A Possible New Diagnostic Biomarker in Early Diagnosis of Alzheimers Disease

The Impact of Mechanical Cues on the Metabolomic and Transcriptomic Profiles of Human Dermal Fibroblasts Cultured in Ultrashort Self-Assembling Peptide 3D Scaffolds

Cells' interactions with their microenvironment influence their morphological features and regulate crucial cellular functions including proliferation, differentiation, metabolism, and gene expression. Most biological data available are based on in vitro two-dimensional (2D) cellular models, which fail to recapitulate the three-dimensional (3D) in vivo systems. This can be attributed to the lack of cell-matrix interaction and the limitless access to nutrients and oxygen, in contrast to in vivo systems. Despite the emergence of a plethora of 3D matrices to address this challenge, there are few reports offering a proper characterization of these matrices or studying how the cell-matrix interaction influences cellular metabolism in correlation with gene expression. In this study, two tetrameric ultrashort self-assembling peptide sequences, FFIK and FIIK, were used to create in vitro 3D models using well-described human dermal fibroblast cells. The peptide sequences are derived from naturally occurring amino acids that are capable of self-assembling into stable hydrogels without UV or chemical cross-linking. Our results showed that 2D cultured fibroblasts exhibited distinct metabolic and transcriptomic profiles compared to 3D cultured cells. The observed changes in the metabolomic and transcriptomic profiles were closely interconnected and influenced several important metabolic pathways including the TCA cycle, glycolysis, MAPK signaling cascades, and hemostasis. Data provided here may lead to clearer insights into the influence of the surrounding microenvironment on human dermal fibroblast metabolic patterns and molecular mechanisms, underscoring the importance of utilizing efficient 3D in vitro models to study such complex mechanisms.

Towards a biochemical approach to occupational stress management

Given the immense and growing cost of occupational stress to society through lost productivity and the burden to healthcare systems, current best practices for detecting, managing and reducing stress in the workplace are clearly sub-optimal and substantially better methods are required. Subjective, self-reported psychology and psychiatry-based instruments are prone to biases whereas current objective, biology-based measures produce conflicting results and are far from reliable. A multivariate approach to occupational stress research is required that reflects the broad, coordinated, physiological response to demands placed on the body by exposure to diverse occupational stressors. A literature review was conducted to determine the extent of application of the emerging multivariate technology of metabolomics to occupational stress research. Of 170 articles meeting the search criteria, three were identified that specifically studied occupational stressors using metabolomics. A further ten studies were not specifically occupational or were of indirect or peripheral relevance. The occupational studies, although limited in number highlight the technological challenges associated with the application of metabolomics to investigate occupational stress. They also demonstrate the utility to evaluate stress more comprehensively than univariate biomarker studies. The potential of this multivariate approach to enhance our understanding of occupational stress has yet to be established. This will require more studies with broader analytical coverage of the metabolome, longitudinal sampling, combination with experience sampling methods and comparison with psychometric models of occupational stress. Progress will likely involve combining multi-omic data into a holistic, systems biology approach to detecting, managing and reducing occupational stress and optimizing workplace performance.

Metabolomic profiling identifies a novel mechanism for heat stroke‑related acute kidney injury

Heat stroke can induce a systemic inflammatory response, which may lead to multi‑organ dysfunction including acute kidney injury (AKI) and electrolyte disturbances. To investigate the pathogenesis of heat stroke (HS)‑related AKI, a mouse model of HS was induced by increasing the animal's core temperature to 41˚C. Blood samples obtained from the tail vein were used to measure plasma glucose and creatinine levels. Micro‑positron emission tomography‑computed tomography (micro‑PET/CT), H&E staining and transmission electron microscopy were conducted to examine metabolic and morphological changes in the mouse kidneys. Immunohistochemistry (IHC) and western blot analyses were performed to investigate the expression of apoptosis‑inducing factor mitochondria‑associated 2 (Aifm2), high‑mobility group box 1 (HMGB1) and receptor for advanced glycosylation end products (RAGE). Liquid chromatography‑mass spectrometry analysis was conducted to find differential metabolites and signaling pathways. The HS mouse model was built successfully, with significantly increased creatinine levels detected in the serum of HS mice compared with controls, whereas micro‑PET/CT revealed active metabolism in the whole body of HS mice. H&E and TUNEL staining revealed that the kidneys of HS mice exhibited signs of hemorrhage and apoptosis. IHC and western blotting demonstrated significant upregulation of Aifm2, HMGB1 and RAGE in response to HS. Finally, 136 differential metabolites were screened out, and enrichment of the 'biosynthesis of unsaturated fatty acids' pathway was detected. HS‑associated AKI is the renal manifestation of systemic inflammatory response syndrome, and may be triggered by the HMGB1/RAGE pathway. Metabolomics indicated increased adrenic acid, docosahexaenoic acid and eicosapentaenoic acid may serve as metabolic biomarkers for AKI in HS. The findings suggested that a correlation between the HMGB1/RAGE pathway and biosynthesis of unsaturated fatty acids may contribute to the progression of HS‑related AKI.

