MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels.

Predicting protein localization and understanding its mechanisms are critical in biology and pathology. In this context, we propose a new web application of MULocDeep with improved performance, result interpretation, and visualization. By transferring the original model into species-specific models, MULocDeep achieved competitive prediction performance at the subcellular level against other state-of-the-art methods. It uniquely provides a comprehensive localization prediction at the suborganellar level. Besides prediction, our web service quantifies the contribution of single amino acids to localization for individual proteins; for a group of proteins, common motifs or potential targeting-related regions can be derived. Furthermore, the visualizations of targeting mechanism analyses can be downloaded for publication-ready figures. The MULocDeep web service is available at https://www.mu-loc.org/.

Prostanoid receptors in hyperfiltration-mediated glomerular injury: Novel agonists and antagonists reveal opposing roles for EP2 and EP4 receptors.

Increased fluid-flow shear stress (FFSS) contributes to hyperfiltration-induced podocyte and glomerular injury resulting in progression of chronic kidney disease (CKD). We reported that increased FFSS in vitro and in vivo upregulates PGE2 receptor EP2 (but not EP4 expression), COX2-PGE2 -EP2 axis, and EP2-linked Akt-GSK3ß-ß-catenin signaling pathway in podocytes. To understand and use the disparities between PGE2 receptors, specific agonists, and antagonists of EP2 and EP4 were used to assess phosphorylation of Akt, GSK3ß and ß-catenin in podocytes using Western blotting, glomerular filtration barrier function using in vitro albumin permeability (Palb ) assay, and mitigation of hyperfiltration-induced injury in unilaterally nephrectomized (UNX) mice at 1 and 6 months. Results show an increase in Palb by PGE2 , EP2 agonist (EP2AGO ) and EP4 antagonist (EP4ANT ), but not by EP2 antagonist (EP2ANT ) or EP4 agonist (EP4AGO ). Pretreatment with EP2ANT blocked the effect of PGE2 or EP2AGO on Palb . Modulation of EP2 and EP4 also induced opposite effects on phosphorylation of Akt and ß-Catenin. Individual agonists or antagonists of EP2 or EP4 did not induce significant improvement in albuminuria in UNX mice. However, treatment with a combination EP2ANT + EP4AGO for 1 or 6 months caused a robust decrease in albuminuria. EP2ANT + EP4AGO combination did not impact adaptive hypertrophy or increased serum creatinine. Observed differences between expression of EP2 and EP4 on the glomerular barrier highlight these receptors as potential targets for intervention. Safe and effective mitigating effect of EP2ANT + EP4AGO presents a novel opportunity to delay the progression of hyperfiltration-associated CKD as seen in transplant donors.

The role of extreme high humidex in depression in chongqing, China: A time series-analysis.

As global climate change intensifies, people are paying increasing attention to the impact of temperature changes on adverse mental health outcomes, especially depression. While increasing attention has been paid to the effect of temperature, there is little research on the effect of humidity. We aimed to investigate the association between humidex, an index combining temperature and humidity to reflect perceived temperature, and outpatient visits for depression from 2014 to 2019 in Chongqing, the largest and one of the most hot and humid cities of China. We also aimed to further identify susceptible subgroups. A distributed lag non-linear model (DLNM) was used to explore the concentration-response relationship between humidex and depression outpatient visits. Hierarchical analysis was carried out by age and gender. A total of 155,436 visits for depression were collected from 2014 to 2019 (2191 days). We found that depression outpatient visits were significantly associated with extremely high humidex (≥40). The significant positive single-lag day effect existed at lag 0 (RR = 1.029, 95%CI: 1.000-1.059) to lag 2 (RR = 1.01, 95%CI: 1.004-1.028), and lag 12 (RR = 1.013, 95%CI: 1.002-1.024). The significant cumulative adverse effects lasted from lag 01 to lag 014. Hierarchical analyses showed that females and the elderly (≥60 years) appeared to be more susceptible to extremely high humidex. The attributable numbers (AN) and fraction (AF) of extremely high humidex on depression outpatients were 1709 and 1.10%, respectively. Extremely high humidex can potentially increase the risk of depression, especially in females and the elderly. More protective measures should be taken in vulnerable populations.

Association between ambient carbon monoxide levels and hospitalization costs of patients with myocardial infarction: Potential effect modification by ABO blood group.

