Wastewater-based epidemiology applied at the building-level reveals distinct virome profiles based on the age of the contributing individuals.

BACKGROUND: Human viruses released into the environment can be detected and characterized in wastewater. The study of wastewater virome offers a consolidated perspective on the circulation of viruses within a population. Because the occurrence and severity of viral infections can vary across a person's lifetime, studying the virome in wastewater samples contributed by various demographic segments can provide valuable insights into the prevalence of viral infections within these segments. In our study, targeted enrichment sequencing was employed to characterize the human virome in wastewater at a building-level scale. This was accomplished through passive sampling of wastewater in schools, university settings, and nursing homes in two cities in Catalonia. Additionally, sewage from a large urban wastewater treatment plant was analysed to serve as a reference for examining the collective excreted human virome. RESULTS: The virome obtained from influent wastewater treatment plant samples showcased the combined viral presence from individuals of varying ages, with astroviruses and human bocaviruses being the most prevalent, followed by human adenoviruses, polyomaviruses, and papillomaviruses. Significant variations in the viral profiles were observed among the different types of buildings studied. Mamastrovirus 1 was predominant in school samples, salivirus and human polyomaviruses JC and BK in the university settings while nursing homes showed a more balanced distribution of viral families presenting papillomavirus and picornaviruses and, interestingly, some viruses linked to immunosuppression. CONCLUSIONS: This study shows the utility of building-level wastewater-based epidemiology as an effective tool for monitoring the presence of viruses circulating within specific age groups. It provides valuable insights for public health monitoring and epidemiological studies.

Exploring the Relationship between Diamine Oxidase and Psychotropic Medications in Fibromyalgia Treatment, Finding No Reduction in Diamine Oxidase Levels and Activity except with Citalopram.

Background: Histamine intolerance manifests when there is an imbalance between the production of histamine and the body's capacity to metabolise it. Within the gastrointestinal tract, diamine oxidase (DAO) plays a pivotal role in breaking down ingested histamine. Insufficient levels of DAO have been linked to various diseases affecting the respiratory, cardiovascular, nervous, muscular, and digestive systems; some of these symptoms are evidenced in fibromyalgia syndrome. This underscores the crucial role of DAO in maintaining the histamine balance and highlights its association with diverse physiological systems and health conditions. The management of fibromyalgia commonly involves the use of psychotropic medications; however, their potential interactions with DAO remain not fully elucidated. Methods: This study delved into the influence of various psychotropic medications on DAO activity through in vitro experiments. Additionally, we explored their impact on the human intestinal cell line Caco-2, examining alterations in DAO expression at both the mRNA and protein levels along with DAO activity. Results: Notably, the examined drugs-sertraline, pregabalin, paroxetine, alprazolam, and lorazepam-did not exhibit inhibitory effects on DAO activity or lead to reductions in DAO levels. In contrast, citalopram demonstrated a decrease in DAO activity in in vitro assays without influencing DAO levels and activity in human enterocytes. Conclusions: These findings imply that a collaborative approach involving psychotropic medications and DAO enzyme supplementation for individuals with fibromyalgia and a DAO deficiency could offer potential benefits for healthcare professionals in their routine clinical practice.

Diamine Oxidase Interactions with Anti-Inflammatory and Anti-Migraine Medicines in the Treatment of Migraine.

Histamine intolerance arises when there is a disparity between the production of histamine and the body's ability to break it down. In the gastrointestinal tract, the primary enzyme responsible for metabolizing ingested histamine is diamine oxidase (DAO), and a shortage of this enzyme has been associated with some diseases related to the respiratory, cardiovascular, nervous, muscular, and digestive systems, in addition to migraines. The treatment of migraines typically revolves around the utilization of both anti-migraine and anti-inflammatory drugs, but their interaction with DAO is not thoroughly understood. In this study, we examined the impact of nonsteroidal anti-inflammatory drugs (NSAIDs) and anti-migraine medications on DAO activity through in vitro experiments. We also investigated their effects on the human intestinal cell line Caco-2, assessing changes in DAO expression (both at the mRNA and protein levels) as well as DAO activity. The tested drugs, including ibuprofen, acetylsalicylic acid, paracetamol, a combination of acetylsalicylic acid with paracetamol and caffeine, zolmitriptan, and sumatriptan, did not inhibit DAO activity or reduce their levels. However, naproxen reduced DAO protein levels in human enterocyte cultures while not affecting DAO activity. These results suggest that combining anti-inflammatory and anti-migraine drugs with DAO enzyme supplementation for migraine patients with DAO deficiency could be beneficial for healthcare professionals in their daily practice.

