NME2 Human

What is NME2 and to which protein family does it belong?

NME2 (also known as NM23-H2, NDPK-B, and nucleoside diphosphate kinase B) is a member of the NDPK/NME superfamily. It belongs to Group I of this superfamily, which consists of proteins sharing 58-88% identity with each other . NME2 functions as a nucleoside diphosphate kinase (NDK) and exists as a hexamer composed of both 'A' (encoded by NME1) and 'B' (encoded by NME2) isoforms . The protein has dual enzymatic functions, with EC designations 2.7.4.6 and 2.7.13.3, indicating its role as both a nucleoside diphosphate kinase and a histidine protein kinase .

What are the key biological functions of NME2?

NME2 is involved in several critical cellular processes, including:

• Regulation of T-cell function and cytokine production through K+ channel Kca3.1 activation

• Cardiac function regulation, as demonstrated in zebrafish knockdown models

• Transcriptional regulation, suggested by its alternative name as c-myc purine-binding transcription factor (PUF)

• Ribonucleoside triphosphate biosynthetic processes

• Cellular response to glucose stimulus and growth hormone

Compared to other NME family members, NME2 has distinct functions, though some overlap exists with NME1 due to their high homology .

What is the tissue distribution pattern of NME2 in humans?

NME2 is widely expressed throughout human tissues. According to the Human Protein Atlas, expression data is available across brain tissues, single-cell types, subcellular localizations, cancer tissues, and blood . NME1 and NME2 are by far the most abundant proteins in the NME family . Unlike some other family members like NME5 and NME7 that are predominantly found in ciliated structures, NME2 shows a more ubiquitous distribution pattern .

What is the role of NME2 in cancer progression and metastasis?

NME2 demonstrates a complex relationship with cancer progression that differs from NME1:

• While NME1 frequently shows reduced expression correlated with metastatic spread, NME2 may show distinct expression patterns .

• There is biphasic expression of both NME1 and NME2 in cancer:

  • Overexpression of both isoforms in most human solid tumors at early stages of development

  • Specifically decreased expression of NME1 (but not necessarily NME2) in primary tumors correlates with metastatic spread

• The differential roles are highlighted by RNA interference studies showing that silencing of NME1, but not NME2, confers a metastatic phenotype to non-invasive human epithelial tumor cell lines .

• In some cancer types such as neuroblastoma, hematopoietic malignancies, and osteosarcoma, high NME expression correlates with poor outcomes .

Research challenges include the difficulty in discriminating between NME1 and NME2 in many studies due to antibody cross-reactivity and probe specificity limitations .

How is NME2 being used as a biomarker in cancer research?

NME2 is currently being investigated as a biomarker by the Early Detection Research Network (EDRN) of the National Cancer Institute . The biomarker is under review for multiple organ systems. While specific organ-targeted information is restricted in public databases, research shows:

• NME2 has been specifically validated under review for breast cancer applications .

• Expression patterns differ significantly between cancer types, with some showing positive correlation with prognosis and others showing negative correlation .

• Diagnostic challenges exist due to heterogeneous expression within primary tumors and the need for precise criteria to evaluate and grade NME2 expression in clinical samples .

Methodologically, researchers should be aware that many antibodies and probes used historically failed to discriminate between NME1 and NME2, potentially confounding earlier studies .

What knockout/knockdown models are available for studying NME2 function?

Several genetic models have been developed to study NME2 function:

When designing experiments with these models, researchers should consider that NME2 knockouts alone show milder phenotypes than double knockouts, suggesting functional redundancy with NME1. Conditional knockout systems would be valuable for future research to understand tissue-specific and developmental stage-specific roles .

What experimental design considerations are important when studying NME2 in drug discovery?

When studying NME2 in drug discovery contexts, several experimental design approaches can enhance research quality:

• D-Optimal Onion Design (DOOD) is recommended for compound selection:

  • This design divides chemical space into layers according to each object's distance to the center point

  • D-optimal selection is then applied to each layer

  • This approach enables model-based selections in discrete spaces while allowing for experimenter's prior knowledge to influence selection

  • DOOD has been successfully used to select lead series of compounds in Type-Three Secretion inhibitor research

• Rectangular Experimental Designs for Multi-Unit Platforms (RED-MUP):

  • Specifically developed for assay optimization in multi-well formats (96-, 384-, 1536-well)

  • Combines classical experimental designs orthogonally onto rectangular platforms

  • Facilitates execution of Design of Experiments (DOE) on these platforms

  • Particularly useful for NME2 assay development where optimization can be a major bottleneck

• Higher information content assays:

  • Consider developing assays that are more informative rather than solely focusing on high throughput

  • Improving signal-to-noise ratio increases precision for all compounds

  • Reduces probability of incorrectly classifying weakly active NME2-interacting compounds

  • Cell-based systems may provide more informative data than receptor-based assays

How should researchers address the conflicting data in NME2 expression studies?

