Biology subjects

Shao, X.

Publications and source records attributed to Shao, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Automated container-less cell processing method for single-cell proteomics

Single-cell genomics and transcriptomics studies enabled us to characterize cell heterogeneity in various tissues, which helped us to better understand the biological system and disease progression. Single-cell proteomics, which directly measures the protein expression level, has the potential to further enhance our knowledge by providing not only a more direct measurement but also crucial information cannot be captured by genomics or transcriptomics study, such as protein activation states and post-translation modification events. One of the main challenges of single-cell proteomics is the large sample loss during sample preparation, which is largely unavoidable using standard proteomics protocols. Protein and peptide loss to the container surface is a well-known phenomenon but often overlooked in larger-scale (>1 {micro}g) proteomics experiments. When it comes to single-cell proteomics with only picograms of protein samples, this loss becomes non-negligible and often dictates the outcomes of the experiment. More importantly, sample processing through multiple pipette tips and containers often introduces random errors, which undermine the ability to detect true heterogenous cellular events. To solve these problems and further improve the throughput and reproducibility of the single-cell proteomics experiments, we developed an automated container-less cell processing platform, utilizing acoustic levitation to process cell samples in the air. Our platform automatically performs cell lysis, protein reduction, alkylation, digestion, and peptide labeling in the air, without any sample transfer step or container. The digested and labeled peptides are then directly injected into the capillary LC-MS/MS system for analysis, eliminating manual steps and conserving most of the sample materials for proteomics analysis. Our initial test shows at least 30% improvement in peptide signals over conventional methods. This process can be performed in parallel to further improve sample processing throughput.

biochemistry↗

bioRxiv2022

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We demonstrated that scSpace can define biologically meaningful cell subpopulations neglected by single-cell RNA-seq or spatially resolved transcriptomics. The use of scSpace to uncover the spatial association within single-cell data, reproduced, the hierarchical distribution of cells in the brain cortex and liver lobules, and the regional variation of cells in heart ventricles and the intestinal villus. scSpace identified cell subclusters in intratelencephalic neurons, which were confirmed by their biomarkers. The application of scSpace in melanoma and Covid-19 exhibited a broad prospect in the discovery of spatial therapeutic markers.

bioinformatics↗

bioRxiv2022

The role of dynamic DNA methylation in liver transplant rejection in children

BackgroundTranscriptional regulation of liver transplant (LT) rejection may reveal novel predictive and therapeutic targets. PurposeTo test the role of differential DNA methylation in children with biopsy-proven acute cellular rejection (rejectors, R) after LT. MethodsPaired peripheral blood DNA samples were obtained before and after LT from 17 children, including 4R and 13 non-rejector (NR), and assayed with MethylC capture sequencing (MCC-Seq) approach covering 5 million CpGs in immune-cell specific regulatory elements. Differentially methylated CpGs (DMCs) were identified using generalized linear regression models adjusting for sex and age and merged into differentially methylated regions (DMR) comprising 3 or more DMCs. ResultsContrasting R vs NR, we identified 2238 DMCs in post-LT and 2620 DMCs in pre-LT samples, which clustered in 216 and 282 DMRs respectively. DMCs associated with R were enriched in enhancers and depleted in promoters. The proportion of hypomethylated versus hypermethylated DMRs increased from 22% to 48% (p<0.0001) in pre-LT vs. post-LT DMCs, respectively. The highest-ranked biological processes enriched in post-LT DMCs were antigen processing and presentation via MHC class I, MHC class I complex, and peptide binding (p<7.92E-17), respectively. Top-ranked DMRs mapped to genes which mediate B-cell receptor signaling (ADAP1) or regulate several immune cells (ARRB2) (p<3.75E-08). DMRs in MHC class I genes were enriched for SNPs which bind TFs, affect gene expression and splicing, or alter peptide-binding amino acid sequences. ConclusionsDynamic methylation in distal regulatory regions reveals known transplant-relevant MHC-dependent rejection pathways, and identifies novel loci for future mechanistic evaluations in pediatric transplant subcohorts.

