Food adulteration and traceability tests using stable carbon isotope technologies

Due to the fractionation of stable carbon isotopes in plant photosynthesis, bio-decomposition processes, environmental factors, plant physiology, geographical factors, climatic conditions and agricultural practices, different foods exhibit significant differences in stable carbon isotope ratios. Therefore, stable carbon isotope ratio analysis (SCIRA) presents an effective tool for detecting food adulteration and food traceability control. In addition, stable carbon isotopes can frequently be used as markers to identify veterinary drug residues, pesticide residues and toxic substances remaining in foods by isotope dilution mass spectrometry (IDMS). The emphasis of this review, which will help readers to modify stable carbon isotope technologies more easily and extend their application in adulteration and traceability for foods, is on the characteristics of various instruments and the data processing methods in SCIRA and IDMS technologies. The latest research is also reviewed and highlighted. This paper reviews potential applications of these technologies to improve current food detection and protect consumers’ rights

The gut microbiome promotes arsenic metabolism and alleviates the metabolic disorder for their mammal host under arsenic exposure

Gut microbiome can participate in arsenic metabolism. However, its efficacy in the host under arsenic stress is still controversial. To clarify their roles in fecal arsenic excretion, tissue arsenic accumulation, host physiological states and metabolism, in this study, ninety-six C57BL/6 male mice were randomly divided to four groups, groups A and B were given sterile water, and groups C and D were given the third generation of broad-spectrum antibiotic (ceftriaxone) to erase the background gut microbiome. Subsequently, groups B and D were subchronicly exposed to arsenic containing feed prepared by adding arsenical mixture (rice arsenic composition) into control feed. In group D, the fecal total arsenic (CtAs) decreased by 25.5 %, iAsIII composition increased by 46.9 %, unclarified As (uAs) composition decreased by 92.4 %, and the liver CtAs increased by 26.7 %; the fecal CtAs was positively correlated with microbial richness and some metabolites (organic acids, amino acids, carbohydrates, SCFAs, hydrophilic bile acids and their derivatives); and fecal DMA was positively correlated with microbial richness and some metabolites (ferulic acid, benzenepropanoic acid and pentanoic acid); network analysis showed that the numbers of modules, nodes, links were decreased and vulnerability was increased; some SCFAs and hydrophilic bile acid decreased, and hydrophobic bile acids increased (Ps < 0.05). In the tissue samples of group D, Il-18 and Ifn-γ gene expression increased and intestinal barrier-related genes Muc2, Occludin and Zo-1 expression decreased (Ps < 0.05); serum glutathione and urine malondialdehyde significantly increased (Ps < 0.05); urine metabolome significantly changed and the variation was correlated with six SCFAs-producing bacteria, and some SCFAs including isobutyric acid, valeric acid and heptanoic acid decreased (Ps < 0.05). Therefore, the normal gut microbiome increases fecal arsenic excretion and biotransformation, which can maintain a healthier microbiome and metabolic functions, and alleviate the metabolic disorder for their mammal host under arsenic exposure

Short-term exposure to antibiotics begets long-term disturbance in gut microbial metabolism and molecular ecological networks

Abstract Background Antibiotic exposure can occur in medical settings and from environmental sources. Long-term effects of brief antibiotic exposure in early life are largely unknown. Results Post a short-term treatment by ceftriaxone to C57BL/6 mice in early life, a 14-month observation was performed using 16S rRNA gene-sequencing technique, metabolomics analysis, and metagenomics analysis on the effects of ceftriaxone exposure. Firstly, the results showed that antibiotic pre-treatment significantly disturbed gut microbial α and β diversities (P < 0.05). Both Chao1 indices and Shannon indices manifested recovery trends over time, but they didn’t entirely recover to the baseline of control throughout the experiment. Secondly, antibiotic pre-treatment reduced the complexity of gut molecular ecological networks (MENs). Various network parameters were affected and manifested recovery trends over time with different degrees, such as nodes (P < 0.001, R 2 = 0.6563), links (P < 0.01, R 2 = 0.4543), number of modules (P = 0.0672, R 2 = 0.2523), relative modularity (P = 0.6714, R 2 = 0.0155), number of keystones (P = 0.1003, R 2 = 0.2090), robustness_random (P = 0.79, R 2 = 0.0063), and vulnerability (P = 0.0528, R 2 = 0.28). The network parameters didn't entirely recover. Antibiotic exposure obviously reduced the number of key species in gut MENs. Interestingly, new keystones appeared during the recovery process of network complexity. Changes in network stability might be caused by variations in network complexity, which supports the ecological theory that complexity begets stability. Besides, the metabolism profiles of the antibiotic group and control were significantly different. Correlation analysis showed that antibiotic-induced differences in gut microbial metabolism were related to MEN changes. Antibiotic exposure also caused long-term effects on gut microbial functional networks in mice. Conclusions These results suggest that short-term antibiotic exposure in early life will cause long-term negative impacts on gut microbial diversity, MENs, and microbial metabolism. Therefore, great concern should be raised about children’s brief exposure to antibiotics if the results observed in mice are applicable to humans. Video Abstrac