Interaction of Diamine Oxidase with Psychostimulant Drugs for ADHD Management.

Histamine intolerance occurs when there is an imbalance between histamine production and the capacity for histamine degradation. Diamine oxidase (DAO) is the main enzyme for the catabolism of ingested histamine degradation in the gastrointestinal tract and its deficiency has been linked to allergy-like symptoms. Psychostimulant drugs are commonly used to treat Attention Deficit Hyperactivity Disorder (ADHD), but their interaction with DAO is not well characterized. In this work, we evaluated the effects of psychostimulant drugs (methylphenidate and lisdexamfetamine) on in vitro DAO activity and in the human cell line of enterocytes (Caco-2), evaluating DAO expression (mRNA and protein) and DAO activity. Methylphenidate and lisdexamfetamine did not repress the in vitro DAO activity. In addition, in Caco-2 cells, lisdexamfetamine promoted a strong upregulation of DAO mRNA levels, whereas methylphenidate tended to induce DAO activity. To sum up, methylphenidate and lisdexamfetamine treatments do not reduce DAO activity. These findings could be useful for physicians prescribing these two drugs to ADHD patients affected by DAO deficiency.

Fucosylated N-glycans as early biomarkers of COVID-19 severity.

Background: The pathological mechanisms of SARS-CoV-2 in humans remain unclear and the unpredictability of COVID-19 progression may be attributed to the absence of biomarkers that contribute to the prognosis of this disease. Therefore, the discovery of biomarkers is needed for reliable risk stratification and to identify patients who are more likely to progress to a critical stage. Methods: Aiming to identify new biomarkers we analysed N-glycan traits in plasma from 196 patients with COVID-19. Samples were classified into three groups according to their severity (mild, severe and critical) and obtained at diagnosis (baseline) and at 4 weeks of follow-up (postdiagnosis), to evaluate their behaviour through disease progression. N-glycans were released with PNGase F and labelled with Rapifluor-MS, followed by their analysis by LC-MS/MS. The Simglycan structural identification tool and Glycostore database were employed to predict the structure of glycans. Results: We determined that plasma from SARS-CoV-2-infected patients display different N-glycosylation profiles depending on the disease severity. Specifically, levels of fucosylation and galactosylation decreased with increasing severity and Fuc1Hex5HexNAc5 was identified as the most suitable biomarker to stratify patients at diagnosis and distinguish mild from critical outcomes. Conclusion: In this study we explored the global plasma glycosignature, reflecting the inflammatory state of the organs during the infectious disease. Our findings show the promising potential of glycans as biomarkers of COVID-19 severity.

Gut Microbiome and Small RNA Integrative-Omic Perspective of Meconium and Milk-FED Infant Stool Samples.

The human gut microbiome plays an important role in health, and its initial development is conditioned by many factors, such as feeding. It has also been claimed that this colonization is guided by bacterial populations, the dynamic virome, and transkingdom interactions between host and microbial cells, partially mediated by epigenetic signaling. In this article, we characterized the bacteriome, virome, and smallRNome and their interaction in the meconium and stool samples from infants. Bacterial and viral DNA and RNA were extracted from the meconium and stool samples of 2- to 4-month-old milk-fed infants. The bacteriome, DNA and RNA virome, and smallRNome were assessed using 16S rRNA V4 sequencing, viral enrichment sequencing, and small RNA sequencing protocols, respectively. Data pathway analysis and integration were performed using the R package mixOmics. Our findings showed that the bacteriome differed among the three groups, while the virome and smallRNome presented significant differences, mainly between the meconium and stool of milk-fed infants. The gut environment is rapidly acquired after birth, and it is highly adaptable due to the interaction of environmental factors. Additionally, transkingdom interactions between viruses and bacteria can influence host and smallRNome profiles. However, virome characterization has several protocol limitations that must be considered.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies.

