HGC: fast hierarchical clustering for large-scale single-cell data.

SUMMARY: Clustering is a key step in revealing heterogeneities in single-cell data. Most existing single-cell clustering methods output a fixed number of clusters without the hierarchical information. Classical hierarchical clustering (HC) provides dendrograms of cells, but cannot scale to large datasets due to high computational complexity. We present HGC, a fast Hierarchical Graph-based Clustering tool to address both problems. It combines the advantages of graph-based clustering and HC. On the shared nearest-neighbor graph of cells, HGC constructs the hierarchical tree with linear time complexity. Experiments showed that HGC enables multiresolution exploration of the biological hierarchy underlying the data, achieves state-of-the-art accuracy on benchmark data and can scale to large datasets. AVAILABILITY AND IMPLEMENTATION: The R package of HGC is available at https://bioconductor.org/packages/HGC/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High Methane Emissions Largely Attributed to Ebullitive Fluxes from a Subtropical River Draining a Rice Paddy Watershed in China.

Rivers are of increasing concern as sources of atmospheric methane (CH4), while estimates of global CH4 emissions from rivers are poorly constrained due to a lack of representative measurements in tropical and subtropical latitudes. Measurements of complete CH4 flux components from subtropical rivers draining agricultural watersheds are particularly important since these rivers are subject to large organic and nutrient loads. Two-year measurements of CH4 fluxes were taken to assess the magnitude of CH4 emissions from the Lixiahe River (a tributary of the Grand Canal) draining a subtropical rice paddy watershed in China. Over the two-year period, annual CH4 emissions averaged 29.52 mmol m-2 d-1, amounting to 10.78 mol m-2 yr-1, making the river a strong source of atmospheric CH4. The CH4 emissions from rivers during the rice-growing season (June-October) accounted for approximately 70% of the annual total, with flux rates at 1-2 orders of magnitude greater than those for rice paddies in this area. Ebullition contributed to 44-56% of the overall CH4 emissions from the rivers and dominated the emission pathways during the summer months. Our data highlight that rivers draining agricultural watersheds may constitute a larger component of anthropogenic CH4 emissions than is currently documented in China.

Climatic role of terrestrial ecosystem under elevated CO2 : a bottom-up greenhouse gases budget.

The net balance of greenhouse gas (GHG) exchanges between terrestrial ecosystems and the atmosphere under elevated atmospheric carbon dioxide (CO2 ) remains poorly understood. Here, we synthesise 1655 measurements from 169 published studies to assess GHGs budget of terrestrial ecosystems under elevated CO2 . We show that elevated CO2 significantly stimulates plant C pool (NPP) by 20%, soil CO2 fluxes by 24%, and methane (CH4 ) fluxes by 34% from rice paddies and by 12% from natural wetlands, while it slightly decreases CH4 uptake of upland soils by 3.8%. Elevated CO2 causes insignificant increases in soil nitrous oxide (N2 O) fluxes (4.6%), soil organic C (4.3%) and N (3.6%) pools. The elevated CO2 -induced increase in GHG emissions may decline with CO2 enrichment levels. An elevated CO2 -induced rise in soil CH4 and N2 O emissions (2.76 Pg CO2 -equivalent year-1 ) could negate soil C enrichment (2.42 Pg CO2 year-1 ) or reduce mitigation potential of terrestrial net ecosystem production by as much as 69% (NEP, 3.99 Pg CO2 year-1 ) under elevated CO2 . Our analysis highlights that the capacity of terrestrial ecosystems to act as a sink to slow climate warming under elevated CO2 might have been largely offset by its induced increases in soil GHGs source strength.

Systematic analysis on the horse-shoe-like effect in PCA plots of scRNA-seq data.

Motivation: In single-cell studies, principal component analysis (PCA) is widely used to reduce the dimensionality of dataset and visualize in 2D or 3D PC plots. Scientists often focus on different clusters within PC plot, overlooking the specific phenomenon, such as horse-shoe-like effect, that may reveal hidden knowledge about underlying biological dataset. This phenomenon remains largely unexplored in single-cell studies. Results: In this study, we investigated into the horse-shoe-like effect in PC plots using simulated and real scRNA-seq datasets. We systematically explain horse-shoe-like phenomenon from various inter-related perspectives. Initially, we establish an intuitive understanding with the help of simulated datasets. Then, we generalized the acquired knowledge on real biological scRNA-seq data. Experimental results provide logical explanations and understanding for the appearance of horse-shoe-like effect in PC plots. Furthermore, we identify a potential problem with a well-known theory of 'distance saturation property' attributed to induce horse-shoe phenomenon. Finally, we analyse a mathematical model for horse-shoe effect that suggests trigonometric solutions to estimated eigenvectors. We observe significant resemblance after comparing the results of mathematical model with simulated and real scRNA-seq datasets. Availability and implementation: The code for reproducing the results of this study is available at: https://github.com/najeebullahshah/PCA-Horse-Shoe.

Benchmarking compound activity prediction for real-world drug discovery applications.

Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a Compound Activity benchmark for Real-world Applications (CARA). Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that although current models can make successful predictions for certain proportions of assays, their performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.

PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction.

Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.

Sulfonamide antibiotics alter gaseous nitrogen emissions in the soil-plant system: A mesocosm experiment and meta-analysis.

