iTRAQ-based quantitative proteomic analysis of cerebrospinal fluid reveals NELL2 as a potential diagnostic biomarker of tuberculous meningitis

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.

Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.

Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.

Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

NELL2 as a potential marker of outcome in the cerebrospinal fluid of patients with tuberculous meningitis: preliminary results from a single-center observational study

OBJECTIVE

To detect the changes in Nel-like 2 (NELL2) in cerebrospinal fluid (CSF) in the outcome of tuberculous meningitis (TBM) patients and to initially evaluate its potential as a marker.

METHODS

We collected the clinical data of patients with suspected TBM in the First People's Hospital of Zunyi from November 2017 to January 2021 and retained their CSF. According to the selection and exclusion criteria, the TBM group (11 cases) and the control group (18 cases) were obtained. Western blotting (WB) was used to detect the level of NELL2 in the CSF of the two groups, especially the change in NELL2 before and after treatment in TBM patients.

RESULTS

The level of NELL2 in the TBM group was lower than that in the control group (P < 0.05), and the level of NELL2 showed an increasing trend after anti-tuberculosis treatment in the TBM group.

CONCLUSIONS

NELL2 in the CSF of TBM patients decreased significantly. Anti-tuberculosis treatment can improve the level of NELL2, which may become one of the potential markers of outcome in the cerebrospinal fluid of patients with tuberculous meningitis.

Identification of protein biomarkers in host cerebrospinal fluid for differential diagnosis of tuberculous meningitis and other meningitis

Background and purpose

The diagnosis of tuberculous meningitis (TBM) is difficult due to the lack of sensitive methods. Identification of TBM-specific biomarkers in the cerebrospinal fluid (CSF) may help diagnose and improve our understanding of TBM pathogenesis.

Patients and methods

Of the 112 suspected patients with TBM prospectively enrolled in the study, 32 patients with inconclusive diagnosis, non-infectious meningitis, and long-term treatment with hormones and immunosuppressants were excluded. The expression of 8 proteins in the CSF was analyzed using ELISA in 22 patients with definite TBM, 18 patients with probable TBM, and 40 patients with non-TBM.

Results

Significant differences in the expression of 7 proteins were detected between the TBM and non-TBM groups (P < 0.01). Unsupervised hierarchical clustering (UHC) analysis revealed a disease-specific profile consisting of 7 differentially expressed proteins for TBM diagnosis, with an accuracy of 82.5% (66/80). Logistic regression with forward stepwise analysis indicated that a combination of 3 biomarkers (APOE_APOAI_S100A8) showed a better ability to discriminate TBM from patients with non-TBM [area under the curve (AUC) = 0.916 (95%CI: 0.857–0.976)], with a sensitivity of 95.0% (95%CI: 83.1–99.4%) and a specificity of 77.5% (95%CI: 61.5–89.2%).

Conclusion

Our results confirmed the potential ability of CSF proteins to distinguish TBM from patients with non-TBM and provided a useful panel for the diagnosis of TBM.

NELL2 modulates cell proliferation and apoptosis via ERK pathway in the development of benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is a quite common illness but its etiology and mechanism remain unclear. Neural epidermal growth factor-like like 2 (NELL2) plays multifunctional roles in neural cell growth and is strongly linked to the urinary tract disease. Current study aims to determine the expression, functional activities and underlying mechanism of NELL2 in BPH. Human prostate cell lines and tissues from normal human and BPH patients were utilized. Immunohistochemical staining, immunofluorescent staining, RT-polymerase chain reaction (PCR) and Western blotting were performed. We further generated cell models with NELL2 silenced or overexpressed. Subsequently, proliferation, cycle, and apoptosis of prostate cells were determined by cell counting kit-8 (CCK-8) assay and flow cytometry analysis. The epithelial-mesenchymal transition (EMT) and fibrosis process were also analyzed. Our study revealed that NELL2 was up-regulated in BPH samples and localized in the stroma and the epithelium compartments of human prostate tissues. NELL2 deficiency induced a mitochondria-dependent cell apoptosis, and inhibited cell proliferation via phosphorylating extracellular signal-regulated kinase 1/2 (ERK1/2) activation. Additionally, suppression of ERK1/2 with U0126 incubation could significantly reverse NELL2 deficiency triggered cell apoptosis. Consistently, overexpression of NELL2 promoted cell proliferation and inhibited cell apoptosis. However, NELL2 interference was observed no effect on EMT and fibrosis process. Our novel data demonstrated that up-regulation of NELL2 in the enlarged prostate could contribute to the development of BPH through enhancing cell proliferation and inhibited a mitochondria-dependent cell apoptosis via the ERK pathway. The NELL2-ERK system might represent an important target to facilitate the development of future therapeutic approaches in BPH.

