Metabolite Profiles of the Cerebrospinal Fluid in Neurosyphilis Patients Determined by Untargeted Metabolomics Analysis

Omics detection and treatment of syphilis

Treponema pallidum is the source of the chronic systemic sexually transmitted illness syphilis. T. pallidum can evade immunity and spread. A hard chancre, enlarged lymph nodes, and a syphilis rash are the primary clinical signs. The condition may affect the nervous or cardiovascular system and even become fatal after being neglected. Omics technology is a cutting-edge technique that maps the entire regulatory network of gene and protein metabolism using high-throughput sequencing and other techniques, such as transcriptomics, proteomics, metabolomics, and genomics, to perform more efficient and methodical research on biological samples. Owing to the diverse and intricate biological roles and gene expression of T. pallidum, a single omics study is frequently insufficient and limited. This review focused on and summarized the use of several omics methods for investigating T. pallidum by referencing several different studies in the literature.

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

LC-MS metabolomics and lipidomics in cerebrospinal fluid from viral and bacterial CNS infections: a review

There is compelling evidence that a dysregulated immune inflammatory response in neuroinfectious diseases results in modifications in metabolic processes and altered metabolites, directly or indirectly influencing lipid metabolism within the central nervous system (CNS). The challenges in differential diagnosis and the provision of effective treatment in many neuroinfectious diseases are, in part, due to limited understanding of the pathophysiology underlying the disease. Although there are numerous metabolomics studies, there remains a deficit in neurolipidomics research to provide a comprehensive understanding of the connection between altered metabolites and changes in lipid metabolism. The brain is an inherently high-lipid organ; hence, understanding neurolipidomics is the key to future breakthroughs. This review aims to provide an integrative summary of altered cerebrospinal fluid (CSF) metabolites associated with neurolipid metabolism in bacterial and viral CNS infections, with a particular focus on studies that used liquid chromatography-mass spectrometry (LC-MS). Lipid components (phospholipids) and metabolites (carnitine and tryptophan) appear to be the most significant indicators in both bacterial and viral infections. On the basis of our analysis of the literature, we recommend employing neurolipidomics in conjunction with existing neurometabolomics data as a prospective method to enhance our understanding of the cross link between dysregulated metabolites and lipid metabolism in neuroinfectious diseases.

Large-Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as "the great imitator," NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients.

Neurosyphilis: insights into its pathogenesis, susceptibility, diagnosis, treatment, and prevention

Background and aim

Invasion of the central nervous system by Treponema pallidum can occur at any stage of syphilis. In the event that T. pallidum is not cleared promptly, certain individuals may experience progression to neurosyphilis, which manifests as cognitive and behavioral abnormalities, limb paralysis, and potentially fatal outcomes. Early identification or prevention of neurosyphilis is therefore crucial. The aim of this paper is to conduct a critical and narrative review of the latest information focusing exclusively to the pathogenesis and clinical management of neurosyphilis.

Methodology

To compile this review, we have conducted electronic literature searches from the PubMed database relating to neurosyphilis. Priority was given to studies published from the past 10 years (from 2013 to 2023) and other studies if they were of significant importance (from 1985 to 2012), including whole genome sequencing results, cell structure of T. pallidum, history of genotyping, and other related topics. These studies are classic or reflect a developmental process.

Results

Neurosyphilis has garnered global attention, yet susceptibility to and the pathogenesis of this condition remain under investigation. Cerebrospinal fluid examination plays an important role in the diagnosis of neurosyphilis, but lacks the gold standard. Intravenous aqueous crystalline penicillin G continues to be the recommended therapeutic approach for neurosyphilis. Considering its sustained prominence, it is imperative to develop novel public health tactics in order to manage the resurgence of neurosyphilis.

Conclusion

This review gives an updated narrative description of neurosyphilis with special emphasis on its pathogenesis, susceptibility, diagnosis, treatment, and prevention.

Resurgence of syphilis: focusing on emerging clinical strategies and preclinical models

