Biology subjects

Gao, A.

Publications and source records attributed to Gao, A..

39 records · Page 3Linked to original sources

bioRxiv2018

Mechanism of replication-coupled DNA-protein crosslink proteolysis by SPRTN and the proteasome

DNA-protein crosslinks (DPCs) are bulky DNA lesions that interfere with DNA metabolism and therefore threaten genomic integrity. Recent studies implicate the metalloprotease SPRTN in S-phase removal of DPCs, but how SPRTN activity is coupled to DNA replication is unknown. Using Xenopus egg extracts that recapitulate replication-coupled DPC proteolysis, we show that DPCs can be degraded by SPRTN or the proteasome, which act as independent DPC proteases. Proteasome recruitment requires DPC polyubiquitylation, which is triggered by single-stranded DNA, a byproduct of DNA replication. In contrast, SPRTN-mediated DPC degradation is independent of DPC polyubiquitylation but requires polymerase extension of a nascent strand to the lesion. Thus, SPRTN and proteasome activities are coupled to DNA replication by distinct mechanisms and together promote replication across immovable protein barriers.\n\nHighlightsO_LIThe proteasome, in addition to SPRTN, degrades DPCs during DNA replication\nC_LIO_LIProteasome-dependent DPC degradation requires DPC ubiquitylation\nC_LIO_LIDPC ubiquitylation is triggered by ssDNA and does not require the replisome\nC_LIO_LISPRTN-dependent DPC degradation is a post-replicative process\nC_LI

molecular biology↗

bioRxiv2017

Identification of key pathways and metabolic fingerprints of longevity in C. elegans

Impaired insulin/IGF-1 signaling (IIS) pathway and caloric restriction (CR) are two well-established interventions to prolong lifespan in worm C. elegans. However, a cross comparison of these longevity pathways using a multi-omics integration approach is lacking. In this study, we aimed to identify key pathways and metabolite fingerprints of longevity that are shared between IIS and CR worm models using a multi-omics integration approach. We generated transcriptomics and metabolomics data from two long-lived mutant worm strains, i.e. daf-2 (impaired IIS pathway) and eat-2 (CR model) and compared them with the N2 strain. Transcriptional profiling identified shared longevity signatures between the two strains, such as an upregulation of lipid storage and defense responses and downregulation of macromolecule synthesis and developmental processes. The shared longevity signatures revealed by metabolomics profiling included an increase in the levels of glycerol-3P, ademine, xanthine, and AMP, and a decrease in the levels of the amino acid pool, the C18:0 and C17:1 fatty acids. After we integrated transcriptomics and metabolomics data based on the annotations in KEGG, our results highlighted a downregulation of pyrimidine metabolism and upregulation of purine metabolism as a commonality between the two longevity mechanisms. Overall, our findings point towards the existence of shared metabolic pathways that are likely important for lifespan extension and provide novel insights into potential regulators and metabolic fingerprints for longevity.

biochemistry↗

bioRxiv2017

Natural genetic variation in C. elegans reveals genomic loci controlling metabolite levels

Metabolic homeostasis is sustained by complex biological networks responding to nutrient availability. Disruption of this equilibrium involving intricate interactions between genetic and environmental factors can lead to metabolic disorders, including obesity and type 2 diabetes. To identify the genetic factors controlling metabolism, we applied a quantitative genetic strategy using a Caenorhabditis elegans population consisting of 199 recombinant inbred lines (RILs) originally derived from crossing parental strains Bristol N2 and Hawaii CB4856. We focused on the genetic factors that control metabolite levels and measured fatty acid (FA) and amino acid (AA) composition in the 199 RILs using targeted metabolomics. For both FA and AA profiles, we observed large variation in metabolite levels with 32-82% heritability between the RILs. We performed metabolite-metabolite correlation analysis and detected strongly co-correlated metabolite clusters. To identify natural genetic variants responsible for the observed metabolite variations, we performed QTL mapping and detected 36 significant metabolite QTL (mQTL). We focused on the mQTL that displayed high significant linkage and heritability, including an mQTL for the FA C14:1 on chromosome I, and another mQTL for the FA C18:2 on chromosome IV. Using introgression lines (ILs) we were able to narrow down both mQTL to a 1.4 Mbp and a 3.6 Mbp region, respectively. Overall, this systems approach provides us with a powerful platform to study the genetic basis of C. elegans metabolism. It also allows us to investigate additional interventions, such as nutrients and stresses that maintain or disturb the regulatory network controlling metabolic homeostasis, and identify gene-by-environment interactions.

genomics↗