Osmosensor-mediated control of Ca2+ spiking in pollen germination.

Higher plants survive terrestrial water deficiency and fluctuation by arresting cellular activities (dehydration) and resuscitating processes (rehydration). However, how plants monitor water availability during rehydration is unknown. Although increases in hypo-osmolarity-induced cytosolic Ca2+ concentration (HOSCA) have long been postulated to be the mechanism for sensing hypo-osmolarity in rehydration1,2, the molecular basis remains unknown. Because osmolarity triggers membrane tension and the osmosensing specificity of osmosensing channels can only be determined in vivo3-5, these channels have been classified as a subtype of mechanosensors. Here we identify bona fide cell surface hypo-osmosensors in Arabidopsis and find that pollen Ca2+ spiking is controlled directly by water through these hypo-osmosensors-that is, Ca2+ spiking is the second messenger for water status. We developed a functional expression screen in Escherichia coli for hypo-osmosensitive channels and identified OSCA2.1, a member of the hyperosmolarity-gated calcium-permeable channel (OSCA) family of proteins6. We screened single and high-order OSCA mutants, and observed that the osca2.1/osca2.2 double-knockout mutant was impaired in pollen germination and HOSCA. OSCA2.1 and OSCA2.2 function as hypo-osmosensitive Ca2+-permeable channels in planta and in HEK293 cells. Decreasing osmolarity of the medium enhanced pollen Ca2+ oscillations, which were mediated by OSCA2.1 and OSCA2.2 and required for germination. OSCA2.1 and OSCA2.2 convert extracellular water status into Ca2+ spiking in pollen and may serve as essential hypo-osmosensors for tracking rehydration in plants.

Palmitoylation at a conserved cysteine residue facilitates gasdermin D-mediated pyroptosis and cytokine release.

Gasdermin D (GSDMD)-mediated pyroptotic cell death drives inflammatory cytokine release and downstream immune responses upon inflammasome activation, which play important roles in host defense and inflammatory disorders. Upon activation by proteases, the GSDMD N-terminal domain (NTD) undergoes oligomerization and membrane translocation in the presence of lipids to assemble pores. Despite intensive studies, the molecular events underlying the transition of GSDMD from an autoinhibited soluble form to an oligomeric pore form inserted into the membrane remain incompletely understood. Previous work characterized S-palmitoylation for gasdermins from bacteria, fungi, invertebrates, as well as mammalian gasdermin E (GSDME). Here, we report that a conserved residue Cys191 in human GSDMD was S-palmitoylated, which promoted GSDMD-mediated pyroptosis and cytokine release. Mutation of Cys191 or treatment with palmitoyltransferase inhibitors cyano-myracrylamide (CMA) or 2-bromopalmitate (2BP) suppressed GSDMD palmitoylation, its localization to the membrane and dampened pyroptosis or IL-1ß secretion. Furthermore, Gsdmd-dependent inflammatory responses were alleviated by inhibition of palmitoylation in vivo. By contrast, coexpression of GSDMD with palmitoyltransferases enhanced pyroptotic cell death, while introduction of exogenous palmitoylation sequences fully restored pyroptotic activities to the C191A mutant, suggesting that palmitoylation-mediated membrane localization may be distinct from other molecular events such as GSDMD conformational change during pore assembly. Collectively, our study suggests that S-palmitoylation may be a shared regulatory mechanism for GSDMD and other gasdermins, which points to potential avenues for therapeutically targeting S-palmitoylation of gasdermins in inflammatory disorders.

Genetic susceptibility to chronic diseases leads to heart failure among Europeans: the influence of leukocyte telomere length.

