A new alkaline pectin lyase with novel thermal and pH stability from Bacilus velezensis.

Pectin lyases are important in various industries, including tobacco leaves processing. In this paper, a novel pectin lyase Pel04 from Bacillus velezensis was characterized. Pel04 molecular weight (Mw) and isoelectric point (pI) of the protein sequence after removing the signal peptide are 43.0 kDa. The optimal temperature and pH of Pel04 is 50 °C and 9.0, respectively. Pel04 was stable in the range of 30-50 °C, and pH 9.5-11. Ca2+ can significantly stimulate the enzyme activity, while Cu2+, Co2+, Fe3+, and Mn2+ have inhibitory effects on Pel04. By Pel04 treatment, the overall content of acids, alcohols, esters and other aromas in tobacco leaves increased, while the contents of phenolic and heterocyclic substances decreased. Pel04 has important potential for industrial application particularly in improving quality of tobacco leaves.

Integrative Analyses of Mendelian Randomization and Transcriptomic Data Reveal No Association between Leptin and Chronic Obstructive Pulmonary Disease.

As a key adipokine, leptin has been extensively investigated for its potential role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, concordant conclusions have not been attained. In this study, we investigated the relationship between leptin and COPD using an integrative analysis that combined a Mendelian randomization (MR) study with transcriptomic data analysis. Here, the MR analysis was performed on the online platform MR-Base, and the bioinformatics analyses were performed with the aid of R Bioconductor packages. No evidence was found by the integrative analysis to support the association of the two attributes. All methods detected a null causal effect of leptin on COPD in the MR analysis. In particular, when the genetically predicted leptin level increased one unit, the risk of developing COPD was estimated as 0.999 (p = 0.943), 0.920 (p = 0.516), 1.002 (p = 0.885), and 1.002 (p = 0.906) by the Inverse Variance Weighted (IVW), MR-Egger, weighted median, and weighted mode method, respectively. Furthermore, no leptin-associated genes except one were identified as being differentially expressed between COPD and control in bioinformatics analysis. The observed association between leptin and COPD in previous observational studies may be attributable to unmeasured confounding effects or reverse causation.

An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated, inflammatory disorder of the skin with chronic inflammation and hyper-proliferation of the epidermis. Since psoriasis has genetic components and the diseased tissue of psoriasis is very easily accessible, it is natural to use high-throughput technologies to characterize psoriasis and thus seek targeted therapies. Transcriptional profiles change correspondingly after an intervention. Unlike cross-sectional gene expression data, longitudinal gene expression data can capture the dynamic changes and thus facilitate causal inference. METHODS: Using the iCluster method as a building block, an ensemble method was proposed and applied to a longitudinal gene expression dataset for psoriasis, with the objective of identifying key lncRNAs that can discriminate the responders from the non-responders to two immune treatments of psoriasis. RESULTS: Using support vector machine models, the leave-one-out predictive accuracy of the 20-lncRNA signature identified by this ensemble was estimated as 80%, which outperforms several competing methods. Furthermore, pathway enrichment analysis was performed on the target mRNAs of the identified lncRNAs. Of the enriched GO terms or KEGG pathways, proteasome, and protein deubiquitination is included. The ubiquitination-proteasome system is regarded as a key player in psoriasis, and a proteasome inhibitor to target ubiquitination pathway holds promises for treating psoriasis. CONCLUSIONS: An integrative method such as iCluster for multiple data integration can be adopted directly to analyze longitudinal gene expression data, which offers more promising options for longitudinal big data analysis. A comprehensive evaluation and validation of the resulting 20-lncRNA signature is highly desirable.

Exploring exosome data to identify prognostic gene signatures for lung adenocarcinoma.

Background: Exosomes are involved in tumorigenesis, growth and metastasis. However, the prognostic value of exosome-related genes in lung adenocarcinoma (LUAD) remains unclear. Methods: Clinical and transcriptome data from The Cancer Genome Atlas LUAD cohort were used to construct a model based on exosome-related genes, which was validated with LUAD data from the Gene Expression Omnibus (GEO). Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were used to explore underlying mechanisms; the single-sample gene set enrichment analysis score was used to determine immune functions. Results: A 19-exosome-related gene signature for overall survival in LUAD was predictive in both The Cancer Genome Atlas and GEO LUAD cohorts. Immune-related and extracellular matrix-related pathways were enriched in differentially expressed genes. Immune states differed between high- and low-risk groups. Conclusion: The novel signature can be used to predict outcomes in LUAD.

