Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics

The longevity factor spermidine is part of a highly heritable complex erythrocyte phenotype associated with longevity

Extreme longevity in humans is known to be a heritable trait. In a well-established twin erythrocyte metabolomics and proteomics database, we identified the longevity factor spermidine and a cluster of correlated molecules with high heritability estimates. Erythrocyte spermidine is 82% heritable and significantly correlated with 59 metabolites and 22 proteins. Thirty-eight metabolites and 19 proteins were >20% heritable, with a mean heritability of 61% for metabolites and 49% for proteins. Correlated metabolites are concentrated in energy metabolism, redox homeostasis, and autophagy pathways. Erythrocyte mean cell volume (MCV), an established heritable trait, was consistently negatively correlated with the top 25 biomolecules most strongly correlated with spermidine, indicating that smaller MCVs are associated with higher concentrations of spermidine and correlated molecules. Previous studies have linked larger MCVs with poorer memory, cognition, and all-cause mortality. Analysis of 432,682 unique patient records showed a linear increase in MCV with age but a significant deviation toward smaller than expected MCVs above age 86, suggesting that smaller MCVs are associated with extreme longevity. Consistent with previous reports, a subset of 78,158 unique patient records showed a significant skewing toward larger MCV values in a deceased cohort compared to an age-matched living cohort. Our study supports the existence of a complex, heritable phenotype in erythrocytes associated with health and longevity.

Comprehensive proteomic characterization of urethral stricture disease in the Chinese population

Background

Male urethral stricture disease (USD) is predominantly characterized by scar formation. There are few effective therapeutic drugs, and comprehensive molecular characterizations of USD formation remain undefined.

Methods

The proteomic profiling of twelve scar tissues and five matched normal adjacent tissues (NATs). Proteomic analysis methods were applied to explore the molecular characterizations of USD formation, including uncovering mechanistic pathways and providing novel biomarkers for scar formation.

Results

Comparative proteomic analysis showed that the extracellular matrix (ECM) and complement cascade signaling were predominant in scar tissues. COL11A1 and CD248 significantly contributed to the accumulation of ECM components. Our study presented diverse molecular mechanisms of scar formation across different ages and suggested the potential effects of PXK in Age 1 (<45) patients. Furthermore, immune infiltration studies indicated the therapeutic potential of inhibiting the complement system (C4A, C4B) in Age 2 (≥45) patients, providing a potential clinical strategy for USD.

Conclusion

This study illustrated the pathogenesis of USD formation and the diverse characteristics of USD patients with different ages, enhancing our understanding of the disease's pathogenesis and providing a valuable resource for USD treatment.

The Effects of Ketogenic Diet on Brain Gene Expressions in Type 2 Diabetes Background

Type 2 diabetes mellitus (T2DM) is a major risk factor of a number of neurodegenerative diseases (NDDs). Ketogenic diet (KD) has significant beneficial effects on glycemic control and may act effectively against NDDs, but the mechanism remains unclear. In this study, we aimed to investigate the potential effects of KD on gene expressions in the brains of T2DM model mice. Male db/db mice at the age of 9 weeks were fed with KD or normal diet to the age of 6 months, and the whole brains were subjected to mRNA-seq analysis for differentially expressed genes. KD significantly lowered fasting glucose and body weights in db/db mice (P < 0.05), and the expression of 189 genes in the brain were significantly changed (P < 0.05, |log2| > 1). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the differentially expressed genes upon KD are involved in inflammatory responses and the functions of biosynthesis. In inflammatory responses, NF-κB signaling pathway, viral protein interaction with cytokine and cytokine receptor, and cytokine-cytokine receptor interaction pathways were enriched, and in biosynthesis pathways, genes functioning in lipid and amino acid metabolism, protein synthesis, and energy metabolism were enriched. Moreover, consistent with the gene set enrichment analysis results, proteasomal activity measured biochemically were enhanced in KD-fed T2DM mice. These data may facilitate the understanding of how KD can be protective to the brain in T2DM background. KD could be a new strategy for the prevention of NDDs in T2DM patients.

Ultraphotostable Phosphorescent Nanosensors for Sensing the Lysosomal pH at the Single-Cell Level over Long Durations

Ultraphotostable phosphorescent nanosensors have been designed for continuously sensing the lysosome pH over a long duration. The nanosensors exhibited excellent photostability, high accuracy, and capability to measure pH values during cell proliferation for up to 7 days. By arranging a metal-ligand complex of long phosphorescence lifetime and pH indicator in silica nanoparticles, we discover efficient Förster resonance energy transfer, which converts the pH-responsive UV-vis absorption signal of the pH indicator into a phosphorescent signal. Both the phosphorescent intensity and lifetime change at different pH values, and intracellular pH values can be accurately measured by our custom-built rapid phosphorescent lifetime imaging microscopy. The excellent photostability, high accuracy, and good biocompatibility prove that these nanosensors are a useful tool for tracing the fluctuation of pH values during endocytosis. The methodology can be easily adapted to design new nanosensors with different pKa or for different kinds of intracellular ions, as there are hundreds of pH and ion indicators readily available.