Screening and identification of potential protein biomarkers for the early diagnosis of acute myocardial infarction

Background

Acute myocardial infarction (AMI) is the most serious type of heart disease. Clinically, there is an urgent need to discover diagnostic biomarkers for the early diagnosis of AMI.

Methods

Serum proteomic profiles in AMI patients, healthy controls, and stable angina pectoris (SAP) patients were explored and compared by iTRAQ-2DLC-MS/MS. The clinical data of AMI patients were also analyzed. Differentially expressed proteins were validated by enzyme linked immunosorbent assay (ELISA), and diagnostic models were constructed.

Results

A total of 39 differentially expressed proteins were identified in AMI patients. The results showed that the serum levels of apolipoprotein E (APOE) in AMI patients were notably higher than those in the healthy controls (P=0.0172). The serum levels of aspartate aminotransferase (AATC) in AMI patients were markedly higher than those in the healthy controls and SAP patients (P<0.0001 and P<0.0001, respectively). The serum levels of fibronectin (FINC) in SAP patients were significantly higher than those in the healthy controls and AMI patients (P=0.0043 and P=0.0044, respectively). Clinical data analysis showed a considerable difference in blood glucose levels, troponin I (TNI), and creatine kinase (CK) in AMI patients compared with SAP patients and healthy controls. A diagnostic model consisting of AATC and clinical indicators [lactate dehydrogenase (LDH) and CK] was established to distinguish between AMI patients and healthy controls, with an area under the curve (AUC) value of 0.993 sensitivity and specificity of 96.2% and 96.3%, respectively. A diagnostic model consisting of AATC and CK was established to distinguish between AMI patients and SAP patients, with an AUC value of 0.975 and a sensitivity and specificity of 85.2% and 79.30%, respectively.

Conclusions

In this study, differentially expressed proteins in AMI patients were combined with clinical indexes, LDH and CK, and two diagnostic models were constructed. This study may provide meaningful data for the early diagnosis of AMI.

Prognostic signature of lung adenocarcinoma based on stem cell-related genes

Lung adenocarcinoma (LUAD) is characterized by high infiltration and rapid growth. The function of the stem cell population is to control and maintain cell regeneration. Therefore, it is necessary to study the prognostic value of stem cell-related genes in LUAD. Signature genes were screened out from 166 stem cell-related genes according to the least absolute shrinkage operator (LASSO) and subsequently multivariate Cox regression analysis, and then established risk model. Immune infiltration and nomogram model were used to evaluate the clinical efficacy of signature. A signature consisting of 10 genes was used to dichotomize the LUAD patients into two groups (cutoff, 1.314), and then validated in GSE20319 and GSE42127. There was a significant correlation between signature and clinical characteristics. Patients with high-risk had a shorter overall survival. Furthermore, significant differences were found in multiple immune cells between the high-risk group and low-risk group. A high correlation was also reflected between signature and immune infiltration. What’s more, the signature could effectively predict the efficacy of chemotherapy in patients with LUAD, and a nomogram based on signature might accurately predict the prognosis of patients with LUAD. The signature-based of stem cell-related genes might be contributed to predicting prognosis of patients with LUAD.

Label-Free Quantitative Proteomics Identifies Novel Biomarkers for Distinguishing Tuberculosis Pleural Effusion from Malignant Pleural Effusion

PURPOSE

To identify potential protein biomarkers for distinguishing tuberculosis plural effusion (TBPE) from malignant plural effusion (MPE).

EXPERIMENTAL DESIGN

Five independent samples from each group (TBPE and MPE) are enrolled for label-free quantitative proteomics analyses. The differentially expressed proteins are validated by western blot and ELISA. Logistic regression analysis is used to obtain the optimal diagnostic model.

RESULTS

In total, 14 proteins with significant difference are identified between TBPE and MPE. Seven differentially expressed proteins are validated using western blot, and the expression patterns of these seven proteins are similar with those in proteomics analysis. Statistically significant differences in four proteins (AGP1, ORM2, C9, and SERPING1) are noted between TBPE and MPE in the training set (n = 230). Logistic regression analysis shows the combination of AGP1-ORM2-C9 presents a sensitivity of 73.0% (92/126) and specificity of 89.4% (93/104) in discriminating TBPE from MPE. Additional validation is performed to evaluate the diagnostic model in an independent blind testing set (n = 80), and yielded a sensitivity of 74.4% (32/43) and specificity of 91.9% (34/37) in discriminating TBPE from MPE.

