Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Functional analysis of a wheat class III peroxidase gene, TaPer12-3A, in seed dormancy and germination.

BACKGROUND: Class III peroxidases (PODs) perform crucial functions in various developmental processes and responses to biotic and abiotic stresses. However, their roles in wheat seed dormancy (SD) and germination remain elusive. RESULTS: Here, we identified a wheat class III POD gene, named TaPer12-3A, based on transcriptome data and expression analysis. TaPer12-3A showed decreasing and increasing expression trends with SD acquisition and release, respectively. It was highly expressed in wheat seeds and localized in the endoplasmic reticulum and cytoplasm. Germination tests were performed using the transgenic Arabidopsis and rice lines as well as wheat mutant mutagenized with ethyl methane sulfonate (EMS) in Jing 411 (J411) background. These results indicated that TaPer12-3A negatively regulated SD and positively mediated germination. Further studies showed that TaPer12-3A maintained H2O2 homeostasis by scavenging excess H2O2 and participated in the biosynthesis and catabolism pathways of gibberellic acid and abscisic acid to regulate SD and germination. CONCLUSION: These findings not only provide new insights for future functional analysis of TaPer12-3A in regulating wheat SD and germination but also provide a target gene for breeding wheat varieties with high pre-harvest sprouting resistance by gene editing technology.

In silico analysis of the wheat BBX gene family and identification of candidate genes for seed dormancy and germination.

BACKGROUND: B-box (BBX) proteins are a type of zinc finger proteins containing one or two B-box domains. They play important roles in development and diverse stress responses of plants, yet their roles in wheat remain unclear. RESULTS: In this study, 96 BBX genes were identified in the wheat genome and classified into five subfamilies. Subcellular localization prediction results showed that 68 TaBBXs were localized in the nucleus. Protein interaction prediction analysis indicated that interaction was one way that these proteins exerted their functions. Promoter analysis indicated that TaBBXs may play important roles in light signal, hormone, and stress responses. qRT-PCR analysis revealed that 14 TaBBXs were highly expressed in seeds compared with other tissues. These were probably involved in seed dormancy and germination, and their expression patterns were investigated during dormancy acquisition and release in the seeds of wheat varieties Jing 411 and Hongmangchun 21, showing significant differences in seed dormancy and germination phenotypes. Subcellular localization analysis confirmed that the three candidates TaBBX2-2 A, TaBBX4-2 A, and TaBBX11-2D were nuclear proteins. Transcriptional self-activation experiments further demonstrated that TaBBX4-2A was transcriptionally active, but TaBBX2-2A and TaBBX11-2D were not. Protein interaction analysis revealed that TaBBX2-2A, TaBBX4-2A, and TaBBX11-2D had no interaction with each other, while TaBBX2-2A and TaBBX11-2D interacted with each other, indicating that TaBBX4-2A may regulate seed dormancy and germination by transcriptional regulation, and TaBBX2-2A and TaBBX11-2D may regulate seed dormancy and germination by forming a homologous complex. CONCLUSIONS: In this study, the wheat BBX gene family was identified and characterized at the genomic level by bioinformatics analysis. These observations provide a theoretical basis for future studies on the functions of BBXs in wheat and other species.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Identification of genetic loci and candidate genes underlying freezing tolerance in wheat seedlings.