Syphilis, a sexually transmitted disease (STD) caused by Treponema pallidum (T. pallidum), has had a worldwide resurgence in recent years and remains a public health threat. As such, there has been a great deal of research into clinical strategies for the disease, including diagnostic biomarkers and possible strategies for treatment and prevention. Although serological testing remains the predominant laboratory diagnostic method for syphilis, it is worth noting that investigations pertaining to the DNA of T. pallidum, non-coding RNAs (ncRNAs), chemokines, and metabolites in peripheral blood, cerebrospinal fluid, and other bodily fluids have the potential to offer novel perspectives on the diagnosis of syphilis. In addition, the global spread of antibiotic resistance, such as macrolides and tetracyclines, has posed significant challenges for the treatment of syphilis. Fortunately, there is still no evidence of penicillin resistance. Hence, penicillin is the recommended course of treatment for syphilis, whereas doxycycline, tetracycline, ceftriaxone, and amoxicillin are viable alternative options. In recent years, efforts to discover a vaccine for syphilis have been reignited with better knowledge of the repertoire of T. pallidum outer membrane proteins (OMPs), which are the most probable syphilis vaccine candidates. However, research on therapeutic interventions and vaccine development for human subjects is limited due to practical and ethical considerations. Thus, the preclinical model is ideal for conducting research, and it plays an important role in clinical transformation. Different preclinical models have recently emerged, such as in vitro culture and mouse models, which will lay a solid foundation for clinical treatment and prevention of syphilis. This review aims to provide a comprehensive summary of the most recent syphilis tactics, including detection, drug resistance treatments, vaccine development, and preclinical models in clinical practice.

Identification of potential biomarkers for diagnosis of syphilis from the cerebrospinal fluid based on untargeted metabolomic analysis

The infection rate of syphilis continues to rise globally, and the difficulty in diagnosis of neurosyphilis promptly needs to be resolved. More specific and sensitive diagnostic markers for latent syphilis and neurosyphilis should be found. Here the metabolic profiles of 88 cerebrospinal fluid samples from syphilis patients and controls were analyzed by LC/MS-based untargeted metabolomics. In total, 272 metabolites based on 3937 features obtained in ESI- mode and 252 metabolites based on 3799 features in ESI+ mode were identified. The experimental process was evaluated by principal component analysis, partial least squares discriminant analysis, and hierarchical cluster analysis. A clear separation between latent syphilis and neurosyphilis was found. Levels of lipid and linoleic acid metabolites, such as 9-oxo-octadecadienoic acid and 9,10,13-trihydroxyoctadecenoic acid, were increased in syphilis patients. In patients with neurosyphilis, significant changes in levels of 5-hydroxy-L-tryptophan (5-HTP) and acetyl-N-formyl-5-methoxykynurenamine (AFMK) in the tryptophan-kynurenine pathway were also detected. Only one metabolite, theophylline, differed significantly between symptomatic and asymptomatic neurosyphilis patients. Additionally, KEGG analysis revealed significant enrichment of tryptophan metabolism pathways, indicating a high correlation between tryptophan metabolism and syphilis symptoms. Levels of linoleic acid metabolites, 5-HTP, AFMK and theophylline were significantly altered in different patients. The role of these differential metabolites in the development of syphilis is worthy of further exploration. Our results may promote the development of biomarkers for diagnosis of latent syphilis from neurosyphilis, and for that of asymptomatic neurosyphilis from symptomatic neurosyphilis in the future.

Diagnostic biomarker panels of osteoarthritis: UPLC-QToF/MS-based serum metabolic profiling

Osteoarthritis (OA) is the most common joint disease in the world, characterized by pain and loss of joint function, which has led to a serious reduction in the quality of patients' lives. In this work, ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-QToF/MS) in conjunction with multivariate pattern recognition methods and an univariate statistical analysis scheme were applied to explore the serum metabolic signatures within OA group (n = 31), HC (healthy controls) group (n = 57) and non-OA group (n = 19) for early diagnosis and differential diagnosis of OA. Based on logistic regression analysis and receiver operating characteristic (ROC) curve analysis, seven metabolites, including phosphatidylcholine (18:0/22:6), p-cresol sulfate and so on, were identified as critical metabolites for the diagnosis of OA and HC and yielded an area under the curve (AUC) of 0.978. The other panel of unknown m/z 239.091, phosphatidylcholine (18:0/18:0) and phenylalanine were found to distinguish OA from non-OA and achieved an AUC of 0.888. These potential biomarkers are mainly involved in lipid metabolism, glucose metabolism and amino acid metabolism. It is expected to reveal new insight into OA pathogenesis from changed metabolic pathways.

Advances in laboratory diagnostic methods for cerebrospinal fluid testing for neurosyphilis

Neurosyphilis is a chronic infectious disease caused by the invasion of Treponema pallidum into the central nervous system. In recent years, with the increase in the latent syphilis infection rate, the incidence of neurosyphilis has gradually increased, the typical symptoms of neurosyphilis have decreased, atypical manifestations have increased, and the clinical manifestations have become increasingly diverse. Cerebrospinal fluid testing plays an important role in the diagnosis of neurosyphilis. In recent years, there have been many advances in cerebrospinal fluid testing. This review focuses on the current and potential laboratory indicators of neurosyphilis in cerebrospinal fluid, aiming to provide a reference for clinical application and ideas for future experimental research of neurosyphilis.

