Longitudinal Metabolomics and Lipidomics Analyses Reveal Alterations Associated with Envenoming by Bothrops asper and Daboia russelii in an Experimental Murine Model

Abstract

Longitudinal metabolomics and lipidomics analyses were carried out on the blood plasma of mice injected intramuscularly with venoms of the viperid species Bothrops asper or Daboia russelii. Blood samples were collected 1, 3, 6, and 24 h after venom injection, and a control group of non-envenomed mice was included. Significant perturbations in metabolomics and lipidomics were observed at 1, 3, and 6 h, while values returned close to those of control mice by 24 h, hence reflecting a transient pattern of metabolic disturbance. Both venoms induced significant changes in amino acids, as well as in several purines and pyrimidines, and in some metabolites of the tricarboxylic acid cycle. KEGG analysis of metabolic pathways that showed those with the greatest change included aminoacyl tRNA synthesis and amino acid biosynthesis and metabolism pathways. With regard to lipid metabolism, there was an increase in triglycerides and some acyl carnitines and a concomitant drop in the levels of some phospholipids. In addition, envenomed mice had higher levels of cortisol, heme, and some oxidative stress markers. The overall pattern of metabolic changes in envenomed mice bears similarities with the patterns described in several traumatic injuries, thus underscoring a metabolic response/adaptation to the injurious action of the venoms.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

“… 19 ; (3) Dataset MM, which was from the Mus musculus samples in ref. 33 ; (4) Dataset SO, which was from soil samples in ref. 34 ; (5) Dataset GUS, which was about global untargeted serum metabolomic samples from ref.…”

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis

“…Further, we tested the generalizability boundary of DeepRTAlign on the seven test sets and seven other datasets (five metabolomic datasets and two proteomic datasets). The five metabolomic datasets include (1) Dataset NCC19, a large-scale plasma analysis about SARS-CoV-2 from the paper of Barberis et al 23 ; (2) Dataset SM1100, standard mixtures consisting of 1100 compounds, from the paper of Li et al 24 ; (3) Dataset MM, which was from the Mus musculus samples in the paper of Wase et al 25 ; (4) Dataset SO, which was from soil samples in the paper of Swenson et al 26 ; (5) Dataset GUS, which was about global untargeted serum metabolomic samples from the paper of Gibson et al 27 . The two proteomic datasets are (1) Dataset MI, which was about mouse intestinal proteomes from the paper of Lichtman et al 28 ; Dataset CD, which was obtained from the gut microbiota of patients with Crohn’s disease in the paper of Mottawea et al 29 .…”

A Complex Pattern of Gene Expression in Tissue Affected by Viperid Snake Envenoming: The Emerging Role of Autophagy-Related Genes

“…Furthermore, they show variations in the patterns of increments in some inflammatory mediators [ 16 ]. Lipidomic and metabolomic analyses of plasma from mice injected with these venoms demonstrated both similarities and differences [ 17 ]. However, there is a notorious lack of information on the patterns of expression of genes that might be involved in the events occurring in tissue injected with venoms.…”