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UNLABELLED: In the face of carbon, nitrogen, and phosphorus starvation, microorganisms have evolved adaptive mechanisms to maintain growth. In a previous study, we identified a protein predicted to contain acyl-CoA-binding domains in the plant pathogenic fungus Verticillium dahliae. The predicted protein, designated VdAcb1, possesses an atypical signal peptide. However, the functions of this acyl-CoA-binding protein in V. dahliae are not clear. In this research, in vivo or in vitro assays confirmed that VdAcb1 is secreted extracellularly from V. dahliae, although it does not have the typical signal peptide. Furthermore, the unconventional secretion of VdAcb1 was dependent on VdGRASP, a member of the compartment for unconventional protein secretion (CUPS). The deletion mutant strain of VdAcb1 (ΔVdAcb1) exhibited significant sensitivity to carbon starvation. RNA-seq revealed that the expression of genes related to filamentous growth (MSB2 pathway) and sugar transport were regulated by VdAcb1 under conditions of carbon starvation. Yeast one-hybrid experiments further showed that the expression of VdAcb1 was positively regulated by the transcription factor VdMsn4. The ΔVdAcb1 strain showed significantly reduced virulence on Gossypium hirsutum and Nicotiana benthamiana. We hypothesize that under conditions of carbon starvation, the expression of VdAcb1 is activated by VdMsn4 and VdAcb1 is secreted into the extracellular space. In turn, this activates the downstream MAPK pathway to enhance filamentous growth and virulence of V. dahliae. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42994-024-00175-3.

[Objective] Identification and characterization of the BRX family genes in tea plants, along with analysis of their expression patterns in various tissues and under stress conditions. The research lays the groundwork for investigation into the functions of the tea plant BRX genes. [Methods] Using the tea plant genome database and bioinformatics softwares such as TBtools, we identified and analyzed the BRX genes with the tea plant variety ‘Shuchazao’. Different treatments, including salt, drought, high temperature, and low temperature stress, as well as exogenous GA3 application, were applied for various durations. The relative transcript abundance of the BRX gene family in different tissues and under stress conditions was quantified using RT-qPCR. [Results] A total of 5 CsBRX genes was identified, encoding proteins composed of 313-370 amino acids. The structure of genes was similar and highly conserved. The promoter regions contained hormone and stress response elements. Based on evolutionary relationships, BRX proteins were divided into 4 subfamilies. CsBRX1, CsBRX3, and CsBRX5 were all expressed in young leaves, while CsBRX4 was exclusively expressed in old leaves. Under salt stress for 12 h, the transcriptional levels of CsBRX1, CsBRX2, and CsBRX4 were elevated to the greatest extend. CsBRX2 exhibited a swift response to low temperature stress, with its relative expression peaked at 1 h. CsBRX2 demonstrated a positive response to exogenous GA signaling, and its expression reached a peak at 24 h interval. [Conclusion] The CsBRX genes demonstrate tissue-specific expression patterns, responding variably to multiple stresses and GA signal, which offers insights into stress tolerance mechanisms of tea plants.

Abstract Background Researchers gradually focus on the relationship between phage and cancer. Objective To summarize the research hotspots and trends in the field of bacteriophage and cancer. Methods The downloaded articles were searched from the Web of Science Core Collection database from January 2008 to June 2023. Bibliometric analysis was carried out through CiteSpace, including the analysis of cooperative networks (country/region, institution, and author), co-citations of references, and key words.Visual analysis of three topics, including gut phage, phage and bacteria, and phage and tumor, was conducted. Results Overall, the United States and China have the most phage-related research. In terms of gut phage, the future research directions are “gut microbiome”, “database” and “microbiota”. The bursting citations explored the phage-dominated viral genome to discover its diversity and individual specificity and investigated associations among bacteriome, metabolome, and virome. In terms of phage and bacteria, “lipopolysaccharide” and “microbiota” are future research directions. Future research hotspots should mainly concentrate on the further exploration and application of phage properties. As for phages and tumors, the future research directions should be "colorectal cancer", "protein" and "phage therapy". Future directions are likely to focus on the research on phages in cancer mechanisms, cancer diagnosis, and cancer treatment combined with genetic engineering techniques. Conclusion Phage therapy would become a hot spot and research direction of tumor and phage research, and the relationship between phage and tumor, especially colorectal cancer (CRC), is expected to be further explored.

