Table_1_Causal relationships between type 2 diabetes, glycemic traits and keratoconus.XLSX

PurposeThe relationship between diabetes mellitus and keratoconus remains controversial. This study aimed to assess the potential causal relationships among type 2 diabetes, glycemic traits, and the risk of keratoconus.MethodsWe used a two-sample Mendelian randomization (MR) design based on genome-wide association summary statistics. Fasting glucose, proinsulin levels, adiponectin, hemoglobin A1c (HbA1c) and type 2 diabetes with and without body mass index (BMI) adjustment were used as exposures and keratoconus was used as the outcome. MR analysis was performed using the inverse-variance weighted method, MR-Egger regression method, weighted-mode method, weighted median method and the MR-pleiotropy residual sum and outlier test (PRESSO).ResultsResults showed that genetically predicted lower fasting glucose were significantly associated with a higher risk of keratoconus [IVW: odds ratio (OR) = 0.382; 95% confidence interval (CI) = 0.261–0.560; p = 8.162 × 10−7]. Genetically predicted lower proinsulin levels were potentially linked to a higher risk of keratoconus (IVW: OR = 0.739; 95% CI = 0.568–0.963; p = 0.025). In addition, genetically predicted type 2 diabetes negatively correlated with keratoconus (IVW: BMI-unadjusted: OR = 0.869; 95% CI = 0.775–0.974, p = 0.016; BMI-adjusted: OR = 0.880, 95% CI = 0.789–0.982, p = 0.022). These associations were further corroborated by the evidence from all sensitivity analyses.ConclusionThese findings provide genetic evidence that higher fasting glucose levels are associated with a lower risk of keratoconus. However, further studies are required to confirmed this hypothesis and to understand the mechanisms underlying this putative causative relationship.</p

Table_2_Causal relationships between type 2 diabetes, glycemic traits and keratoconus.XLSX

PurposeThe relationship between diabetes mellitus and keratoconus remains controversial. This study aimed to assess the potential causal relationships among type 2 diabetes, glycemic traits, and the risk of keratoconus.MethodsWe used a two-sample Mendelian randomization (MR) design based on genome-wide association summary statistics. Fasting glucose, proinsulin levels, adiponectin, hemoglobin A1c (HbA1c) and type 2 diabetes with and without body mass index (BMI) adjustment were used as exposures and keratoconus was used as the outcome. MR analysis was performed using the inverse-variance weighted method, MR-Egger regression method, weighted-mode method, weighted median method and the MR-pleiotropy residual sum and outlier test (PRESSO).ResultsResults showed that genetically predicted lower fasting glucose were significantly associated with a higher risk of keratoconus [IVW: odds ratio (OR) = 0.382; 95% confidence interval (CI) = 0.261–0.560; p = 8.162 × 10−7]. Genetically predicted lower proinsulin levels were potentially linked to a higher risk of keratoconus (IVW: OR = 0.739; 95% CI = 0.568–0.963; p = 0.025). In addition, genetically predicted type 2 diabetes negatively correlated with keratoconus (IVW: BMI-unadjusted: OR = 0.869; 95% CI = 0.775–0.974, p = 0.016; BMI-adjusted: OR = 0.880, 95% CI = 0.789–0.982, p = 0.022). These associations were further corroborated by the evidence from all sensitivity analyses.ConclusionThese findings provide genetic evidence that higher fasting glucose levels are associated with a lower risk of keratoconus. However, further studies are required to confirmed this hypothesis and to understand the mechanisms underlying this putative causative relationship.</p

Molecular Dynamics Study on the Inhibition Mechanisms of Drugs CQ_1–3 for Alzheimer Amyloid‑β₄₀ Aggregation Induced by Cu²⁺

The aggregation of amyloid-β (Aβ) peptide induced by Cu2+ is a key factor in development of Alzheimer’s disease (AD), and metal ion chelation therapy enables treatment of AD. Three CQi (i = 1, 2, and 3 with R = H, Cl, and NO2, respectively) drugs had been verified experimentally to be much stronger inhibitors than the pioneer clioquinol (CQ) in both disaggregation of Aβ40 aggregate and reduction of toxicity induced by Cu2+ binding at low pH. Due to the multiple morphologies of Cu2+–Aβ40 complexes produced at different pH states, we performed a series of molecular dynamics simulations to explain the structural changes and morphology characteristics as well as intrinsic disaggregation mechanisms of three Cu2+–Aβ40 models in the presence of any of the three CQi drugs at both low and high pH states. Three inhibition mechanisms for CQi were proposed as “insertion”, “semi-insertion”, and “surface” mechanisms, based on the morphologies of CQi–model x (CQi–x, x = 1, 2, and 3) and the strengths of binding between CQi and the corresponding model x. The insertion mechanism was characterized by the morphology with binding strength of more than 100 kJ/mol and by CQi being inserted or embedded into the hydrophobic cavity of model x. In those CQi–x morphologies with lower binding strength, CQi only attaches on the surface or inserts partly into Aβ peptide. Given the evidence that the binding strength is correlated positively with the effectiveness of drug to inhibit Aβ aggregation and thus to reduce toxicity, the data of binding strength presented here can provide a reference for one to screen drugs. From the point of view of binding strength, CQ2 is the best drug. Because of the special role of Asp23 in both Aβ aggregation and stabilizing the Aβ fibril, the generation of a H-bond between CQ3 and Asp23 of the Aβ40 peptide is believed to be responsible for CQ3 having the strongest disaggregation capacity. Therefore, besides strong binding, stronger propensity to H-bond with Asp23 would be another key factor to be taken seriously into account in drug screens. Meanwhile, the structural characteristics of drug CQi itself are also worthy of attention. First, the increasing polarity from CQ1 and CQ2 to CQ3 in turn results in increasing probability and strength of the interaction between the drug and the N-terminal (NT) region of Aβ40, which obviously inhibits Aβ peptide aggregation induced by Cu2+ binding. Second, both the benzothiazole ring and phenol ring of CQi can overcome the activation energy barrier (∼16 kJ/mol) to rotate flexibly around the intramolecular C7–N14 bond to achieve the maximum match and interaction with the ambient Aβ40 residues. Such a structural feature of CQi paves the new way for ones in selection and modification of a drug