KEY MESSAGE: Ten stable loci for freezing tolerance (FT) in wheat were detected by genome-wide association analysis. The putative candidate gene TaRPM1-7BL underlying the major locus QFT.ahau-7B.2 was identified and validated. Frost damage restricts wheat growth, development, and geographical distribution. However, the genetic mechanism of freezing tolerance (FT) remains unclear. Here, we evaluated FT phenotypes of 245 wheat varieties and lines, and genotyped them using a Wheat 90 K array. The association analysis showed that ten stable loci were significantly associated with FT (P < 1 × 10-4), and explained 6.45-26.33% of the phenotypic variation. In particular, the major locus QFT.ahau-7B.2 was consistently related to all nine sets of FT phenotypic data. Based on five cleaved amplified polymorphic sequence (CAPS) markers closely linked to QFT.ahau-7B.2, we narrowed down the target region to the 570.67-571.16 Mb interval (0.49 Mb) on chromosome 7B, in which four candidate genes were annotated. Of these, only TaRPM1-7BL exhibited consistent differential expression after low temperature treatment between freezing-tolerant and freezing-sensitive varieties. The results of cloning and whole-exome capture sequencing indicated that there were two main haplotypes for TaRPM1-7BL, including freezing-tolerant Hap1 and freezing-sensitive Hap2. Based on the representative SNP (+1956, A/G), leading to an amino acid change in the NBS domain, a CAPS marker (CAPS-TaRPM1-7BL) was developed and validated in 431 wheat varieties (including the above 245 materials) and 318 F2 lines derived from the cross of 'Annong 9267' (freezing-tolerant) × 'Yumai 9' (freezing-sensitive). Subsequently, the TaRPM1-7BL gene was silenced in 'Yumai 9' by virus-induced gene silencing (VIGS), and these silenced wheat seedlings exhibited enhanced FT phenotypes, suggesting that TaRPM1-7BL negatively regulates FT. These findings are valuable for understanding the complex genetic basis of FT in wheat.

A new method to estimate the histological stage of primary biliary cholangitis.

OBJECTIVE: To analyze the diagnostic efficacy of the periportal hypoechoic band (PHB) in the histological stage of patients with primary biliary cholangitis (PBC). METHODS: We prospectively included 77 cases of PBC pathologically or clinically confirmed, and high-frequency ultrasound (HFUS) measurements of the PHB were performed in all included patients. Ludwig staging system of histopathology was used as the gold standard. RESULTS: The width of the PHB was positively correlated with histological staging (r = 0.844, p < 0.001). By area under the receiving operating characteristic curve (AUROC), the best cutoff value for PHB for advanced stage (≥ stage 3) was 2.4 mm (AUROC: 0.934; 95%CI: 0.841-0.981) and 0.93 for sensitivity, and 0.91 for specificity, the concordance rates of PHB vs. liver biopsy was 90.3%. The correct rate for early-stage PBC was 87.9% and for the progressive stage was 93.1%. After multi-factor regression analysis, the PHB (OR = 1.331, CI = 1.105-1.603, p = 0.003) and total bilirubin (OR = 1.156, CI = 1.041-1.285, p = 0.007) were independent influencing factors for progressive PBC. CONCLUSIONS: Measurement of the PHB to assess advanced PBC is a simple and effective method. This method may complement current methods for the histological staging assessment of patients with PBC. REGISTRATION: Clinical trial registration: ChiCTR 2000032053, 2020/04/19. CLINICAL RELEVANCE STATEMENT: The measurement of periportal hypoechoic band (PHB) provides a simple and easy assessment of the degree of disease progression in patients with PBC and provides an important clinical reference in predicting the histological staging of PBC from an ultrasound perspective. KEY POINTS: • The PHB is correlated with histological staging in the patient with PBC. • The area under the ROC curves of PHB for detecting advanced stage (≥ stage 3) were 0.934 and 0.93 for sensitivity, and 0.91 for specificity, the concordance rates of PHB vs. liver biopsy was 90.3%. The application of PHB can better assess the advanced PBC. • Measurement of the PHB to assess advanced PBC is a simple and effective method that can significantly reduce the need for liver biopsy.

Primary biliary cholangitis has causal effects on systemic rheumatic diseases: a Mendelian randomization study.

