DNA methylation and genomic instability are critical drivers of cancer initiation and malignant progression. However, the roles of methylation aberrations and genomic instability in malignant progression have not been thoroughly investigated in intrahepatic cholangiocarcinoma (ICC). To address this, we identified differentially methylated regions (DMRs) and somatic copy number alterations (SCNAs) from 341 ICC samples across various stages. Our findings revealed that stages IAIB, II, IIIA, and IIIB exhibited comparable methylation changes, whereas stage IV ICC showed a pronounced accumulation of stage-specific methylation alterations. Leveraging these findings, we developed a classification model that effectively distinguished stage IV ICC from earlier stages with high accuracy using 15 DMRs. Furthermore, stage IV ICC exhibited slightly higher genomic instability, including an elevated aneuploidy score and a greater proportion of focal amplifications. We also observed a positive correlation between SCNA burden and DNA methylation entropy in the promoter, gene body, and CpG island regions, with the gene body of MDM2 serving as a notable example. These findings highlight the potential of DNA methylation as a biomarker for metastasis diagnosis and the interplay between local genomic instability and aberrant methylation, emphasizing their synergistic roles in driving the evolutionary trajectory of ICC. Intrahepatic cholangiocarcinoma (ICC) is a rare but aggressive type of liver cancer with a poor prognosis. In this study, we analyzed DNA methylation and genetic alterations in 341 ICC samples to understand how these molecular changes contribute to cancer progression. DNA methylation is a chemical modification to DNA that can switch genes on or off without changing the DNA sequence itself. Genomic instability, which refers to a high frequency of mutations across the genome, is a common feature of cancer. We found that abnormal DNA methylation occurs more frequently in advanced-stage ICC and can help distinguish advanced from early-stage disease, suggesting it could serve as a potential biomarker for cancer spread. We also observed greater genomic instability in advanced-stage ICC. Notably, the abnormal DNA methylation patterns were closely linked to genomic instability – especially in key cancer-related genes – potentially working together to disrupt normal gene function. Our findings provide new insights into how epigenetic and genetic alterations drive the development and progression of ICC and may support future efforts to develop new diagnostic tools or targeted treatments.

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and carbon dioxide (CO₂) removal. While various CO₂ removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable, and sustainable approaches to achieve climate targets. Here we show that preserving woody debris in managed forests can remove gigatons of CO₂ from the atmosphere sustainably based on a carbon cycle analysis using three earth system models. Woody debris is produced from logging, sawmill, and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving the yearly produced woody debris in managed forests has the capacity to remove 769-937 Gt CO₂ from the atmosphere cumulatively from 2025 to 2100 if its residence time is lengthened for 100-2,000 years and after 5% CO₂ removal is discounted to account for CO₂ emission due to machine operation for wood debris preservation. This translates to a reduction in global temperatures between 0.35 - 0.42°C. Given the large potential, relatively low cost and long durability, future efforts should be focused on establishing large-scale demonstration projects for this technology in a variety of contexts, with rigorous monitoring of carbon dioxide removal, its co-benefits and side-effects.

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and carbon dioxide (CO₂) removal. While various CO₂ removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable, and sustainable approaches to achieve climate targets. Here we show that preserving woody debris in managed forests can remove gigatons of CO₂ from the atmosphere sustainably based on a carbon cycle analysis using three earth system models. Woody debris is produced from logging, sawmill, and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving the yearly produced woody debris in managed forests has the capacity to remove 769-937 Gt CO₂ from the atmosphere cumulatively from 2025 to 2100 if its residence time is lengthened for 100-2,000 years and after 5% CO₂ removal is discounted to account for CO₂ emission due to machine operation for wood debris preservation. This translates to a reduction in global temperatures between 0.35 - 0.42°C. Given the large potential, relatively low cost and long durability, future efforts should be focused on establishing large-scale demonstration projects for this technology in a variety of contexts, with rigorous monitoring of carbon dioxide removal, its co-benefits and side-effects.

Currently, the Omicron variant of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate globally. In our multiplex respiratory pathogen detection, we identified numerous instances of co-infection with Echovirus (ECHO) among Coronavirus Disease 2019 (COVID-19) patients, which exacerbated the clinical symptoms of these patients. Such co-infections are likely to impact the subsequent medical treatment. To date, there are no reports on the pathogenic mechanisms related to COVID-19 co-infected with ECHO. Therefore, this study employed the TM Widely-Targeted metabolomics approach to analyze the serum metabolomes of COVID-19 patients with single SARS-CoV-2 infection (COVID-19), COVID-19 patients co-infected with ECHO (COVID-19  +  ECHO), and healthy individuals (Control) recruited from routine physical examinations during the same period. Concurrent clinical laboratory tests were performed on the patients to reveal the differences in metabolomic characteristics between the COVID-19 patients and the COVID-19  +  ECHO patients, as well as to explore potential metabolic pathways that may exacerbate disease progression. Our findings indicate that both clinical examination indicators and the pathways enriched by differential metabolites confirm that patients with dual infection exhibit higher inflammatory and immune responses compared to those with single COVID-19 infections. This difference is likely reflected through abnormalities in the glycerophospholipid metabolic pathway, with the metabolite Sn-Glycero-3-Phosphocholine playing a crucial role in this process. Finally, we established a diagnostic model based on logistic regression using five differential metabolites, which accurately differentiates between the dual infection population and the single COVID-19 infection population (AUC = 0.828).

