Identification and validation of three potential biomarkers and immune microenvironment for in severe asthma in microarray and single-cell datasets

Objective: The aim of this study was to identify genetic biomarkers and cellular communications associated with severe asthma in microarray data sets and single cell data sets. The potential gene expression levels were verified in a mouse model of asthma.Methods: We identified differentially expressed genes from the microarray datasets (GSE130499 and GSE63142) of severe asthma, and then constructed models to screen the most relevant biomarkers to severe asthma by machine learning algorithms (LASSO and SVM-RFE), with further validation of the results by GSE43696. Single-cell datasets (GSE193816 and GSE227744) were identified for potential biomarker-specific expression and intercellular communication. Finally, The expression levels of potential biomarkers were verified with a mouse model of asthma.Results: The 73 genes were differentially expressed between severe asthma and normal control. LASSO and SVM-RFE recognized three genes BCL3, DDIT4 and S100A14 as biomarkers of severe asthma and had good diagnostic effect. Among them, BCL3 transcript level was down-regulated in severe asthma, while S100A14 and DDIT4 transcript levels were up-regulated. The transcript levels of the three genes were confirmed in the mouse model. Infiltration of neutrophils and mast cells were found to be increased in severe asthma and may be associated with bronchial epithelial cells through BMP and NRG signalingConclusions: We identified three differentially expressed genes (BCL3, DDIT4 and S100A14) of diagnostic significance that may be involved in the development of severe asthma and these gene expressions could be serviced as biomarker of severe asthma and investigating the function roles could bring new insights into the underlying mechanisms.

Changes in Adipokine, Resitin, and BDNF Concentrations in Treatment-Resistant Depression after Electroconvulsive Therapy

OBJECTIVES

One of the current challenges in psychiatry is the search for answers on how to effectively manage drug-resistant depression. The occurrence of drug resistance in patients is an indication for the use of electroconvulsive therapy (ECT). This method is highly effective and usually results in relatively quick health improvement. Despite the knowledge of how ECT works, not all of the biological pathways activated during its use have been identified. Hence, based on the neuroinflammatory hypothesis of depression, we investigated the concentration of two opposite-acting adipokines (anti-inflammatory adiponectin and proinflammatory resistin) and BDNF in antidepressant-resistant patients undergoing ECT.

METHODS

The study group comprised 52 patients hospitalized due to episodes of depression in the course of unipolar and bipolar affective disorder. The serum concentration of adipokines and BDNF was determined before and after the therapeutic intervention using an ELISA method. In the analyses, we also included comparisons considering the type of depression, sex, and achieving remission.

RESULTS

Adiponectin, resistin, and BDNF concentrations change after ECT treatment. These changes are correlated with an improvement in the severity of depressive symptoms and are more or less pronounced depending on the type of depression.

CONCLUSIONS

Although not all observed changes reach statistical significance, adipokines in particular remain exciting candidates for biomarkers in assessing the course of the disease and response to ECT treatment.

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

Serum Metabolic Profiling Analysis of Chronic Gastritis and Gastric Cancer by Untargeted Metabolomics

Purpose

Gastric cancer is a common tumor of the digestive system. Identification of potential molecules associated with gastric cancer progression and validation of potential biomarkers for gastric cancer diagnosis are very important. Thus, the aim of our study was to determine the serum metabolic characteristics of the serum of patients with chronic gastritis (CG) or gastric cancer (GC) and validate candidate biomarkers for disease diagnosis.

Experimental Design

A total of 123 human serum samples from patients with CG or GC were collected for untargeted metabolomic analysis via UHPLC-Q-TOF/MS to determine characteristics of the serum. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and heat map were used for multivariate analysis. In addition, commercial databases were used to identify the pathways of metabolites. Differential metabolites were identified based on a heat map with a t-test threshold (p < 0.05), fold-change threshold (FC > 1.5 or FC < 2/3) and variable importance in the projection (VIP >1). Then, differential metabolites were analyzed by receiver operating characteristic (ROC) curve to determine candidate biomarkers. All samples were analyzed for fasting lipid profiles.

Results

Analysis of serum metabolomic profiles indicated that most of the altered metabolic pathways in the three groups were associated with lipid metabolism (p < 0.05) and lipids and lipid-like molecules were the predominating metabolites within the top 100 differential metabolites (p < 0.05, FC > 1.5 or FC < 2/3, and VIP >1). Moreover, differential metabolites, including hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine had high diagnostic performance according to PLS-DA. In addition, fasting lipid profile analysis showed the serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (Apo-A1) were decreased concomitant to the progression of the progression of the disease compared with those in the control group (p < 0.05).

Conclusions

Thus, this study demonstrated that lipid metabolism may influence the development of CG to GC. Hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine were selected as candidate diagnostic markers for CG and GC.

Clinical and Molecular-Genetic Insights into the Role of Oxidative Stress in Diabetic Retinopathy: Antioxidant Strategies and Future Avenues

Reactive oxygen species (ROS) overproduction and ROS-signaling pathways activation attack the eyes. We evaluated the oxidative stress (OS) and the effects of a daily, core nutritional supplement regimen containing antioxidants and omega 3 fatty acids (A/ω3) in type 2 diabetics (T2DM). A case-control study was carried out in 480 participants [287 T2DM patients with (+)/without (-) diabetic retinopathy (DR) and 193 healthy controls (CG)], randomly assigned to a daily pill of A/ω3. Periodic evaluation through 38 months allowed to outline patient characteristics, DR features, and classic/OS blood parameters. Statistics were performed by the SPSS 24.0 program. Diabetics displayed significantly higher circulating pro-oxidants (p = 0.001) and lower antioxidants (p = 0.0001) than the controls. Significantly higher plasma malondialdehyde/thiobarbituric acid reactive substances (MDA/TBARS; p = 0.006) and lower plasma total antioxidant capacity (TAC; p = 0.042) and vitamin C (0.020) was found in T2DM + DR versus T2DM-DR. The differential expression profile of solute carrier family 23 member 2 (SLC23A2) gene was seen in diabetics versus the CG (p = 0.001), and in T2DM + DR versus T2DM - DR (p < 0.05). The A/ω3 regime significantly reduced the pro-oxidants (p < 0.05) and augmented the antioxidants (p < 0.05). This follow-up study supports that a regular A/ω3 supplementation reduces the oxidative load and may serve as a dietary prophylaxis/adjunctive intervention for patients at risk of diabetic blindness.