genomics↗

bioRxiv2022

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Spatially resolved transcriptomics (ST) provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications from ST data, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells, decomposed from ST data through a non-negative linear model and spatial mapping between single-cell RNA-sequencing and ST data. The performance of SpaTalk benchmarked on public single-cell ST datasets was superior to that of existing cell-cell communication inference methods. SpaTalk was then applied to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based ST data universally, providing new insights into spatial inter-cellular dynamics.

bioinformatics↗

bioRxiv2022

Single-cell analysis reveals a pathogenic cellular module associated with early allograft dysfunction after liver transplantation

Liver transplantation (LT) is the standard therapy for patients with end-stage liver disease. Although LT technology has markedly progressed in recent decades, early allograft dysfunction (EAD) exacerbates the current organ shortage and impacts the prognosis of recipients. However, understanding of cellular characteristics and molecular events contributing to EAD is limited. Here, a large single-cell transcriptomic atlas of transplanted livers collected from four patients is constructed, including 58,243 cells, which are classified into 14 cell types and 29 corresponding subtypes with known markers, including liver parenchymal cells and non-parenchymal cells with different cell states. Compared to the pre-LT livers, graft remodeling is noted in the post-LT livers, with marked changes in several immune cells in either cell ratios or cell states. More importantly, an EAD-associated pathogenic cellular module is identified, consisting of mucosal-associated invariant T (MAIT) cells, granzyme B (GZMB)+ granzyme K (GZMK)+ natural killer (NK) cells, and S100A12+ neutrophils, all of which are elevated in EAD patient after LT. This cellular module is also verified in two independent datasets. Collectively, these results reveal the cellular characteristics of transplanted livers and the EAD-associated pathogenic cellular module at the single-cell level, offering new insights into the EAD occurrence after LT.

genomics↗

bioRxiv2022

Spatially resolved single-cell deconvolution of bulk RNA-seq using Bulk2Space

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space (https://github.com/ZJUFanLab/bulk2space), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

bioinformatics↗

bioRxiv2022

Time-lapsed proteomics reveals a role for the novel protein, SNED1, in modulating ECM composition and protein folding

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes including proliferation and migration. Alterations of ECM structure and/or composition has been shown to lead to, or accompany, many pathological processes including cancer and fibrosis. To understand how the ECM contributes to diseases, we first need to obtain a comprehensive characterization of the ECM of tissues and of its changes during disease progression. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundance in the ECM, nor do they permit to achieve the high coverage needed to gain finer biochemical information, including the presence of isoforms or post-translational modifications. In addition, broadly adopted proteomic methods relying on extended trypsin digestion do not provide structural information on ECM proteins, yet, gaining insights into ECM protein structure is critical to better understanding protein functions. Here, we present the optimization of a time-lapsed proteomic method using limited proteolysis of partially denatured samples and the sequential release of peptides to achieve superior sequence coverage as compared to standard ECM proteomic workflow. Exploiting the spatio-temporal resolution of this method, we further demonstrate how 3-dimensional time-lapsed peptide mapping can identify protein regions differentially susceptible to trypsin and can thus identify sites of post-translational modifications, including protein-protein interactions. We further illustrate how this approach can be leveraged to gain insight on the role of the novel ECM protein SNED1 in ECM homeostasis. We found that the expression of SNED1 by mouse embryonic fibroblasts results in the alteration of overall ECM composition and the sequence coverage of certain ECM proteins, raising the possibility that SNED1 could modify accessibility to trypsin by engaging in protein-protein interactions.

cell biology↗

bioRxiv2022

Sequence Coverage Visualizer: A web application for protein sequence coverage 3D visualization

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.

bioinformatics↗

bioRxiv2021

Two Stages of Speech Envelope Tracking in Human Auditory Cortex Modulated by Speech Intelligibility

