Immune checkpoint inhibitors (ICIs), especially CTLA-4 inhibitors (αCTLA-4), exhibit a high incidence of colitis as an immune-related adverse event (irAE) during cancer treatment, severely limiting patient benefit. Clinically, both treatment interruption and existing intervention drugs for ICI-mediated colitis may compromise antitumor efficacy. However, there is inadequate research on the pathogenesis of ICI-mediated colitis, with findings often conflicting. Here, we first established multiple clinically relevant animal models, including an immuno-humanized ICI-mediated colitis model. Through time-series transcriptomics, we discovered that αCTLA-4-induced colonic toxicity exhibits characteristics ranging from early metabolic reprogramming represented by glycolysis to later immune disorders represented by Th17 responses. By targeting colonic CTLA-4+ T cells, αCTLA-4 blocked CD80/CD86-CTLA-4 interaction, thereby activating the PI3K-AKT-mTOR pathway. Subsequently, mTOR mediated metabolic reprogramming in T cells, shifting them from Treg-biased oxidative phosphorylation to Th17-biased glycolysis. The colonic toxicity of αCTLA-4 has also been demonstrated to depend on the PI3K-AKT-mTOR pathway, glycolysis, and Th17 responses. Notably, metformin significantly relieved ICI-mediated colitis by inhibiting mTOR without impeding antitumor efficacy. Collectively, these findings highlighted the metabolic-immune axis in the colonic toxicity of ICI and provided a clinically superior intervention strategy. ### Competing Interest Statement The authors have declared no competing interest.

In hepatocellular carcinoma (HepG2), aberrant histone modifications are closely linked to long non-coding RNA (lncRNA) expression. However, existing computational models lack physical interpretability at specific promoter coordinates. To address this, we developed a position-specific statistical scoring model based on adjacent and next-adjacent nucleotide frequencies. We trained two independent, position-specific matrices representing increased and decreased modification states across 600 bp promoter windows centered on the true signal summits. Finally, ten-fold cross-validation revealed that significant energy differences between sequences with increased and decreased histone signals enable excellent classification performance. These results indicted a strong correlation between the total energy of local DNA structures and histone modification signal.

In hepatocellular carcinoma (HCC), aberrant histone modifications are linked to the dysregulation of long non-coding RNA (lncRNA) expression. Although existing computational models can accurately predict some associations, they lack deep physical interpretability. We constructed an energy model based on the physical principle that energy determines molecular structure. Total DNA segment energy was calculated by summing adjacent trinucleotide interaction energies and applied to analyze 11 key histone modifications in HCC, specifically within lncRNA promoter regions where modification signals were increased or decreased. Finally, ten-fold cross-validation revealed that significant energy differences between sequences with increased and decreased histone signals enable excellent classification performance. These results indicted a strong correlation between the total energy of local DNA structures and histone modification signal. Furthermore, introducing longer k-mers led to computational redundancy without a consistent improvement, confirming that the trinucleotide model most effectively acquires the local DNA structural changes associated with histone modification levels. Our model can effectively distinguish DNA sequences associated with different histone modification levels from a physical energy perspective. This model serves as an interpretable tool for epigenetic research while providing a new understanding a new perspective for understanding the dysregulation of lncRNA expression in HCC.

Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very challenging problem. So far, there has not been a dataset specifically for pNETs available to researchers. To address this issue, we propose a pNETs dataset, a well-annotated Contrast-Enhanced Computed Tomography (CECT) dataset focused exclusively on Pancreatic Neuroendocrine Tumors, containing data from 469 patients. This is the first dataset solely dedicated to pNETs, distinguishing it from previous collections. Additionally, we provide the baseline detection networks with a new slice-wise weight loss function designed for the UNet-based model, improving the overall pNET segmentation performance. We hope that our dataset can enhance the understanding and diagnosis of pNET Tumors within the medical community, facilitate the development of more accurate diagnostic tools, and ultimately improve patient outcomes and advance the field of oncology.