Previous researches have reported the association between air pollution and various diseases. However, few researches have investigated whether air pollutants are associated with the economic loss resulting from patients' hospitalization, especially the economic loss of hospitalization due to acute cardiovascular events. The purpose of our research was to explore the association between the levels of carbon monoxide (CO), taken as an index of pollution, and the hospitalization costs of myocardial infarction (MI), and the potential effect modification by the ABO blood group. A total of 3237 MI inpatients were included in this study. A multiple linear regression model was used to evaluate the association between ambient CO levels and hospitalization costs of MI patients. Moreover, we performed stratified analyses by age, gender, body mass index (BMI), season, hypertension, and ABO blood types. There was a positive association between the levels of CO in the air and the costs of hospitalization caused by MI. Furthermore, such association was stronger in males, BMI ≥25, <65 years, with hypertension, and non-O blood group. Interestingly, we found the association was particularly significant in patients with blood group B. Overall, our study first found that ambient CO levels could have an impact on the hospitalization costs for MI patients, and those with blood group B can be more sensitive.

DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism.

Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computational methods for mRNA subcellular localization prediction provide a useful approach for studying mRNA functions. However, few computational methods were designed for mRNA subcellular localization prediction and their performance have room for improvement. Especially, there is still no available tool to predict for mRNAs that have multiple localization annotations. In this paper, we propose a multi-head self-attention method, DM3Loc, for multi-label mRNA subcellular localization prediction. Evaluation results show that DM3Loc outperforms existing methods and tools in general. Furthermore, DM3Loc has the interpretation ability to analyze RNA-binding protein motifs and key signals on mRNAs for subcellular localization. Our analyses found hundreds of instances of mRNA isoform-specific subcellular localizations and many significantly enriched gene functions for mRNAs in different subcellular localizations.

Bioinformatics for plant and agricultural discoveries in the age of multiomics: A review and case study of maize nodal root growth under water deficit.

Advances in next-generation sequencing and other high-throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in-house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future.

The association between short-term exposure to ambient carbon monoxide and hospitalization costs for bronchitis patients: A hospital-based study.

Ambient carbon monoxide (CO) is associated with bronchitis morbidity, but there is no evidence concerning its correlation with hospitalization costs for bronchitis patients. This study aimed to investigate the relationship between short-term ambient CO exposure and hospitalization costs for bronchitis patients in Chongqing, China. Baseline data for 3162 hospitalized bronchitis patients from November 2013 to December 2019 were collected. Multiple linear regression analysis was used to determine the association, delayed and cumulative, between short-term CO exposure and hospitalization costs. Additionally, subgroup analyses were performed by gender, age, season, and comorbidity. Positive association between CO and hospitalization costs for bronchitis patients was observed. The strongest association was observed at lag 015 days, with per 1 mg/m3 increase of CO concentrations corresponded to 5834.40 Chinese Yuan (CNY) (95% CI: 2318.71, 9350.08; P < 0.001) (845.97 US dollars) increment in hospitalization costs. Stratified analysis results showed that the association was more obvious among those males, elderly, with comorbidities, and in warm seasons. More importantly, there was strongest correlation between CO and bronchitis patients with coronary heart disease. In summary, short-term exposure to ambient CO, even lower than Chinese and WHO standards, can be associated with increased hospitalization costs for bronchitis. Controlling CO exposure can be helpful to reduce medical burden associated with bronchitis patients. The results also suggest that when setting air quality standards and formulating preventive measures, susceptible subpopulations ought to be considered.

MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.

MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.

A dynamic programing approach to integrate gene expression data and network information for pathway model generation.

MOTIVATION: As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. RESULTS: To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. AVAILABILITY AND IMPLEMENTATION: IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation.