The envelope is essential for speech perception. Recent studies have shown that cortical activity can track the acoustic envelope. However, whether the tracking strength reflects the extent of speech intelligibility processing remains controversial. Here, using stereo-electroencephalogram (sEEG) technology, we directly recorded the activity in human auditory cortex while subjects listened to either natural or noise-vocoded speech. These two stimuli have approximately identical envelopes, but the noise-vocoded speech does not have speech intelligibility. We found two stages of envelope tracking in auditory cortex: an early high-{gamma} (60-140 Hz) power stage (delay {approx} 49 ms) that preferred the noise-vocoded speech, and a late {theta} (4-8 Hz) phase stage (delay {approx} 178 ms) that preferred the natural speech. Furthermore, the decoding performance of high-{gamma} power was better in primary auditory cortex than in non-primary auditory cortex, consistent with its short tracking delay. We also found distinct lateralization effects: high-{gamma} power envelope tracking dominated left auditory cortex, while {theta} phase showed better decoding performance in right auditory cortex. In sum, we suggested a functional dissociation between high-{gamma} power and {theta} phase: the former reflects fast and automatic processing of brief acoustic features, while the latter correlates to slow build-up processing facilitated by speech intelligibility.

neuroscience↗

bioRxiv2021

Ancient genomes redate the extinction of Sussemionus, a subgenus of Equus, to late Holocene

The exceptionally-rich fossil record available for the equid family has provided textbook examples of macroevolutionary changes. Horses, asses and zebras represent three extant subgenus of Equus lineage, while the Sussemionus subgenus is another remarkable Equus lineage ranging from North America to Ethiopia in Pleistocene. We sequenced 26 archaeological specimens from northern China in Holocene showing morphological features reminiscent of Equus ovodovi, a species representative of Sussemionus, and further confirmed them as this species by genetic analyses. Thus, we present the first high-quality complete genome of the Sussemionus that we sequenced to 12.0x depth-of-coverage and demonstrate that it survived until [~]3,500 years ago, despite the continued demographic collapse during the Last Glacial Maximum and the great human expansion in East Asia. We also confirmed the Equus phylogenetic tree, and found Sussemionus diverged from the ancestor of non-caballine equids [~]2.3-2.7 Million years ago and admixture events could have taken place between them. Our works suggest the small genetic diversity but not the enhanced inbreeding mainly limited the chances of survival of the species, and illustrates how ancient DNA can inform on extinction dynamics and the long-term resilience of species surviving in cryptic population pockets.

genomics↗

bioRxiv2021

Optical Control of Invertebrate nAChR and Behaviors with Dithienylethene-Imidacloprid

Photopharmacology has changed established methods of studying receptor functions, allowing for increasing spatiotemporal resolution. However, no photopharmacological tools are available for the invertebrate nicotinic acetylcholine receptor (nAChR). Here, we report a photochromic ligand, dithienylethene-imidacloprid (DitIMI), targeting invertebrate nAChR. We demonstrated that DitIMI has low spontaneous in vivo and in vitro activity but can be photoisomerized to a highly active closed-form. This photoisomerization can further be translated to photomodulation of neuron membrane potential and behavioral responses of living mosquito larvae and American cockroaches. Furthermore, we discovered that DitIMI is a specific reporter for fluorescence polarization based high-throughput screening of nAChR ligands.

molecular biology↗

bioRxiv2021

Integrative COVID-19 Biological Network Inference with Probabilistic Core Decomposition

The SARS-CoV-2 coronavirus is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network, and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs, and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The majority types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

bioinformatics↗

bioRxiv2021

Controllable drug release of pH-sensitive liposomes encapsulating artificial cytosol system

The fabrication of cell model containing artificial cytosol is challenging. Herein we constructed an artificial cytosol contained cell model by electroformation method. Agarose was selected as the main component of the artificial cytosol. Sucrose was added into agarose to regulate the sol viscosity and phase transition temperature (Tm). The viscosity of the sol with the mass ratio (agarose-sucrose) 1:9 was closest to the natural cytosolic. DSPC/20 mol% Chol was used to form large unilamellar vesicle (LUV) as cell model compartment. The rhodamine release experiment confirmed that the release of rhodamine from LUVs containing artificial cytosol took more time than that from LUVs containing pure water. The unique release profile makes agarose-sucrose@LUVs suitable as a drug carrier. Doxorubicin (DOX) is loaded in the agarose-sucrose@LUVs, and their half maximum inhibition concentration on HeLa cells is 0.015 mol L-1, which means 31.7 times increase in inhibition efficiency over free DOX.