BACKGROUND: An association has been observed between primary biliary cholangitis (PBC) and systemic rheumatic diseases (SRDs) in observational studies, however the exact causal link remains unclear. We aimed to evaluate the causal effects of PBC on SRDs through Mendelian randomization (MR) analysis. METHODS: The genome-wide association study (GWAS) summary data were obtained from MRC IEU OpenGWAS and FinnGen databases. Independent genetic variants for PBC were selected as instrumental variables. Inverse variance weighted was used as the main approach to evaluate the causal effects of PBC on Sjögren syndrome (SS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), mixed connective tissue disease (MCTD) and polymyositis (PM). Horizontal pleiotropy and heterogeneity were measured by MR‒Egger intercept test and Cochran's Q value, respectively. RESULTS: PBC had causal effects on SS (OR = 1.177, P = 8.02e-09), RA (OR = 1.071, P = 9.80e-04), SLE (OR = 1.447, P = 1.04e-09), SSc (OR = 1.399, P = 2.52e-04), MCTD (OR = 1.306, P = 4.92e-14), and PM (OR = 1.416, P = 1.16e-04). Based on the MR‒Egger intercept tests, horizontal pleiotropy was absent (all P values > 0.05). The robustness of our results was further enhanced by the leave-one-out method. CONCLUSIONS: Our research has provided new insights into PBC and SRDs, indicating casual effects on various SRDs.

A New Co(II)-coordination Polymer: Fluorescence Performances, Loaded with Paclitaxel-hydrogel on Breast Cancer and Molecular Docking Study.

In the current context of the increasing incidence of breast cancer, we aim to develop an efficient drug carrier for breast cancer by constructing an innovative complex consisting of a metal-organic framework (MOF) and a hydrogel. The aim of this initiative is to provide new ideas and tools for breast cancer treatment strategies through scientific research, so as to address the current challenges in breast cancer treatment. In the present study, by employment of a new Co(II)-based coordination polymer with the chemical formula of [Co(H2O)(CH3OH)L]n (1) (H2L = 5-(1 H-tetrazol-5-yl)nicotinic acid) was solvothermally synthesized by reaction of Co(NO3)2·6H2O a mixed solvent of MeOH and water. The characteristics of ligand-based absorption and emission, as unveiled by ultraviolet and fluorescence spectroscopy tests, offer insights into the distinctive electronic transitions and structural features originating from the ligand in compound 1. Using natural polysaccharide hyaluronic acid (HA) and carboxymethyl chitosan (CMCS) as raw materials, HA/CMCS hydrogels were successfully prepared by chemical method and their internal morphology was studied by scanning electron microscopy. Using paclitaxel as a drug model, we further designed and synthesized a novel metal gel particle-loaded paclitaxel drug and evaluated its inhibitory effect on breast cancer cells. Finally, the hypothesized interactions between the complex and the receptor have been confirmed through molecular docking simulation, and multiple polar interactions have been verified, which further proves the potential anti-cancer capability and excellent bioactivity. Based on this, this composite material prepared from a novel Co(II)-coordinated polymer with paclitaxel hydrogel could provide a useful pathway for the identification and treatment of breast cancer.

Nuclear cGAS suppresses DNA repair and promotes tumorigenesis.

Accurate repair of DNA double-stranded breaks by homologous recombination preserves genome integrity and inhibits tumorigenesis. Cyclic GMP-AMP synthase (cGAS) is a cytosolic DNA sensor that activates innate immunity by initiating the STING-IRF3-type I IFN signalling cascade1,2. Recognition of ruptured micronuclei by cGAS links genome instability to the innate immune response3,4, but the potential involvement of cGAS in DNA repair remains unknown. Here we demonstrate that cGAS inhibits homologous recombination in mouse and human models. DNA damage induces nuclear translocation of cGAS in a manner that is dependent on importin-α, and the phosphorylation of cGAS at tyrosine 215-mediated by B-lymphoid tyrosine kinase-facilitates the cytosolic retention of cGAS. In the nucleus, cGAS is recruited to double-stranded breaks and interacts with PARP1 via poly(ADP-ribose). The cGAS-PARP1 interaction impedes the formation of the PARP1-Timeless complex, and thereby suppresses homologous recombination. We show that knockdown of cGAS suppresses DNA damage and inhibits tumour growth both in vitro and in vivo. We conclude that nuclear cGAS suppresses homologous-recombination-mediated repair and promotes tumour growth, and that cGAS therefore represents a potential target for cancer prevention and therapy.