Fungal chitin deacetylase (CDA) plays a crucial role in pathogen-plant interactions, which is regarded as an innovative and promising target for fungicides. In this study, a pharmacophore-based virtual screening strategy was employed to identify compounds VS-24 and VS-25 as potent inhibitors against Puccinia striiformis f. sp. tritici CDA (PstCDA). Further bioassays demonstrated that VS-24 exhibited a protective effect of 61.2% against rice blast at 100 μg/mL, while VS-25 showed a superior protective effect of 45.5% against corn rust at 5 μg/mL, both superior to the reported CDA inhibitor benzohydroxamic acid (BHA). Molecular dynamics simulations revealed that multiple key interactions involving the Zn2+ ion and residues His207 and Tyr152 of PstCDA are critical for the binding of VS-24 or VS-25, with electrostatic interactions contributing most significantly to the binding free energy. Finally, toxicity predictions confirmed the potential biosafety of VS-24 and VS-25. Overall, this study identified two promising lead compounds targeting fungal CDA to control plant diseases.

Stomach adenocarcinoma (STAD) has high incidence and mortality rates. Long non-coding RNAs (lncRNAs) and angiogenesis are closely related to the pathogenesis and metastasis of STAD. Recently, emerging evidence demonstrated that DNA methylation plays crucial roles in the development of STAD. This study explored the relationship between DNA methylation and the abnormal expression of angiogenesis-related lncRNAs (ARlncRNAs) in stomach adenocarcinoma, aiming to identify prognostic biomarkers. Moreover, a Cox analysis and Lasso regression were used to establish an ARlncRNA feature set related to angiogenesis. The prognostic model was evaluated by using a Kaplan-Meier (KM) analysis, ROC curves, and nomograms. Based on the identified 18 key ARlncRNAs, a prognostic predictive model was constructed. In addition, a specific ARlncRNA with abnormal methylation in the model, LINC00511, showed significant differences in expression and methylation across different subgroups. The methylation and expression of LINC00511 were analyzed by a correlation and co-expression analysis. The correlation analysis indicated that promoter methylation may improve LINC00511 expression. Further analysis found 355 mRNAs co-expressed with LINC00511 which may interact with 6 miRNAs to regulate target gene expression. The abnormal methylation of LINC00511 could significantly contribute to the progression of stomach adenocarcinoma.

Overall cancer hypomethylation had been identified in the past, but it is not clear exactly which hypomethylation site is the more important for the occurrence of cancer. To identify key hypomethylation sites, we studied the effect of hypomethylation in twelve regions on gene expression in colon adenocarcinoma (COAD). The key DNA methylation sites of cg18949415, cg22193385 and important genes of C6orf223, KRT7 were found by constructing a prognostic model, survival analysis and random combination prediction a series of in-depth systematic calculations and analyses, and the results were validated by GEO database, immune microenvironment, drug and functional enrichment analysis. Based on the expression values of C6orf223, KRT7 genes and the DNA methylation values of cg18949415, cg22193385 sites, the least diversity increment algorithm were used to predict COAD and normal sample. The 100 % reliability and 97.12 % correctness of predicting tumor samples were obtained in jackknife test. Moreover, we found that C6orf223 gene, cg18949415 site play a more important role than KRT7 gene, cg22193385 site in COAD. In addition, we investigate the impact of key methylation sites on three-dimensional chromatin structure. Our results will be help for experimental studies and may be an epigenetic biomarker for COAD.