biochemistry↗

bioRxiv2021

The First High-Quality Reference Genome of Sika Deer Provides Insights for High-Tannin Adaptation

Sika deer are known to prefer oak leaves, which are rich in tannins and toxic to most mammals; however, the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear. In identifying the mechanism responsible for the tolerance of a highly toxic diet, we have made a major advancement in the elucidation of the genomics of sika deer. We generated the first high-quality, chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments. Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food, especially the expansion of UGT genes in a subfamily. The first chromosome-level assembly and genetic characterization of the tolerance toa highly toxic diet suggest that the sika deer genome will serve as an essential resource for understanding evolutionary events and tannin adaptation. Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.

genomics↗

bioRxiv2021

Laminar perfusion imaging with zoomed arterial spin labeling at 7 Tesla

Laminar fMRI based on BOLD and CBV contrast at ultrahigh magnetic fields has been applied for studying the dynamics of mesoscopic brain networks. However, the quantitative interpretations of BOLD/CBV fMRI results are confounded by different baseline physiology across cortical layers. Here we introduce a novel 3D zoomed pseudo-continuous arterial spin labeling technique at 7T that offers the unique capability for quantitative measurements of laminar cerebral blood flow (CBF) both at rest and during task activation with high spatial specificity and sensitivity. We found arterial transit time in superficial layers is [~]100 msec shorter than in middle/deep layers revealing the dynamics of labeled blood flowing from pial arteries to downstream microvasculature. Resting state CBF peaked in the middle layers which is highly consistent with microvascular density measured from human cortex specimens. Finger tapping induced a robust two-peak laminar profile of CBF increases in the superficial (somatosensory and premotor input) and deep (spinal output) layers of M1, while finger brushing task induced a weaker CBF increase in superficial layers (somatosensory input). We further demonstrated that top-down attention induced a predominant CBF increase in deep layers and a smaller CBF increase on top of the lower baseline CBF in superficial layers of V1 (feedback cortical input), while bottom-up stimulus driven activity peaked in the middle layers (feedforward thalamic input). These quantitative laminar profiles of perfusion activity suggest an important role of M1 superficial layers for the computation of finger movements, and that visual attention may amplify deep layer output to the subcortex. Significance StatementCBF or microvascular perfusion measured by arterial spin labeling (ASL) is a key parameter for in vivo assessment of neurovascular function. Compared to BOLD or VASO fMRI, ASL perfusion contrast offers the unique capability for quantitative CBF measurements both at baseline and during task activation, which is critical for quantitative estimation of metabolic activities tightly related to neuronal activation. We proposed a zoomed 3D ASL technique at 7T for laminar perfusion imaging with high spatial specificity and sensitivity. This technique is able to differentiate and quantify the input/output and feedforward/feedback activities of human motor and visual cortex, thereby providing an important tool for quantitative assessment of neurovascular function and metabolic activities of neural circuits across cortical layers.