Effects of dietary arsenic exposure on liver metabolism in mice

Arsenic, a ubiquitous environmental toxicant with various forms and complex food matrix interactions, can reportedly exert differential effects on the liver compared to drinking water exposure. To examine its specific liver-related harms, we targeted the liver in C57BL/6 J mice (n=48, 8-week-old) fed with arsenic-contaminated food (30 mg/kg) for 60 days, mimicking the rice arsenic composition observed in real-world scenarios (iAsV: 7.3%, iAsIII: 72.7%, MMA: 1.0%, DMA: 19.0%). We then comprehensively evaluated liver histopathology, metabolic changes, and the potential role of the gut-liver axis using human hepatocellular carcinoma cells (HepG2) and microbiota/metabolite analyses. Rice arsenic exposure significantly altered hepatic lipid (fatty acids, glycerol lipids, phospholipids, sphingolipids) and metabolite (glutathione, thioneine, spermidine, inosine, indole-derivatives, etc.) profiles, disrupting 33 metabolic pathways (bile secretion, unsaturated fatty acid biosynthesis, glutathione metabolism, ferroptosis, etc.). Pathological examination revealed liver cell necrosis/apoptosis, further confirmed by ferroptosis induction in HepG2 cells. Gut microbiome analysis showed enrichment of pathogenic bacteria linked to liver diseases and depletion of beneficial strains. Fecal primary and secondary bile acids, short-chain fatty acids, and branched-chain amino acids were also elevated. Importantly, mediation analysis revealed significant correlations between gut microbiota, fecal metabolites, and liver metabolic alterations, suggesting fecal metabolites may mediate the impact of gut microbiota and liver metabolic disorders. Gut microbiota and its metabolites may play significant roles in arsenic-induced gut-liver injuries. Overall, our findings demonstrate that rice arsenic exposure triggers oxidative stress, disrupts liver metabolism, and induces ferroptosis

Flanking V and J Sequences of Complementary Determining Region 3 of T Cell Receptor (TCR) δ1 (CDR3δ1) Determine the Structure and Function of TCRγ4δ1*

The γδ T cell receptor (TCR) differs from immunoglobulin and αβ TCR in its overall binding mode. In human, genes δ1, δ2, and δ3 are used for TCRδ chains. Previously, we have studied antigen binding determinants of TCRδ2 derived from dominant γδ T cells residing in peripheral blood. In this study we have investigated the critical determinants for antigen recognition and TCR function in TCRδ1 originated from gastric tumor-infiltrating γδ T lymphocytes using three independent experimental strategies including complementary determining region 3 (CDR3) of TCRδ1 (CDR3δ1)-peptide mediated binding, CDR3δ1-grafted TCR fusion protein-mediated binding, and TCRγ4δ1- and mutant-expressing cell-mediated binding. All three approaches consistently showed that the conserved flanking V and J sequences but not the diverse D segment in CDR3δ1 determine the antigen binding. Most importantly, we found that mutations in the V and J regions of CDR3δ1 also abolish the assembly of TCR and TCR-CD3 complexes in TCRγ4δ1-transduced J.RT3-T3.5 cells. Together with our previous studies on CDR3δ2 binding, our finding suggests that both human TCRδ1 and TCRδ2 recognize antigen predominately via flanking V and J regions. These results indicate that TCRγδ recognizes antigens using conserved parts in their CDR3, which provides an explanation for a diverse repertoire of γδTCRs only recognizing a limited number of antigens