Researchers frequently encounter conflicting data regarding NME2 expression and its correlation with disease progression. To address these inconsistencies:

• Discriminate between NME1 and NME2:

  • Use isoform-specific antibodies or probes that can clearly distinguish between these highly homologous proteins

  • When reviewing literature, critically evaluate whether the study properly distinguished between isoforms

• Account for heterogeneous expression:

  • Consider spatial heterogeneity of expression within tissues (e.g., loss of NME1 observed at invasive fronts of hepatic and colorectal tumors)

  • Sample multiple regions of primary tumors when possible

• Standardize evaluation criteria:

  • Clearly define and consistently apply criteria for grading NME2 expression

  • Report quantitative measurements when possible rather than qualitative assessments

• Context-specific interpretation:

  • For liver, breast, colon, and lung carcinoma and melanoma, most studies report an inverse correlation with metastasis and poor survival

  • For neuroblastoma, hematopoietic malignancies, and osteosarcoma, high expression often correlates with poor outcomes

  • Gastric and ovarian carcinomas show inconsistent results

What statistical approaches are recommended for analyzing NME2 experimental data?

When analyzing experimental data related to NME2:

What are promising areas for further investigation of NME2 function?

Based on current knowledge gaps, several promising research directions for NME2 include:

• Conditional knockout models:

  • Development of tissue-specific and temporally controlled NME2 knockout systems

  • Would help understand specific functions in particular tissues or developmental stages

  • Could clarify role in pathological processes without developmental confounders

• Differentiation of NME1 vs. NME2 functions:

  • Development of tools to specifically target NME2 without affecting NME1

  • Investigation of unique signaling pathways and protein interactions for each isoform

  • Understanding differential regulation in various disease contexts

• Exploration of non-enzymatic functions:

  • NME proteins are involved in multiple physiological and pathological processes beyond their enzymatic roles

  • Further investigation into transcription factor activities and protein-protein interactions

  • Understanding nuclear roles of NME2 distinct from cytoplasmic functions

How can the "People Also Search For" approach be applied to optimize NME2 research?

The "People Also Search For" (PASF) approach from search engine optimization can be adapted to enhance NME2 research:

• Identifying related research questions:

  • PASF presents questions related to initial search queries

  • For NME2 research, this means identifying complementary research areas that frequently co-occur

  • This approach can reveal unexpected connections between NME2 and other biological pathways

• Content development strategy:

  • When developing research proposals or publications, incorporate related concepts that researchers commonly search for in connection with NME2

  • This increases the impact and visibility of research findings

• Keyword integration for research visibility:

  • Understanding search volume for NME2-related keywords provides insights into research interest

  • Incorporating these related keywords into research publications can increase visibility

  • This approach ensures research remains highly relevant to the scientific community

• Research prioritization:

  • By analyzing which NME2-related queries have higher search volumes, researchers can prioritize investigations that address questions of broader interest

  • This can help direct limited research resources toward areas with the greatest potential impact

What assay optimization strategies are recommended for NME2 research?

Developing reliable assays for NME2 research requires careful optimization:

• RED-MUP framework implementation:

  • Specifically designed for multi-well formats commonly used in biochemical and cell-based assays

  • Combines classical experimental designs orthogonally onto rectangular platforms

  • Facilitates DOE execution on standard laboratory platforms (96-, 384-, 1536-well formats)

  • Provides efficient tools for assay optimization, potentially reducing development time

• Signal-to-noise ratio improvement:

  • Focus on developing more informative assays rather than exclusively pursuing higher throughput

  • Improving signal-to-noise ratio increases precision for all compounds

  • Reduces probability of incorrectly classifying weakly active compounds as inactive

  • More compounds would give quantitative values, allowing for earlier SAR development

• Cell-based systems consideration:

  • Cell-based systems often provide more informative data than receptor-based assays

  • Can differentiate between agonists and antagonists where receptor-based assays cannot

  • Consider including these systems early in drug discovery processes to obtain more reliable hits

• Quality over quantity focus:

  • Prioritize quality of screened compounds and obtained data

  • Develop SAR and QSAR models at earlier stages

  • This approach may yield more successful leads than traditional high-throughput approaches