Currently, the Omicron variant of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate globally. In our multiplex respiratory pathogen detection, we identified numerous instances of co-infection with Echovirus (ECHO) among Coronavirus Disease 2019 (COVID-19) patients, which exacerbated the clinical symptoms of these patients. Such co-infections are likely to impact the subsequent medical treatment. To date, there are no reports on the pathogenic mechanisms related to COVID-19 co-infected with ECHO. Therefore, this study employed the TM Widely-Targeted metabolomics approach to analyze the serum metabolomes of COVID-19 patients with single SARS-CoV-2 infection (COVID-19), COVID-19 patients co-infected with ECHO (COVID-19  +  ECHO), and healthy individuals (Control) recruited from routine physical examinations during the same period. Concurrent clinical laboratory tests were performed on the patients to reveal the differences in metabolomic characteristics between the COVID-19 patients and the COVID-19  +  ECHO patients, as well as to explore potential metabolic pathways that may exacerbate disease progression. Our findings indicate that both clinical examination indicators and the pathways enriched by differential metabolites confirm that patients with dual infection exhibit higher inflammatory and immune responses compared to those with single COVID-19 infections. This difference is likely reflected through abnormalities in the glycerophospholipid metabolic pathway, with the metabolite Sn-Glycero-3-Phosphocholine playing a crucial role in this process. Finally, we established a diagnostic model based on logistic regression using five differential metabolites, which accurately differentiates between the dual infection population and the single COVID-19 infection population (AUC = 0.828).

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and carbon dioxide (CO₂) removal. While various CO₂ removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable, and sustainable approaches to achieve climate targets. Here we show that preserving woody debris in managed forests can remove gigatons of CO₂ from the atmosphere sustainably based on a carbon cycle analysis using three earth system models. Woody debris is produced from logging, sawmill, and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving the yearly produced woody debris in managed forests has the capacity to remove 769-937 Gt CO₂ from the atmosphere cumulatively from 2025 to 2100 if its residence time is lengthened for 100-2,000 years and after 5% CO₂ removal is discounted to account for CO₂ emission due to machine operation for wood debris preservation. This translates to a reduction in global temperatures between 0.35 - 0.42°C. Given the large potential, relatively low cost and long durability, future efforts should be focused on establishing large-scale demonstration projects for this technology in a variety of contexts, with rigorous monitoring of carbon dioxide removal, its co-benefits and side-effects.

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and carbon dioxide (CO₂) removal. While various CO₂ removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable, and sustainable approaches to achieve climate targets. Here we show that preserving woody debris in managed forests can remove gigatons of CO₂ from the atmosphere sustainably based on a carbon cycle analysis using three earth system models. Woody debris is produced from logging, sawmill, and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving the yearly produced woody debris in managed forests has the capacity to remove 769-937 Gt CO₂ from the atmosphere cumulatively from 2025 to 2100 if its residence time is lengthened for 100-2,000 years and after 5% CO₂ removal is discounted to account for CO₂ emission due to machine operation for wood debris preservation. This translates to a reduction in global temperatures between 0.35 - 0.42°C. Given the large potential, relatively low cost and long durability, future efforts should be focused on establishing large-scale demonstration projects for this technology in a variety of contexts, with rigorous monitoring of carbon dioxide removal, its co-benefits and side-effects.

DNA methylation and genomic instability are critical drivers of cancer initiation and malignant progression. However, the roles of methylation aberrations and genomic instability in malignant progression have not been thoroughly investigated in intrahepatic cholangiocarcinoma (ICC). To address this, we identified differentially methylated regions (DMRs) and somatic copy number alterations (SCNAs) from 341 ICC samples across various stages. Our findings revealed that stages IAIB, II, IIIA, and IIIB exhibited comparable methylation changes, whereas stage IV ICC showed a pronounced accumulation of stage-specific methylation alterations. Leveraging these findings, we developed a classification model that effectively distinguished stage IV ICC from earlier stages with high accuracy using 15 DMRs. Furthermore, stage IV ICC exhibited slightly higher genomic instability, including an elevated aneuploidy score and a greater proportion of focal amplifications. We also observed a positive correlation between SCNA burden and DNA methylation entropy in the promoter, gene body, and CpG island regions, with the gene body of MDM2 serving as a notable example. These findings highlight the potential of DNA methylation as a biomarker for metastasis diagnosis and the interplay between local genomic instability and aberrant methylation, emphasizing their synergistic roles in driving the evolutionary trajectory of ICC. Intrahepatic cholangiocarcinoma (ICC) is a rare but aggressive type of liver cancer with a poor prognosis. In this study, we analyzed DNA methylation and genetic alterations in 341 ICC samples to understand how these molecular changes contribute to cancer progression. DNA methylation is a chemical modification to DNA that can switch genes on or off without changing the DNA sequence itself. Genomic instability, which refers to a high frequency of mutations across the genome, is a common feature of cancer. We found that abnormal DNA methylation occurs more frequently in advanced-stage ICC and can help distinguish advanced from early-stage disease, suggesting it could serve as a potential biomarker for cancer spread. We also observed greater genomic instability in advanced-stage ICC. Notably, the abnormal DNA methylation patterns were closely linked to genomic instability – especially in key cancer-related genes – potentially working together to disrupt normal gene function. Our findings provide new insights into how epigenetic and genetic alterations drive the development and progression of ICC and may support future efforts to develop new diagnostic tools or targeted treatments.

