Recombinant Mouse ATP synthase lipid-binding protein, mitochondrial (Atp5g3)

What is ATP5G3 and what is its primary function?

ATP5G3 (ATP synthase subunit C3) is a component of mitochondrial ATP synthase, specifically functioning as a lipid-binding protein within this complex. It is encoded by the Atp5g3 gene and plays a critical role in energy production through oxidative phosphorylation. Research indicates that ATP5G3 displays significant changes in gene expression under conditions of oxidative stress, suggesting its involvement in cellular stress response mechanisms . It forms part of the c-ring of the F0 complex of ATP synthase, which is essential for proton transport across the inner mitochondrial membrane during ATP synthesis.

How is ATP5G3 expressed across different tissues in mouse models?

Expression studies using BXD recombinant inbred mice reveal that ATP5G3 is expressed in multiple tissues with varying levels of abundance. According to data from GeneNetwork, expression of Atp5g3 is detectable in several organs relevant to alcoholism research, including the hippocampus, cerebellum, and liver . Notably, the relative expression levels in brain tissues (hippocampus and cerebellum) are higher than those observed in the liver. Additionally, substantial strain-dependent variation exists in expression levels, with this variation being more pronounced in brain tissues compared to liver . This tissue-specific expression pattern suggests potential specialized functions of ATP5G3 in different organ systems.

What are the known partner genes of ATP5G3?

Based on STRING analysis, ATP5G3 interacts with at least 12 partner genes, primarily other components of the mitochondrial respiratory chain and ATP synthesis machinery . Key interaction partners include:

• ATP5A1 (ATP synthase F1 subunit alpha)

• ATP5B (ATP synthase F1 subunit beta)

• ATP5H (ATP synthase peripheral stalk subunit d)

• COX6B1 (Cytochrome c oxidase subunit 6B1)

• UQCRFS1 (Ubiquinol-cytochrome c reductase, Rieske iron-sulfur polypeptide 1)

• NDUFA9 (NADH:ubiquinone oxidoreductase subunit A9)

• PPA2 (Inorganic pyrophosphatase 2)

• LHPP (Phospholysine phosphohistidine inorganic pyrophosphate phosphatase)

The co-expression patterns between ATP5G3 and these partner genes vary substantially across different tissues, suggesting tissue-specific functional interactions .

What role does ATP5G3 play in oxidative stress and cell death pathways?

Research suggests ATP5G3 has a protective role against oxidative stress-induced cell death. In studies using sodium nitroprusside (SNP), which generates oxidative stress, ATP5G3 suppression via siRNA significantly increased cytotoxicity in cervical carcinoma cells . This protective function appears to operate through regulation of the p38 MAPK pathway and Bcl-xL. Specifically, ATP5G3 inhibits p38 activity, which would otherwise promote cell death when activated. The protection conferred by ATP5G3 was negated by Bcl-xL inhibition, suggesting that ATP5G3's protective effect is mediated through the Bcl-xL pathway via inhibition of p38 activity .

Furthermore, the cytotoxicity observed with ATP5G3 suppression was completely blocked by co-transfection with siBax, indicating Bax involvement in the death pathway regulated by ATP5G3 . Interestingly, this cell death process appears to involve autophagic/lysosomal mechanisms rather than classical apoptosis, as pretreatment with lysosomal inhibitors significantly reduced the cytotoxicity associated with ATP5G3 suppression .

How does ATP5G3 expression correlate with alcohol-related phenotypes?

Expression studies reveal significant correlations between Atp5g3 expression levels and various alcohol-related phenotypes, with these relationships being tissue-specific:

In the hippocampus:

• Negative correlation (R = −0.6795) with acute ethanol response (time to ataxia)

• Positive correlation (R = 0.6039) with chronic withdrawal and handling-induced convulsion score

In the cerebellum:

• Strong positive correlation (R = 0.8512) with ethanol preference in males

• Positive correlation (R = 0.8433) with obesity markers, suggesting potential metabolic connections

In the liver:

• Negative correlation with corticosterone plasma levels after ethanol injection

• Negative correlation with locomotor tolerance/sensitization in males

• Strong positive correlation (R = 0.9553) with handling-induced convulsion score 7 hours after ethanol injection

• Negative correlation with obesity-relevant phenotypes in males

These correlations suggest that ATP5G3 may be involved in both the acute response to alcohol and the development of alcohol dependence, with its effects potentially varying by tissue type.

What are the tissue-specific transcriptome mapping patterns of ATP5G3?

Transcriptome mapping analysis reveals that Atp5g3 expression is regulated differently across tissues:

In the hippocampus:

• Expression is primarily regulated by three loci located on chromosomes 4, 5, and 12

• Significance threshold was determined at LOD 3.64, with a suggestive threshold at LOD 2.27

In the cerebellum:

• Two regulatory loci on chromosome 8 were identified

• One locus reached significance (LOD 3.77) while another was at the suggestive level (LOD 2.32)

In the liver:

• One significant regulatory locus on chromosome 1 (LOD 3.60)

• Additional suggestive loci on chromosomes 1, 9, 12, and 18

This differential regulation pattern aligns with the observation that ATP5G3 correlates with different alcohol-related phenotypes in different tissues, suggesting tissue-specific regulatory mechanisms and functions.

How do ATP5G3-partner gene interactions vary across different tissues?

