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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. The authors set out to identify binding motifs for all 301 transcription factors of a plant pathogenic bacterium, Pseudomonas syringae , using HT-SELEX. They successfully identify binding motifs for 100 transcription factors, infer their binding sites on the genome, and validate the predicted interactions and functions.

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. CARPID uses CRISPR technology to navigate biotin ligase to specific lncRNAs, which allows proximal labeling and thus the querying of RNA–protein interactions in living cells.

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.

DNA and histone modifications have notable effects on gene expression 1 . Being the most prevalent internal modification in mRNA, the N 6 -methyladenosine (m 6 A) mRNA modification is as an important post-transcriptional mechanism of gene regulation 2 – 4 and has crucial roles in various normal and pathological processes 5 – 12 . However, it is unclear how m 6 A is specifically and dynamically deposited in the transcriptome. Here we report that histone H3 trimethylation at Lys36 (H3K36me3), a marker for transcription elongation, guides m 6 A deposition globally. We show that m 6 A 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 m 6 A methyltransferase complex (MTC), which in turn facilitates the binding of the m 6 A MTC to adjacent RNA polymerase II, thereby delivering the m 6 A MTC to actively transcribed nascent RNAs to deposit m 6 A co-transcriptionally. In mouse embryonic stem cells, phenocopying METTL14 knockdown, H3K36me3 depletion also markedly reduces m 6 A 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 m 6 A in mRNA, and uncover another layer of gene expression regulation that involves crosstalk between histone modification and RNA methylation. METTL14 recognizes the trimethyl mark on lysine 36 of histone H3 that directs m 6 A modifications co-transcriptionally.

N 6 -methyladenosine (m 6 A) 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 m 6 A readers that target thousands of mRNA transcripts through recognizing the consensus GG(m 6 A)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 m 6 A-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 m 6 A and are critical for their oncogenic functions. Thus, our work reveals a different facet of the m 6 A-reading process that promotes mRNA stability and translation, and highlights the functional importance of IGF2BPs as m 6 A readers in post-transcriptional gene regulation and cancer biology. Huang et al. identify IGF2BPs as an additional class of N 6 -methyladenosine (m 6 A) reader proteins. They find that IGF2BPs selectively bind to m 6 A-containing mRNAs and promote their stability.

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.

A Correction to this paper has been published: https://doi.org/10.1038/s41592-021-01067-w

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

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. 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. 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 ( < 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) ( < 0.001) and GM group (4/20) ( < 0.05). 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.

Systemic inflammatory response is reported to occupy a crucial role in the progression of hepatocellular carcinoma (HCC). The prognostic significance of SII and PNI in HCC has been explored, but the prognostic significance of aggregate index of systemic inflammation (AISI) in HCC is still unknown. This study was designed to determine the prognostic significance of AISI in HCC and explain the potential underlying mechanisms via gut microbiota and fecal metabolomic profiling. A cohort of 109 cases of HCC individuals during January 2023 to August 2024 was included into this clinical research, and the clinical information and fresh fecal samples were collected. The fecal samples were collected for 16S rRNA sequence and metabolomics analysis. Survival analysis revealed that HCC patients in low AISI group tend to experience relatively longer survival time compared with those in high AISI group. Then, we employed ROC analysis to measure the predictive performance of AISI for the survival outcome, and ROC curve showed that levels of AISI had good predictive performance for the survival status with an AUC of 0.771 (95%CI: 0.671-0.871). 16S rRNA sequencing results revealed that levels of were up-regulated in the low AISI group, and levels of were up-regulated in the high AISI group. Metabolic analysis demonstrated that cavipetin A, pemptoporphyrin, and 8-Oxo-dGMP with high VIP value were the most distinct fecal metabolites. AISI is a potential prognostic biomarker in individuals with HCC. A low level of AISI was correlated with high abundance of and some metabolites, indicating that AISI might affect the prognosis of HCC individuals via the regulations of gut microbes and metabolites.