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

Single and Joined Behaviour of Circulating Biomarkers and Metabolic Parameters in High-Fit and Low-Fit Healthy Females.

Biomarkers are important in the assessment of health and disease, but are poorly studied in still healthy individuals with a (potential) different risk for metabolic disease. This study investigated, first, how single biomarkers and metabolic parameters, functional biomarker and metabolic parameter categories, and total biomarker and metabolic parameter profiles behave in young healthy female adults of different aerobic fitness and, second, how these biomarkers and metabolic parameters are affected by recent exercise in these healthy individuals. A total of 102 biomarkers and metabolic parameters were analysed in serum or plasma samples from 30 young, healthy, female adults divided into a high-fit (V̇O2peak ≥ 47 mL/kg/min, N = 15) and a low-fit (V̇O2peak ≤ 37 mL/kg/min, N = 15) group, at baseline and overnight after a single bout of exercise (60 min, 70% V̇O2peak). Our results show that total biomarker and metabolic parameter profiles were similar between high-fit and low-fit females. Recent exercise significantly affected several single biomarkers and metabolic parameters, mostly related to inflammation and lipid metabolism. Furthermore, functional biomarker and metabolic parameter categories corresponded to biomarker and metabolic parameter clusters generated via hierarchical clustering models. In conclusion, this study provides insight into the single and joined behavior of circulating biomarkers and metabolic parameters in healthy females, and identified functional biomarker and metabolic parameter categories that may be used for the characterisation of human health physiology.

Brain N-Glycosylation and Lipidomic Profile Changes Induced by a High-Fat Diet in Dyslipidemic Hamsters.

The consumption of diets rich in saturated fats is known to be associated with higher mortality. The adoption of healthy habits, for instance adhering to a Mediterranean diet, has proved to exert a preventive effect towards cardiovascular diseases and dyslipidemia. Little is known about how a suboptimal diet can affect brain function, structure, and the mechanisms involved. The aims of this study were to examine how a high-fat diet can alter the brain N-glycan and lipid profile in male Golden Syrian hamsters and to evaluate the potential of a Mediterranean-like diet to reverse this situation. During twelve weeks, hamsters were fed a normal fat diet (CTRL group), a high-fat diet (HFD group), and a high-fat diet followed by a Mediterranean-like diet (MED group). Out of seventy-two identified N-glycans, fourteen were significant (p < 0.05) between HFD and CTRL groups, nine between MED and CTRL groups, and one between MED and HFD groups. Moreover, forty-nine lipids were altered between HFD and CTRL groups, seven between MED and CTRL groups, and five between MED and HFD groups. Our results suggest that brain N-glycan composition in high-fat diet-fed hamsters can produce events comparable to those found in some neurodegenerative diseases, and may promote brain ageing.

Comparative Proteomic Analysis of an Ethyl Tert-Butyl Ether-Degrading Bacterial Consortium.

A bacterial consortium capable of degrading ethyl tert-butyl ether (ETBE) as a sole carbon source was enriched and isolated from gasoline-contaminated water. Arthrobacter sp., Herbaspirillum sp., Pseudacidovorax sp., Pseudomonas sp., and Xanthomonas sp. were identified as the initial populations with the 16S rDNA analysis. The consortium aerobically degraded 49% of 50 mg/L of ETBE, in 6 days. The ETBE degrading efficiency of the consortium increased to 98% even with the higher concentrations of ETBE (1000 mg/L) in the subsequent subcultures, which accumulated tert-butyl alcohol (TBA). Xanthomonas sp. and Pseudomonas sp. were identified as the predominant ETBE degrading populations in the final subculture. The metaproteome of the ETBE-grown bacterial consortium was compared with the glucose-grown bacterial consortium, using 2D-DIGE. Proteins related to the ETBE metabolism, stress response, carbon metabolism and chaperones were found to be abundant in the presence of ETBE while proteins related to cell division were less abundant. The metaproteomic study revealed that the ETBE does have an effect on the metabolism of the bacterial consortium. It also enabled us to understand the responses of the complex bacterial consortium to ETBE, thus revealing interesting facts about the ETBE degrading bacterial community.