Immune-related gene signature improves prognosis prediction in patients with breast cancer and associates it with tumor immunity and inflammatory response.

BACKGROUND: The prognostic potential of immune-related genes, particularly immune checkpoint inhibitors (ICIs) and long non-coding RNAs (lncRNAs), is gaining attention for evaluating the prognosis of breast cancer patients. METHODS: We analyzed 23 datasets to identify 15 ICI-related mRNAs and 5 immune-related lncRNAs, creating a robust immune score (IS). This score was used to classify patients into high and low IS groups and assess their survival outcomes. RESULTS: Patients with high IS showed significantly poorer overall survival (OS) and progression-free survival (PFS) compared to those with low IS. Multivariate Cox regression analysis confirmed IS as an independent prognostic factor. Additionally, high IS was associated with higher mutation loads and neoantigen profiles, while low IS correlated with enhanced immune cell infiltration. CONCLUSIONS: The immune score developed from ICI-related mRNAs and lncRNAs effectively predicts the prognosis of breast cancer patients and highlights the differential immune and inflammatory responses between patients with varying levels of immune score. This underscores the relevance of IS in guiding therapeutic decisions and tailoring patient management strategies in clinical settings.

Distribution of EGFR fusions in 35,023 Chinese patients with solid tumors-the frequency, fusion partners and clinical outcome.

BACKGROUND: Epidermal growth factor receptor (EGFR) fusions are rare but potentially actionable oncogenic drivers across multiple solid tumors. However, the distribution and molecular characteristics of EGFR fusions in Chinese patients with solid malignancies have not been explored. METHODS: Panel-based next-generation sequencing (NGS) data of 35,023 patients with various types of solid tumors was collected and analyzed from the Simcere Diagnostics (Nanjing, China) database. A 9563-patient cohort was derived from The Cancer Genome Atlas (TCGA) to explore the relationship between EGFR fusion status and overall survival (OS). RESULTS: In this study, prevalence of functional EGFR fusions was 0.303% (106/35,023) in total across solid tumors, which occur more commonly in gastroesophageal junction cancer (1/61, 1.613%), followed by medulloblastoma (1/66, 1.515%) and glioma (33/2409, 1.370%). Analysis showed a prevalence for fusion partners in different tumor types. The top 3 co-mutant genes with EGFR fusion were TP53 (mutation frequency, MF: 65%), BRCA2 (MF: 43%), and ALK (MF: 41%). Furthermore, patients in the EGFR fusion group had a significantly shorter OS than those in the non-EGFR fusion group (p < 0.0001) in the TCGA cohort, suggesting that EGFR fusion might be a high-risk factor for poor prognosis. CONCLUSIONS: Our study is the first retrospective analysis of EGFR fusions in a large-scale solid tumor population, which may provide a reference for future EGFR-TKI clinical trials with EGFR fusions.

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China.

BACKGROUND: Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations. OBJECTIVE: This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health. METHODS: This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed. RESULTS: The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions. CONCLUSIONS: Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental Verification.

DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying eight candidates for DNA-PKcs targets. Subsequent experiments revealed the effective inhibition of DNA-PKcs-mediated cell proliferation by three small molecules (5025-0002, M769-1095, and V008-1080). These molecules exhibited anticancer activity with IC50 (inhibitory concentration at 50%) values of 152.6 µM, 30.71 µM, and 74.84 µM, respectively. Notably, V008-1080 enhanced homology-directed repair (HDR) mediated by CRISPR/Cas9 while inhibiting non-homologous end joining (NHEJ) efficiency. Further investigations into the structure-activity relationships unveiled the binding sites and critical interactions between these small molecules and DNA-PKcs. This is the first application of DeepBindGCN_RG in a real drug screening task, and the successful discovery of a novel DNA-PKcs inhibitor demonstrates its efficiency as a core component in the screening pipeline. Moreover, this study provides important insights for exploring novel anticancer therapeutics and advancing the development of gene editing techniques by targeting DNA-PKcs.