Data repository for the paper "Size, distribution and vulnerability of the global soil inorganic carbon (Huang et al. 2024, Science, DOI: 10.1126/science.adi7918)". The global soil inorganic carbon (SIC) database provides high-resolution (30 arc-seconds) global gridded SIC data for three soil depths (0-30cm, 30-100cm, 100-200cm). SIC is expressed as carbonate equivalent in fine earth (i.e., ≤ 2 mm), in unit of gC/kg(soil).This dataset includes six gridded data files in NetCDF format. Three files provide the estimations of present-day SIC for three soil depths. The other three files contain values of the standard deviations of each estimation based on machine learning models built during 10-fold cross validation. These data are generated through linking measured SIC content (N > 200,000) to spatially explicit environment covariates by using the machine learning models (two-part models: binary classification and regression model) (Materials and Methods).There are two csv files documenting the compiled measured SIC at different locations across the globe. Site-level measurements were collected from the ISRIC World Soil Information Service (WoSIS) with standardised soil profile data, supplemented by additional databases from National Soil Surveys, resamplings and new measurements from China, from literature via “China National Knowledge Infrastructure” (https://www.cnki.net/) and “Web of Science” (http://www.isiknowledge.com/), from additional samples collected in South-West Germany, Egypt and Russia. University of Hohenheim holds the ownership of the measured data with the label "Germany_xxx". A small portion of entries of measured samples located in northeast China are left empty due to data sharing policies. For users need this part of data, please address to Dr. Songbai Hong ([email protected]). The gridded NetCDF data is likely to be less accurate in observation limited regions compared to regions with enough measurements. Caution is advised when applying this data in such areas. Variable names follow the conventions used in WoSIS snapshot - September 2019. The study of "Significant loss of soil inorganic carbon at the continental scale" (National Science Review, Volume 9, Issue 2, February 2022, nwab120) and Supplementary Table S12 provide literature lists from which additional measurements were extracted.This archive also hosts programming codes for reading compiled data, training predictive models, generating NetCDF files or creating visual displays.

Data repository for the paper "Size, distribution and vulnerability of the global soil inorganic carbon (Huang et al. 2024, Science, DOI: 10.1126/science.adi7918)". The global soil inorganic carbon (SIC) database provides high-resolution (30 arc-seconds) global gridded SIC data for three soil depths (0-30cm, 30-100cm, 100-200cm). SIC is expressed as carbonate equivalent in fine earth (i.e., ≤ 2 mm), in unit of gC/kg(soil).This dataset includes six gridded data files in NetCDF format. Three files provide the estimations of present-day SIC for three soil depths. The other three files contain values of the standard deviations of each estimation based on machine learning models built during 10-fold cross validation. These data are generated through linking measured SIC content (N > 200,000) to spatially explicit environment covariates by using the machine learning models (two-part models: binary classification and regression model) (Materials and Methods).There are two csv files documenting the compiled measured SIC at different locations across the globe. Site-level measurements were collected from the ISRIC World Soil Information Service (WoSIS) with standardised soil profile data, supplemented by additional databases from National Soil Surveys, resamplings and new measurements from China, from literature via “China National Knowledge Infrastructure” (https://www.cnki.net/) and “Web of Science” (http://www.isiknowledge.com/), from additional samples collected in South-West Germany, Egypt and Russia. University of Hohenheim holds the ownership of the measured data with the label "Germany_xxx". A small portion of entries of measured samples located in northeast China are left empty due to data sharing policies. For users need this part of data, please address to Dr. Songbai Hong ([email protected]). The gridded NetCDF data is likely to be less accurate in observation limited regions compared to regions with enough measurements. Caution is advised when applying this data in such areas. Variable names follow the conventions used in WoSIS snapshot - September 2019. The study of "Significant loss of soil inorganic carbon at the continental scale" (National Science Review, Volume 9, Issue 2, February 2022, nwab120) and Supplementary Table S12 provide literature lists from which additional measurements were extracted.This archive also hosts programming codes for reading compiled data, training predictive models, generating NetCDF files or creating visual displays.