NMR-based metabolomics with enhanced sensitivity

NMR-based metabolomics, which emerged along with mass spectrometry techniques, is the preferred method for studying metabolites in medical research and food industries. However, NMR techniques suffer from inherently low sensitivity, regardless of their superior reproducibility. To overcome this, we made two beneficial modifications: we detuned the probe to reach a position called "Spin Noise Tuning Optimum" (SNTO), and we replaced the conventional cylindrical 5 mm NMR tube with an electric field component-optimized shaped tube. We found that concerted use of both modifications can increase the sensitivity (signal to noise ratio per unit volume) and detection of metabolites and decrease the measurement time by order of magnitude. In this study, we demonstrate and discuss the achieved signal enhancement of metabolites on model non-human (bovine serum, amino acid standard mixture) and human urine samples.

The tip of the iceberg for diagnostic dilemmas: Performance of current diagnostics and future complementary screening approaches

Genetic testing is currently the leading edge of clinical care when it comes to diagnostics. However, many questions remain unanswered even when employing next-generation sequencing techniques due to our inability to decode genetic variations and our limited repertoire of available diagnoses. Accordingly, diagnostic yields for current genomic screenings are <50% and fail to provide the whole picture, leaving the remaining patients without a definitive diagnosis. Human phenotypic/disease expression is explained by alterations not only at the genome, but also at the transcriptome, proteome and metabolome levels. These "higher" complexity levels represent at wealth of information, and diagnostic screenings tests at these levels have been shown to significantly improve diagnostic yields in specific populations compared to conventional diagnostic workup or gold standards in use (7-30% increase in diagnostic yields, depending on the population, approach and gold standard being compared against). However, these are not yet routinely available to clinicians. Due to their dynamic and modifiable nature, tapping into data from different omics will improve our understanding of the pathophysiological bases underlying (many yet to characterize) human disorders. We herein review how alterations at these levels (e.g. post-transcriptional and post-translational) may be pathogenic, how such tests may be implemented and in which situations they are of significant utility.

Search for biomarkers of Alzheimer's disease: Recent insights, current challenges and future prospects

Due to the trend of prolonged lifespan leading to higher incidence of age-related diseases, the demand for reliable biomarkers of dementia rises. In this review, we present novel biomarkers of high potential, especially those found in blood, urine or saliva, which could lead to a more comfortable patient experience and better time- and cost-effectivity, compared to the currently used diagnostic methods. We focus on biomarkers that might allow for the detection of Alzheimer's disease before its clinical manifestations. Such biomarkers might be helpful for better understanding the etiology of the disease and identifying its risk factors. Moreover, it could be a base for developing new treatment or at least help to prolong the presymptomatic stage in patients suffering from Alzheimer's disease. As potential candidates, we present, for instance, neurofilament light in both cerebrospinal fluid and blood plasma or amyloid β in plasma. Above all, we provide an overview of different approaches to the diagnostics, analyzing patient's biofluids as a whole using molecular spectroscopy. Infrared and Raman spectroscopy and especially chiroptical methods provide information not only on the chemical composition, but also on molecular structure. Therefore, these techniques are promising for the diagnostics of Alzheimer's disease, as the accumulation of amyloid β in abnormal conformation is one of the hallmarks of this disease.

NMR metabolomics of cerebrospinal fluid differentiates inflammatory diseases of the central nervous system

BACKGROUND

Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample.

METHODOLOGY/PRINCIPAL FINDINGS

1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity.

CONCLUSIONS/SIGNIFICANCE

These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes.

Golgin A4 in CSF and granulovacuolar degenerations of patients with Alzheimer disease

OBJECTIVE

To isolate and identify a new, as yet unknown molecule in CSF that could serve as marker for Alzheimer disease.

METHODS

We immunized mice with human CSF and fused hybridomas for monoclonal antibodies and screened these antibodies for their capacity to discriminate CSF of patients with Alzheimer disease from CSF of controls. We then chromatographically isolated the antigen to the best discriminating antibody and identified the antigen using mass spectrometric methods. Thereafter, we quantified the CSF concentration of the antigen in a new cohort of patients with Alzheimer disease and controls and performed immunohistochemistry of postmortem brain tissue derived from patients with Alzheimer disease and controls.