Lay abstract Exosome products are related to tumorigenesis, growth and metastasis, and also have potential prognostic value in lung cancer. The data and information for lung adenocarcinoma patients retrieved from databases were used to develop a risk score model. The molecular mechanisms and immune system activity in high- and low-risk groups of patients with lung adenocarcinoma based on the median risk score were obviously different. The risk score model contained 19 related genes. Patients with low-risk scores had better prognoses. The novel risk score model can be used to predict the outcome for patients with lung adenocarcinoma.

A ferroptosis-related gene signature predicts overall survival in patients with lung adenocarcinoma.

Aims: To elucidate the association between ferroptosis-related genes and prognosis in patients with lung adenocarcinoma (LUAD). Materials & methods: A ferroptosis-related gene signature was made by lasso regression analysis through the LUAD datasets of the Cancer Genome Atlas. The prognostic value of the multigene signature was externally validated in the GSE72094 dataset from the Gene Expression Omnibus database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were used to explore underlying mechanisms. Results & conclusion: We established a novel ferroptosis-related gene signature for overall survival in LUAD that was predictive in both the training and validation cohorts. Immune-related pathways were significantly enriched, and immune status differed between the high- and low-risk groups. Targeting ferroptosis is a potential therapeutic option in LUAD. These results still need to be confirmed by more studies.

Lay abstract Lung adenocarcinoma (LUAD) is a common type of lung cancer, a major contributor to cancer-related death in men and women worldwide. Ferroptosis is a form of regulated cell death that is dependent on iron. The relationship between ferroptosis-related gene expression and survival in patients with LUAD remains to be elucidated. In this article, the public datasets the Cancer Genome Atlas and the Gene Expression Omnibus were used to create a model with 12 ferroptosis genes to separate LUAD patients into high- and low-risk groups. The low-risk group had better survival than the high-risk group. We also found that the immune status was different in high-risk and low-risk patients. In conclusion, our study established a novel ferroptosis-related gene signature for survival in LUAD. The underlying mechanisms involve tumor immunity.

Predictive factors for pleural drainage volume after uniportal video-assisted thoracic surgery lobectomy for non-small cell lung cancer: a single-institution retrospective study.

OBJECTIVE: To identify the predictive factors associated with pleural drainage volume (PDV) after uniportal video-assisted thoracic surgery (VATS) lobectomy for non-small cell lung cancer (NSCLC). METHODS: A total of 440 consecutive NSCLC patients who underwent uniportal VATS lobectomy were enrolled in this study between November 2016 and July 2019. Thirty-four parameters, including patients' clinicopathological characteristics and other potential predictors were collected. Daily drainage volume was summed up as PDV. Univariate analysis and multivariate regression models were fitted to identify independent predictive factors for PDV. RESULTS: The median PDV was 840 ml during the median drainage duration of 4 days. A strong correlation was observed between PDV and drainage duration (correlation coefficient = 0.936). On univariate analysis, age, forced expiratory volume in 1 s % predicted (FEV1%), left ventricular ejection fraction (LVEF), operation time, serum total protein (TP), and body mass index (BMI) showed a significant correlation with PDV (P value, < 0.001, < 0.001, 0.003, 0.008, 0.028, and 0.045, respectively). Patients with smoking history (P = 0.030) or who underwent lower lobectomy (P = 0.015) showed significantly increased PDV than never smokers or those who underwent upper or middle lobectomy, respectively. On multivariate regression analysis, older age (P< 0.001), lower FEV1% (P< 0.001), lower LVEF (P = 0.011), lower TP (P = 0.013), and lower lobectomy (P = 0.016) were independent predictors of increased PDV. CONCLUSIONS: Predictive factors of PDV can be identified. Based on these predictors, patients can be treated with tailored individualized safe chest tube management.

Identification of monotonically differentially expressed genes for non-small cell lung cancer.

BACKGROUND: Monotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important. RESULTS: With the aid of a feature selection algorithm capable of identifying MEGs - the MFSelector method - two sets of potential MEGs were selected in this study: the MEGs across the different pathologic stages and the MEGs across the risk levels of death for the NSCLC patients at early stages. For the lung adenocarcinoma (AC) subtypes no statistically significant MEGs were identified across pathologic stages, however dozens of MEGs were identified across the risk levels of death. By contrast, for the squamous cell lung carcinoma (SCC) there were no statistically significant MEGs as either stage or risk level advanced. CONCLUSIONS: The pathologic stage of non-small cell lung cancer patients at early stages has no prognostic value, making the identification of prognostic gene signatures for them more meaningful and highly desirable.