CONCLUSION

The study uncovers the proteomic profiles of TBPE and MPE, and provides new potential diagnostic biomarkers for distinguishing TBPE from MPE.

Mass spectrometry-based proteomic techniques to identify cerebrospinal fluid biomarkers for diagnosing suspected central nervous system infections. A systematic review

OBJECTIVES

Central nervous system (CNS) infections account for considerable death and disability every year. An urgent research priority is scaling up diagnostic capacity, and introduction of point-of-care tests. We set out to assess current evidence for the application of mass spectrometry (MS) peptide sequencing in identification of diagnostic biomarkers for CNS infections.

METHODS

We performed a systematic review (PROSPEROCRD42018104257) using PRISMA guidelines on use of MS to identify cerebrospinal fluid (CSF) biomarkers for diagnosing CNS infections. We searched PubMed, Embase, Web of Science, and Cochrane for articles published from 1 January 2000 to 1 February 2019, and contacted experts. Inclusion criteria involved primary research except case reports, on the diagnosis of infectious diseases except HIV, applying MS to human CSF samples, and English language.

RESULTS

4,620 papers were identified, of which 11 were included, largely confined to pre-clinical biomarker discovery, and eight (73%) published in the last five years. 6 studies performed further work termed verification or validation. In 2 of these studies, it was possible to extract data on sensitivity and specificity of the biomarkers detected by ELISA, ranging from 89-94% and 58-92% respectively.

CONCLUSIONS

The findings demonstrate feasibility and potential of the methods in a variety of infectious diseases, but emphasise the need for strong interdisciplinary collaborations to ensure appropriate study design and biomarker validation.

Potential biomarkers for antidiastole of tuberculous and malignant pleural effusion by proteome analysis

AIM

To investigate novel potential biomarkers for antidiastole of tuberculous pleural effusion (TPE) from malignant pleural effusion (MPE).

MATERIALS & METHODS

iTRAQTM-coupled LC-MS/MS were applied to analyze the proteome of TPE and MPE samples. The candidate proteins were verified by enzyme-linked immunosorbent assay.

RESULTS

A total of 432 differential proteins were identified. Enzyme-linked immunosorbent assay revealed significantly higher levels of fibronectin (FN) and cathepsin G (CTSG) in MPE than in TPE, but lower levels of leukotriene-A4 hydrolase (LTA4H). The receiver operator characteristic values were 0.285 for FN, 0.64 for LTA4H, 0.337 for CTSG and 0.793 for a combination of these candidate markers.

CONCLUSION

FN, LTA4H and CTSG were identified as potential biomarkers to differentiate TPE from MPE and their combination exhibited higher diagnostic capacity.

Determination of Cerebrospinal Fluid Proteome Variations by Isobaric Labeling Coupled with Strong Cation-Exchange Chromatography and Tandem Mass Spectrometry

Cerebrospinal fluid (CSF) is in direct contact with the brain and represents a valuable source of mediators that reflect metabolic processes occurring in the central nervous system (CNS). In this sense, mass spectrometry (MS) methods have proven to be sensitive in quantifying the proteomic profiles of CSF, therefore being able to detect biomarker candidates for neurological disorders. In particular, a key development has been the use of multiplexing technologies to easily identify and quantify complex protein mixtures. This chapter describes a workflow suitable for the analysis of CSF proteome using isobaric labeling coupled to strong cation-exchange chromatography fractionation for its potential use as a biomarker discovery platform. In this case, the isobaric tags for relative and absolute quantitation (iTRAQ) label all proteins in a sample via free amines at the N-terminus and on the side chain of lysine residues. Then, the labeled samples are pooled and chromatographically fractionated. These fractions with the pooled samples are afterward analyzed by tandem mass spectrometry (MS/MS), and proteins are quantified by the relative intensities of the reporter ions in the MS/MS spectra, simultaneously obtaining the amino acid sequence. This method complements the neuroproteomic toolbox to identify new protein biomarkers not only for the early clinical diagnosis and disease staging of CNS-related disorders but also to elucidate the molecular mechanisms related to the pathophysiology of these symptoms.

