Lipid Dysregulation Induced by Gasoline and Diesel Exhaust Exposure and the Interaction with Age.

Limited knowledge exists regarding gasoline and diesel exhaust effects on lipid metabolism. This study collected gasoline and diesel exhaust under actual driving conditions and conducted inhalation exposure on male young and middle-aged C57BL/6J mice for 4 h/day for 5 days to simulate commuting exposure intensity. Additionally, PM2.5 from actual roadways, representing gasoline and diesel vehicles, was generated for exposure to human umbilical vein endothelial cells (HUVECs) and normal liver cells (LO2) for 24, 48, and 72 h to further investigate exhaust particle toxicity. Results showed that diesel exhaust reduced total cholesterol and low-density lipoprotein cholesterol levels in young mice, indicating disrupted lipid metabolism. Aspartate aminotransferase and alanine aminotransferase levels increased by 53.7% and 21.7%, respectively, suggesting potential liver injury. Diesel exhaust exposure decreased superoxide dismutase and increased glutathione peroxidase levels. Cell viability decreased, and reactive oxygen species levels increased in HUVECs and LO2 following exposure to exhaust particles, with dose- and time-dependent effects. Diesel exhaust particles exhibited more severe inhibition of cell proliferation and oxidative damage compared to gasoline exhaust particles. These findings provide novel evidence of the risk of disrupted lipid metabolism due to gasoline and diesel exhaust, emphasizing the toxicity of diesel exhaust.

Expansive discovery of chemically diverse structured macrocyclic oligoamides.

Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.

Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.

Design of amyloidogenic peptide traps.

Segments of proteins with high ß-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in ß-strand and ß-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid ß1-42 (Aß42). The Aß binders block the assembly of Aß fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aß42 species.

A novel capsid-XL32-derived adeno-associated virus serotype prompts retinal tropism and ameliorates choroidal neovascularization.

Gene therapy has been adapted, from the laboratory to the clinic, to treat retinopathies. In contrast to subretinal route, intravitreal delivery of AAV vectors displays the advantage of bypassing surgical injuries, but the viral particles are more prone to be nullified by the host neutralizing factors. To minimize such suppression of therapeutic effect, especially in terms of AAV2 and its derivatives, we introduced three serine-to-glycine mutations, based on the phosphorylation sites identified by mass spectrum analysis, to the XL32 capsid to generate a novel serotype named AAVYC5. Via intravitreal administration, AAVYC5 was transduced more effectively into multiple retinal layers compared with AAV2 and XL32. AAVYC5 also enabled successful delivery of anti-angiogenic molecules to rescue laser-induced choroidal neovascularization and astrogliosis in mice and non-human primates. Furthermore, we detected fewer neutralizing antibodies and binding IgG in human sera against AAVYC5 than those specific for AAV2 and XL32. Our results thus implicate this capsid-optimized AAVYC5 as a promising vector suitable for a wide population, particularly those with undesirable AAV2 seroreactivity.

De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Honokiol ameliorates cigarette smoke-induced damage of airway epithelial cells via the SIRT3/SOD2 signalling pathway.

Cigarette smoking can cause damage of airway epithelial cells and contribute to chronic obstructive pulmonary disease (COPD). Honokiol is originally isolated from Magnolia obovata with multiple biological activities. Here, we investigated the protective effects of honokiol on cigarette smoke extract (CSE)-induced injury of BEAS-2B cells. BEAS-2B cells were treated with 300 mg/L CSE to construct an in vitro cell injury model, and cells were further treated with 2, 5 and 10 µM honokiol, then cell viability and LDH leakage were analysed by CCK-8 and LDH assay kits, respectively. Apoptosis was detected by flow cytometry analysis. ELISA was used to measure the levels of tumour necrosis factor (TNF)-ɑ, IL-1ß, IL-6, IL-8 and MCP-1. The results showed that honokiol (0.5-20 µM) showed non-toxic effects on BEAS-2B cells. Treatment with honokiol (2, 5 and 10 µM) reduced CSE (300 mg/L)-induced decrease in cell viability and apoptosis in BEAS-2B cells. Honokiol also decreased CSE-induced inflammation through inhibiting expression and secretion of inflammatory cytokines, such as TNF-ɑ, IL-1ß, IL-6, IL-8 and MCP-1. Moreover, honokiol repressed CSE-induced reactive oxygen species (ROS) production, decrease of ATP content and mitochondrial biogenesis, as well as mitochondrial membrane potential. Mechanistically, honokiol promoted the expression of SIRT3 and its downstream target genes, which are critical regulators of mitochondrial function and oxidative stress. Silencing of SIRT3 reversed the protective effects of honokiol on CSE-induced damage and mitochondrial dysfunction in BEAS-2B cells. These results indicated that honokiol attenuated CSE-induced damage of airway epithelial cells through regulating SIRT3/SOD2 signalling pathway.

