A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019

Comparisons of different extraction methods and solvents for saliva samples

INTRODUCTION

Changes in the categories and concentrations of salivary metabolites may be closely related to oral, intestinal or systemic diseases. To study salivary metabolites, the first analytical step is to extract them from saliva samples as much as possible, while reducing interferences to a minimum. Frequently used extraction methods are protein precipitation (PPT), liquid-liquid extraction (LLE) and solid-phase extraction (SPE), with various organic solvents. The types and quantities of metabolites extracted with different methods may vary greatly, but few studies have systematically evaluated them.

OBJECTIVES

This study aimed to select the most suitable methods and solvents for the extraction of saliva according to different analytical targets.

METHODS

An untargeted metabolomics approach based on liquid chromatography-mass spectrometry was applied to obtain the raw data. The numbers of metabolites, repeatability of the data and intensities of mass spectrometry signals were used as evaluation criteria.

RESULTS

PPT resulted in the highest coverage. Among the PPT solvents, acetonitrile displayed the best repeatability and the highest coverage, while acetone resulted in the best signal intensities for the extracted compounds. LLE with the mixture of chloroform and methanol was the most suitable for the extraction of small hydrophobic compounds.

CONCLUSION

PPT with acetonitrile or acetone was recommended for untargeted analysis, while LLE with the mixture of chloroform and methanol was recommended for small hydrophobic compounds.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Electronic Tongue for Direct Assessment of SARS-CoV-2-Free and Infected Human Saliva-A Feasibility Study

An electronic tongue is a powerful analytical instrument based on an array of non-selective chemical sensors with a partial specificity for data gathering and advanced pattern recognition methods for data analysis. Connecting electronic tongues with electrochemical techniques for data collection has led to various applications, mostly within sensing for food quality and environmental monitoring, but also in biomedical research for the analyses of different bioanalytes in human physiological fluids. In this paper, an electronic tongue consisting of six electrodes (viz., gold, platinum, palladium, titanium, iridium, and glassy carbon) was designed and tested in authentic (undiluted, unpretreated) human saliva samples from eight volunteers, collected before and during the COVID-19 pandemic. Investigations of 11 samples using differential pulse voltammetry and a principal component analysis allowed us to distinguish between SARS-CoV-2-free and infected authentic human saliva. This work, as a proof-of-principle demonstration, provides a new perspective for the use of electronic tongues in the field of enzyme-free electrochemical biosensing, highlighting their potential for future applications in non-invasive biomedical analyses.

Effect of short-term use of FFP2 (N95) masks on the salivary metabolome of young healthy volunteers: a pilot study

The use of face masks has become an integral part of public life in the post-pandemic era. However, the understanding of the effect of wearing masks on physiology remains incomplete and is required for informing public health policies. For the first time, we report the effects of wearing FFP2 masks on the metabolic composition of saliva, a proximal matrix to breath, along with cardiopulmonary parameters. Un-induced saliva was collected from young (31.2 ± 6.3 years) healthy volunteers (n = 10) before and after wearing FFP2 (N95) masks for 30 minutes and analyzed using GCMS. The results showed that such short-term mask use did not cause any significant change in heart rate, pulse rate or SpO₂. Three independent data normalization approaches were used to analyze the changes in metabolomic signature. The individuality of the overall salivary metabotype was found to be unaffected by mask use. However, a trend of an increase in the salivary abundance of L-fucose, 5-aminovaleric acid, putrescine and phloretic acid was indicated irrespective of the method of data normalization. Quantitative analysis confirmed increases in concentrations of these metabolites in saliva of paired samples amid high inter-individual variability. The results showed that while there was no significant change in measured physiological parameters and individual salivary metabotypes, mask use was associated with correlated changes in these metabolites plausibly originating from altered microbial metabolic activity. These results might also explain the change in odour perception reported to be associated with mask use. Potential implications of these changes on mucosal health and immunity warrants further investigation to evolve more prudent mask use policies.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Untargeted saliva metabolomics by liquid chromatography—Mass spectrometry reveals markers of COVID-19 severity

Background

The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes–and whether markers can be found in different biofluids–are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum.

Methods

Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques.

Results

Positive percent agreement of 1.00 between a partial least squares–discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified.