Veterinary antibiotics are widely used in many countries worldwide to treat diseases and protect the health of animals. However, the effects of sulfonamide antibiotics introduced via manure and wastewater irrigation on nitrogen (N) loss in the soil-plant system remain poorly understood. Here, we conducted a pot experiment to assess the effects of sulfamethazine (SMZ) and its degradation product (2-amino-4,6-dimethylpyrimidine, ADPD) at four concentration gradients (i.e., 0, 1, 10, 100 mg kg-1) on nitrous oxide (N2O) and ammonia (NH3) emissions, and the abundances of N-cycling functional genes and sulfonamide resistance genes. We also collated 350 observations from 62 published papers and performed a meta-analysis of antibiotic addition effects on N2O emission and soil net nitrification and denitrification. Antibiotics additions showed an inhibitory effect on N2O emissions, which accords with the trend of our meta-analysis showing a significant decrease of 32%. The decreased N2O emissions were attributed to the significant reduction in the abundances of total bacterial communities, ammonia oxidizers, and nir-type denitrifiers and to the resultant changes in soil inorganic N. N2O emissions did not differ between non-environmentally relevant concentrations for SMZ but lowered with increasing ADPD concentrations. This discrepancy can be explained by differential responses of the gene abundances of ammonia oxidizers and nirK-type denitrifiers and the development of antibiotic resistance genes in the highest concentration following antibiotic additions. Antibiotic additions increased soil NH3 volatilization but did not affect vegetable yield. Therefore, these findings provide insight into how the prevalence of antibiotics in soils could alter the N-cycling process and associated gas emissions, with implications for understanding the ecological risks of antibiotics in agriculture.

Effects of nanopolystyrene addition on nitrogen fertilizer fate, gaseous loss of N from the soil, and soil microbial community composition.

Nanoplastics and microplastics are the degradation products of plastics waste and have become a dominant pollutant in the environment. However, little is known about the ecological impacts of nanoplastic particles in the agroecosystem. We conducted a mesocosm experiment to examine nanopolystyrene effects on fertilizer nitrogen (N) fate, N gaseous losses and soil microbial communities using Chinese cabbage (Brassica Campestris ssp.) as the model plant. The two-factorial experiment was designed as the addition of 15N-labeled urea exposed without and with ~50 nm nanopolystyrene (0, 0.05%, and 0.1%). Nanopolystyrene addition had a detectable effect on soil mineral N content. The 15N uptake of plants was reduced in aboveground biomass but enhanced in roots with increasing nanopolystyrene concentration. Nanopolystyrene addition decreased soil nitrous oxide and ammonia emissions by 27% and 37%, respectively. Nanopolystyrene addition consistently reduced the abundance of ammonia oxidizer genes but showed contrasting effects on denitrifying genes. Metagenomic sequencing data revealed no significant effects of nanopolystyrene on the N-cycle pathway, while it significantly altered the composition of bacterial and fungal communities. This study provided the first insights into the nanopolystyrene induced linkage of root growth with more root N uptake and less gaseous N losses and the associated changes in the microbial community.

hECA: The cell-centric assembly of a cell atlas.

The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We developed a unified informatics framework for seamless cell-centric data assembly and built the human Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled human cells from 116 published datasets, covering 38 organs and 11 systems. We invented three new methods of atlas applications based on the cell-centric assembly: "in data" cell sorting for targeted data retrieval with customizable logic expressions, "quantitative portraiture" for multi-view representations of biological entities, and customizable reference creation for generating references for automatic annotations. Case studies on agile construction of user-defined sub-atlases and "in data" investigation of CAR-T off-targets in multiple organs showed the great potential enabled by the cell-centric ensemble atlas.

Increased ZnO nanoparticle toxicity to wheat upon co-exposure to phenanthrene.

Polycyclic aromatic hydrocarbons and zinc oxide nanoparticles are ubiquitous pollutants in the environment. However, little information is available about their toxicity interaction in food crops. In this study, seed germination and hydroponic experiments were conducted to assess the impact of ZnO (NPs and bulk at 250, 500 and 1000 mg L-1) individual and combined with phenanthrene (1 mg L-1) on wheat growth for 15 days. Under ZnO (NPs and bulk) alone and combined with phenanthrene exposure, dose-dependent toxicity in some indexes (germination rate, biomass, shoot height, root length) was observed. Both ZnO NPs and bulk inhibited plant growth at high concentrations, but no significant difference was observed between them (P > 0.05). The chlorophyll concentration of wheat leaves decreased by 0.43-0.60 fold when the levels of ZnO NPs and bulk treated were elevated. There was a negative correlation between ZnO (NPs and bulk) and total chlorophyll. Hill reaction activity also exhibited the same tendency. Through transmission electron microscopy, ZnO NPs were found in wheat seedling root apoplast and symplasm at 1000 mg L-1 with or without phenanthrene. High doses (500 and 1000 mg L-1) of ZnO (NPs and bulk) caused more DNA damage to wheat seedling root cells, and ZnO NPs induced stronger genotoxicity than bulk ones to wheat root cells. Superoxide dismutase (SOD) and catalase (CAT) activities of wheat seedling roots decreased at 1000 mg L-1 ZnO (NPs and bulk), especially in the co-exposure treatments. Hence, ZnO (NPs and bulk) combined with phenanthrene cause more damage to wheat seedling roots, and even destroy the antioxidant system. Our findings are helpful for not only assessing the individual and combined toxicity between phenanthrene and ZnO (NPs and bulk), but also for understanding the different response of plants to individual and combined pollution.