BACKGROUND: Genetic susceptibility to various chronic diseases has been shown to influence heart failure (HF) risk. However, the underlying biological pathways, particularly the role of leukocyte telomere length (LTL), are largely unknown. We investigated the impact of genetic susceptibility to chronic diseases and various traits on HF risk, and whether LTL mediates or modifies the pathways. METHODS: We conducted prospective cohort analyses on 404 883 European participants from the UK Biobank, including 9989 incident HF cases. Multivariable Cox regression was used to estimate associations between HF risk and 24 polygenic risk scores (PRSs) for various diseases or traits previously generated using a Bayesian approach. We assessed multiplicative interactions between the PRSs and LTL previously measured in the UK Biobank using quantitative PCR. Causal mediation analyses were conducted to estimate the proportion of the total effect of PRSs acting indirectly through LTL, an integrative marker of biological aging. RESULTS: We identified 9 PRSs associated with HF risk, including those for various cardiovascular diseases or traits, rheumatoid arthritis (P = 1.3E-04), and asthma (P = 1.8E-08). Additionally, longer LTL was strongly associated with decreased HF risk (P-trend = 1.7E-08). Notably, LTL strengthened the asthma-HF relationship significantly (P-interaction = 2.8E-03). However, LTL mediated only 1.13% (P < 0.001) of the total effect of the asthma PRS on HF risk. CONCLUSIONS: Our findings shed light onto the shared genetic susceptibility between HF risk, asthma, rheumatoid arthritis, and other traits. Longer LTL strengthened the genetic effect of asthma in the pathway to HF. These results support consideration of LTL and PRSs in HF risk prediction.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction.

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

A genome-wide CRISPR/Cas9 knockout screen identifies TMEM239 as an important host factor in facilitating African swine fever virus entry into early endosomes.

African swine fever (ASF) is a highly contagious, fatal disease of pigs caused by African swine fever virus (ASFV). The complexity of ASFV and our limited understanding of its interactions with the host have constrained the development of ASFV vaccines and antiviral strategies. To identify host factors required for ASFV replication, we developed a genome-wide CRISPR knockout (GeCKO) screen that contains 186,510 specific single guide RNAs (sgRNAs) targeting 20,580 pig genes and used genotype II ASFV to perform the GeCKO screen in wild boar lung (WSL) cells. We found that knockout of transmembrane protein 239 (TMEM239) significantly reduced ASFV replication. Further studies showed that TMEM239 interacted with the early endosomal marker Rab5A, and that TMEM239 deletion affected the co-localization of viral capsid p72 and Rab5A shortly after viral infection. An ex vivo study showed that ASFV replication was significantly reduced in TMEM239-/- peripheral blood mononuclear cells from TMEM239 knockout piglets. Our study identifies a novel host factor required for ASFV replication by facilitating ASFV entry into early endosomes and provides insights for the development of ASF-resistant breeding.

Diet, Pace of Biological Aging, and Risk of Dementia in the Framingham Heart Study.

OBJECTIVE: People who eat healthier diets are less likely to develop dementia, but the biological mechanism of this protection is not well understood. We tested the hypothesis that healthy diet protects against dementia because it slows the pace of biological aging. METHODS: We analyzed Framingham Offspring Cohort data. We included participants ≥60 years-old, free of dementia and having dietary, epigenetic, and follow-up data. We assessed healthy diet as long-term adherence to the Mediterranean-Dash Intervention for Neurodegenerative Delay diet (MIND, over 4 visits spanning 1991-2008). We measured the pace of aging from blood DNA methylation data collected in 2005-2008 using the DunedinPACE epigenetic clock. Incident dementia and mortality were defined using study records compiled from 2005 to 2008 visit through 2018. RESULTS: Of n = 1,644 included participants (mean age 69.6, 54% female), n = 140 developed dementia and n = 471 died over 14 years of follow-up. Greater MIND score was associated with slower DunedinPACE and reduced risks for dementia and mortality. Slower DunedinPACE was associated with reduced risks for dementia and mortality. In mediation analysis, slower DunedinPACE accounted for 27% of the diet-dementia association and 57% of the diet-mortality association. INTERPRETATION: Findings suggest that slower pace of aging mediates part of the relationship of healthy diet with reduced dementia risk. Monitoring pace of aging may inform dementia prevention. However, a large fraction of the diet-dementia association remains unexplained and may reflect direct connections between diet and brain aging that do not overlap other organ systems. Investigation of brain-specific mechanisms in well-designed mediation studies is warranted. ANN NEUROL 2024;95:1069-1079.

Sulfatase-Induced In Situ Formulation of Antineoplastic Supra-PROTACs.