Aberrant DNA methylation plays a crucial role in breast cancer progression by regulating gene expression. However, the regulatory pattern of DNA methylation in long noncoding RNAs (lncRNAs) for breast cancer remains unclear. In this study, we integrated gene expression, DNA methylation, and clinical data from breast cancer patients included in The Cancer Genome Atlas (TCGA) database. We examined DNA methylation distribution across various lncRNA categories, revealing distinct methylation characteristics. Through genome-wide correlation analysis, we identified the CpG sites located in lncRNAs and the distally associated CpG sites of lncRNAs. Functional genome enrichment analysis, conducted through the integration of ENCODE ChIP-seq data, revealed that differentially methylated CpG sites (DMCs) in lncRNAs were mostly located in promoter regions, while distally associated DMCs primarily acted on enhancer regions. By integrating Hi-C data, we found that DMCs in enhancer and promoter regions were closely associated with the changes in three-dimensional chromatin structures by affecting the formation of enhancer-promoter loops. Furthermore, through Cox regression analysis and three machine learning models, we identified 11 key methylation-driven lncRNAs (DIO3OS, ELOVL2-AS1, MIAT, LINC00536, C9orf163, AC105398.1, LINC02178, MILIP, HID1-AS1, KCNH1-IT1, and TMEM220-AS1) that were associated with the survival of breast cancer patients and constructed a prognostic risk scoring model, which demonstrated strong prognostic performance. These findings enhance our understanding of DNA methylation's role in lncRNA regulation in breast cancer and provide potential biomarkers for diagnosis.

IntroductionLong non-coding RNAs (lncRNAs) play crucial roles in genetic markers, genome rearrangement, chromatin modifications, and other biological processes. Increasing evidence suggests that lncRNA functions are closely related to their subcellular localization. However, the distribution of lncRNAs in different subcellular localizations is imbalanced. The number of lncRNAs located in the nucleus is more than ten times that in the exosome.MethodsIn this study, we propose a new oversampling method to construct a predictive dataset and develop a predictive model called LncSTPred. This model improves the Adaboost algorithm for subcellular localization prediction using 3-mer, 3-RF sequence, and minimum free energy structure features.Results and DiscussionBy using our improved Adaboost algorithm, better prediction accuracy for lncRNA subcellular localization was obtained. In addition, we evaluated feature importance by using the F-score and analyzed the influence of highly relevant features on lncRNAs. Our study shows that the ANA features may be a key factor for predicting lncRNA subcellular localization, which correlates with the composition of stems and loops in the secondary structure of lncRNAs.

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer.Database URL: http://www.positionprediction.cn/

Hi-C can obtain three-dimensional chromatin structure information and is widely used for genome assembly. We constructed the GutHi-C technology. As shown in the graphical abstract, it is a highly efficient and quick-to-operate method and can be widely used for human, livestock, and poultry gut microorganisms. It provides a reference for the Hi-C methodology of the microbial metagenome. DPBS, Dulbecco's phosphate-buffered saline; Hi-C, high-through chromatin conformation capture; LB, Luria-Bertani; NGS, next-generation sequencing; PCR, polymerase chain reaction; QC, quality control.

Acute myeloid leukemia (AML) is a rare tumor that invades the blood and bone marrow, it is rapidly progressive, highly aggressive, and difficult to cure. Studies have shown that long non-coding RNA (lncRNA) and ferroptosis play important roles in AML. However, few studies have been done on ferroptosis-related lncRNA for AML. To investigate the role of ferroptosis-related lncRNA in AML prognosis, we screened the differentially expressed genes related to ferroptosis and lncRNA. Ferroptosis-related lncRNA associated with AML prognosis was obtained by Pearson correlation analysis. By using univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis, the ten prognostic genes were used for constructing the prognostic model. The model was then validated using a Kaplan-Meier analysis and Cox regression analysis. The ROC results have shown that the model could better predict AML survival. We identified some mutated genes that may affect the poor prognosis based on the somatic mutation analysis. The enrichment pathway analysis of prognostic genes revealed that these genes were mainly enriched in some immune pathways and cancer pathways. By immune infiltration analysis, we found that high-risk patients may respond better to immunotherapy.