Covalent and non-covalent interaction of myofibrillar protein and cyanidin-3-O-glucoside: focus on structure, binding sites and in vitro digestion properties.

BACKGROUND: The aim of this study was to investigate the effects of covalent and non-covalent interactions between myofibrillar protein (MP) and cyanidin-3-O-glucoside (C3G) on protein structure, binding sites, and digestion properties. Four methods of inducing covalent cross-linking were used in the preparation of MP-C3G conjugates, including tyrosinase-catalyzed oxidation, alkaline pH shift treatment, free radical grafting, and ultrasonic treatment. A comparison was made between MP-C3G conjugates and complexes, and the analysis included sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), C3G binding ratio, liquid chromatography-tandem mass spectrometry (LC-MS/MS), protein side-chain amino acids, circular dichroism spectroscopy, three-dimensional fluorescence, particle size, and in vitro simulated digestion. RESULTS: Covalent bonding between C3G and amino acid side chains in MP was confirmed by LC-MS/MS. In covalent bonding, tryptophan residues, free amino groups and sulfhydryl groups were all implicated. Among the 22 peptides covalently modified by C3G, 30 modification sites were identified, located in lysine, histidine, tryptophan, arginine and cysteine. In vitro simulated digestion experiments showed that the addition of C3G significantly reduced the digestibility of MP, with the covalent conjugate showing lower digestibility than the non-covalent conjugate. Moreover, the digestibility of protein decreased more during intestinal digestion, possibly because covalent cross-linking of C3G and MP further inhibited trypsin targeting sites (lysine and arginine). CONCLUSION: Covalent cross-linking of C3G with myofibrillar proteins significantly affected protein structure and reduced protein digestibility by occupying more trypsin binding sites. © 2023 Society of Chemical Industry.

Floral nectar reabsorption and a sugar concentration gradient in two long-spurred Habenaria species (Orchidaceae).

BACKGROUND: Floral nectar is the most common reward flowers offered to pollinators. The quality and quantity of nectar produced by a plant species provide a key to understanding its interactions with pollinators and predicting rates of reproductive success. However, nectar secretion is a dynamic process with a production period accompanied or followed by reabsorption and reabsorption remains an understudied topic. In this study, we compared nectar volume and sugar concentration in the flowers of two long-spurred orchid species, Habenaria limprichtii and H. davidii (Orchidaceae). We also compared sugar concentration gradients within their spurs and rates of reabsorption of water and sugars. RESULTS: Both species produced diluted nectar with sugar concentrations from 17 to 24%. Analyses of nectar production dynamics showed that as flowers of both species wilted almost all sugar was reabsorbed while the original water was retained in their spurs. We established a nectar sugar concentration gradient for both species, with differences in sugar concentrations at their spur's terminus and at their spur's entrance (sinus). Sugar concentration gradient levels were 1.1% in H. limprichtii and 2.8% in H. davidii, both decreasing as flowers aged. CONCLUSION: We provided evidence for the reabsorption of sugars but not water occurred in wilted flowers of both Habenaria species. Their sugar concentration gradients vanished as flowers aged suggesting a slow process of sugar diffusion from the nectary at the spur's terminus where the nectar gland is located. The processes of nectar secretion/reabsorption in conjunction with the dilution and hydration of sugar rewards for moth pollinators warrant further study.

Longitudinal association of remnant cholesterol with joint arteriosclerosis and atherosclerosis progression beyond LDL cholesterol.