Nowadays, large amounts of omics data have been generated and contributed to increasing knowledge about associated biological mechanisms. A new challenge coming along is how to identify the active pathways and extract useful insights from these data with huge background information and noise. Although biologically meaningful modules can often be detected by many existing informatics tools, it is still hard to interpret or make use of the results towards in silico hypothesis generation and testing. To address this gap, we previously developed the IMPRes (Integrative MultiOmics Pathway Resolution) v 1.0 algorithm, a new step-wise active pathway detection method using a dynamic programming approach. This approach enables the network detection one step at a time, making it easy for researchers to trace the pathways, and leading to more accurate drug design and more effective treatment strategies. In this paper, we present IMPRes-Pro, an enhancement to IMPRes v1.0 by integrating proteomics data along with transcriptomics data and constructing a heterogeneous background network. The evaluation experiment conducted on human primary breast cancer dataset has shown the advantage over the original IMPRes v1.0 method. Furthermore, a case study on human metastatic breast cancer dataset was performed and we have provided several insights regarding the selection of optimal therapy strategy. IMPRes-Pro algorithm and visualization tool is available as a web service at http://digbio.missouri.edu/impres.

Dose-response association between sugar- and artificially sweetened beverage consumption and the risk of metabolic syndrome: a meta-analysis of population-based epidemiological studies.

OBJECTIVE: The associations between sugar-sweetened beverage (SSB) and artificially sweetened beverage (ASB) consumption and the risk of metabolic syndrome (MetS) remain controversial. A quantitative assessment of dose-response associations has not been reported. This study aims to assess the associations between the risk of MetS and SSB, ASB, and total sweetened beverage (TSB, the combination of SSB and ASB) consumption by reviewing population-based epidemiological studies. DESIGN: Meta-analysis. SETTING: We searched PubMed, Embase and Web of Science databases prior to 4 November 2019, for relevant studies investigating the SSB-MetS and ASB-MetS associations. A random effects model was used to estimate pooled relative risks (RR) and 95 % CI. Dose-response association was assessed using a restricted cubic splines model. PARTICIPANTS: We identified seventeen articles (twenty-four studies, including 93 095 participants and 20 749 MetS patients). RESULTS: The pooled RR for the risk of MetS were 1·51 (95 % CI 1·34, 1·69), 1·56 (1·32, 1·83) and 1·44 (1·19, 1·75) in high consumption group of TSB, SSB and ASB, respectively; and 1·20 (1·13, 1·28), 1·19 (1·11, 1·28) and 1·31 (1·05, 1·65) per 250 ml/d increase in TSB, SSB and ASB consumption, respectively. Additionally, we found evidence of non-linear, TSB-MetS and SSB-MetS dose-response associations and a linear ASB-MetS dose-response association. CONCLUSIONS: TSB, SSB and ASB consumption was associated with the risk of MetS. The present findings provide evidence that supports reducing intake of these beverages to lower the TSB-, SSB- and ASB-related risk of MetS.

Upregulated proteoglycan-related signaling pathways in fluid flow shear stress-treated podocytes.

The ultrafiltrate flow over the major processes and cell body generates fluid flow shear stress (FFSS) on podocytes. Hyperfiltration-associated increase in FFSS can lead to podocyte injury and detachment. Previously, we showed that FFSS-induced upregulation of the cyclooxygenase 2 (COX2)-PGE2-prostaglandin E receptor 2 (EP2) axis in podocytes activates Akt-glycogen synthase kinase-3ß-ß-catenin and MAPK/ERK signaling in response to FFSS. Integrative MultiOmics Pathway Resolution (IMPRes) is a new bioinformatic tool that enables simultaneous time-series analysis of more than two groups to identify pathways and molecular connections. In the present study, we used previously characterized COX2 [prostaglandin-endoperoxide synthase 2 (Ptgs2)], EP2 (Ptger2), and ß1-catenin (Ctnnb1) as "seed genes" from an array data set of four groups analyzed over a time course. The 3 seed genes shared 7 pathways and 50 genes of 14 pathways and 89 genes identified by IMPRes. A composite of signaling pathways highlighted the temporal molecular connections during mechanotransduction signaling in FFSS-treated podocytes. We investigated the "proteoglycans in cancer" and "galactose metabolism" pathways predicted by IMPRes. A custom-designed PCR array validated 60.7% of the genes predicted by IMPRes analysis, including genes for the above-named pathways. Further validation using Western blot analysis showed increased expression of phosho-Erbb2, phospho-mammalian target of rapamycin (mTOR), CD44, and hexokinase II (Hk2); decreased total Erbb2, galactose mutarotase (Galm), and ß-1,4-galactosyltransferase 1 (B4galt1); and unchanged total mTOR and AKT3. These findings corroborate our previously reported results. This study demonstrates the potential of the IMPRes method to identify novel pathways. Identifying the "proteoglycans in cancer" and "galactose metabolism" pathways has generated a lead to study the significance of FFSS-induced glycocalyx remodeling and possible detachment of podocytes from the glomerular matrix.

