Plasma metabolome and cytokine profile reveal glycylproline modulating antibody fading in convalescent COVID-19 patients

The impact and inflammatory characteristics of SARS-CoV-2 infection during ovarian stimulation on the outcomes of assisted reproductive treatment

Introduction

Despite the global prevalence of coronavirus disease 2019 (COVID-19), limited research has been conducted on the effects of SARS-CoV-2 infection on human reproduction. The aims of this study were to investigate the impact of SARS-CoV-2 infection during controlled ovarian stimulation (COS) on the outcomes of assisted reproductive treatment (ART) and the cytokine status of patients.

Methods

This retrospective cohort study included 202 couples who received ART treatment, 101 couples infected with SARS-CoV-2 during COS and 101 matched uninfected couples. The parameters of ovarian stimulation and pregnancy outcomes were compared between the two groups. The All-Human Inflammation Array Q3 kit was utilized to measure cytokine levels in both blood and follicular fluid.

Results

No difference was found in the number of good-quality embryos (3.3 ± 3.1 vs. 3.0 ± 2.2, P = 0.553) between the infected and uninfected groups. Among couples who received fresh embryo transfers, no difference was observed in clinical pregnancy rate (53.3% vs. 51.5%, P = 0.907). The rates of fertilization, implantation, miscarriage, ectopic pregnancy and live birth were also comparable between the two groups. After adjustments were made for confounders, regression models indicated that the quality of embryos (B = 0.16, P = 0.605) and clinical pregnancy rate (P = 0.206) remained unaffected by SARS-CoV-2 infection. The serum levels of MCP-1, TIMP-1, I-309, TNF-RI and TNF-RII were increased, while that of eotaxin-2 was decreased in COVID-19 patients. No significant difference was found in the levels of cytokines in follicular fluid between the two groups.

Conclusion

Asymptomatic or mild COVID-19 during COS had no adverse effects on ART outcomes. Although mild inflammation was present in the serum, it was not detected in the follicular fluid of these patients. The subsequent immune response needs further investigation.

Comprehensive Profiling of Amine-Containing Metabolite Isomers with Chiral Phosphorus Reagents

Metabolite isomers play diverse and crucial roles in various metabolic processes. However, in untargeted metabolomics analysis, it remains a great challenge to distinguish between the constitutional isomers and enantiomers of amine-containing metabolites due to their similar chemical structures and physicochemical properties. In this work, the triplex stable isotope N-phosphoryl amino acids labeling (SIPAL) is developed to identify and relatively quantify the amine-containing metabolites and their isomers by using chiral phosphorus reagents coupled with high-resolution tandem mass spectroscopy. The constitutional isomers could be effectively distinguished with stereo isomers by using the diagnosis ions in MS/MS spectra. The in-house software MS-Isomerism has been parallelly developed for high-throughput screening and quantification. The proposed strategy enables the unbiased detection and relative quantification of isomers of amine-containing metabolites. Based on the characteristic triplet peaks with SIPAL tags, a total of 854 feature peaks with 154 isomer groups are successfully recognized as amine-containing metabolites in liver cells, in which 37 amine-containing metabolites, including amino acids, polyamines, and small peptides, are found to be significantly different between liver cancer cells and normal cells. Notably, it is the first time to identify S-acetyl-glutathione as an endogenous metabolite in liver cells. The SIPAL strategy could provide spectacular insight into the chemical structures and biological functions of the fascinating amine-containing metabolite isomers. The feasibility of SIPAL in isomeric metabolomics analysis may reach a deeper understanding of the mirror-chemistry in life and further advance the discovery of novel biomarkers for disease diagnosis.

Epidemiology, clinical presentation, pathophysiology, and management of long COVID: an update

The increasing number of coronavirus disease 2019 (COVID-19) infections have highlighted the long-term consequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection called long COVID. Although the concept and definition of long COVID are described differently across countries and institutions, there is general agreement that it affects multiple systems, including the immune, respiratory, cardiovascular, gastrointestinal, neuropsychological, musculoskeletal, and other systems. This review aims to provide a synthesis of published epidemiology, symptoms, and risk factors of long COVID. We also summarize potential pathophysiological mechanisms and biomarkers for precise prevention, early diagnosis, and accurate treatment of long COVID. Furthermore, we suggest evidence-based guidelines for the comprehensive evaluation and management of long COVID, involving treatment, health systems, health finance, public attitudes, and international cooperation, which is proposed to improve the treatment strategies, preventive measures, and public health policy making of long COVID.

Peripheral Blood Omics and Other Multiplex-based Systems in Pulmonary and Critical Care Medicine

Over the last years, the use of peripheral blood-derived big datasets in combination with machine learning technology has accelerated the understanding, prediction, and management of pulmonary and critical care conditions. The goal of this article is to provide readers with an introduction to the methods and applications of blood omics and other multiplex-based technologies in the pulmonary and critical care medicine setting to better appreciate the current literature in the field. To accomplish that, we provide essential concepts needed to rationalize this approach and introduce readers to the types of molecules that can be obtained from the circulating blood to generate big datasets; elaborate on the differences between bulk, sorted, and single-cell approaches; and the basic analytical pipelines required for clinical interpretation. Examples of peripheral blood-derived big datasets used in recent literature are presented, and limitations of that technology are highlighted to qualify both the current and future value of these methodologies.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

COVID-19 currently represents one of the major health challenges worldwide. Albeit its infectious character, with onset affectation mainly at the respiratory track, it is clear that the pathophysiology of COVID-19 has a systemic character, ultimately affecting many organs. This feature enables the possibility of investigating SARS-CoV-2 infection using multi-omic techniques, including metabolomic studies by chromatography coupled to mass spectrometry or by nuclear magnetic resonance (NMR) spectroscopy. Here we review the extensive literature on metabolomics in COVID-19, that unraveled many aspects of the disease including: a characteristic metabotipic signature associated to COVID-19, discrimination of patients according to severity, effect of drugs and vaccination treatments and the characterization of the natural history of the metabolic evolution associated to the disease, from the infection onset to full recovery or long-term and long sequelae of COVID.

Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients

Metabolomic analysis of blood plasma samples from COVID-19 patients is a promising approach allowing for the evaluation of disease progression. We performed the metabolomic analysis of plasma samples of 30 COVID-19 patients and the 19 controls using the high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometric detection (LC-MS/MS). In our analysis, we identified 103 metabolites enriched in KEGG metabolic pathways such as amino acid metabolism and the biosynthesis of aminoacyl-tRNAs, which differed significantly between the COVID-19 patients and the controls. Using ANDSystem software, we performed the reconstruction of gene networks describing the potential genetic regulation of metabolic pathways perturbed in COVID-19 patients by SARS-CoV-2 proteins. The nonstructural proteins of SARS-CoV-2 (orf8 and nsp5) and structural protein E were involved in the greater number of regulatory pathways. The reconstructed gene networks suggest the hypotheses on the molecular mechanisms of virus-host interactions in COVID-19 pathology and provide a basis for the further experimental and computer studies of the regulation of metabolic pathways by SARS-CoV-2 proteins. Our metabolomic analysis suggests the need for nonstructural protein-based vaccines and the control strategy to reduce the disease progression of COVID-19.