BACKGROUND: Chitin synthase (CHS) is an important target for pesticide development as chitin biosynthesis is essential for the survival and reproduction of various organisms, such as oomycetes, fungi and insects. Small-molecule inhibitors of CHS have potential applications for the control of agricultural pests and diseases. RESULTS: In this study, exploiting the cryo-EM structures of PsChs1, the CHS indispensable to the sporangial production and virulence of soybean root rot pathogenic oomycete Phytophthora sojae, a virtual screening method combining by molecular docking, inhibitory activity measurement and biological activity determination was conducted, to identify novel small-molecule inhibitors of CHS. A chemical library containing approximate to 1.8 million compounds was screened, and four potent inhibitors (HS-20, HS-24, HS-36 and HS-40) were identified. Amongst these compounds, HS-20 showed the most potent inhibitory activity with a Ki value of 4.2 +/- 0.2 mu M. Besides inhibitory activities towards PsChs1, these compounds were effective in decreasing sporangial production and preventing zoospore infection. When inoculated with zoospores, HS-20 and HS-24 completely inhibited the growth of P. sojae, suggesting their potential in its prevention and control. CONCLUSION: This study identified four new compounds with potent chitin synthase (CHS) inhibitory activity, all of which significantly reduce sporangia production and zoospore infection. It also presents promising in silico techniques and small molecule candidates for the design and development of novel CHS inhibitors. (c) 2024 Society of Chemical Industry. Published by John Wiley & Sons Ltd

Previous evidence has confirmed that branched-chain aminotransferase-1 (BCAT1), a key enzyme governing branched-chain amino acid (BCAA) metabolism, has a role in cancer aggression partly by restricting αKG levels and inhibiting the activities of the αKG-dependent enzyme family. The oncogenic role of BCAT1, however, was not fully elucidated in acute myeloid leukemia (AML). In this study, we investigated the clinical significance and biological insight of BCAT1 in AML. Using q-PCR, we analyzed BCAT1 mRNAs in bone marrow samples from 332 patients with newly diagnosed AML. High BCAT1 expression independently predicts poor prognosis in patients with AML. We also established BCAT1 knockout (KO)/over-expressing (OE) AML cell lines to explore the underlying mechanisms. We found that BCAT1 affects cell proliferation and modulates cell cycle, cell apoptosis, and DNA damage/repair process. Additionally, we demonstrated that BCAT1 regulates histone methylation by reducing intracellular αKG levels in AML cells. Moreover, high expression of BCAT1 enhances the sensitivity of AML cells to the Poly (ADP-ribose) polymerase (PARP) inhibitor both in vivo and in vitro. Our study has demonstrated that BCAT1 expression can serve as a reliable predictor for AML patients, and PARP inhibitor BMN673 can be used as an effective treatment strategy for patients with high BCAT1 expression.

Probiotics are considered to exert beneficial effects in humans and animals by modulating the structure and metabolic functions of the gut microbiota. Therefore, the identification of functional probiotics and in-depth exploration of the characteristics and applicability of probiotics are of paramount significance for the development of more effective probiotic products and the formulation of personalized probiotic treatment strategies in the fields of human health and livestock farming. However, due to current limitations in sequencing technologies and considering that microbial communities may encompass closely related lineages, rendering metagenome assembly complex, the generation of complete metagenome-assembled genomes (cMAGs) is hindered. This limitation constrains our comprehensive resolution at the probiotic strain level. In this review, we summarized the effects of probiotics on gut microbiota balance and host health from a functional perspective. The technical methods of functional probiotics identification were summarized from the technical point of view. Furthermore, we introduced methods for microbial metagenome assembly to elucidate the associated progress and advantages and disadvantages of these approaches. Finally, we highlight more advanced metagenomic assembly techniques that may help us assemble high-fidelity intestinal metagenomes, providing powerful tools for the identification of functional probiotics.