Characterization of a proteomic profile associated with organ dysfunction and mortality of sepsis and septic shock.

INTRODUCTION: The search for new biomarkers that allow an early diagnosis in sepsis and predict its evolution has become a necessity in medicine. The objective of this study is to identify, through omics techniques, potential protein biomarkers that are expressed in patients with sepsis and their relationship with organ dysfunction and mortality. METHODS: Prospective, observational and single-center study that included adult patients (≥ 18 years) who were admitted to a tertiary hospital and who met the criteria for sepsis. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination classification and cross-validation with a vector classifier, an association of these proteins with mortality and organ dysfunction was established. The protein-protein interaction network was analyzed with String software. RESULTS: 141 patients were enrolled in this study. Mass spectrometry identified 177 proteins. Of all of them, and by recursive feature elimination, nine proteins (GPX3, APOB, ORM1, SERPINF1, LYZ, C8A, CD14, APOC3 and C1QC) were associated with organ dysfunction (SOFA > 6) with an accuracy of 0.82 ± 0.06, precision of 0.85 ± 0.093, sensitivity 0.81 ± 0.10, specificity 0.84 ± 0.10 and AUC 0.82 ± 0.06. Twenty-two proteins (CLU, LUM, APOL1, SAA1, CLEBC3B, C8A, ITIH4, KNG1, AGT, C7, SAA2, APOH, HRG, AFM, APOE, APOC1, C1S, SERPINC1, IGFALS, KLKB1, CFB and BTD) were associated with mortality with an accuracy of 0.86 ± 0.05, a precision of 0.91 ± 0.05, a sensitivity of 0.91 ± 0.05, a specificity of 0.72 ± 0.17, and an area under the curve (AUC) of 0.81 ± 0.08 with a confidence interval of 95%. CONCLUSION: In sepsis there are proteomic patterns associated with organ dysfunction and mortality.

Microbial bile salt hydrolase activity influences gene expression profiles and gastrointestinal maturation in infant mice.

The mechanisms by which early microbial colonizers of the neonate influence gut development are poorly understood. Bacterial bile salt hydrolase (BSH) acts as a putative colonization factor that influences bile acid signatures and microbe-host signaling pathways and we considered whether this activity can influence infant gut development. In silico analysis of the human neonatal gut metagenome confirmed that BSH enzyme sequences are present as early as one day postpartum. Gastrointestinal delivery of cloned BSH to immature gnotobiotic mice accelerated shortening of the colon and regularized gene expression profiles, with monocolonised mice more closely resembling conventionally raised animals. In situ expression of BSH decreased markers of cell proliferation (Ki67, Hes2 and Ascl2) and strongly increased expression of ALPI, a marker of cell differentiation and barrier function. These data suggest an evolutionary paradigm whereby microbial BSH activity potentially influences bacterial colonization and in-turn benefits host gastrointestinal maturation.

Transcriptomic and Proteomic Profiles for Elucidating Cisplatin Resistance in Head-and-Neck Squamous Cell Carcinoma.