Silencing AXL by covalent siRNA-gelatin-antibody nanoconjugate inactivates mTOR/EMT pathway and stimulates p53 for TKI sensitization in NSCLC.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality with the 5-year survival rate at a dismal 16% for the past 40 years. Drug resistance is a major obstacle to achieving long-term patient survival. Identifying and validating molecular biomarkers responsible for resistance and thereby adopting multi-directional therapy is necessary to improve the survival rate. Previous studies indicated ~20% of tyrosine kinase inhibitor (TKI) resistant NSCLC patients overexpress AXL with increase in EMT and decrease in p53 expression. To overcome the resistance, we designed gelatin nanoparticles covalently conjugated with EGFR targeting antibody and siRNA (GAbsiAXL). GAbsiAXL efficiently silences AXL, decreases mTOR and EMT signaling with concomitant increase in p53 expression. Because of the molecular changes, the AXL silencing sensitizes the cells to TKI. Our results show AXL overexpression has an important role in driving TKI resistance through close association with energy-dependent mitochondrial pathways.

Essential protein identification based on essential protein-protein interaction prediction by Integrated Edge Weights.

Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein-protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein-protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision-recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein-protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms' essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.

S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure.

Large protein language models (PLMs) present excellent potential to reshape protein research by encoding the amino acid sequences into mathematical and biological meaningful embeddings. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily depending on 3D structures. To address this issue, we introduce S-PLM, a 3D structure-aware PLM utilizing multi-view contrastive learning to align the sequence and 3D structure of a protein in a coordinate space. S-PLM applies Swin-Transformer on AlphaFold-predicted protein structures to embed the structural information and fuses it into sequence-based embedding from ESM2. Additionally, we provide a library of lightweight tuning tools to adapt S-PLM for diverse protein property prediction tasks. Our results demonstrate S-PLM's superior performance over sequence-only PLMs, achieving competitiveness in protein function prediction compared to state-of-the-art methods employing both sequence and structure inputs.

Scaffold protein SH3BP2 signalosome is pivotal for immune activation in nephrotic syndrome.

Despite clinical use of immunosuppressive agents, the immunopathogenesis of minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) remains unclear. Src homology 3-binding protein 2 (SH3BP2), a scaffold protein, forms an immune signaling complex (signalosome) with 17 other proteins, including phospholipase Cγ2 (PLCγ2) and Rho-guanine nucleotide exchange factor VAV2 (VAV2). Bioinformatic analysis of human glomerular transcriptome (Nephrotic Syndrome Study Network cohort) revealed upregulated SH3BP2 in MCD and FSGS. The SH3BP2 signalosome score and downstream MyD88, TRIF, and NFATc1 were significantly upregulated in MCD and FSGS. Immune pathway activation scores for Toll-like receptors, cytokine-cytokine receptor, and NOD-like receptors were increased in FSGS. Lower SH3BP2 signalosome score was associated with MCD, higher estimated glomerular filtration rate, and remission. Further work using Sh3bp2KI/KI transgenic mice with a gain-in-function mutation showed ~6-fold and ~25-fold increases in albuminuria at 4 and 12 weeks, respectively. Decreased serum albumin and unchanged serum creatinine were observed at 12 weeks. Sh3bp2KI/KI kidney morphology appeared normal except for increased mesangial cellularity and patchy foot process fusion without electron-dense deposits. SH3BP2 co-immunoprecipitated with PLCγ2 and VAV2 in human podocytes, underscoring the importance of SH3BP2 in immune activation. SH3BP2 and its binding partners may determine the immune activation pathways resulting in podocyte injury leading to loss of the glomerular filtration barrier.

Association between maternal short-term exposure to ambient air pollution and the risk of fetal distress: A matched case-control study.