Molecular Dynamics Study on the Inhibition Mechanisms of Drugs CQ_1–3 for Alzheimer Amyloid‑β₄₀ Aggregation Induced by Cu²⁺

The aggregation of amyloid-β (Aβ) peptide induced by Cu²⁺ is a key factor in development of Alzheimer’s disease (AD), and metal ion chelation therapy enables treatment of AD. Three CQ_<i>i</i> (i = 1, 2, and 3 with R = H, Cl, and NO₂, respectively) drugs had been verified experimentally to be much stronger inhibitors than the pioneer clioquinol (CQ) in both disaggregation of Aβ₄₀ aggregate and reduction of toxicity induced by Cu²⁺ binding at low pH. Due to the multiple morphologies of Cu²⁺–Aβ₄₀ complexes produced at different pH states, we performed a series of molecular dynamics simulations to explain the structural changes and morphology characteristics as well as intrinsic disaggregation mechanisms of three Cu²⁺–Aβ₄₀ models in the presence of any of the three CQ_<i>i</i> drugs at both low and high pH states. Three inhibition mechanisms for CQ_<i>i</i> were proposed as “insertion”, “semi-insertion”, and “surface” mechanisms, based on the morphologies of CQ_<i>i</i>–model <i>x</i> (CQ_<i>i</i>–<i>x</i>, <i>x</i> = 1, 2, and 3) and the strengths of binding between CQ_<i>i</i> and the corresponding model <i>x</i>. The insertion mechanism was characterized by the morphology with binding strength of more than 100 kJ/mol and by CQ_<i>i</i> being inserted or embedded into the hydrophobic cavity of model <i>x</i>. In those CQ_<i>i</i>–<i>x</i> morphologies with lower binding strength, CQ_<i>i</i> only attaches on the surface or inserts partly into Aβ peptide. Given the evidence that the binding strength is correlated positively with the effectiveness of drug to inhibit Aβ aggregation and thus to reduce toxicity, the data of binding strength presented here can provide a reference for one to screen drugs. From the point of view of binding strength, CQ₂ is the best drug. Because of the special role of Asp23 in both Aβ aggregation and stabilizing the Aβ fibril, the generation of a H-bond between CQ₃ and Asp23 of the Aβ₄₀ peptide is believed to be responsible for CQ₃ having the strongest disaggregation capacity. Therefore, besides strong binding, stronger propensity to H-bond with Asp23 would be another key factor to be taken seriously into account in drug screens. Meanwhile, the structural characteristics of drug CQ_<i>i</i> itself are also worthy of attention. First, the increasing polarity from CQ₁ and CQ₂ to CQ₃ in turn results in increasing probability and strength of the interaction between the drug and the N-terminal (NT) region of Aβ₄₀, which obviously inhibits Aβ peptide aggregation induced by Cu²⁺ binding. Second, both the benzothiazole ring and phenol ring of CQ_<i>i</i> can overcome the activation energy barrier (∼16 kJ/mol) to rotate flexibly around the intramolecular C7–N14 bond to achieve the maximum match and interaction with the ambient Aβ₄₀ residues. Such a structural feature of CQ_<i>i</i> paves the new way for ones in selection and modification of a drug

Table_2_Metabolomic profiling of Marek’s disease virus infection in host cell based on untargeted LC-MS.XLSX

Marek’s disease (MD) caused by Marek’s disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.</p

Table_1_Metabolomic profiling of Marek’s disease virus infection in host cell based on untargeted LC-MS.XLSX

Marek’s disease (MD) caused by Marek’s disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.</p

Table_3_Metabolomic profiling of Marek’s disease virus infection in host cell based on untargeted LC-MS.XLSX

Marek’s disease (MD) caused by Marek’s disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.</p

Image_1_Metabolomic profiling of Marek’s disease virus infection in host cell based on untargeted LC-MS.TIF

Marek’s disease (MD) caused by Marek’s disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.</p