Fundamental asymmetry of insertions and deletions in genomes size evolution.

The origin of large genomes that underlies the long standing "C-value enigma" is only partially explained by selfish DNA. We investigated insertions and deletions (indels) of nucleotides and discussed their relevance in size evolution of random biological sequences (RBS) and genomes. By developing a probabilistic model of RBS based on size evolution of expandable sites in a thought perfect genome, it was found that insertion bias engenders exponential increase of average RBS sizes. When combined with existing large segments of genome that are not subject to selection pressure (e.g. selfish DNA), such insertion bias results in explosive expansion of genomes, and therefore helps explain the "C value enigma" besides selfish DNA. Such increase of RBS size is caused by the fundamental asymmetry of indels, with insertions result in more available sites and deletions result in less deletable nucleotides. In qualitative agreement with the size distribution of known genomes, tails of RBS size distributions exhibit exponential decay with probabilities of larger RBS segments being smaller. Unsurprisingly, a slight deletion bias (higher deletions probabilities) results in a slow decrease of average RBS size and may lead to their eventual vanishing. Contrary to intuition, strictly balanced insertion and deletion results in linearly increasing instead of completely fixed RBS size. Nonetheless, such slow linear increase of average RBS sizes with time are small in magnitude and are consequently not influential on genome size evolution, and certainly not a major contributor for the "C-value enigma". Our model suggested that insertion bias of nucleotides may provide complementary explanation for large genomes besides selfish DNA. The fundamental indel asymmetry is applicable for all forms of genomic insertions and deletions. Long-lasting exponential increase of genome size present energy and material requirement that is impossible to sustain. We therefore concluded that if there were explosively accelerating expansion caused by significant effective insertion bias for any survival species, it must have occurred sporadically. Our model also provided an explanation for the observed proportional evolution of genome size.

A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time.

BACKGROUND: Feature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection. METHODS: We adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data. RESULTS: Using a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene's expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time. CONCLUSIONS: We believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data.

Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information.

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible.

Spectroscopic Study on the Interaction of Human Cytoglobin with Copper(â ¡) Ion.

Cytoglobin (Cygb), a recently discovered member of the vertebrate globin family, exhibits a traditional globin fold with a three-over-three α-helical sandwich. The interaction between copper(Ⅱ) ion (Cu2+) and Cygb has been investigated by UV-Vis, fluorescence, synchronous fluorescence and circular dichroism (CD) spectra. Results showed that the absorption intensity of Cygb at 280 nm increased and the intrinsic fluorescence of Cygb was quenched when Cu2+ was added. This fluorescence quenching of Cygb has been proven that it belongs to static quenching. The synchronous fluorescence spectra indicated that there were small changes about the microenvironment of tryptophan residues and tyrosine residues; furthermore, the binding site of Cu2+ is closer to tryptophan residues than tyrosine residues. No obvious change was observed about the secondary structure of Cygb with the addition of Cu2+ from the CD spectra.

Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus.

BACKGROUND: Over the last decade, metabolomics has evolved into a mainstream enterprise utilized by many laboratories globally. Like other "omics" data, metabolomics data has the characteristics of a smaller sample size compared to the number of features evaluated. Thus the selection of an optimal subset of features with a supervised classifier is imperative. We extended an existing feature selection algorithm, threshold gradient descent regularization (TGDR), to handle multi-class classification of "omics" data, and proposed two such extensions referred to as multi-TGDR. Both multi-TGDR frameworks were used to analyze a metabolomics dataset that compares the metabolic profiles of hepatocellular carcinoma (HCC) infected with hepatitis B (HBV) or C virus (HCV) with that of cirrhosis induced by HBV/HCV infection; the goal was to improve early-stage diagnosis of HCC. RESULTS: We applied two multi-TGDR frameworks to the HCC metabolomics data that determined TGDR thresholds either globally across classes, or locally for each class. Multi-TGDR global model selected 45 metabolites with a 0% misclassification rate (the error rate on the training data) and had a 3.82% 5-fold cross-validation (CV-5) predictive error rate. Multi-TGDR local selected 48 metabolites with a 0% misclassification rate and a 5.34% CV-5 error rate. CONCLUSIONS: One important advantage of multi-TGDR local is that it allows inference for determining which feature is related specifically to the class/classes. Thus, we recommend multi-TGDR local be used because it has similar predictive performance and requires the same computing time as multi-TGDR global, but may provide class-specific inference.

