NME4 Human

What is NME4 and what are its primary functions in human cells?

NME4, also known as NDPK-M or NM23-H4, is a nucleoside diphosphate kinase belonging to the nonmetastatic 23 (NM23) family. It specifically localizes to the mitochondrial intermembrane space via its N-terminal sequence, which must be cleaved to enable its catalytic activity. In human cells, NME4 is widely expressed at high levels in the liver, intermediate levels in the heart and colon, and lower levels in the brain, testis, and peripheral leukocytes .

The primary functions of NME4 include:

• Regulation of lipid metabolism through interaction with key enzymes in CoA metabolism

• Modulation of acetyl-CoA and malonyl-CoA levels

• Influence on triglyceride synthesis and accumulation

• Potential roles in mitochondrial function and energy metabolism

NME4 binds to the inner mitochondrial membrane via anionic phospholipids, particularly cardiolipin, which positions it strategically for its role in cellular metabolism .

How does NME4 expression vary across human tissues and what techniques are most effective for detection?

NME4 demonstrates tissue-specific expression patterns in humans. The most reliable detection techniques for studying NME4 expression across tissues include:

• RT-qPCR: For quantitative measurement of mRNA expression levels, which has revealed high expression in liver tissue, moderate in heart and colon, and lower levels in brain, testis, and leukocytes .

• Western blotting: For protein detection, using specific antibodies against NME4.

• Immunohistochemistry: For visualization of NME4 in tissue sections, enabling analysis of its spatial distribution.

• Tandem mass spectrometry: For precise protein identification and quantification, which has been successfully applied to identify over 8,000 proteins in NME4 knockout studies .

When designing experiments to measure NME4 expression, researchers should consider analyzing multiple tissue types simultaneously and include appropriate housekeeping genes or proteins as controls for normalization.

What is the relationship between NME4 and mitochondrial function in human cells?

NME4 specifically localizes to the mitochondrial intermembrane space and plays a role in mitochondrial metabolism. Research has shown that:

To study this relationship experimentally, researchers should consider:

• Mitochondrial isolation techniques to study NME4 localization

• Oxygen consumption measurements to assess mitochondrial function

• Analysis of mitochondrial membrane potential

• Expression analysis of mitochondrial genes in parallel with NME4 expression studies

How does NME4 contribute to lipid metabolism regulation and NAFLD progression?

NME4 plays a significant role in lipid metabolism through multiple mechanisms:

• CoA Metabolism Interaction: NME4 interacts with key enzymes in CoA metabolism, influencing acetyl-CoA and malonyl-CoA levels, which are crucial intermediates in fatty acid synthesis .

• Triglyceride Regulation: Studies have demonstrated that NME4 increases triglyceride levels and promotes lipid accumulation in the liver. Knockdown of NME4 significantly decreases triglyceride levels in hepatocytes, while overexpression increases them .

• NAFLD Progression: NME4 expression positively correlates with the level of steatosis in high-fat diet (HFD) fed mice. NME4 depletion in mouse liver impedes the progression of NAFLD induced by HFD, resulting in decreased liver weight and serum ALT levels (an indicator of liver injury) .

Methodologically, researchers investigating NME4's role in lipid metabolism should consider:

• Lipidomic analyses to comprehensively assess changes in lipid profiles

• In vitro assays for triglyceride and cholesterol quantification

• Targeted gene knockdown or overexpression in hepatic cell lines

• In vivo models using liver-specific gene modulation via AAV-mediated delivery

What experimental models are most effective for studying NME4's role in human metabolic disorders?

Based on current research, the following experimental models have proven effective:

In vitro models:

• Human hepatic cell lines with NME4 knockout or overexpression using CRISPR-Cas9 technology

• Primary human hepatocytes treated with fatty acids to induce steatosis

In vivo models:

• Liver-specific Nme4 knockdown in mice using AAV-delivered shRNA

• High-fat diet (HFD) mouse models to study NAFLD progression

• Conditional knockout mouse models for tissue-specific studies

Analytical approaches:

• TMT 6plex-based quantitative proteomics for protein expression profiling

• Targeted and untargeted lipidomics to comprehensively analyze lipid changes

• Metabolomic analyses focusing on acetyl-CoA, malonyl-CoA, and other CoA derivatives

• Histological assessment of liver tissue for lipid accumulation

What are the established protein-protein interactions of NME4 and how do they impact its metabolic functions?

NME4's protein interaction network has been characterized using multiple complementary approaches:

• Tandem Affinity Purification Mass Spectrometry (TAP-MS): This technique identified 89 high-confidence interacting proteins (HCIPs) with MUSE scores greater than 0.80 .

• Proximity-dependent Biotinylating with Mass Spectrometry (TurboID-MS): This approach helps identify proteins in close proximity to NME4, particularly in the mitochondrial environment .