BACKGROUND: Ambient air pollution has been linked to gestational complications. However, the evidence on the relationship between air pollution and fetal distress is limited. OBJECTIVES: To investigate the relationship between maternal short-term air pollution exposure and fetal distress, and to identify a potential susceptible population. METHODS: This matched case-control study, involving 313 pregnancy women with fetal distress was conducted in Xi'an, the largest city in Northwest China from 2013 to 2016. Each woman with fetal distress was randomly matched with four women without fetal distress of the same age, same gestational week, and registration in the same period (n = 1252). Inverse distance-weighted (IDW) interpolation was applied to estimate maternal air pollution exposure based on the residential addresses. We employed conditional logistic regression model to evaluate the relationship between air pollutants and fetal distress. Distributed lag nonlinear model (DLNM) was performed to examine the exposure-response relationship between air pollutants and fetal distress. RESULTS: Maternal short-term exposure to PM10, PM2.5-10 (PMc), SO2, NO2, and CO was associated with increased risk of fetal distress. Each 10 µg/m3 increment in PM10, PMc, SO2 at lag 014, and NO2 at lag 010, the odds ratio (ORs) of fetal distress were 1.027 (95 % confidence interval (CI): 1.004, 1.050), 1.058 (95 % CI: 1.014, 1.105), 1.140 (95 % CI: 1.029, 1.264), and 1.158 (95 % CI: 1.046, 1.283), respectively. Similarly, with a 0.1 mg/m3 increment in CO at lag 014, the OR of fetal distress was 1.029 (95 % CI: 1.002, 1.058). Stratified analyses showed that the estimate associations of PM10, PM2.5 and CO appeared to be stronger, although not statistically significantly, among women with gestational complications. CONCLUSION: Maternal short-term exposure to ambient air pollution may increase the risk of fetal distress. Understanding the detrimental role of air pollution in fetal distress can help us better develop preventative methods in reducing its' impact on maternal and fetal health.

Single-cell biological network inference using a heterogeneous graph transformer.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

The association between short-term ambient sulfur dioxide exposure and hospitalization costs of ischemic stroke: a hospital-based study in Chongqing, China.

Evidence of the short-term effects of ambient sulfur dioxide (SO2) exposure on the economic burden of ischemic stroke is limited. This study aimed to explore the association between short-term ambient SO2 exposure and hospitalization costs for ischemic stroke in Chongqing, the most populous city in China. The hospital-based study included 7271 ischemic stroke inpatients. Multiple linear regression models were used to estimate the association between SO2 concentration and hospitalization costs. Propensity score matching was used to compare the patients' characteristics when exposed to SO2 concentrations above and below 20 µg/m3. It is found that short-term SO2 exposure was positively correlated with the hospitalization costs of ischemic stroke. The association was more evident in males, people younger than 65, and people hospitalized in the cool seasons. Besides, among the components of hospitalization costs, medicine costs were most significantly associated with SO2. More interesting, the lower concentration of SO2, the higher costs associated with 1 µg/m3 SO2 change. Above all, SO2 was positively associated with hospitalization costs of ischemic stroke, even at its low levels. The measures to reduce the level of SO2 can help reduce the burden of ischemic stroke.

Single-cell RNA sequencing to characterize the response of pancreatic cancer to anti-PD-1 immunotherapy.

Pancreatic cancer (PaC) is resistant to immune checkpoint therapy, but the underlying mechanisms are largely unknown. In this study, we have established four orthotopic PaC murine models with different PaC cell lines by intra-pancreatic inoculation. Therapeutic examinations demonstrate that only tumors induced with Panc02-H7 cells respond to αPD-1 antibody treatment, leading to significantly reduced tumor growth and increased survival in the recipient mice. Transcriptomic profiling at a single-cell resolution characterizes the molecular activity of different cells within tumors. Comparative analysis and validated experiments demonstrate that αPD-1-sensitive and -resistant tumors differently shape the immune landscape in the tumor microenvironment (TME) and markedly altering effector CD8+ T cells and tumor-associated macrophages (TAMs) in their number, frequency, and gene profile. More exhausted effector CD8+ T cells and increased M2-like TAMs with a reduced capacity of antigen presentation are detected in resistant Panc02-formed tumors versus responsive Panc02-H7-formed tumors. Together, our data highlight the correlation of tumor-induced imbalance of macrophages with the fate of tumor-resident effector CD8+ T cells and PaC response to αPD-1 immunotherapy. TAMs as a critical regulator of tumor immunity and immunotherapy contribute to PaC resistance to immune checkpoint blockade.