To identify the novel genes involved in chemoresistance in head and neck squamous cell carcinoma (HNSCC), we explored the expression profiles of the following cisplatin (CDDP) resistant (R) versus parental (sensitive) cell lines by RNA-sequencing (RNA-seq): JHU029, HTB-43 and CCL-138. Using the parental condition as a control, 30 upregulated and 85 downregulated genes were identified for JHU029-R cells; 263 upregulated and 392 downregulated genes for HTB-43-R cells, and 154 upregulated and 68 downregulated genes for CCL-138-R cells. Moreover, we crossed-checked the RNA-seq results with the proteomic profiles of HTB-43-R (versus HTB-43) and CCL-138-R (versus CCL-138) cell lines. For the HTB-43-R cells, 21 upregulated and 72 downregulated targets overlapped between the proteomic and transcriptomic data; whereas in CCL-138-R cells, four upregulated and three downregulated targets matched. Following an extensive literature search, six genes from the RNA-seq (CLDN1, MAGEB2, CD24, CEACAM6, IL1B and ISG15) and six genes from the RNA-seq and proteomics crossover (AKR1C3, TNFAIP2, RAB7A, LGALS3BP, PSCA and SSRP1) were selected to be studied by qRT-PCR in 11 HNSCC patients: six resistant and five sensitive to conventional therapy. Interestingly, the high MAGEB2 expression was associated with resistant tumours and is revealed as a novel target to sensitise resistant cells to therapy in HNSCC patients.

The Catalan Surveillance Network of SARS-CoV-2 in Sewage: design, implementation, and performance.

Wastewater-based epidemiology has shown to be an efficient tool to track the circulation of SARS-CoV-2 in communities assisted by wastewater treatment plants (WWTPs). The challenge comes when this approach is employed to help Health authorities in their decision-making. Here, we describe the roadmap for the design and deployment of SARSAIGUA, the Catalan Surveillance Network of SARS-CoV-2 in Sewage. The network monitors, weekly or biweekly, 56 WWTPs evenly distributed across the territory and serving 6 M inhabitants (80% of the Catalan population). Each week, samples from 45 WWTPs are collected, analyzed, results reported to Health authorities, and finally published within less than 72 h in an online dashboard ( https://sarsaigua.icra.cat ). After 20 months of monitoring (July 20-March 22), the standardized viral load (gene copies/day) in all the WWTPs monitored fairly matched the cumulative number of COVID-19 cases along the successive pandemic waves, showing a good fit with the diagnosed cases in the served municipalities (Spearman Rho = 0.69). Here we describe the roadmap of the design and deployment of SARSAIGUA while providing several open-access tools for the management and visualization of the surveillance data.

Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples.

Advances in metabolomics analysis and data treatment increase the knowledge of complex biological systems. One of the most used methodologies is gas chromatography-mass spectrometry (GC-MS) due to its robustness, high separation efficiency, and reliable peak identification through curated databases. However, methodologies are not standardized, and the derivatization steps in GC-MS can introduce experimental errors and take considerable time, exposing the samples to degradation. Here, we propose the injection-port derivatization (IPD) methodology to increase the throughput in plasma metabolomics analysis by GC-MS. The IPD method was evaluated and optimized for different families of metabolites (organic acids, amino acids, fatty acids, sugars, sugar phosphates, etc.) in terms of residence time, injection-port temperature, and sample/derivatization reagent ratio. Finally, the method's usefulness was validated in a study consisting of a cohort of obese patients with or without nonalcoholic steatohepatitis. Our results show a fast, reproducible, precise, and reliable method for the analysis of biological samples by GC-MS. Raw data are publicly available at MetaboLights with Study Identifier MTBLS5151.

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Red-Fleshed Apples Rich in Anthocyanins and White-Fleshed Apples Modulate the Aorta and Heart Proteome in Hypercholesterolaemic Rats: The AppleCOR Study.

The impact of a red-fleshed apple (RFA) rich in anthocyanins (ACNs), a white-fleshed apple (WFA) without ACNs, and an extract infusion from Aronia fruit (AI) equivalent in dose of cyanidin-3-O-galactoside (main ACN) as RFA was determined by the proteome profile of aorta and heart as key cardiovascular tissues. Hypercholesterolaemic Wistar rats were separated into six groups (n = 6/group; three males and three females) and the proteomic profiles were analyzed using nanoliquid chromatography coupled to mass spectrometry. No adverse events were reported and all products were well tolerated. RFA downregulated C1QB and CFP in aorta and CRP in heart. WFA downregulated C1QB and CFP in aorta and C9 and C3 in aorta and heart, among other proteins. AI downregulated PRKACA, IQGAP1, and HSP90AB1 related to cellular signaling. Thus, both apples showed an anti-inflammatory effect through the complement system, while RFA reduced CRP. Regardless of the ACN content, an apple matrix effect was observed that involved different bioactive components, and inflammatory proteins were reduced.