Key findings from these interaction studies include:

• Most NME4 interacting partners are enzymes, kinases, and peptidases

• These interactions suggest NME4's regulatory role in metabolic processes

• Interacting proteins are highly enriched in pathways related to metabolic diseases and liver complications, including liver steatosis, hyperplasia, and hepatocellular carcinoma

To investigate these interactions experimentally, researchers should consider:

• Co-immunoprecipitation studies to validate specific interactions

• Proximity labeling approaches for mitochondrial interactions

• Functional assays to assess the impact of specific interactions on enzymatic activities

• Structural studies to determine interaction domains and binding mechanisms

What are the optimal techniques for NME4 gene manipulation in experimental models?

For effective NME4 gene manipulation, researchers should consider the following approaches based on their specific experimental goals:

For in vitro studies:

• CRISPR-Cas9 system: This has been successfully used to generate NME4 knockout cell lines, enabling comprehensive proteomics analysis of the resulting phenotypes .

• Lentiviral or plasmid-based overexpression: For gain-of-function studies.

• siRNA or shRNA: For transient knockdown experiments.

For in vivo studies:

• AAV-mediated shRNA delivery: This approach has been effectively used to knock down Nme4 specifically in the liver, with viral particles primarily targeting hepatocytes rather than adipose tissue .

• Conditional knockout models: For tissue-specific and temporally controlled deletion.

• Transgenic overexpression: For studying the effects of constitutive or inducible NME4 overexpression.

When designing these experiments, researchers should:

• Confirm knockdown or overexpression efficiency at both mRNA and protein levels

• Consider potential off-target effects, particularly with CRISPR-Cas9 approaches

• Include appropriate controls, such as scrambled shRNA or empty vector controls

How can researchers effectively analyze NME4's impact on cellular lipid profiles and metabolism?

Comprehensive analysis of NME4's impact on lipid metabolism requires a multi-omics approach:

Lipidomic analyses:

• Untargeted lipidomics can reveal broad changes in the lipidome after NME4 manipulation

• Targeted lipidomics should focus on triglycerides, which are significantly affected by NME4

• Multiple lipid extraction methods should be employed to ensure comprehensive coverage

Metabolomic analyses:

• Targeted quantification of acetyl-CoA and malonyl-CoA, which are directly influenced by NME4

• Analysis of CoA derivatives and intermediates in fatty acid synthesis

• Stable isotope tracing to track metabolic flux through relevant pathways

Biochemical assays:

• In vitro enzymatic assays to measure activities of key lipogenic enzymes

• Quantification of cellular triglycerides and cholesterol using commercial kits

• Oil Red O staining for visual assessment of lipid accumulation

Integrative approaches:

• Correlation of lipidomic data with proteomic changes

• Pathway analysis to identify key nodes affected by NME4 manipulation

• Network analysis to elucidate the broader metabolic impact

What proteomics approaches are most informative for studying NME4's interactome and functional networks?

Several complementary proteomics approaches have proven valuable for characterizing NME4's protein interactions and functional networks:

Interaction proteomics:

• Tandem Affinity Purification coupled with Mass Spectrometry (TAP-MS): This approach identified 707 proteins, with 89 high-confidence interacting proteins after statistical filtering with the MUSE algorithm .

• Proximity-dependent Biotinylation (TurboID-MS): This technique is particularly useful for identifying transient or weak interactions in the native cellular environment, especially within mitochondria .

• Co-immunoprecipitation followed by mass spectrometry: For validation of specific interactions.

Quantitative proteomics:

• Tandem Mass Tag (TMT) 6plex-based quantitative proteomics: This approach identified over 8,000 proteins and quantified changes in protein expression following NME4 depletion .

• Label-free quantification: As an alternative approach for protein quantification.

• Targeted proteomics: For precise quantification of specific proteins of interest.

Bioinformatic analysis:

• Gene ontology enrichment analysis to identify biological processes affected by NME4

• Proteomap analysis to visualize protein expression changes in different functional categories

• Network analysis to identify functional modules within the NME4 interactome

When implementing these approaches, researchers should consider:

• Appropriate controls to distinguish true interactors from background

• Statistical filtering methods like MUSE to increase confidence in interaction data

• Validation of key interactions through orthogonal methods

How does NME4 function differ between normal and pathological states in human tissues?

This complex question requires integrative research approaches:

Comparative expression analysis:

• Analysis of NME4 expression in healthy versus diseased human tissues (particularly liver)

• Correlation of expression levels with disease progression markers

• Single-cell RNA sequencing to identify cell-type specific changes in expression

Functional alterations:

• Investigation of post-translational modifications of NME4 in disease states

• Analysis of subcellular localization changes in pathological conditions

• Assessment of catalytic activity alterations in disease models

Interactome changes:

• Comparative interaction proteomics between normal and diseased states

• Analysis of altered protein complex formation

• Investigation of pathway rewiring in pathological conditions

Research has shown that Nme4 expression is positively correlated with steatosis levels in HFD-fed mice, suggesting upregulation in pathological states . Further studies in human samples are needed to fully characterize these changes and their functional implications.

What are the emerging therapeutic strategies targeting NME4 for metabolic disorders?