Precisely patterned nanofibres made from extendable protein multiplexes.

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

De novo design of buttressed loops for sculpting protein functions.

In natural proteins, structured loops play central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that generates structured loops buttressed by extensive hydrogen bonding interactions with two neighboring loops and with secondary structure elements. We use this approach to design tandem repeat proteins with buttressed loops ranging from 9 to 14 residues in length. Experimental characterization shows the designs are folded and monodisperse, highly soluble, and thermally stable. Crystal structures are in close agreement with the computational design models, with the loops structured and buttressed by their neighbors as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute quite generally to efforts to design new protein functions.

Design of stimulus-responsive two-state hinge proteins.

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

Intervention of Dietary Protein Levels on Muscle Quality, Antioxidation, and Autophagy in the Muscles of Triploid Crucian Carp (Carassius carassius Triploid).

The aim of this study is to investigate the effect of dietary protein levels on flesh quality, oxidative stress, and autophagy status in the muscles of triploid crucian carp (Carassius carassius triploid), and the related molecular mechanisms. Six experimental diets with different protein levels (26%, 29%, 32%, 35%, 38%, 41%) were formulated. A total of 540 fish with an initial weight of 11.79 ± 0.09 g were randomly assigned to 18 cages and six treatments with three replicates of 30 fish each for 8 weeks feeding. It could be found that the whole-body ash content significantly increased in high protein level groups (p < 0.05). The 29% dietary protein level group exhibited the highest muscle moisture, although there was an inconspicuous decrease in the chewiness of the muscles when compared with the other groups. The dietary protein level influenced the content of free amino acids and nucleotides, especially the content of flavor amino acids, which exhibited an increasing tendency along with the increasing protein level, such as alanine and glutamic acid, while the flavor nucleotides showed different fluctuation trends. Moreover, the genes related to muscle development were shown to be influenced by the dietary protein level, especially the expression of MRF4, which was up-regulated with the increasing dietary protein levels. The 29% dietary protein level promoted the majority of analyzed muscle genes expression to the highest level when compared to other dietary levels, except the Myostain, whose expression reached its highest at 38% dietary protein levels. Furthermore, the effect of dietary protein levels on antioxidant signaling pathway genes were also examined. High protein levels would boost the expression of GSTα; GPX1 and GPX4α mRNA expression showed the highest level at the 32% dietary protein group. The increasing dietary protein level decreased both mRNA and protein expressions of Nrf2 by up-regulating Keap1. Autophagy-related gene expression levels reached the peak at 32% dietary protein level, as evidenced by a similar change in protein expression of FoxO1. In summary, muscle nutritional composition, antioxidative pathways, and autophagy levels were affected by the dietary protein levels. A total of 29-32% dietary protein level would be the appropriate level range to improve muscle quality and promote the antioxidant and autophagy capacity of triploid crucian carp muscles.

Stepwise design of pseudosymmetric protein hetero-oligomers.

Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign problems, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 15 new homo-oligomers and recombined them to make 17 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system share a common backbone, and hence are ideal building blocks for generating and functionalizing larger symmetric assemblies.

YAP inhibitor verteporfin suppresses tumor angiogenesis and overcomes chemoresistance in esophageal squamous cell carcinoma.

PURPOSE: Targeting angiogenesis is an attractive strategy for the effective treatment of cancer. This study aimed to investigate the anti-cancer activities of YAP inhibitor verteporfin (VP) in esophageal squamous cell carcinoma (ESCC) cells through its inhibitory effect on tumor angiogenesis. METHODS: Cell proliferation, apoptosis, migration and invasion abilities were estimated by MTT, colony formation, DAPI staining, wound healing and transwell assays, respectively. Human umbilical vein endothelial cell (HUVEC) tube formation assay and chick embryo chorioallantoic membrane (CAM) model were used to observe angiogenesis in vitro and in vivo. The interactions between ESCC cells and HUVECs were assessed by cell chemotactic migration and adhesion assays. The expression levels of angiogenesis-related molecules were detected by Western blot. RESULTS: We found that VP was potential to inhibit ESCC cell proliferation, migration, invasion and induce apoptosis in the dose-dependent fashion. VP also significantly suppressed proliferation, migration, and tube formation of HUVECs and promoted apoptosis of HUVECs, and reduced angiogenesis in CAM. Moreover, VP inhibited ESCC cell-induced angiogenesis in vitro by decreasing HUVEC chemotactic migration, adhesion and tube formation, and also reduced ESCC cell-induced neovascularization of the CAM in vivo. In addition, VP suppressed the expression of pro-angiogenic molecules such as VEGFA, MMP-2 and ß-catenin in ESCC cells. Furtherly, VP increased the chemosensitivity of ESCC-resistant cells to paclitaxel (PTX). The combination of VP and PTX attenuated the resistant cell-mediated angiogenesis in vitro and in vivo. CONCLUSION: These results reveal for the first time that VP potently inhibits malignant progression and overcomes chemoresistance of ESCC cells via inhibition of tumor angiogenesis. It provides insight into a new strategy for the treatment of ESCC that VP could be a potential drug candidate for targeting tumor angiogenesis.