Mendelian randomization analyses reveal no genetic causal effects of major adipokines on systemic lupus erythematosus.

Epidemiological studies have shown that the levels of serum adipokine such as leptin and resistin are associated with the risk of developing systemic lupus erythematosus (SLE). Nevertheless, whether either leptin or resistin has causal impacts on the risk of SLE is still unknown. In this study, two-sample univariable MR analyses and multivariable MR analysis were performed to explore the causal relationships between adipokines and SLE. Additionally, the potential causal effects of SLE on major adipokines were evaluated using reverse MR analyses. The results of inverse-variance weighted (IVW), weighted median, weighted mode and MR‒Egger methods concordantly supported that major adipokines have no causal effects on the risk of SLE. In the multivariable MR IVW analysis with leptin and resistin as covariates, neither leptin (odds ratio (OR) = 3.093, P = 0.067) nor resistin (OR = 0.477, P = 0.311) was identified as an independent risk factor for SLE, which is in line with the univariable MR results. In conclusion, our analyses revealed no evidence to support that these three major adipokines are risk factors for SLE.

Exploring the causal relationship between Takayasu arteritis and inflammatory bowel disease using Mendelian randomization.

Takayasu arteritis (TA) and inflammatory bowel disease (IBD) are two distinct diseases; however, previous studies have reported many cases of IBD-TA coexistence. Additionally, the incidence of IBD in patients with TA is estimated to be significantly higher than the incidence in the general population. Therefore, the two diseases are anticipated to be linked. Mendelian randomization (MR) analysis assesses whether an exposure might causally affect an outcome by using genetic variants inherited randomly at conception, thereby reducing the impact of confounding and reverse causality. The present study aimed to investigate the potential causal relationship between TA and IBD using MR analysis. Two-sample MR analysis, in which TA and IBD were regarded as the exposure and outcome, respectively, was conducted to investigate whether the two diseases are causally related using the R TwoSampleMR package. Summary GWAS data of TA consisted of 516 Turkish cohorts and 462 controls, and 119 patients and 993 controls of European ancestry. Summary data of IBD was from a sub-study of the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) that comprised 31,665 cases and 33,977 controls of European ancestry. Additionally, separate MR analyses stratified by the two major subtypes of IBD, Crohn's disease (CD) and ulcerative colitis (UC), were performed. Various statistical tests, including the intercept of MR-Egger regression, funnel plots, Cochran's Q tests, and leave-one-out sensitivity analyses, were employed to assess the presence of heterogeneity and horizontal pleiotropy among single nucleotide polymorphisms (SNPs). In the primary analysis using the inverse-variance weighted (IVW) method, the risk of developing IBD for a patient with TA compared to a non-TA control increased 1.053 times (Odds Ratio (OR) = 1.053, P = 0.065). The MR-Egger method (OR = 1.025, P = 0.470) yielded results consistent with this null finding. However, both the weighted median method (OR = 1.038, P = 0.002) and the weighted mode method (OR = 1.051, P = 0.009) identified a significant harmful causal effect. The MR outcomes from separate subgroup analyses slightly diverged from those of IBD and TA. Specifically, for CD, three methods indicated that TA is a risk factor: IVW estimated the OR as 1.045 (P = 0.032), MR-Egger as 0.997 (P = 0.957), weighed median as 1.028 (P = 0.021), and weighted mode as 1.031 (P = 0.022), respectively. This study represents one of the initial investigations into the potential causal association between TA and IBD. With three MR methods, including the primary IVW approach, indicating a notable effect on TA on CD, our analysis findings offer some indication that TA could be a contributing risk factor for CD.

Association between liver enzymes and type 2 diabetes: a real-world study.