Background: CEBPA mutation is a prevalent molecular abnormality in acute myeloid leukemia (AML). Recent studies have reported that in-frame mutations in bZIP domain of CEBPA(CEBPAbZIP-inf) delineate a unique subgroup of AML patients who exhibit favorable prognosis to conventional chemotherapeutic regimens. Furthermore, CEBPA mutations are implicated in activating the BCL2 P2 promoter, a process facilitated by their interaction with nuclear factor-κB (NF-κB) p50, thereby enhancing BCL2 expression in hematopoietic cell lines. Nevertheless, the specific impact of BCL2 inhibitor venetoclax on CEBPAbZIP-inf AML patients remains understudied. Therefore, this study retrospectively analyzed the clinical characteristics, co-mutational spectrum, and the impact of induction regimens on the prognosis of patients with acute myeloid leukemia (AML) harboring CEBPAbZIP-inf. Method: A total of 103 de novo CEBPAbZIP-inf AML patients with biological data were enrolled, 44 cases were inducted by conventional “7+3” intensive chemotherapy (IC), 20 cases were by venetoclax plus hypomethylating agent regimens (VEN+HMA) and 27 cases were by venetoclax plus intensive chemotherapy (VEN+IC). Clinical characteristics, co-mutational spectrum, overall response rate, composite complete response (CRc) rate, overall survival (OS), and event free survival (EFS) were analyzed. Results: The age of patients in the VEN+HMA group is greater than that of the IC and VEN+IC groups. The median age was 47.0 (IC), 69.0 (VEN+HMA) and 42.0 (VEN+IC) years at diagnosis respectively. Co-mutation of ASXL1/2(20%), TET2(40%) and NPM1(20%) are relatively more common among the VEN+HMA cohort (p=0.0051, p=0.02, p=0.0051, respectively). 35(79.55%) patients attained CR/CRi after once cycle of induction therapy, and 11(31.4%) patients relapsed in IC group; 16(80%) patients attained CR/CRi, 8(50%) relapsed in VEN+HMA group; simultaneously, 26(96.3) patients attained CR/CRi, 9(34.6%) relapsed in VEN+IC group. The median follow-up of the patients in IC, VEN+HMA and VEN+IC was 815.0days, 599.0days and 673.0days, respectively. The median OS was not reached, 652.0days and not reached. In patients treated with IC and VEN+IC, the median OS was significantly longer compared with that in the VEN+HMA group (IC vs.VEN+HMA, p= 0.0025; VEN+IC vs. VEN+HMA, p=0.0183), but no significant difference was observed when comparing IC and VEN+IC group (IC vs. VEN+IC p= 0.928). The median EFS was not reached, 439.0days and not reached (IC vs.VEN+HMA, p= 0.168; VEN+IC vs. VEN+HMA, p= 0.227; IC vs. VEN+IC p= 0.915). When the patients were analyzed by age stratification, regardless of whether the patients are aged 60 or younger, or older than 60, there is no significant difference in OS and EFS among the three cohorts. The median OS of the patients in the IC, VEN+HMA and VEN+IC was not reached, not reached and not reached (IC vs.VEN+HMA, p= 0.746; VEN+IC vs. VEN+HMA, p= 0.657; IC vs. VEN+IC p= 0.704), and the EFS was not reached, 222.0days and not reached in patients aged 60 or younger (IC vs.VEN+HMA, p= 0.0904; VEN+IC vs. VEN+HMA, p= 0.204; IC vs. VEN+IC p=0.713). The median OS of the patients in the IC, VEN+HMA and VEN+IC was not reached, 652 and not reached (IC vs.VEN+HMA, p= 0.522; VEN+IC vs. VEN+HMA, p= 0.497; IC vs. VEN+IC p= 0.617), and the EFS was 294.0days, 790.0days and not reached in patients older than 60 (IC vs.VEN+HMA, p= 0.267; VEN+IC vs. VEN+HMA, p= 0.431; IC vs. VEN+IC p=0.299). Conclusion: In conclusion, IC, VEN + HMA, VEN+IC regimens showed similar CRc rates after one cycle of induction therapy, and had no significant impact on the prognosis of CEBPAbZIP-inf patients.