Conclusions

In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics

The majority of metabolomics studies to date have utilised blood serum or plasma, biofluids that do not necessarily address the full range of patient pathologies. Here, correlations between serum metabolites, salivary metabolites and sebum lipids are studied for the first time. 83 COVID-19 positive and negative hospitalised participants provided blood serum alongside saliva and sebum samples for analysis by liquid chromatography mass spectrometry. Widespread alterations to serum-sebum lipid relationships were observed in COVID-19 positive participants versus negative controls. There was also a marked correlation between sebum lipids and the immunostimulatory hormone dehydroepiandrosterone sulphate in the COVID-19 positive cohort. The biofluids analysed herein were also compared in terms of their ability to differentiate COVID-19 positive participants from controls; serum performed best by multivariate analysis (sensitivity and specificity of 0.97), with the dominant changes in triglyceride and bile acid levels, concordant with other studies identifying dyslipidemia as a hallmark of COVID-19 infection. Sebum performed well (sensitivity 0.92; specificity 0.84), with saliva performing worst (sensitivity 0.78; specificity 0.83). These findings show that alterations to skin lipid profiles coincide with dyslipidaemia in serum. The work also signposts the potential for integrated biofluid analyses to provide insight into the whole-body atlas of pathophysiological conditions.

Improving the Diagnostic Potential of Extracellular miRNAs Coupled to Multiomics Data by Exploiting the Power of Artificial Intelligence

In recent years, the clinical use of extracellular miRNAs as potential biomarkers of disease has increasingly emerged as a new and powerful tool. Serum, urine, saliva and stool contain miRNAs that can exert regulatory effects not only in surrounding epithelial cells but can also modulate bacterial gene expression, thus acting as a “master regulator” of many biological processes. We think that in order to have a holistic picture of the health status of an individual, we have to consider comprehensively many “omics” data, such as miRNAs profiling form different parts of the body and their interactions with cells and bacteria. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms coupled to other multiomics data (i.e., big data) could help researchers to classify better the patient’s molecular characteristics and drive clinicians to identify personalized therapeutic strategies. Here, we highlight how the integration of “multiomic” data (i.e., miRNAs profiling and microbiota signature) with other omics (i.e., metabolomics, exposomics) analyzed by AI algorithms could improve the diagnostic and prognostic potential of specific biomarkers of disease.

Opioid-Use, COVID-19 Infection, and Their Neurological Implications

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an imminent threat to human health and public safety. ACE2 and transmembrane serine protease 2 proteins on host cells provide the viral entry point to SARS-CoV-2. Although SARS-CoV-2 mainly infects the respiratory system, there have been reports of viral neurotropism and central nervous system injury as indicated by plasma biomarkers, including neurofilament light chain protein and glial fibrillary acidic protein. Even with a small proportion of infections leading to neurological manifestation, the overall number remains high. Common neurological manifestations of SARS-CoV-2 infection include anosmia, ageusia, encephalopathy, and stroke, which are not restricted to only the most severe infection cases. Opioids and opioid antagonists bind to the ACE2 receptor and thereby have been hypothesized to have therapeutic potential in treating COVID-19. However, in the case of other neurotropic viral infections such as human immunodeficiency virus (HIV), opioid use has been established to exacerbate HIV-mediated central nervous system pathogenesis. An analysis of electronic health record data from more than 73 million patients shows that people with Substance Use Disorders are at higher risk of contracting COVID-19 and suffer worse consequences then non-users. Our in-vivo and in-vitro unpublished studies show that morphine treatment causes increased expression of ACE2 in murine lung and brain tissue as early as 24 h post treatment. At the same time, we also observed morphine and lipopolysaccharides treatment lead to a synergistic increase in ACE2 expression in the microglial cell line, SIM-A9. This data suggests that opioid treatment may potentially increase neurotropism of SARS-CoV-2 infection. We have previously shown that opioids induce gut microbial dysbiosis. Similarly, gut microbiome alterations have been reported with SARS-CoV-2 infection and may play a role in predicting COVID-19 disease severity. However, there are no studies thus far linking opioid-mediated dysbiosis with the severity of neuron-specific COVID-19 infection.