Genome Regulation and Gene Interaction Networks Inferred From Muscle Transcriptome Underlying Feed Efficiency in Pigs

Improvement of feed efficiency (FE) is key for Sustainability and cost reduction in pig production. Our aim was to characterize the muscle transcriptomic profiles in Danbred Duroc (Duroc; n = 13) and Danbred Landrace (Landrace; n = 28), in relation to FE for identifying potential biomarkers. RNA-seq data on the 41 pigs was analyzed employing differential gene expression methods, gene-gene interaction and network analysis, including pathway and functional analysis. We also compared the results with genome regulation in human exercise data, hypothesizing that increased FE mimics processes triggered in exercised muscle. In the differential expression analysis, 13 genes were differentially expressed, including: MRPS11, MTRF1, TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, the divergent count, we performed pathway enrichment analysis. We found five significantly enriched pathways related to feed conversion ratio (FCR). These pathways were mainly related to mitochondria, and summarized in the mitochondrial translation elongation (MTR) pathway. In the gene interaction analysis, the most interesting genes included the mitochondrial genes: PPIF, MRPL35, NDUFS4 and the fat metabolism and obesity genes: AACS, SMPDL3B, CTNNBL1, NDUFS4, and LIMD2. In the network analysis, we identified two modules significantly correlated with FCR. Pathway enrichment of module genes identified MTR, electron transport chain and DNA repair as enriched pathways. The network analysis revealed the mitochondrial gene group NDUF as key network hub genes, showing their potential as biomarkers. Results show that genes related to human exercise were enriched in identified FCR related genes. We conclude that mitochondrial activity is a key driver for FCR in muscle tissue, and mitochondrial genes could be potential biomarkers for FCR in pigs.

Differentially Expressed Genes in Osteomyelitis Induced by Staphylococcus aureus Infection

Osteomyelitis (OM) is a complicated and serious disease and its underlying molecular signatures of disease initiation and progression remain unclear. Staphylococcus aureus (S. aureus) is the most common causative agent of OM. Previous study of Banchereau et al. has established a link between whole blood transcription profiles and clinical manifestations in patients infected with S. aureus. However, the differentially expressed genes (DEGs) in OM induced by S. aureus infection have not been intensively investigated. In this study, we downloaded the gene expression profile dataset GSE30119 from Gene Expression Omnibus, and performed bioinformatic analysis to identify DEGs in S. aureus infection induced OM from the transcriptional level. The study consisted of 143 whole blood samples, including 44 healthy controls, 42 OM-free, and 57 OM infection patients. A total of 209 S. aureus infection-related genes (SARGs) and 377 OM-related genes (OMRGs) were identified. The SARGs were primarily involved in the immune response by GO functional and pathway enrichment analysis. Several proteins adhere to neutrophil extracellular traps may be critical for the immune response to the process of S. aureus infection. By contrast, the OMRGs differ from the SARGs. The OMRGs were enriched in transmembrane signaling receptor and calcium channel activity, cilium morphogenesis, chromatin silencing, even multicellular organism development. Several key proteins, including PHLPP2 and EGF, were hub nodes in protein–protein interaction network of the OMRGs. In addition, alcoholism, systemic lupus erythematosus and proteoglycans in cancer were the top pathways influenced by the OMRGs associated with OM. Thus, this study has further explored the DEGs and their biological functions associated with S. aureus infection and OM, comparing with the previous study, and may light the further insight into the underlying molecular mechanisms and the potential critical biomarkers in OM development.

Large-Scale SRM Screen of Urothelial Bladder Cancer Candidate Biomarkers in Urine

Urothelial bladder cancer is a condition associated with high recurrence and substantial morbidity and mortality. Noninvasive urinary tests that would detect bladder cancer and tumor recurrence are required to significantly improve patient care. Over the past decade, numerous bladder cancer candidate biomarkers have been identified in the context of extensive proteomics or transcriptomics studies. To translate these findings in clinically useful biomarkers, the systematic evaluation of these candidates remains the bottleneck. Such evaluation involves large-scale quantitative LC-SRM (liquid chromatography-selected reaction monitoring) measurements, targeting hundreds of signature peptides by monitoring thousands of transitions in a single analysis. The design of highly multiplexed SRM analyses is driven by several factors: throughput, robustness, selectivity and sensitivity. Because of the complexity of the samples to be analyzed, some measurements (transitions) can be interfered by coeluting isobaric species resulting in biased or inconsistent estimated peptide/protein levels. Thus the assessment of the quality of SRM data is critical to allow flagging these inconsistent data. We describe an efficient and robust method to process large SRM data sets, including the processing of the raw data, the detection of low-quality measurements, the normalization of the signals for each protein, and the estimation of protein levels. Using this methodology, a variety of proteins previously associated with bladder cancer have been assessed through the analysis of urine samples from a large cohort of cancer patients and corresponding controls in an effort to establish a priority list of most promising candidates to guide subsequent clinical validation studies.