Identifying a small set of effective biomarkers from multi-omics data is important for the discrimination of different cell types and helpful for the early detection diagnosis of complex diseases. However, it is challenging to identify optimal biomarkers from the high throughput molecular data. Here, we present a method called protein-protein interaction affinity and co-expression network (PPIA-coExp), a linear programming model designed to discover context-specific biomarkers based on co-expressed networks and protein-protein interaction affinity (PPIA), which was used to estimate the concentrations of protein complexes based on the law of mass action. The performance of PPIA-coExp excelled over the traditional node-based approaches in both the small and large samples. We applied PPIA-coExp to human aging and Alzheimer's disease (AD) and discovered some important biomarkers. In addition, we performed the integrative analysis of transcriptome and epigenomic data, revealing the correlation between the changes in gene expression and different histone modification distributions in human aging and AD.

Proper cell fate determination relies on precise spatial and temporal genome-wide cooperation between regulatory elements (REs) and their targeted genes. However, the lengths of REs defined using different methods vary, which indicates that there is sequence redundancy and that the context of the genome may be unintelligible. We developed a method called MAE-seq (Massive Active Enhancers by Sequencing) to experimentally identify functional REs at a 25-bp scale. In this study, MAE-seq was used to identify 626879, 541617 and 554826 25-bp enhancers in mouse embryonic stem cells (mESCs), C2C12 and HEK 293T, respectively. Using ∼1.6 trillion 25 bp DNA fragments and screening 12 billion cells, we identified 626879 as active enhancers in mESCs as an example. Comparative analysis revealed that most of the histone modification datasets were annotated by MAE-Seq loci. Furthermore, 33.85% (212195) of the identified enhancers were identified as de novo ones with no epigenetic modification. Intriguingly, distinct chromatin states dictate the requirement for dissimilar cofactors in governing novel and known enhancers. Validation results show that these 25-bp sequences could act as a functional unit, which shows identical or similar expression patterns as the previously defined larger elements, Enhanced resolution facilitated the identification of numerous cell-specific enhancers and their accurate annotation as super enhancers. Moreover, we characterized novel elements capable of augmenting gene activity. By integrating with high-resolution Hi-C data, over 55.64% of novel elements may have a distal association with different targeted genes. For example, we found that the Cdh1 gene interacts with one novel and two known REs in mESCs. The biological effects of these interactions were investigated using CRISPR-Cas9, revealing their role in coordinating Cdh1 gene expression and mESC proliferation. Our study presents an experimental approach to refine the REs at 25-bp resolution, advancing the precision of genome annotation and unveiling the underlying genome context. This novel approach not only advances our understanding of gene regulation but also opens avenues for comprehensive exploration of the genomic landscape.

Circular RNAs (circRNAs), a type of endogenous noncoding RNA (ncRNA), exert vital roles in leukemia progression and are promising prognostic factors. Here, we report a novel circRNA, circSLC25A13 (hsa_circ_0081188), which was increased in acute myeloid leukemia (AML) patients with poor overall survival (OS) comparing to patients with good prognosis. Knockdown of circSLC25A13 in AML cells inhibited proliferation and increased cell apoptosis in vitro and in vivo. Enhanced circSLC25A13 expression promoted the survival of AML cells. Mechanistically, circSLC25A13 played as a microRNA sponge of miR-616-3p, which inhibited the expression of adenylate cyclase 2 (ADCY2). Downregulation of miR-616-3p and overexpression of ADCY2 partially rescued circSLC25A13 deficient induced cell growth arrest. In summary, through competitive absorption of miR-616-3p and thereby upregulating ADCY2 expression, circSLC25A13 promoted AML progression. Moreover, circSLC25A13 may represent a potential novel biomarker for the prognosis of AML and offer a potential therapeutic target for AML treatment.