RESULTS

We generated >200 hybridomas and selected 1 antibody that discriminated CSF from patients with Alzheimer disease from that of controls. We identified golgin A4 as the antigen detected by this antibody. Golgin A4 concentration was significantly higher in CSF from patients with Alzheimer disease than in CSF of controls (145 [interquartile range 125-155] vs 115 [ 99-128] pg/mL, p < 0.001) and demonstrated a substantial discriminative power (area under the receiver operating characteristic curve 0.80, 95% confidence interval 0.67-0.94). Immunohistochemistry of postmortem brain sections from patients with Alzheimer disease revealed a significant accumulation of golgin A4 in granulovacuolar degeneration bodies (GVBs).

CONCLUSIONS

These results support the notion that golgin A4 could serve as a diagnostic marker in Alzheimer disease. For validation of this notion, prospective multicenter diagnostic studies will evaluate golgin A4 as diagnostic marker for Alzheimer disease. Furthermore, it has to be determined whether the association of golgin A4 with GVBs is an epiphenomenon or whether golgin A4 plays a more direct role in Alzheimer disease, allowing it to serve as a target in therapeutic treatment strategies.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that elevated CSF golgin A4 levels identify patients with Alzheimer disease.

System-wide assembly of pathways and modules hierarchically reveal metabolic mechanism of cerebral ischemia

The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions. Based on the fusing maps of comprehensive serum metabolome, fatty acid and amino acid profiling, we identified 35 potential metabolic biomarkers for ischemic stroke. Our analyses revealed 8 significantly altered pathways by MetPA (Metabolomics Pathway Analysis, impact score >0.10) and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased. We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways. We provided a new perspective from the viewpoint of abnormal metabolites for the overall study of ischemic stroke as well as a new method to simplify the network analysis by selecting the more closely connected edges and nodes to build a module map of stroke.

Raman spectroscopy of blood serum for Alzheimer's disease diagnostics: specificity relative to other types of dementia

The key moment for efficiently and accurately diagnosing dementia occurs during the early stages. This is particularly true for Alzheimer's disease (AD). In this proof-of-concept study, we applied near infrared (NIR) Raman microspectroscopy of blood serum together with advanced multivariate statistics for the selective identification of AD. We analyzed data from 20 AD patients, 18 patients with other neurodegenerative dementias (OD) and 10 healthy control (HC) subjects. NIR Raman microspectroscopy differentiated patients with more than 95% sensitivity and specificity. We demonstrated the high discriminative power of artificial neural network (ANN) classification models, thus revealing the high potential of this developed methodology for the differential diagnosis of AD. Raman spectroscopic, blood-based tests may aid clinical assessments for the effective and accurate differential diagnosis of AD, decrease the labor, time and cost of diagnosis, and be useful for screening patient populations for AD development and progression. Multivariate data analysis of blood serum Raman spectra allows for the differentiation between patients with Alzheimer's disease, other types of dementia and healthy individuals.

1H NMR Analysis of Cerebrospinal Fluid from Alzheimer's Disease Patients: An Example of a Possible Misinterpretation Due to Non-Adjustment of pH

Two publications from the same research group reporting on the detection of new possible biomarkers for the early diagnosis of Alzheimer’s disease (AD), based on the analysis of cerebrospinal fluid samples (CSF) with ¹H Nuclear Magnetic Resonance (NMR), are at the origin of the present study. The authors observed significant differences in ¹H NMR spectra of CSF from AD patients and healthy controls and, thus, proposed some NMR signals (without attribution) as possible biomarkers. However, this work was carried out in non-standardized pH conditions. Our study aims at warning about a possible misinterpretation that can arise from ¹H NMR analyses of CSF samples if pH adjustment is not done before NMR analysis. Indeed, CSF pH increases rapidly after removal and is subject to changes over conservation time. We first identify the NMR signals described by the authors as biomarkers. We then focus on the chemical shift variations of their NMR signals as a function of pH in both standard solutions and CSF samples. Finally, a principal component analysis of ¹H NMR data demonstrates that the same CSF samples recorded at pH 8.1 and 10.0 are statistically differentiated.

Analysis of bacterial biofilms using NMR-based metabolomics

Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this 'social structure' of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.

Magnetic resonance imaging of Alzheimer's disease: from diagnosis to therapeutic evaluation

Alzheimer's disease (AD) is a devastating late-life dementia that produces progressive loss of memory and mental faculties in elderly people. It is important to identify the earliest evidence of AD and to monitor the development of this disease for us to make positive response to its management. Magnetic resonance imaging (MRI) is powerful to image the tissue or organ without damnification. MRI can be employed to diagnose the early AD development and monitor the key biomarker development in AD. MRI may be helpful not only in diagnosing early AD, but also in evaluating its development. This article reviews the progress of MRI on the diagnosis and detection of AD, and makes comments on its therapeutic application.