Considerations on the Analysis of E-900 Food Additive: An NMR Perspective.

Food additives are in widespread use in the food industry to extend the shelf life of food, improve its organoleptic characteristics or facilitate industrial processing. Their use is not without controversy, which makes regulation and control crucial for food safety and public health. Among food additives, silicone-based antifoaming agents (polysiloxanes or E900) are difficult to analyze and quantify due to their polymeric nature. Currently, there is no official method of quantifying this additive in foods. In this context, nuclear magnetic resonance (NMR) is a quantitative method for speciation analysis of silicon compounds almost without known interferents. In this work, we describe the evolution of the regulation of the E900 additive, discuss different analytic methods quantifying polydimethylsiloxanes (PDMS), and propose a new method based on NMR suitable for analyzing the content of E900 in the form of PDMS in various types of food from dietary oils to marmalades and jellies, among others. The proposed method consists of a previous quantitative concentration of PDMS by liquid-liquid extraction and the monitoring of the quantification using a bis(trimethylsilyl)benzene (BTMSB) standard to control the variability, ranging within 2-7%, depending on the food. This simple, direct, and reproducible procedure for aqueous and lipidic foods may help to monitor and fill a gap in regulatory legislation regarding the E900 additive.

The Interplay between Pathophysiological Pathways in Early-Onset Severe Preeclampsia Unveiled by Metabolomics.

INTRODUCTION: Preeclampsia is a multi-system disorder unique to pregnancy responsible for a great part of maternal and perinatal morbidity and mortality. The precise pathogenesis of this complex disorder is still unrevealed. METHODS: We examined the pathophysiological pathways involved in early-onset preeclampsia, a specific subgroup representing its most severe presentation, using LC-MS/MS metabolomic analysis based on multi-level extraction of lipids and small metabolites from maternal blood samples, collected at the time of diagnosis from 14 preeclamptic and six matched healthy pregnancies. Statistical analysis comprised multivariate and univariate approaches with the application of over representation analysis to identify differential pathways. RESULTS: A clear difference between preeclamptic and control pregnancies was observed in principal component analysis. Supervised multivariate analysis using orthogonal partial least square discriminant analysis provided a robust model with goodness of fit (R2X = 0.91, p = 0.002) and predictive ability (Q2Y = 0.72, p < 0.001). Finally, univariate analysis followed by 5% false discovery rate correction indicated 82 metabolites significantly altered, corresponding to six overrepresented pathways: (1) aminoacyl-tRNA biosynthesis; (2) arginine biosynthesis; (3) alanine, aspartate and glutamate metabolism; (4) D-glutamine and D-glutamate metabolism; (5) arginine and proline metabolism; and (6) histidine metabolism. CONCLUSION: Metabolomic analysis focusing specifically on the early-onset severe form of preeclampsia reveals the interplay between pathophysiological pathways involved in this form. Future studies are required to explore new therapeutic approaches targeting these altered metabolic pathways in early-onset preeclampsia.

Looking for a needle in a haystack. SARS-CoV-2 variant characterization in sewage.

SARS-CoV-2 variants are emerging worldwide, and monitoring them is key in providing early warnings. Here, we summarize the different analytical approaches currently used to study the dissemination of SARS-CoV-2 variants in wastewater and discuss their advantages and disadvantages. We also provide preliminary results of two sensitive and cost-effective approaches: variant-specific reverse transcription-nested PCR assays and a nonvariant-specific amplicon deep sequencing strategy that targets three key regions of the viral spike protein. Next-generation sequencing approaches enable the simultaneous detection of signature mutations of different variants of concern in a single assay and may be the best option to explore the real picture at a particular time. Targeted PCR approaches focused on specific signature mutations will need continuous updating but are sensitive and cost-effective.