Proteolysis targeting chimera (PROTAC) technology is an innovative strategy for cancer therapy, which, however, suffers from poor targeting delivery and limited capability for protein of interest (POI) degradation. Here, we report a strategy for the in situ formulation of antineoplastic Supra-PROTACs via intracellular sulfatase-responsive assembly of peptides. Coassembling a sulfated peptide with two ligands binding to ubiquitin VHL and Bcl-xL leads to the formation of a pro-Supra-PROTAC, in which the ratio of the two ligands is rationally optimized based on their protein binding affinity. The resulting pro-Supra-PROTAC precisely undergoes enzyme-responsive assembly into nanofibrous Supra-PROTACs in cancer cells overexpressing sulfatase. Mechanistic studies reveal that the pro-Supra-PROTACs selectively cause apparent cytotoxicity to cancer cells through the degradation of Bcl-xL and the activation of caspase-dependent apoptosis, during which the rationally optimized ligand ratio improves the bioactivity for POI degradation and cell death. In vivo studies show that in situ formulation enhanced the tumor accumulation and retention of the pro-Supra-PROTACs, as well as the capability for inhibiting tumor growth with excellent biosafety when coadministrating with chemodrugs. Our findings provide a new approach for enzyme-regulated assembly of peptides in living cells and the development of PROTACs with high targeting delivering and POI degradation efficiency.

Characterization of linoleate dioxygenases in basidiomycetes and the functional role of CcLdo1 in regulating fruiting body development in Coprinopsis cinerea.

Coprinopsis cinerea, a model fungus, is utilized for investigating the developmental mechanisms of basidiomycetes. The development of basidiomycetes is a highly organized process that requires coordination among genetic, environmental, and physiological factors. Oxylipins, a class of widely distributed signaling molecules, play crucial roles in fungal biology. Among oxylipins, the sexual pheromone-inducing factors (psi factors) have been identified as key regulators of the balance between asexual and sexual spore development in Ascomycetes. Linoleate dioxygenases are enzymes involved in the biosynthesis of psi factors, yet their specific physiological functions in basidiomycete development remain unclear. In this study, linoleate dioxygenases in basidiomycetes were identified and characterized. Phylogenetic analysis revealed that linoleate dioxygenases from Basidiomycota formed a distinct clade, with linoleate dioxygenases from Agaricomycetes segregating into three groups and those from Ustilaginomycetes forming a separate group. Both basidiomycete and ascomycete linoleate dioxygenases shared two characteristic domains: the N-terminal of linoleate dioxygenase domain and the C-terminal of cytochrome P450 domain. While the linoleate dioxygenase domains exhibited similarity between basidiomycetes and ascomycetes, the cytochrome P450 domains displayed high diversity in key sites. Furthermore, the gene encoding the linoleate dioxygenase Ccldo1 in C. cinerea was knocked out, resulting in a significant increase in fruiting body formation without affecting asexual conidia production. This observation suggests that secondary metabolites synthesized by CcLdo1 negatively regulate the sexual reproduction process in C. cinerea while not influencing the asexual reproductive process. This study represents the first identification of a gene involved in secondary metabolite synthesis that regulates basidiocarp development in a basidiomycete.

Advances in metabolism pathways of theaflavins: digestion, absorption, distribution and degradation.

Theaflavins, a major kind of component in black tea, have been reported to show a variety of biological activities and health effects. However, the unstable chemical properties, low bioavailability and unclear metabolism pathways of theaflavins have left much to be desired in terms of its specific efficacy and applications. This paper provides a comprehensive knowledge on the digestion, absorption, metabolism, distribution and excretion of theaflavins. We find that pH-dependent stability, efflux transport proteins are closely related to the low absorption rate and low bioavailability of theaflavins. When pass through the gastrointestinal tract, TFDG, TF2A and TF2B are gradually degraded to TF1, and release gallic acid. Then, the theaflavins skeleton are degraded into small molecular phenolic substances under the action of enzymes and microorganisms. In addition, theaflavins are widely distributed in the human body including brain, lung, heart, kidney, liver, blood tissue in a low content and can be excreted through feces. However, the influence of digestive enzymes barrier and gut microbial barrier on theaflavins are still unclear. Importantly, most findings are reported by in vitro methods and animal experiments, the metabolites and metabolic pathways of theaflavins in human body are not fully understood and need to be further investigated. We hope to lay a theoretical basis for exploring methods to improve the bioavailability of theaflavins and expanding the application of theaflavins in health foods as well as pharmaceuticals.

Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay.