Based on current understanding of NME4's role in lipid metabolism and NAFLD progression, several potential therapeutic approaches could be explored:

Direct inhibition approaches:

• Small molecule inhibitors targeting NME4's catalytic activity

• Allosteric modulators affecting its protein-protein interactions

• Peptide-based inhibitors targeting specific interaction domains

Gene therapy approaches:

• AAV-mediated liver-specific knockdown, which has shown promising results in mouse models

• CRISPR-based approaches for targeted gene editing

• Antisense oligonucleotides for transient knockdown

Metabolic bypass strategies:

• Interventions targeting downstream metabolic pathways affected by NME4

• Supplementation with metabolites to counteract NME4-induced changes

Combination therapies:

• Synergistic approaches combining NME4 modulation with other metabolic interventions

• Diet and lifestyle modifications to complement molecular interventions

For researchers pursuing these approaches, careful consideration of off-target effects, tissue specificity, and long-term consequences is essential. Preclinical studies should include comprehensive assessment of both efficacy and safety parameters.

How can human clinical study designs be optimized to investigate NME4's role in metabolic diseases?

While current NME4 research has primarily used cell and animal models, translation to human studies requires careful design considerations:

Observational studies:

• Cross-sectional studies comparing NME4 expression between healthy individuals and those with metabolic disorders

• Longitudinal studies tracking NME4 expression over disease progression

• Correlation analyses between NME4 levels and established biomarkers of metabolic disease

Clinical sample analysis:

• Liver biopsies from patients with various stages of NAFLD for NME4 expression analysis

• Multi-omics profiling of patient samples to correlate NME4 with broader metabolic signatures

• Single-cell analyses to identify cell-type specific alterations

Biomarker development:

• Assessment of NME4 or its downstream effectors as potential diagnostic or prognostic biomarkers

• Longitudinal evaluation of candidate biomarkers during disease progression

• Correlation with treatment response markers

Intervention studies:

• Pharmacological interventions targeting pathways related to NME4 function

• Dietary interventions that might modulate NME4 activity or expression

• Traceable human experiment design research (THEDRE) methodology for well-controlled human studies

When designing such studies, researchers should consider the principles of human-centered research as outlined in the Traceable Human Experiment Design Research framework, which provides methodological guidance for conducting human-centered informatics research .

How does NME4 function integrate with other mitochondrial proteins in human cellular metabolism?

NME4's mitochondrial localization places it within a complex network of mitochondrial proteins involved in energy metabolism. To investigate these relationships:

Mitochondrial interactome analysis:

• Proximity labeling specifically within mitochondria using mitochondrially-targeted TurboID

• Comparison of NME4 interactors with known mitochondrial protein complexes

• Analysis of co-expression patterns across tissues and conditions

Functional relationships:

• Assessment of how NME4 manipulation affects other mitochondrial functions

• Investigation of potential coordination between NME4 and mitochondrial lipid metabolism

• Analysis of energy production pathways in the context of NME4 activity

Evolutionary perspectives:

• Comparative analysis of NME4 function across species

• Assessment of co-evolution with interacting mitochondrial proteins

• Identification of conserved functional domains and interaction motifs

What computational models best predict NME4's influence on metabolic networks in human cells?

Advanced computational approaches can help elucidate NME4's role in broader metabolic networks:

Network modeling approaches:

• Protein-protein interaction network analysis incorporating NME4 interactome data

• Metabolic flux balance analysis to predict the impact of NME4 modulation

• Bayesian network modeling to infer causal relationships in metabolic pathways

Machine learning applications:

• Predictive models of NME4's impact on cellular phenotypes

• Feature selection to identify key factors influenced by NME4

• Pattern recognition in multi-omics data sets related to NME4 function

Systems biology integration:

• Multi-scale modeling incorporating molecular, cellular, and tissue-level data

• Dynamic simulation of metabolic responses to NME4 perturbation

• Integration of experimental data with existing metabolic models

Researchers should validate computational predictions with targeted experimental approaches, creating an iterative cycle of prediction, validation, and model refinement.

How can researchers effectively design experiments to resolve contradictory findings about NME4 function?

Scientific literature may contain seemingly contradictory findings about NME4 function. To address these contradictions:

Standardization approaches:

• Establish consistent experimental protocols for NME4 research

• Define standard cell lines and animal models for comparative studies

• Create reference datasets for NME4 expression and function

Contextual analysis:

• Systematically investigate context-dependent functions of NME4

• Identify factors that may explain differential effects (cell type, metabolic state, etc.)

• Design experiments that explicitly test the influence of contextual factors

Meta-analysis techniques:

• Systematic review of existing literature to identify patterns in contradictory findings

• Statistical meta-analysis of comparable datasets

• Identification of moderator variables that explain heterogeneity in results

Direct replication and extension:

• Independent replication of key experiments using identical methods

• Systematic variation of experimental parameters to identify boundary conditions

• Collaborative multi-lab studies to increase robustness of findings

By addressing contradictions systematically, researchers can develop a more nuanced understanding of NME4's complex roles in human cellular metabolism.