BACKGROUND: Arteriosclerosis and atherosclerosis are closely related with cardiovascular disease (CVD) risk. Remnant cholesterol (RC) could predict CVD. However, its effect on joint arteriosclerosis and atherosclerosis progression remains unclear. This study aims to evaluate the association of RC with joint arteriosclerosis and atherosclerosis progression trajectories in the general population. METHODS: This study collected data across five biennial surveys of the Beijing Health Management Cohort from 2010 to 2019. Multi-trajectory model was used to determine the joint arteriosclerosis and atherosclerosis progression patterns by brachial-ankle pulse wave velocity (baPWV) and ankle brachial index (ABI). We also performed discordance analyses for RC vs. low density lipoprotein cholesterol (LDL-C) using ordinal logistics model. RESULTS: A total of 3186 participants were included, with three clusters following distinct arteriosclerosis and atherosclerosis progression patterns identified using a multi-trajectory model. In the multivariable-adjusted ordinal logistics analyses, RC was significantly associated with baPWV and ABI progression (OR: 1.20; 95% CI: 1.13-1.28, per 10 mg/dL). For the discordance analyses, the discordant low RC group was associated with decreased risk compared to the concordant group (OR: 0.73; 95% CI: 0.60-0.89). People with a high RC level were at an increased risk of joint arteriosclerosis and atherosclerosis progression, even with optimal LDL-C. CONCLUSIONS: RC is independently associated with joint arteriosclerosis and atherosclerosis progression beyond LDL-C. RC could be an earlier risk factor than LDL-C of arteriosclerosis and atherosclerosis in the general population.

Association of impaired sensitivity to thyroid hormones with hyperuricemia through obesity in the euthyroid population.

BACKGROUND: Impaired sensitivity to thyroid hormones is a newly proposed clinical entity associated with hyperuricemia in the subclinical hypothyroid population. However, it is unknown whether the association exists in the euthyroid population. This study aimed to explore the association of impaired sensitivity to thyroid hormones (assessed by the thyroid feedback quantile-based index [TFQI], parametric thyroid feedback quantile-based index [PTFQI], thyrotrophic thyroxine resistance index [TT4RI] and thyroid-stimulating hormone index [TSHI]) with hyperuricemia and quantify the mediating effect of body mass index BMI in the euthyroid population. METHODS: This cross-sectional study enrolled Chinese adults aged ≥ 20 years who participated in the Beijing Health Management Cohort (2008-2019). Adjusted logistic regression models were used to explore the association between indices of sensitivity to thyroid hormones and hyperuricemia. Odds ratios [OR] and absolute risk differences [ARD] were calculated. Mediation analyses were performed to estimate direct and indirect effects through BMI. RESULTS: Of 30,857 participants, 19,031 (61.7%) were male; the mean (SD) age was 47.3 (13.3) years; and 6,515 (21.1%) had hyperuricemia. After adjusting for confounders, individuals in the highest group of thyroid hormone sensitivity indices were associated with an increased prevalence of hyperuricemia compared with the lowest group (TFQI: OR = 1.18, 95% CI 1.04-1.35; PTFQI: OR = 1.20, 95% CI 1.05-1.36; TT4RI: OR = 1.17, 95% CI 1.08-1.27; TSHI: OR = 1.12, 95% CI 1.04-1.21). BMI significantly mediated 32.35%, 32.29%, 39.63%, and 37.68% of the associations of TFQI, PTFQI, TT4RI and TSHI with hyperuricemia, respectively. CONCLUSIONS: Our research revealed that BMI mediated the association between impaired sensitivity to thyroid hormones and hyperuricemia in the euthyroid population. These findings could provide useful evidence for understanding the interaction between impaired sensitivity to thyroid hormone and hyperuricemia in euthyroid individuals and suggest the clinical implications of weight control in terms of impaired thyroid hormones sensitivity.