Lactate dehydrogenase A (LDHA) is highly expressed in many tumor cells and promotes the conversion of pyruvate to lactic acid in the glucose pathway, providing energy and synthetic precursors for rapid proliferation of tumor cells. Therefore, inhibition of LDHA has become a widely concerned tumor treatment strategy. However, the research and development of highly efficient and low toxic LDHA small molecule inhibitors still faces challenges. To discover potential inhibitors against LDHA, virtual screening based on molecular docking techniques was performed from Specs database of more than 260,000 compounds and Chemdiv-smart database of more than 1,000 compounds. Through molecular dynamics (MD) simulation studies, we identified 12 potential LDHA inhibitors, all of which can stably bind to human LDHA protein and form multiple interactions with its active central residues. In order to verify the inhibitory activities of these compounds, we established an enzyme activity assay system and measured their inhibitory effects on recombinant human LDHA. The results showed that Compound 6 could inhibit the catalytic effect of LDHA on pyruvate in a dose-dependent manner with an EC50 value of 14.54 ± 0.83 µM. Further in vitro experiments showed that Compound 6 could significantly inhibit the proliferation of various tumor cell lines such as pancreatic cancer cells and lung cancer cells, reduce intracellular lactic acid content and increase intracellular reactive oxygen species (ROS) level. In summary, through virtual screening and in vitro validation, we found that Compound 6 is a small molecule inhibitor for LDHA, providing a good lead compound for the research and development of LDHA related targeted anti-tumor drugs.

α-Ketoglutarate alleviates osteoarthritis by inhibiting ferroptosis via the ETV4/SLC7A11/GPX4 signaling pathway.

Osteoarthritis (OA) is the most common degenerative joint disorder that causes disability in aged individuals, caused by functional and structural alterations of the knee joint. To investigate whether metabolic drivers might be harnessed to promote cartilage repair, a liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics approach was carried out to screen serum biomarkers in osteoarthritic rats. Based on the correlation analyses, α-ketoglutarate (α-KG) has been demonstrated to have antioxidant and anti-inflammatory properties in various diseases. These properties make α-KG a prime candidate for further investigation of OA. Experimental results indicate that α-KG significantly inhibited H2O2-induced cartilage cell matrix degradation and apoptosis, reduced levels of reactive oxygen species (ROS) and malondialdehyde (MDA), increased superoxide dismutase (SOD) and glutathione (GSH)/glutathione disulfide (GSSG) levels, and upregulated the expression of ETV4, SLC7A11 and GPX4. Further mechanistic studies observed that α-KG, like Ferrostatin-1 (Fer-1), effectively alleviated Erastin-induced apoptosis and ECM degradation. α-KG and Fer-1 upregulated ETV4, SLC7A11, and GPX4 at the mRNA and protein levels, decreased ferrous ion (Fe2+) accumulation, and preserved mitochondrial membrane potential (MMP) in ATDC5 cells. In vivo, α-KG treatment inhibited ferroptosis in OA rats by activating the ETV4/SLC7A11/GPX4 pathway. Thus, these findings indicate that α-KG inhibits ferroptosis via the ETV4/SLC7A11/GPX4 signaling pathway, thereby alleviating OA. These observations suggest that α-KG exhibits potential therapeutic properties for the treatment and prevention of OA, thereby having potential clinical applications in the future.

Effects of genetically proxied lipid-lowering drugs on acute myocardial infarction: a drug-target mendelian randomization study.