Abstract Background More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. Objective To analyze the characteristic microbes in AA. Methods Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. Results The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Conclusion Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Abstract Escherichia coli (E. coli) is a ubiquitous symbiotic bacterium in the gut, and the diversity of E. coli genes determines the diversity of its functions. In this review, the two‐edged sword theory was innovatively proposed. For the question ‘how can we harness the ambivalent nature of E. coli to screen and treat CRC?’, in terms of CRC screening, the variations in the abundance and subtypes of E. coli across different populations present an opportunity to utilise it as a biomarker, while in terms of CRC treatment, the natural beneficial effect of E. coli on CRC may be limited, and engineered E. coli, particularly certain subtypes with probiotic potential, can indeed play a significant role in CRC treatment. It seems that the favourable role of E. coli as a genetic tool lies not in its direct impact on CRC but its potential as a research platform that can be integrated with various technologies such as nanoparticles, imaging methods, and synthetic biology modification. The relationship between gut microflora and CRC remains unclear due to the complex diversity and interaction of gut microflora. Therefore, the application of E. coli should be based on the ‘One Health’ view and take the interactions between E. coli and other microorganisms, host, and environmental factors, as well as its own changes into account. In this paper, the two‐edged sword role of E. coli in CRC is emphasised to realise the great potential of E. coli in CRC screening and treatment.

Understanding how plant pathogenic fungi adapt to their hosts is of critical importance to securing optimal crop productivity. In response to pathogenic attack, plants produce reactive oxygen species (ROS) as part of a multipronged defense response. Pathogens, in turn, have evolved ROS scavenging mechanisms to undermine host defense. Thioredoxins (Trx) are highly conserved oxidoreductase enzymes with a dithiol-disulfide active site, and function as antioxidants to protect cells against free radicals, such as ROS. However, the roles of thioredoxins in Verticillium dahliae, an important vascular pathogen, are not clear. Through proteomics analyses, we identified a putative thioredoxin (VdTrx1) lacking a signal peptide. VdTrx1 was present in the exoproteome of V. dahliae cultured in the presence of host tissues, a finding that suggested that it plays a role in host-pathogen interactions. We constructed a VdTrx1 deletion mutant ΔVdTrx1 that exhibited significantly higher sensitivity to ROS stress, H2O2, and tert-butyl hydroperoxide (t-BOOH). In vivo assays by live-cell imaging and in vitro assays by western blotting revealed that while VdTrx1 lacking the signal peptide can be localized within V. dahliae cells, VdTrx1 can also be secreted unconventionally depending on VdVps36, a member of the ESCRT-II protein complex. The ΔVdTrx1 strain was unable to scavenge host-generated extracellular ROS fully during host invasion. Deletion of VdTrx1 resulted in higher intracellular ROS levels of V. dahliae mycelium, displayed impaired conidial production, and showed significantly reduced virulence on Gossypium hirsutum, and model plants, Arabidopsis thaliana and Nicotiana benthamiana. Thus, we conclude that VdTrx1 acts as a virulence factor in V. dahliae.