neuroscience↗

bioRxiv2021

The impact of PNPLA3 and TM6SF2 in cirrhosis related complications

Patatin-like phospholipase domain-containing 3 (PNPLA3) and transmembrane 6-superfamily member 2 (TM6SF2) polymorphisms have major impact for fibrosis due to steatohepatitis. However, there are scant data about correlations between cirrhosis-related complications and the polymorphisms of these genes. Therefore, we aimed to determine the role of the PNPLA3 and TM6SF2 polymorphisms in fibrosis progression for patients with liver cirrhosis. A multicenter study was performed at six hospitals in Japan enrolling 400 patients with liver cirrhosis caused by virus (n = 157), alcohol (n = 104), nonalcoholic fatty liver disease (NAFLD) (n = 106), or autoimmune disease (n = 33). These cirrhotic patients included those with complications of variceal bleeding, hepatic ascites, and/or hepatic encephalopathy and those without. To assess the role of the PNPLA3 and TM6SF2 polymorphisms in patients with cirrhosis related complications, we calculated the odds ratio and relative risk for the rs738409 and rs58542926 polymorphisms. We also accessed whether or not the interaction between these two polymorphisms contributed to cirrhosis related complications. As a result, the odds ratio for complications in the NAFLD group significantly increased in the presence of the rs738409 GG genotype when the CC genotype was used as the reference. There were no significant risks between complications and the presence of the rs738409 G allele in the virus or alcohol groups. There were no significant risks of complications in the frequency of the rs58542926 T polymorphism regardless of the etiology of liver cirrhosis. The interaction between the rs738409 and rs58542926 polymorphisms had the highest odds ratio of 2.415 for complications in the rs738409 GG + rs58542926 (CT+TT) group when rs738409 (CC+CG) + TM6SF2 CC was used as the reference in the NAFLD group although there was no statistically interaction between these gene polymorphisms (P=0.085).

genetics↗

bioRxiv2020

Novel enzyme for dimethyl sulfide-releasing in bacteria reveals a missing route in the marine sulfur cycle

Dimethylsulfoniopropionate (DMSP) is an abundant and ubiquitous organosulfur molecule and plays important roles in the global sulfur cycle. Cleavage of DMSP produces volatile dimethyl sulfide (DMS), which has impacts on the global climate. Multiple pathways for DMSP catabolism have been identified. Here we identified yet another novel pathway, the ATP DMSP lysis pathway. The key enzyme, AcoD, is an ATP-dependent DMSP lyase. AcoD belongs to the acyl-CoA synthetase superfamily, which is totally different from other DMSP lyases, showing a new evolution route. AcoD catalyses the conversion of DMSP to DMS by a two-step reaction: the ligation of DMSP with CoA to form the intermediate DMSP-CoA, which is then cleaved to DMS and acryloyl-CoA. The novel catalytic mechanism was elucidated by structural and biochemical analyses. AcoD is widely distributed in many bacterial lineages including Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Firmicutes, revealing this new pathway plays important roles in global DMSP/DMS cycles.

ecology↗

bioRxiv2020

Neonicotinoids stimulate H2-limited methane emission in Periplaneta americana through the regulation of gut bacterium community

Methane emitted by insects is considered to be an important source of atmospheric methane. Here we report the stimulation of methane emission in Periplaneta americana, an insect species with abundant methanogens, by neonicotinoids, insecticides widely used to control insect pests. The application of cycloxaprid (CYC) and imidacloprid (IMI) caused foregut expansion in P. americana, and increased the methane production and emission. Antibiotics could mostly eliminate the stimulatory effects. In P. americana gut, hydrogen levels increased and pH values decreased, which could be significantly explained by the gut bacterium community change. The proportion of several bacterium genera increased in guts following CYC treatment, and four genera from five with increased proportions could generate hydrogen at anaerobic conditions. Hydrogen is a central intermediate in methanogenesis. Gut methanogens could use the increased hydrogen to produce more methane, especially at acidic conditions. Following neonicotinoid applications, all increased methanogens in both foregut and hindgut used hydrogen as electron donor to produce methane. Besides, the up-regulation of mcrA, encoding the enzyme that catalyzes the final step of methanogenesis, suggested an enhanced methane production ability in present methanogens. In the termite Coptotermes chaohuensis, another methanogen-abundant insect species, hydrogen levels in gut and methane emission significantly increased after neonicotinoid treatment, which was similar to the results in P. americana. In summary, neonicotinoids changed bacterium community in P. americana gut to generate more hydrogen, which then stimulate gut methanogens to produce and emit more methane. The finding raised a new concern over neonicotinoid applications, and might be a potential environmental risk associated with global warming.

ecology↗