OBJECTIVE: High low-density-lipoprotein (LDL) cholesterol has been associated with an increased risk of coronary artery diseases (CAD) including acute myocardial infarction (AMI). However, whether lipids lowering drug treatment is causally associated with decreased risk of AMI remains largely unknown. We used Mendelian randomization (MR) to evaluate the influence of genetic variation affecting the function of lipid-lowering drug targets on AMI. METHODS: Single-nucleotide polymorphisms (SNPs) associated with lipids as instruments were extracted from the Global Lipids Genetics Consortium (GLGC). The genome-wide association study (GWAS) data for AMI were obtained from UK Biobank. Two sample MR analysis was used to study the associations between high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides (TG) with AMI (n = 3,927). Genetic variants associated with LDL cholesterol at or near drug target gene were used to mimic drug effects on the AMI events in drug target MR. RESULTS: Genetically predicted higher LDL-C (per one SD increase in LDL-C of 38.67 mg/dL, OR 1.006, 95% CI 1.004-1.007) and TG (per one SD increase in TG of 90.72 mg/dL, 1.004, 1.002-1.006) was associated with increased risk of AMI, but decreased risk for higher HDL-C (per one SD increase in HDL-C of 15.51 mg/dL, 0.997, 0.995-0.999) in univariable MR. Association remained significant for LDL-C, but attenuated toward the null for HDL-C and TG in multivariable MR. Genetically proxied lower LDL-C with genetic variants at or near the PCSK9 region (drug target of evolocumab) and NPC1L1 (drug target of ezetimibe) were associated with decreased risk of AMI (0.997, 0.994-0.999 and 0.986, 0.975-0.998, respectively), whereas genetic variants at HMGCR region (drug target of statin) showed marginal association with AMI (0.995, 0.990-1.000). After excluding drug target-related SNPs, LDL-C related SNPs outside the drug target region remained a causal effect on AMI (0.994, 0.993-0.996). CONCLUSIONS: The findings suggest that genetically predicted LDL-C may play a predominant role in the development of AMI. The drug MR results imply that ezetimibe and evolocumab may decrease the risk of AMI due to their LDL-C lowering effect, and there are other non-drug related lipid lowering pathways that may be causally linked to AMI.

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy.

AIM: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference. METHODS AND RESULTS: We retrospectively analyzed images from 50 adult patients (median age 51, interquartile range 32-62 42% women) who had undergone CMR within 1 month of 2DE. RV planimetry of the myocardial border was performed in end-diastole (ED) and end-systole (ES) for eight standardized 2DE RV views with calculation of areas. The DL model comprised a Feature Tokenizer module and a stack of Transformer layers. Age, gender and calculated areas were used as inputs, and the output was RV volume in ED/ES. The dataset was randomly split into training, validation and testing subsets (35, 5 and 10 patients respectively). Mean RVEDV, RVESV and RV ejection fraction (EF) were 163 ± 70 mL, 82 ± 42 mL and 51% ± 8% respectively without differences among the subsets. The proposed method achieved good prediction of RV volumes (R2  = .953, absolute percentage error [APE] = 9.75% ± 6.23%) and RVEF (APE = 7.24% ± 4.55%). Per CMR, there was one patient with RV dilatation and three with RV dysfunction in the testing dataset. The DL model detected RV dilatation in 1/1 case and RV dysfunction in 4/3 cases. CONCLUSIONS: An attention-based DL method for 2DE RV quantification showed feasibility and promising accuracy. The method requires validation in larger cohorts with wider range of RV size and function. Further research will focus on the reduction of the number of required 2DE to make the method clinically applicable.

Deep learning based detection and classification of fetal lip in ultrasound images.

OBJECTIVES: Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep learning technology to develop a new model aimed at quickly and accurately assessing the development of fetal lips during prenatal examinations. This model can detect ultrasound images of the fetal lips and classify them, aiming to provide a more objective prediction for the development of fetal lips. METHODS: This study included 632 pregnant women in their mid-pregnancy stage, who underwent ultrasound examinations of the fetal lips, collecting both normal and abnormal fetal lip ultrasound images. To improve the accuracy of the detection and classification of fetal lips, we proposed and validated the Yolov5-ECA model. RESULTS: The experimental results show that, compared with the currently popular 10 models, our model achieved the best results in the detection and classification of fetal lips. In terms of the detection of fetal lips, the mAP@0.5 and mAP@0.5:0.95 were 0.920 and 0.630, respectively. In the classification of fetal lip ultrasound images, the accuracy reached 0.925. CONCLUSIONS: The deep learning algorithm has accuracy consistent with manual evaluation in the detection and classification process of fetal lips. This automated recognition technology can provide a powerful tool for inexperienced young doctors, helping them to accurately conduct examinations and diagnoses of fetal lips.

Soil Marginal Effect and LSTM Model in Chinese Solar Greenhouse.

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse.

ARIP: A Tool for Precise Interatomic Contact Area and Volume Calculation in Proteins.

