Biomarker candidates for progression and clinical management of COVID-19 associated pneumonia at time of admission

COVID-19 pathophysiology is currently not fully understood, reliable prognostic factors remain elusive, and few specific therapeutic strategies have been proposed. In this scenario, availability of biomarkers is a priority. MS-based Proteomics techniques were used to profile the proteome of 81 plasma samples extracted in four consecutive days from 23 hospitalized COVID-19 associated pneumonia patients. Samples from 10 subjects that reached a critical condition during their hospital stay and 10 matched non-severe controls were drawn before the administration of any COVID-19 specific treatment and used to identify potential biomarkers of COVID-19 prognosis. Additionally, we compared the proteome of five patients before and after glucocorticoids and tocilizumab treatment, to assess the changes induced by the therapy on our selected candidates. Forty-two proteins were differentially expressed between patients' evolution groups at 10% FDR. Twelve proteins showed lower levels in critical patients (fold-changes 1.20–3.58), of which OAS3 and COG5 found their expression increased after COVID-19 specific therapy. Most of the 30 proteins over-expressed in critical patients (fold-changes 1.17–4.43) were linked to inflammation, coagulation, lipids metabolism, complement or immunoglobulins, and a third of them decreased their expression after treatment. We propose a set of candidate proteins for biomarkers of COVID-19 prognosis at the time of hospital admission. The study design employed is distinctive from previous works and aimed to optimize the chances of the candidates to be validated in confirmatory studies and, eventually, to play a useful role in the clinical practice.

Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients

Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

Association of Lipids’ Metabolism with Vitamin D Receptor (rs10735810, rs222857) and Angiotensinogen (rs699) Genes Polymorphism in Essential Hypertensive Patients

BACKGROUND: Cardiovascular (CV) diseases are the most spread cause of mortality in the world. Essential arterial hypertension (EAH), as a major risk factor for the development of CV diseases, is a multifactorial disease involving environmental and genetic factors together with risk-conferring behaviors. AIM: The purpose of this study was to analyze lipid metabolism changes in patients with EAH depending on the Vitamin D receptor (VDR rs2228570 (aka rs10735810)) and angiotensinogen (AGT rs699) genes polymorphism. MATERIALS AND METHODS: The single-stage study involved 100 patients suffering from Stage 2 EAH, 1–3 degrees of blood pressure increase, high and very high CV risks, 21% (21) men, and 79% (79) women. The average age of patients was 59.86 ± 6.22 years old. The control group included 60 practically healthy individuals of an appropriate age and sex distribution. To examine the VDR gene (rs10735810, rs2228570) and AGT gene (rs699) polymorphism, a qualitative real-time polymerase chain reaction was made. The lipid metabolism was studied by determining the blood plasma content of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TGs). RESULTS: Т allele of AGT gene is associated with reduced HDL-C level in men and increased TGs level in women. The EAH risk increases 4.5 times as much among the ТС-genotype carriers and lowered HDL-C level (odds ratio [OR] = 6.43; p = 0.01). The EAH risk increases as far as the HDL-C level reduction, irrespective of the VDR gene alleles condition 1.83 times (OR = 2.37; OR 95% confidence interval [CI]: 1.02–5.51; p = 0.04) and 1.9 times (OR=2.43; OR 95% CI: 0.99–5.97; p = 0.04). HDL-C reduction and LDL-C elevation in women increase the EAH risk 2.4 times (OR = 3.27; p = 0.01) and 1.24 times (OR = 3.67; p = 0.01), respectively. CONCLUSIONS: The EAH risk increases with a reduced HDL-C level in the TC genotype carriers of the AGT gene and irrespective of VDR gene polymorphic variants.

Serum Metabolomic And Lipidomic Profiling Identifies Diagnostic Biomarkers For Seropositive And Seronegative Rheumatoid Arthritis Patients

Background: Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods: We carried out metabolomic and lipidomic profiling of metabolites and lipids in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results: Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions: A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

An Integrated Metabolomic Study of Osteoporosis: Discovery and Quantification of Hyocholic Acids as Candidate Markers

Osteoporosis is becoming a highly prevalent disease in a large proportion of the global aged population. Serum metabolite markers may be important for the treatment and early prevention of osteoporosis. Serum samples from 32 osteoporosis and 32 controls were analyzed by untargeted metabolomics and lipidomic approaches performed on an ultra-high performance liquid chromatography and high-resolution mass spectrometry (UHPLC-HRMS) system. To find systemic disturbance of osteoporosis, weighted gene correlation network analysis (WGCNA) and statistical methods were employed for data-mining. Then, an in-depth targeted method was utilized to determine potential markers from the family of key metabolites. As a result, 1,241 metabolites were identified from untargeted methods and WGCNA indicated that lipids metabolism is deregulated and glycerol phospholipids, sphingolipids, fatty acids, and bile acids (BA) are majorly affected. As key metabolites of lipids metabolism, 66 bile acids were scanned and 49 compounds were quantified by a targeted method. Interestingly, hyocholic acids (HCA) were found to play essential roles during the occurrence of osteoporosis and may be potential markers. These metabolites may be new therapeutic or diagnosis targets for the screening or treatment of osteoporosis. Quantified measurement of potential markers also enables the establishment of diagnostic models for the following translational research in the clinic.

EFFECT OF HONEY ON BLOOD SUGAR LEVEL AND LIPIDS METABOLISM IN MALE RABBITS

The present work was done to investigate the ability of Yemeni Sider honey to ameliorate the level of blood sugar and lipid profile in rabbits. For this goal 36 rabbits were used, after adaptation period the animals were divided into 6 groups as follows: group 1 and 2 served as control, and other 4 groups were served as treatment groups. Metformin was used as comparison in alloxan –induced diabetic rabbits. After the end of experiment (day 27) our results showed stabilization of sugar level and lipid profile,cholesterol HDL, LDL and triglycerides. We concluded that the use of honey in addition to metformin is more effective and ability of this drug in dealing with the metabolism of carbohydrates and fats.

In Silico Insights into the Mechanism of Action of Epoxy-α-Lapachone and Epoxymethyl-Lawsone in Leishmania spp.

Epoxy-α-lapachone (Lap) and Epoxymethyl-lawsone (Law) are oxiranes derived from Lapachol and have been shown to be promising drugs for Leishmaniases treatment. Although, it is known the action spectrum of both compounds affect the Leishmania spp. multiplication, there are gaps in the molecular binding details of target enzymes related to the parasite’s physiology. Molecular docking assays simulations were performed using DockThor server to predict the preferred orientation of both compounds to form stable complexes with key enzymes of metabolic pathway, electron transport chain, and lipids metabolism of Leishmania spp. This study showed the hit rates of both compounds interacting with lanosterol C-14 demethylase (−8.4 kcal/mol to −7.4 kcal/mol), cytochrome c (−10.2 kcal/mol to −8.8 kcal/mol), and glyceraldehyde-3-phosphate dehydrogenase (−8.5 kcal/mol to −7.5 kcal/mol) according to Leishmania spp. and assessed compounds. The set of molecular evidence reinforces the potential of both compounds as multi-target drugs for interrupt the network interactions between parasite enzymes, which can lead to a better efficacy of drugs for the treatment of leishmaniases.