Aim: This study aimed to examine the association of liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl-transferase (GGT), with type 2 diabetes (T2D) risk, particularly their dose-response relationship. Methods: This cross-sectional study enrolled participants aged >20 years old who underwent physical examination at our local hospital from November 2022 to May 2023. A generalized additive model (GAM) was fit to assess the dose-response relationship between liver enzymes and T2D risk. Furthermore, data from the UK Biobank (n=217,533) and National Health and Nutrition Examination Survey (NHANES 2011-2018; n= 15,528) were analyzed to evaluate whether the dose-response relationship between liver enzymes and T2D differed by population differences. Results: A total of 14,100 participants were included (1,155 individuals with T2D and 12,945 individuals without diabetes) in the analysis. GAM revealed a non-linear relationship between liver enzymes and T2D risk (P non-linear < 0.001). Specifically, T2D risk increased with increasing ALT and GGT levels (range, <50 IU/L) and then plateaued when ALT and GGT levels were >50 IU/L. Elevated AST within a certain range (range, <35 IU/L) decreased the risk of T2D, whereas mildly elevated AST (>35 IU/L) became a risk factor for T2D. The UK Biobank and NHANES data analysis also showed a similar non-linear pattern between liver enzymes and T2D incidence. Conclusion: Liver enzymes were non-linearly associated with T2D risk in different populations, including China, the UK, and the US. Elevated ALT and GGT levels, within a certain range, could increase T2D risk. More attention should be given to liver enzyme levels for early lifestyle intervention and early T2D prevention. Further studies are necessary to explore the mechanism of the non-linear association between liver enzymes and T2D risk.

White blood cells and type 2 diabetes: A Mendelian randomization study.

BACKGROUND: Observational studies have demonstrated an association between white blood cells (WBC) subtypes and type 2 diabetes (T2D) risk. However, it is unknown whether this relationship is causal. We used Mendelian randomization (MR) to investigate the causal effect of WBC subtypes on T2D and glycemic traits. METHODS: The summary data for neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were extracted from a recent genome-wide association study (n = 173,480). The DIAGRAM and MAGIC consortia offered summary data pertaining to T2D and glycemic characteristics, including fasting glucose (FG) (n = 133,010), glycosylated hemoglobin (HbA1c) (n = 46,368), and homeostatic model assessment-estimated insulin resistance (HOMA-IR) (n = 37,037). A series of MR analyses (univariable MR, multivariable MR, and reverse MR) were used to investigate the causal association of different WBC subtypes with T2D and glycemic traits. RESULTS: Using the inverse-variance weighted method, we found one standard deviation increases in genetically determined neutrophil [odd ratio (OR): 1.086, 95% confidence interval (CI): 0.877-1.345], lymphocyte [0.878 (0.766-1.006)], monocyte [1.010 (0.906-1.127)], eosinophil [0.995 (0.867-1.142)], and basophil [0.960 (0.763-1.207)] were not causally associated with T2D risk. These findings were consistent with the results of three pleiotropy robust methods (MR-Egger, weighted median, and mode-based estimator) and multivariable MR analyses. Reverse MR analysis provided no evidence for the reverse causation of T2D on WBC subtypes. The null causal effects of WBC subtypes on FG, HbA1c, and HOMA-IR were also identified. CONCLUSIONS: WBCs play no causal role in the development of insulin resistance and T2D. The observed association between these factors may be explained by residual confounding.

ncRNAs-mediated TIMELESS overexpression in lung adenocarcinoma correlates with reduced tumor immune cell infiltration and poor prognosis.

Lung adenocarcinoma (LUAD) has a poor prognosis. Circadian genes such as TIMELESS have been associated with several pathologies, including cancer. The expression of TIMELESS and the relationship between TIMELESS, infiltration of tumors and prognosis in LUAD requires further investigation. In this study, we investigated the expression of TIMELESS and its association with survival across several types of human cancer using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Program. Noncoding RNAs (ncRNAs) regulating overexpression of TIMELESS in lung adenocarcinoma (LUAD) were explored with expression, correlation, and survival analyses. Immune cell infiltration and biomarkers were analyzed between different TIMELESS expression levels. The relationship between TIMELESS expression and immunophenoscores, which were used to predict response to immunotherapy, was evaluated. TIMELESS was identified as a potential oncogene in LUAD. NcRNA analysis showed MIR4435-2HG/hsa-miR-1-3p may interact with TIMELESS in a competitive endogenous RNA network in LUAD tumor tissues. Most immune cells were significantly decreased in TCGA LUAD tumor tissues with high TIMELESS expression except for CD4+T cells and Th2 cells. TIMELESS expression in LUAD tumor tissues was significantly negatively correlated with neutrophil biomarkers, dendritic cell biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C) and an immunophenoscore that predicted outcomes associated with the use of immune checkpoint inhibitors. These findings imply that ncRNAs-mediated TIMELESS overexpression in LUAD tumor tissues correlated with poor prognosis, reduced immune cell infiltration in the tumor microenvironment, and poor response to immune checkpoint inhibitors.