The interplay patterns of amino acid residues are pivotal in determining the tertiary structure and flexibility of proteins, which in turn are intricately linked to their functionality and interactions with other molecules. Here, we introduce ARIP, a novel tool designed to identify contact residues within proteins. ARIP employs a modified version of the dr_sasa algorithm and an atomic overlap weighted algorithm to directly calculate the contact area and volume between atoms based on their van der Waals radius. It also allows for the selection of solvent radii, recognizing that not every atom in proteins can interact with water molecules. The solvent parameters were derived from the analysis of approximately 5000 protein and nucleic acid structures with water molecules determined using X-ray crystallography. One advantage of the modified algorithm is its capability to analyze multiple models within a single PDB file, making it suitable for molecular dynamic capture. The contact volume is symmetrically distributed between the interacting atoms, providing more informative results than contact area for the analysis of intra- and intermolecular interactions and the development of scoring functions. Furthermore, ARIP has been applied to four distinct cases: capturing key residue-residue contacts in NMR structures of P4HB, protein-drug binding of CYP17A1, protein-DNA binding of SPI1, and molecular dynamic simulations of BRD4.

Discovery of the Inhibitor Targeting the SLC7A11/xCT Axis through In Silico and In Vitro Experiments.

In the development and progression of cervical cancer, oxidative stress plays an important role within the cells. Among them, Solute Carrier Family 7 Member 11 (SLC7A11/xCT) is crucial for maintaining the synthesis of glutathione and the antioxidant system in cervical cancer cells. In various tumor cells, studies have shown that SLC7A11 inhibits ferroptosis, a form of cell death, by mediating cystine uptake and maintaining glutathione synthesis. Additionally, SLC7A11 is also involved in promoting tumor metastasis and immune evasion. Therefore, inhibiting the SLC7A11/xCT axis has become a potential therapeutic strategy for cervical cancer. In this study, through structure-based high-throughput virtual screening, a compound targeting the SLC7A11/xCT axis named compound 1 (PubChem CID: 3492258) was discovered. In vitro experiments using HeLa cervical cancer cells as the experimental cell model showed that compound 1 could reduce intracellular glutathione levels, increase glutamate and reactive oxygen species (ROS) levels, disrupt the oxidative balance within HeLa cells, and induce cell death. Furthermore, molecular dynamics simulation results showed that compound 1 has a stronger binding affinity with SLC7A11 compared to the positive control erastin. Overall, all the results mentioned above indicate the potential of compound 1 in targeting the SLC7A11/xCT axis and treating cervical cancer both in vitro and in silico.

Trans-eQTL mapping in gene sets identifies network effects of genetic variants.

Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.

Using genetics and proteomics data to identify proteins causally related to COVID-19, healthspan and lifespan: a Mendelian randomization study.

BACKGROUND: COVID-19 pandemic poses a heavy burden on public health and accounts for substantial mortality and morbidity. Proteins are building blocks of life, but specific proteins causally related to COVID-19, healthspan and lifespan have not been systematically examined. METHODS: We conducted a Mendelian randomization study to assess the effects of 1,361 plasma proteins on COVID-19, healthspan and lifespan, using large GWAS of severe COVID-19 (up to 13,769 cases and 1,072,442 controls), COVID-19 hospitalization (32,519 cases and 2,062,805 controls) and SARS-COV2 infection (122,616 cases and 2,475,240 controls), healthspan (n = 300,477) and parental lifespan (~0.8 million of European ancestry). RESULTS: We identified 35, 43, and 63 proteins for severe COVID, COVID-19 hospitalization, and SARS-COV2 infection, and 4, 32, and 19 proteins for healthspan, father's attained age, and mother's attained age. In addition to some proteins reported previously, such as SFTPD related to severe COVID-19, we identified novel proteins involved in inflammation and immunity (such as ICAM-2 and ICAM-5 which affect COVID-19 risk, CXCL9, HLA-DRA and LILRB4 for healthspan and lifespan), apoptosis (such as FGFR2 and ERBB4 which affect COVID-19 risk and FOXO3 which affect lifespan) and metabolism (such as PCSK9 which lowers lifespan). We found 2, 2 and 3 proteins shared between COVID-19 and healthspan/lifespan, such as CXADR and LEFTY2, shared between severe COVID-19 and healthspan/lifespan. Three proteins affecting COVID-19 and seven proteins affecting healthspan/lifespan are targeted by existing drugs. CONCLUSIONS: Our study provided novel insights into protein targets affecting COVID-19, healthspan and lifespan, with implications for developing new treatment and drug repurposing.