De novo design of small beta barrel proteins.

Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.

Discovering Geographical Flock Patterns of CO2 Emissions in China Using Trajectory Mining Techniques.

Carbon dioxide (CO2) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high-low attribute values criterion and the extreme number-duration values criterion. A case study is conducted based on the CO2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR.

Global warming is a serious threat to human survival and health. Facing increasing global warming, the issue of CO2 emissions has attracted more attention. China is a major contributor of anthropogenic CO2 emissions and so it is essential to accurately estimate China's CO2 emissions and analyze their changing characteristics. This study recalculates CO2 emissions from Chinese cities from 2011 to 2020 using the SPNN-GNNWR model and multiple factors to reduce the uncertainty in emission estimates. The SPNN-GNNWR model has excellent predictions (R2: 0.925, 10-fold CV R2: 0.822) when cross-validation is used. The results indicate that the total CO2 emissions in China calculated by the model are close to those accounted for by other authorities in the world, with the total CO2 emissions increasing from 9.122 billion tonnes in 2011 to 9.912 billion tonnes in 2020. The city with the largest increase in CO2 emissions is Tianjin, and the city with the largest decrease is Beijing. The study also reveals the regional differences in CO2 emissions in Chinese mainland, including emissions, emission intensity and per capita emissions. Capturing and understanding the emissions and the related socioeconomic characteristics of different cities can help to develop effective emission mitigation strategies.

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles.

A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.

Exploration of Structured Symmetric Cyclic Peptides as Ligands for Metal-Organic Frameworks.

Despite remarkable advances in the assembly of highly structured coordination polymers and metal-organic frameworks, the rational design of such materials using more conformationally flexible organic ligands such as peptides remains challenging. In an effort to make the design of such materials fully programmable, we first developed a computational design method for generating metal-mediated 3D frameworks using rigid and symmetric peptide macrocycles with metal-coordinating sidechains. We solved the structures of six crystalline networks involving conformationally constrained 6 to 12 residue cyclic peptides with C2, C3, and S2 internal symmetry and three different types of metals (Zn2+, Co2+, or Cu2+) by single-crystal X-ray diffraction, which reveals how the peptide sequences, backbone symmetries, and metal coordination preferences drive the assembly of the resulting structures. In contrast to smaller ligands, these peptides associate through peptide-peptide interactions without full coordination of the metals, contrary to one of the assumptions underlying our computational design method. The cyclic peptides are the largest peptidic ligands reported to form crystalline coordination polymers with transition metals to date, and while more work is required to develop methods for fully programming their crystal structures, the combination of high chemical diversity with synthetic accessibility makes them attractive building blocks for engineering a broader set of new crystalline materials for use in applications such as sensing, asymmetric catalysis, and chiral separation.

Preparation and Characterization of Degradable Cellulose-Based Paper with Superhydrophobic, Antibacterial, and Barrier Properties for Food Packaging.

A great paradigm for foremost food packaging is to use renewable and biodegradable lignocellulose-based materials instead of plastic. Novel packages were successfully prepared from the cellulose paper by coating a mixture of polylactic acid (PLA) with cinnamaldehyde (CIN) as a barrier screen and nano silica-modified stearic acid (SA/SiO2) as a superhydrophobic layer. As comprehensively investigated by various tests, results showed that the as-prepared packages possessed excellent thermal stability attributed to inorganic SiO2 incorporation. The excellent film-forming characteristics of PLA improved the tensile strength of the manufactured papers (104.3 MPa) as compared to the original cellulose papers (70.50 MPa), enhanced by 47.94%. Benefiting from the rough nanostructure which was surface-modified by low surface energy SA, the contact angle of the composite papers attained 156.3°, owning superhydrophobic performance for various liquids. Moreover, the composite papers showed excellent gas, moisture, and oil bacteria barrier property as a result of the reinforcement by the functional coatings. The Cobb300s and WVP of the composite papers were reduced by 100% and 88.56%, respectively, and their antibacterial efficiency was about 100%. As the novel composite papers have remarkable thermal stability, tensile strength, and barrier property, they can be exploited as a potential candidate for eco-friendly, renewable, and biodegradable cellulose paper-based composites for the substitute of petroleum-derived packages.