability and storage of their target mRNAs (for example, MYC) in an m6A-dependent manner under normal and stress conditions and therefore affect gene expression output. Moreover, the K homology domains of IGF2BPs are required for their recognition of m6A and are critical for their oncogenic functions. Thus, our work reveals a different facet of the m6A-reading process that promotes mRNA stability and translation, and highlights the functional importance of IGF2BPs as m6A readers in post-transcriptional gene regulation and cancer biology.

RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data.

More than 100 distinct chemical modifications to RNA have been characterized so far. However, the prevalence, mechanisms and functions of various RNA modifications remain largely unknown. To provide transcriptome-wide landscapes of RNA modifications, we developed the RMBase v2.0 (http://rna.sysu.edu.cn/rmbase/), which is a comprehensive database that integrates epitranscriptome sequencing data for the exploration of post-transcriptional modifications of RNAs and their relationships with miRNA binding events, disease-related single-nucleotide polymorphisms (SNPs) and RNA-binding proteins (RBPs). RMBase v2.0 was expanded with ∼600 datasets and ∼1 397 000 modification sites from 47 studies among 13 species, which represents an approximately 10-fold expansion when compared with the previous release. It contains ∼1 373 000 N6-methyladenosines (m6A), ∼5400 N1-methyladenosines (m1A), ∼9600 pseudouridine (Ψ) modifications, ∼1000 5-methylcytosine (m5C) modifications, ∼5100 2'-O-methylations (2'-O-Me), and ∼2800 modifications of other modification types. Moreover, we built a new module called 'Motif' that provides the visualized logos and position weight matrices (PWMs) of the modification motifs. We also constructed a novel module termed 'modRBP' to study the relationships between RNA modifications and RBPs. Additionally, we developed a novel web-based tool named 'modMetagene' to plot the metagenes of RNA modification along a transcript model. This database will help researchers investigate the potential functions and mechanisms of RNA modifications.

ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed 'Regulator' module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs.

tRF2Cancer: A web server to detect tRNA-derived small RNA fragments (tRFs) and their expression in multiple cancers.

tRNA-derived small RNA fragments (tRFs) are one class of small non-coding RNAs derived from transfer RNAs (tRNAs). tRFs play important roles in cellular processes and are involved in multiple cancers. High-throughput small RNA (sRNA) sequencing experiments can detect all the cellular expressed sRNAs, including tRFs. However, distinguishing genuine tRFs from RNA fragments generated by random degradation remains a major challenge. In this study, we developed an integrated web-based computing system, tRF2Cancer, to accurately identify tRFs from sRNA deep-sequencing data and evaluate their expression in multiple cancers. The binomial test was introduced to evaluate whether reads from a small RNA-seq data set represent tRFs or degraded fragments. A classification method was then used to annotate the types of tRFs based on their sites of origin in pre-tRNA or mature tRNA. We applied the pipeline to analyze 10 991 data sets from 32 types of cancers and identified thousands of expressed tRFs. A tool called 'tRFinCancer' was developed to facilitate the users to inspect the expression of tRFs across different types of cancers. Another tool called 'tRFBrowser' shows both the sites of origin and the distribution of chemical modification sites in tRFs on their source tRNA. The tRF2Cancer web server is available at http://rna.sysu.edu.cn/tRFfinder/.

RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data.

Although more than 100 different types of RNA modifications have been characterized across all living organisms, surprisingly little is known about the modified positions and their functions. Recently, various high-throughput modification sequencing methods have been developed to identify diverse post-transcriptional modifications of RNA molecules. In this study, we developed a novel resource, RMBase (RNA Modification Base, http://mirlab.sysu.edu.cn/rmbase/), to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies. The current release of RMBase includes ∼ 9500 pseudouridine (Ψ) modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼ 1000 5-methylcytosines (m(5)C) predicted from Aza-IP data, ∼ 124 200 N6-Methyladenosine (m(6)A) modifications discovered from m(6)A-seq and ∼ 1210 2'-O-methylations (2'-O-Me) identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. It provides web interfaces to show thousands of relationships between RNA modification sites and microRNA target sites. It can also be used to illustrate the disease-related SNPs residing in the modification sites/regions. RMBase provides a genome browser and a web-based modTool to query, annotate and visualize various RNA modifications. This database will help expand our understanding of potential functions of RNA modifications.

deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data.

Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions.

Serum total bilirubin and long-term outcome in patients undergoing percutaneous coronary intervention.

PURPOSE: The purpose of this study was to investigate the associated between serum total bilirubin (STB) levels and long-term outcomes in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). METHODS: A total of 1,273 consecutive patients were enrolled. Patients were grouped according to their baseline STB levels: Group 1 (STB < 3.4 µmol/L), Group 2 (3.4 µmol/L ≤ STB ≤ 10.3 µmol/L), Group 3 (10.3 µmol/L < STB ≤ 17.1 µmol/L), and Group 4 (STB < 17.1 µmol/L) and the rate of major adverse cardiovascular events (MACE) was determined RESULTS: A total of 1,152 patients were successfully followed up (90.5%) for a mean period of 30 ± 5 months, including 187 patients experiencing a major adverse cardiovascular event (MACE: death from any cause, myocardial infarction, repeat revascularization or readmission). The MACE rate in Groups 3 and 4 was lower than in Groups 1 and 2 (P < 0.01). After adjusted the confounding factors with Cox regression analysis, the MACE rates in Groups 2-4 were still lower than in Group 1 (Group 2, RR=0.293, 95% CI 0.167-0.517, P < 0.01; Group 3, RR=0.142, 95% CI 0.065-0.312, P < 0.01; Group 4, RR=0.134, 95% CI 0.071-0.252, P < 0.01). The cumulative survival rates of Groups 3 and 4 were higher than that of Groups 1and 2 (P < 0.01). CONCLUSIONS: High STB concentration is associated with lower MACE in patients with ACS after PCI.

Discovery of Protein-lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets.

Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein-lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP-lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP-lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.