An integrative pan-cancer analysis of the molecular characteristics of dietary restriction in tumour microenvironment.

BACKGROUND: Dietary restriction (DR), a general term for dieting, has been demonstrated as an effective intervention in reducing the occurrence of cancers. Molecular activities associated with DR are crucial in mediating its anti-cancer effects, yet a comprehensive exploration of the landscape of these activities at the pan-cancer level is still lacking. METHODS: We proposed a computational approach for quantifying DR-related molecular activities and delineating the landscape of these activities across 33 cancer types and 30 normal tissues within 27,320 samples. We thoroughly examined the associations between DR-related molecular activities and various factors, including the tumour microenvironment, immunological phenotypes, genomic features, and clinical prognosis. Meanwhile, we identified two DR genes that show potential as prognostic predictors in hepatocellular carcinoma and verified them by immunohistochemical assays in 90 patients. FINDINGS: We found that DR-related molecular activities showed a close association with tumour immunity and hold potential for predicting immunotherapy responses in various cancers. Importantly, a higher level of DR-related molecular activities is associated with improved overall survival and cancer-specific survival. FZD1 and G6PD are two DR genes that serve as biomarkers for predicting the prognosis of patients with hepatocellular carcinoma. INTERPRETATION: This study presents a robust link between DR-related molecular activities and tumour immunity across multiple cancer types. Our research could open the path for further investigation of DR-related molecular processes in cancer treatment. FUNDING: National Natural Science Foundation of China (Grant No. 82000628) and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (Grant No. 2023LSYS001).

Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer.

Background: Colorectal cancer (CRC) is linked to distinct gut microbiome patterns. The efficacy of gut bacteria as diagnostic biomarkers for CRC has been confirmed. Despite the potential to influence microbiome physiology and evolution, the set of plasmids in the gut microbiome remains understudied. Methods: We investigated the essential features of gut plasmid using metagenomic data of 1,242 samples from eight distinct geographic cohorts. We identified 198 plasmid-related sequences that differed in abundance between CRC patients and controls and screened 21 markers for the CRC diagnosis model. We utilize these plasmid markers combined with bacteria to construct a random forest classifier model to diagnose CRC. Results: The plasmid markers were able to distinguish between the CRC patients and controls [mean area under the receiver operating characteristic curve (AUC = 0.70)] and maintained accuracy in two independent cohorts. In comparison to the bacteria-only model, the performance of the composite panel created by combining plasmid and bacteria features was significantly improved in all training cohorts (mean AUCcomposite = 0.804 and mean AUCbacteria = 0.787) and maintained high accuracy in all independent cohorts (mean AUCcomposite = 0.839 and mean AUCbacteria = 0.821). In comparison to controls, we found that the bacteria-plasmid correlation strength was weaker in CRC patients. Additionally, the KEGG orthology (KO) genes in plasmids that are independent of bacteria or plasmids significantly correlated with CRC. Conclusion: We identified plasmid features associated with CRC and showed how plasmid and bacterial markers could be combined to further enhance CRC diagnosis accuracy.

DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: huzheng1998@163.com, liangjiuxing@m.scnu.edu.cn, lizheyzy@163.com.

Effects of alkali, autoclaving, and Fe+ autoclaving pretreatment on anaerobic digestion performance of coking sludge from the perspective of sludge extracts and methane production.

Pretreatment of activated sludge is an important step in increasing the reaction speed during anaerobic digestion by accelerating the hydrolysis process. It is necessary not only to analyze the changes in the general properties of the sludge before and after pretreatment but also to further analyze and evaluate the sludge structure and extracellular polymeric substances (EPS). In this study, the changes in coking sludge extracts after pretreatments with alkali, autoclaving, and Fe+ autoclaving were analyzed and compared using EPS heat extraction method. Moreover, the methane production potential of the pretreated coking sludge was investigated via biochemical methane potential (BMP) test. The results showed that after alkali, autoclaving, and Fe+ autoclaving, the concentration of protein and polysaccharide in the bound sludge extract accounted for approximately 40% and 28%, 62% and 51%, and 66% and 83% of the total protein and polysaccharide extracted from the sludge, respectively. In the experiment without pretreatment, there is no phenomenon of gas production from coking sludge. According to the BMP test results, Fe+ autoclaving pretreatment showed the highest methane production of 257 mL/gVSS. This study revealed that the analysis of sludge extracts was necessary in assessing the effects of anaerobic digestion pretreatment and methanogenic potential. Moreover, coking sludge showed higher methanogenic potential after Fe+ autoclaving pretreatment.

Influence of macromolecules on aggregation kinetics of diesel soot nanoparticles in aquatic environments.

Soot nanoparticles (SNPs) produced from incomplete combustion have strong impacts on aquatic environments as they eventually reach surface water, where their environmental fate and transport are largely controlled by aggregation. This study investigated the aggregation kinetics of SNPs in the presence of macromolecules including fulvic acid (FA), humic acid (HA), alginate polysaccharide, and bovine serum albumin (BSA, protein) under various environmentally relevant solution conditions. Our results showed that increasing salt concentrations induced SNP aggregation by suppressing electrostatic repulsion and that CaCl2 exhibited stronger effect than NaCl in charge neutralization, which is in agreement with the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. The aggregation rates of SNPs were variously reduced by macromolecules, and such stabilization effect was the greatest by BSA, followed by HA, alginate, and FA. Steric repulsion resulting from macromolecules adsorbed on SNP surfaces was mainly responsible for enhancing SNP stability. Such steric repulsion appeared to be affected by macromolecular structure, as BSA having a more compact globular structure on SNP surfaces imparted long-range steric repulsive forces and retarded the SNP aggregation rate by 10-100 times. In addition, alginate was shown to enhance SNP aggregation by ∼10 times at high CaCl2 concentrations due to alginate gel formation via calcium bridging. The results may bear strong significance for the fate and transport of SNPs in both natural and controlled environmental systems.