The co-expression patterns between ATP5G3 and its partner genes show remarkable tissue specificity, as illustrated by the following examples:

• UQCRFS1 expression positively correlates with ATP5G3 in both cerebellum (R = 0.61) and liver (R = 0.671), but shows negligible correlation in the hippocampus (R = -0.065)

• ATP5A1 (exons 9, 10, 11) shows consistent positive correlation with ATP5G3 across all three tissues (hippocampus: R = 0.717, cerebellum: R = 0.557, liver: R = 0.772)

• Different transcript regions of the same gene can show divergent correlation patterns with ATP5G3. For instance, ATP5A1's 3'-UTR region shows weaker or different correlations compared to its coding exons

This table illustrates these tissue-specific interaction patterns:

These tissue-specific interaction patterns suggest that ATP5G3 may participate in different molecular pathways in different tissues, potentially explaining its diverse roles in various physiological and pathological processes .

What techniques are commonly used to study ATP5G3 expression?

Several approaches have been successfully employed to study ATP5G3 expression:

• Microarray analysis: The GeneNetwork platform contains multiple microarray datasets that have been used to analyze Atp5g3 expression across different tissues in various mouse strains. For example, the Hippocampus Consortium M430v2 dataset provided expression data from 67 BXD recombinant inbred strains .

• Quantitative RT-PCR: For validation of expression differences and for studying expression changes under various experimental conditions.

• Immunoblotting: Used to assess ATP5G3 protein levels and changes in response to treatments. This technique was also used to examine downstream effects, such as p38 phosphorylation levels in response to ATP5G3 manipulation .

• In situ hybridization: Though not explicitly mentioned in the search results, this technique is applicable for spatial analysis of ATP5G3 expression in tissue sections.

When designing expression studies, researchers should consider probe selection carefully, as different probes targeting different regions of the same gene can yield varying results, potentially due to alternative splicing or transcript variants .

How can researchers effectively manipulate ATP5G3 expression for functional studies?

Functional studies of ATP5G3 have successfully employed several approaches to manipulate its expression:

• siRNA transfection: Small interfering RNA targeting ATP5G3 (siATP5G3) has been used to downregulate its expression in cell culture models. This approach revealed the protective role of ATP5G3 against SNP-induced cytotoxicity .

• Co-transfection techniques: Studies have employed co-transfection of siATP5G3 with other siRNAs (e.g., sip38, siBcl-xL, siBax) to investigate pathway interactions. This methodology helped establish that ATP5G3's protective role is mediated by Bcl-xL via inhibition of p38 activity .

• Pharmacological interventions: While not directly manipulating ATP5G3 expression, researchers have used inhibitors of pathways potentially regulated by ATP5G3 (e.g., p38 inhibitors, Bcl-xL inhibitors, lysosomal inhibitors) to characterize its functional role in cell death pathways .

When designing manipulation experiments, researchers should consider:

• Cell type selection based on endogenous ATP5G3 expression levels

• Appropriate controls for transfection procedures

• Verification of knockdown efficiency at both mRNA and protein levels

• Potential compensation by related genes or pathways

What experimental designs are most effective for studying ATP5G3's role in cell death mechanisms?

Based on published research, effective experimental designs for studying ATP5G3's role in cell death include:

• Comparative cytotoxicity assays: Comparing cell viability between ATP5G3-knockdown cells and control cells under various stress conditions (e.g., SNP treatment). This approach revealed increased vulnerability to oxidative stress in ATP5G3-depleted cells .

• Pathway dissection using specific inhibitors: Pretreating cells with pathway-specific inhibitors (p38 inhibitor, DFO, lysosomal inhibitors) before inducing stress helped identify the specific pathways through which ATP5G3 mediates its effects .

• Gene interaction studies: Co-transfecting cells with siRNAs targeting ATP5G3 and potential interacting genes (p38, Bcl-xL, Bax) to determine epistatic relationships. This approach established that ATP5G3's protective effect operates upstream of p38 and involves Bcl-xL .

• Cell death modality assessment: Using specific markers and inhibitors to distinguish between different types of cell death (apoptosis, autophagy, necrosis). This revealed that ATP5G3 depletion primarily induces autophagic/lysosomal cell death rather than apoptosis .

A comprehensive experimental design should include:

• Dose-response and time-course analyses

• Multiple cell death assessment methods

• Subcellular localization studies

• Molecular pathway validation through both gain- and loss-of-function approaches

How can researchers analyze correlations between ATP5G3 expression and phenotypic data?

The search results demonstrate several effective approaches for correlation analysis:

• Utilization of genetic reference populations: BXD recombinant inbred mouse strains provide a powerful platform for correlating gene expression with phenotypes due to their genetic diversity and extensive phenotypic characterization .

• Cross-tissue correlation analysis: Analyzing correlations in multiple tissues can reveal tissue-specific associations. For example, ATP5G3 expression correlates with different alcohol-related phenotypes in hippocampus, cerebellum, and liver .

• Bioinformatic resources: The GeneNetwork platform (http://www.genenetwork.org) offers tools for correlation analysis between gene expression and hundreds of phenotypes across multiple mouse strains .

When conducting correlation analyses, researchers should:

• Report correlation coefficients (R values) and statistical significance

• Consider sex-specific effects, as some correlations may differ between males and females

• Be cautious about causality interpretations from correlational data

• Validate findings using alternative approaches or independent datasets

What approaches are used to investigate transcriptome regulation of ATP5G3?

Several approaches have proven effective for studying the regulation of ATP5G3 expression:

Researchers investigating ATP5G3 regulation should consider:

• The possibility of distant regulatory elements

• Tissue-specific regulatory mechanisms

• Potential involvement of epigenetic mechanisms

• The influence of alternative splicing on gene expression measurements