An Updated Review of Recent Advances in Neurosyphilis

Neurosyphilis is caused by Treponema pallidum invading the central nervous system, of which the incidence is increasing worldwide. Due to its variable clinical manifestations, diagnosis of neurosyphilis remains challenging, especially the asymptomatic form. This review focuses on recent advances in neurosyphilis, including epidemiology, clinical manifestations, laboratory findings, comorbidities, diagnosis, treatment, prognosis, and basic research. The expansion of men who have sex with men and the infection of human immunodeficiency virus mainly accounted for the increasing incidence of neurosyphilis. The rate of some historically described forms of neurosyphilis in the pre-antibiotic era declined significantly; atypical features are more prevalent. Neurosyphilis, regarded as a great mimicker for neuro-ophthalmic, audio-vestibular, and psychiatric disorders, often presents concomitantly with other diseases, including metabolic disorders. Studies on long non-coding RNAs, miRNAs, chemokines, and metabolites in peripheral blood and cerebrospinal fluid may facilitate exploring the pathogenesis and identifying novel biomarkers of neurosyphilis. The drug resistance of Treponema pallidum to penicillin has not been reported; ceftriaxone was proposed to be more effective than penicillin, whereas few randomized controlled trials supported this view. This study may pave the way for further research, especially the diagnosis and treatment of neurosyphilis.

Reproducibility of mass spectrometry based metabolomics data

BACKGROUND

Assessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets.

RESULTS

We apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well.

CONCLUSIONS

This research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

Optimization of pretreatment methods for cerebrospinal fluid metabolomics based on ultrahigh performance liquid chromatography/mass spectrometry

Sample pretreatment of cerebrospinal fluid (CSF) in metabolomics plays an important role in metabolic profiling study, especially for samples related to central nervous system diseases. However, there is few study about optimization of CSF metabolomics pretreatment. Therefore, it is an urgent need to optimize CSF pretreatment in order to promote the extraction efficiency of metabolites. In this study, CSF samples were separately subjected to nine different protein precipitation solvents and five different reconstitution solvents to establish the most effective pretreatment method before hydrophilic interaction (HILIC) and reverse-phase (RP) ultrahigh performance liquid chromatography mass spectrometry (UPLC/MS) analysis. The optimal conditions for different sample pretreatment methods were analyzed based on coverage (number of detected potential metabolites), stability (the relative standard deviation (RSD) distribution of metabolites) and the reproducibility of the data. Our results suggested that using EtOH or MeOH-EtOH-ACN (1:1:1, v/v/v) as the protein precipitation solvents and H₂O-MeOH-ACN (2:1:1, v/v/v) as the reconstitution solvent were optimal methods for T3 column analysis. For HILIC column analysis, using EtOH to precipitate protein and H₂O-MeOH-ACN (2:1:1, v/v/v) to reconstitute or MeOH to precipitate and 5 %ACN to reconstitute performed best. This developed UPLC/MS pretreatment method could provide better protein precipitation solvents and reconstitution solvents for global CSF metabolic analysis, potentially facilitating the comprehensive understanding of many central nervous system diseases.

Predictive Modeling for Metabolomics Data

In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.

Catalytic degradation mechanism of sulfamethazine via photosynergy of monoclinic BiVO4and microalgae under visible-light irradiation

To improve the efficiency of antibiotic degradation, the photosynergistic performance of bismuth vanadate (BiVO₄) with a microalga, Dictyosphaerium sp., was demonstrated under visible-light irradiation for the first time. Sulfamethazine (SM2) was selected as a representative sulfanilamide antibiotic, and the photocatalytic degradation mechanism of SM2 was evaluated in media via the BiVO₄-algae system. The hydrothermally synthesized sample was characterized using X-ray powder diffraction, X-ray photoelectron spectroscopy, ultraviolet-visible diffuse reflectance spectroscopy, transmission electron microscopy, Brunauer-Emmett-Teller surface area, and Fourier transform infrared spectroscopy techniques. The results demonstrated that the prepared photocatalyst corresponded to phase-pure monoclinic scheelite BiVO₄. The synthesized BiVO₄ showed superior photocatalytic properties under irradiation with visible light, and more than 80% of photocatalytic degradation efficiency was obtained by the BiVO₄-algae system. Based on quenching experiments, the photocatalytic degradation of SM2 in the BiVO₄-algae system was primarily accomplished via the generation of triplet state dissolved organic matter, and hydroxyl radicals played a small role in the degradation process. The direct oxidation of holes made no contribution to the degradation. Metabolomics data showed that a total of 91 metabolites were significantly changed between the two comparison groups (algae-SM2 group vs algae group; algae-BiVO₄-SM2 group vs algae-BiVO₄ group). The glycometabolism pathways were increased and the tricarboxylic acid cycle was activated when BiVO₄ was present. The study provides a distinctive approach to remove antibiotics using visible light in the aqueous environment.