Serum Metabolomic Profiles for Distinguishing Lung Cancer From Pulmonary Tuberculosis: Identification of Rapid and Noninvasive Biomarker

BACKGROUND

Pulmonary tuberculosis (PTB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings, which are easily misdiagnosed. There is an urgent need for a noninvasive and accurate biomarker to distinguish LC from PTB.

METHODS

A total of 694 subjects were enrolled and divided into discovery set (n = 122), identification set (n = 214), and validation set (n = 358). Metabolites were identified by multivariate and univariate analyses. Receiver operating characteristic curve were used to evaluate the diagnostic efficacy of biomarkers.

RESULTS

Seven metabolites were identified and validated. Phenylalanylphenylalanine for distinguishing LC from PTB yielded an area under the curve of 0.89, sensitivity of 71%, and specificity of 92%. It also showed good diagnostic abilities in discovery set and identification set. Compared with that in healthy volunteers (median [interquartile range], 1.57 [1.01, 2.34] μg/mL), it was elevated in LC (4.76 [2.74, 7.08] μg/mL; ratio of median, [ROM] = 3.03, P < .01) and reduced in PTB (1.06 [0.51, 2.09] μg/mL; ROM = 0.68, P < .05).

CONCLUSIONS

The metabolomic profile of LC and PTB was described and a key biomarker identified. We produced a rapid and noninvasive method to supplement existing clinical diagnostic examinations for distinguishing LC from PTB.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Advances in peripheral blood biomarkers of patients with Alzheimer's disease: Moving closer to personalized therapies

Recently, measurable peripheral biomarkers in the plasma of patients with Alzheimer's disease (AD) have gained considerable clinical interest. Several studies have identified one or more blood signatures that may facilitate the development of novel diagnostic and therapeutic strategies. For instance, changes in peripheral amyloid β42 (Aβ42) levels have been largely investigated in patients with AD and correlated with the progression of the pathology, although with controversial results. In addition, tumor necrosis factor α (TNFα) has been identified as an inflammatory biomarker strongly associated with AD, and several studies have consistently suggested the pharmacological targeting of TNFα to reduce systemic inflammation and prevent neurotoxicity in AD. Moreover, alterations in plasma metabolite levels appear to predict the progression of systemic processes relevant to brain functions. In this study, we analyzed the changes in the levels of Aβ42, TNFα, and plasma metabolites in subjects with AD and compared the results with those in healthy elderly (HE) subjects. Differences in plasma metabolites of patients with AD were analyzed with respect to Aβ42, TNFα, and the Mini-Mental State Examination (MMSE) score, searching for plasma signatures that changed simultaneously. In addition, the phosphorylation levels of the Tyr682 residue of the amyloid precursor protein (APP), which we previously proposed as a biomarker of AD, were measured in five HE and five AD patients, in whom the levels of Aβ42, TNFα, and two plasma lipid metabolites increased simultaneously. Overall, this study highlights the potential of combining different plasma signatures to define specific clinical phenotypes of patient subgroups, thus paving the way for the stratification of patients with AD and development of personalized approaches.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

PARP16-Mediated Stabilization of Amyloid Precursor Protein mRNA Exacerbates Alzheimer's Disease Pathogenesis

The accumulation and deposition of beta-amyloid (Aβ) are key neuropathological hallmarks of Alzheimer's disease (AD). PARP16, a Poly(ADP-ribose) polymerase, is a known tail-anchored endoplasmic reticulum (ER) transmembrane protein that transduces ER stress during pathological processes. Here, we found that PARP16 was significantly increased in the hippocampi and cortices of APPswe/PS1dE9 (APP/PS1) mice and hippocampal neuronal HT22 cells exposed to Aβ, suggesting a positive correlation between the progression of AD pathology and the overexpression of PARP16. To define the effect of PARP16 on AD progression, adeno-associated virus mediated-PARP16 knockdown was used in APP/PS1 mice to investigate the role of PARP16 in spatial memory, amyloid burden, and neuroinflammation. Knockdown of PARP16 partly attenuated impaired spatial memory, as indicated by the Morris water maze test, and decreased amyloid deposition, neuronal apoptosis, and the production of inflammatory cytokines in the brains of APP/PS1 mice. In vitro experiments demonstrated that the knockdown of PARP16 expression rescued neuronal damage and ER stress triggered by Aβ. Furthermore, we discovered that intracellular PARP16 acts as an RNA-binding protein that regulates the mRNA stability of amyloid precursor protein (APP) and protects targeted APP from degradation, thereby increasing APP levels and AD pathology. Our findings revealed an unanticipated role of PARP16 in the pathogenesis of AD, and at least in part, its association with increased APP mRNA stability.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.