Abstract [Objective] The study aims to identify the NAC transcription factor genes CsNAC79 and Cs- NAC9 in tea plants and analyze their expression patterns under drought, salt, high temperature, and low temperature stresses as well as ABA and GA3 treatments in order to provide references for functional analysis of CsNAC79 and CsNAC9. [Methods] CsNAC79 and CsNAC9 were amplified by RT-PCR from tea plant ‘Longjing 43’ and analyzed by bioinformatics. The expression of CsNAC79 and CsNAC9 under abiotic stresses in tea plants was analyzed by qRT-PCR. [Results] (1) CsNAC79 and CsNAC9 encoded 409 and 282 amino acids, respectively, and contain the conserved NAM domain of the NAC family at 15-150 amino acids and 11-134 amino acids, respectively. (2) CsNAC79 protein was closely related to Actinidia chinensis, Diospyros kaki, and Cynara scolymus, while CsNAC9 protein was closely related to Amygdalus persica, Litchi chinensis, and Durio zibethinus. Both of the NAC transcription factors were hydrophilic proteins without signal peptides and transmembrane structures. The promoter regions of CsNAC79 and CsNAC9 contained abiotic stress- responsive elements. Protein structure analysis showed that Cs- NAC79 and CsNAC9 proteins were mainly composed of random coils and α-helixes. (3) Expression analysis showed that CsNAC79 and CsNAC9 genes were up-regulated under different abiotic stresses. [Conclusion] The CsNAC79 and CsNAC9 genes in tea plants responded to various abiotic stresses.

Abstract Background Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five‐year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA. Review This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA. Conclusion Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation.

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Abstract Background Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined. Objective To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype. Methods The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated. Results Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters. Conclusion This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

Abstract Background The most common toxic side effect after chemotherapy, one of the main treatments for colorectal cancer (CRC), is myelosuppression. Objective To analyze the correlation between gut microbiota and leukopenia after chemotherapy in CRC patients. Methods Stool samples were collected from 56 healthy individuals and 55 CRC patients. According to the leukocytes levels in peripheral blood, the CRC patients were divided into hypoleukocytes group (n = 13) and normal leukocytes group (n = 42). Shannon index, Simpson index, Ace index, Chao index and Coverage index were used to analyze the diversity of gut microbiota. LDA and Student's t-test(St test) were used for analysis of differences. Six machine learning algorithms, including logistic regression (LR) algorithm, random forest (RF) algorithm, neural network (NN) algorithm, support vector machine (SVM) algorithm, catboost algorithm and gradient boosting tree algorithm, were used to construct the prediction model of gut microbiota with leukopenia after chemotherapy for CRC. Results Compared with healthy group, the microbiota alpha diversity of CRC patients was significantly decreased (p

Abstract Background The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor. Objective To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer. Methods Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer. Results There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium , norank_f__Oscillospiraceae , Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas , Erysipelotrichaceae_UCG-003 , Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium. Conclusion The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

Abstract Background Colorectal cancer (CRC) is a multifactorial disease with genetic and environmental factors. Regional differences in risk factors are an important reason for the different incidences of CRC in different regions. Objective The goal was to clarify the intestinal microbial composition and structure of CRC patients in different regions and construct CRC risk prediction models based on regional differences. Methods A metagenomic dataset of 601 samples from 6 countries in the GMrepo and NCBI databases was collected. All whole-genome sequencing (WGS) data were annotated for species by MetaPhlAn2. We obtained the relative abundance of species composition at the species level and genus level. The MicrobiotaProcess package was used to visualize species composition and PCA. LEfSe analysis was used to analyze the differences in the datasets in each region. Spearman correlation analysis was performed for CRC differential species. Finally, the CRC risk prediction model was constructed and verified in each regional dataset. Results The composition of the intestinal bacterial community varied in different regions. Differential intestinal bacteria of CRC in different regions are inconsistent. There was a common diversity of bacteria in all six countries, such as Peptostreptococcus stomatis and Fusobacterium nucleatum at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) are important CRC-related bacteria that are related to other bacteria in different regions. Region has little influence on the accuracy of the CRC risk prediction model. Peptostreptococcus stomatis is an important variable in CRC risk prediction models in all regions. Conclusion Peptostreptococcus stomatis is a common high-risk pathogen of CRC worldwide, and it is an important variable in CRC risk prediction models in all regions. However, regional differences in intestinal bacteria had no significant impact on the accuracy of the CRC risk prediction model.