Clinical Evidence of the Relationship Between Alanine Aminotransferase and Diabetic Kidney Disease.

Aim: Multiple studies have investigated the association between alanine aminotransferase (ALT) and diabetes mellitus (DM); however, only a few studies have specifically examined the relationship between ALT and diabetic kidney disease (DKD). This study aimed to investigate the relationship between ALT and DKD using clinical data. Methods: A cross-sectional study was conducted on 668 individuals that included non-DM (N=281), DM without DKD (N=160), and DKD (N=227) patients. A generalized additive model (GAM) was used to examine the dose-response relationship between ALT and DKD risk. We also analyzed the data from the US National Health and Nutrition Examination Survey (NHANES) 2015-2018 using the same statistical methods; 4481, 1110, and 671 individuals were included in the non-DM, DM without DKD, and DKD groups, respectively. Results: The changes in ALT activity among the non-DM, DM without DKD, and DKD groups showed a similar pattern in both our clinical data and the NHANES dataset. ALT activity increases with the onset of DM, whereas ALT activity decreases when DM progresses to DKD. The GAM revealed a nonlinear U-shaped relationship between ALT and DKD risk in the two datasets, and the lowest range of ALT was 40-50 IU/L. Both lower (<40 IU/L) and higher (>50 IU/L) ALT activity were found to be positively associated with DKD risk. Conclusion: A U-shaped nonlinear association between ALT and DKD was found in our clinical data and NHANES data. DKD risk was increased by both lower or higher ALT activity. To confirm the causality of nonlinear relationship, larger prospective studies or Mendelian randomization analysis are required.

Integrative analysis of Mendelian randomization and gene expression profiles reveals a null causal relationship between adiponectin and diabetic retinopathy.

Observational studies have been conducted to investigate the correlation between adiponectin and diabetic retinopathy (DR), but no consistent relationship has been established. In this study, we employed an integrative analysis that combined Mendelian randomization (MR) and bioinformatics analyses to comprehensively explore the association between DR and adiponectin, aiming to provide a unified answer of their relationship. Using the inverse-variance weighted (IVW) method, the odd ratio (OR) of developing DR per 1 mg/dL increment in genetically predicted log-transformed adiponectin concentration was estimated to be 0.949 (P = 0.557). Other robust MR methods produced consistent results, confirming the absence of a causal effect of adiponectin on DR. Additionally, the expression levels of the six adiponectin-related genes showed no significant differences among normal controls, individuals with diabetes but without DR, and those with DR Furthermore, the biological pathways enriched by these genes were not strongly relevant to DR. At both the individual gene and pathway levels, there were no overlaps between the adiponectin-related genes and the differentially expressed genes, indicating a lack of association between adiponectin and DR based on gene expression profiles. In summary, the integrative analysis, which combined MR and bioinformatics data mining, yielded compelling evidence supporting the notion that adiponectin is not a risk factor for DR.

Mendelian randomization analyses explore the relationship between cathepsins and lung cancer.

Lung cancer, a major contributor to cancer-related fatalities worldwide, involves a complex pathogenesis. Cathepsins, lysosomal cysteine proteases, play roles in various physiological and pathological processes, including tumorigenesis. Observational studies have suggested an association between cathepsins and lung cancer. However, the causal link between the cathepsin family and lung cancer remains undetermined. This study employed Mendelian randomization analyses to investigate this causal association. The univariable Mendelian randomization analysis results indicate that elevated cathepsin H levels increase the overall risk of lung cancer, adenocarcinoma, and lung cancer among smokers. Conversely, reverse Mendelian randomization analyses suggest that squamous carcinoma may lead to increased cathepsin B levels. A multivariable analysis using nine cathepsins as covariates reveals that elevated cathepsin H levels lead to an increased overall risk of lung cancer, adenocarcinoma, and lung cancer in smokers. In conclusion, cathepsin H may serve as a marker for lung cancer, potentially inspiring directions in lung cancer diagnosis and treatment.