Identification of a Novel Serum Biomarker for Tuberculosis Infection in Chinese HIV Patients by iTRAQ-Based Quantitative Proteomics

Tuberculosis (TB) is a major comorbidity in HIV patients as well as a serious co-epidemic. Traditional detection methods are not effective or sensitive for the detection of Mycobacterium tuberculosis at the early stage. TB has become a major cause of lethal on HIV patients. We employed isobaric tags for relative and absolute quantitation (iTRAQ) technology to identify the different host responses in HIV-noTB and HIV-TB patients’ sera. Given the diversity of HIV subtypes, which results in a variety of host responses in different human populations, we focused on the Chinese patients. Of the 25 proteins identified, 7 were increased and 18 were decreased in HIV-TB co-infected patients. These proteins were found to be involved in host immune response processes. We identified a candidate protein, endoglin (ENG), which showed an 4.9 times increase by iTRAQ and 11.5 times increase by ELISA. ENG demonstrated the diagnostic efficacy and presented a novel molecular biomarker for TB in HIV-infected Chinese patients. This study provides new insight into the challenges in the diagnosis and effective management of patients with HIV-TB.

iTRAQ-Based Proteomics Suggests Ephb6 as a Potential Regulator of the ERK Pathway in the Prefrontal Cortex of Chronic Social Defeat Stress Model Mice

PURPOSE

Major depressive disorder (MDD) is a worldwide concern and devastating psychiatric disease. The World Health Organization claims that MDD leads to at least 11.9% of the global burden of disease. However, the underlying pathophysiology mechanisms of MDD remain largely unknown.

EXPERIMENTAL DESIGN

Herein, we proteomic-based strategy is used to compare the prefrontal cortex (PFC) in chronic social defeat stress (CSDS) model mice with a control group. Based on pooled samples, differential proteins are identified in the PFC proteome using iTRAQ coupled with LC-MS/MS.

RESULTS

Ingenuity Pathway Analysis (IPA) is then followed to predict relevant pathways, with the ephrin receptor signaling pathway selected for further research. Additionally, as the selected key proteins of the ephrin receptor signaling pathway, ephrin type-B receptor 6 (EphB6) and the ERK pathway are validated by Western blotting.

CONCLUSION AND CLINICAL RELEVANT

Altogether, increased understanding of the ephrin receptor signaling pathway in MDD is provided, which implicates further investigation of PFC dysfunction induced by CSDS treatment.

Cerebrospinal fluid metabolomic profiling in tuberculous and viral meningitis: Screening potential markers for differential diagnosis

BACKGROUND

Tuberculous meningitis (TBM) is the most severe and frequent form of central nervous system tuberculosis. The current lack of efficient diagnostic tests makes it difficult to differentiate TBM from other common types of meningitis, especially viral meningitis (VM). Metabolomics is an important tool to identify disease-specific biomarkers. However, little metabolomic information is available on adult TBM.

METHODS

We used ¹H nuclear magnetic resonance-based metabolomics to investigate the metabolic features of the CSF from 18 TBM and 20 VM patients. Principal component analysis and orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) were applied to analyze profiling data. Metabolites were identified using the Human Metabolome Database and pathway analysis was performed with MetaboAnalyst 3.0.

RESULTS

The OSC-PLS-DA model could distinguish TBM from VM with high reliability. A total of 25 key metabolites that contributed to their discrimination were identified, including some, such as betaine and cyclohexane, rarely reported before in TBM. Pathway analysis indicated that amino acid and energy metabolism was significantly different in the CSF of TBM compared with VM.

CONCLUSIONS

Twenty-five key metabolites identified in our study may be potential biomarkers for TBM differential diagnosis and are worthy of further investigation.

Laboratory Diagnosis of Central Nervous System Infection

Central nervous system (CNS) infections are potentially life threatening if not diagnosed and treated early. The initial clinical presentations of many CNS infections are non-specific, making a definitive etiologic diagnosis challenging. Nucleic acid in vitro amplification-based molecular methods are increasingly being applied for routine microbial detection. These methods are a vast improvement over conventional techniques with the advantage of rapid turnaround and higher sensitivity and specificity. Additionally, molecular methods performed on cerebrospinal fluid samples are considered the new gold standard for diagnosis of CNS infection caused by pathogens, which are otherwise difficult to detect. Commercial diagnostic platforms offer various monoplex and multiplex PCR assays for convenient testing of targets that cause similar clinical illness. Pan-omic molecular platforms possess potential for use in this area. Although molecular methods are predicted to be widely used in diagnosing and monitoring CNS infections, results generated by these methods need to be carefully interpreted in combination with clinical findings. This review summarizes the currently available armamentarium of molecular assays for diagnosis of central nervous system infections, their application, and future approaches.