GMPR Human

What is GMPR and what is its function in human cells?

GMPR (guanosine monophosphate reductase) is a cytosolic enzyme required for maintaining the cellular balance of adenine and guanine nucleotides . It catalyzes the conversion of GMP (guanosine monophosphate) to IMP (inosine-5′-monophosphate), which is a key step in nucleotide metabolism. This enzyme plays a critical role in cellular function by regulating nucleotide pools that affect DNA replication, RNA synthesis, and energy metabolism.

What are the structural characteristics of human GMPR?

Human GMPR enzyme has specific substrate-binding and product-binding pockets that interact with GMP or IMP during catalysis. Molecular dynamics simulation studies have identified 19 water sites within the enzyme that are responsible for its catalytic activity and structural integrity . The enzyme functions with the cofactor nicotinamide adenine dinucleotide phosphate (NDP), which participates in the reaction mechanism. The water molecules at the binding sites are organized in specific categories that stabilize different components of the substrate or product molecules.

How is the GMPR gene associated with human diseases?

GMPR has been implicated in several human diseases. Recent research has identified GMPR as the 19th locus associated with progressive external ophthalmoplegia (PEO), a late-onset Mendelian mitochondrial disorder characterized by paresis of the extraocular muscles, ptosis, and skeletal-muscle restricted multiple mitochondrial DNA deletions . Additionally, rare missense variants in GMPR have been associated with reticulocyte counts in human blood cell trait studies , suggesting a potential role in erythrocyte development.

What is the role of GMPR in mitochondrial DNA maintenance disorders?

A novel heterozygous c.547G>C variant in GMPR has been identified in a patient who presented with PEO in the seventh decade of life . This variant causes aberrant splicing, resulting in decreased GMPR protein levels in patient skeletal muscle, proliferating and quiescent cells. The mutation is associated with subtle changes in nucleotide homeostasis protein levels and disturbed mitochondrial DNA maintenance in skeletal muscle. Oxidative histochemistry revealed cytochrome c oxidase-deficient fibers and occasional ragged red fibers showing subsarcolemmal mitochondrial accumulation, while molecular studies identified the presence of multiple mtDNA deletions . These findings suggest that GMPR plays a crucial role in maintaining mitochondrial DNA integrity, likely through its function in regulating nucleotide pools.

How do water molecule dynamics affect GMPR enzyme function?

Water molecules play a critical role in the function of human GMPR enzyme. The 19 identified water sites can be categorized according to their functions:

These water molecules mediate specific interactions, such as GMP(N1)···W2···Asp129/Asn158, IMP(N1)···W3···Glu289, and IMP(O6)···W10···Ser270 . These interactions are thermodynamically significant and could potentially be targeted in drug design approaches.

What is the potential of GMPR as a drug target for leukemia?

GMPR, particularly the GMPR-II isoform, has been identified as a potential target for the design of isoform-specific antileukemic agents . The GMP-binding site in cancerous protein (PDB ID: 2C6Q) is significantly expanded and has different dynamics compared to the normal protein (PDB ID: 2BLE). The unique water-mediated interactions at the binding sites provide opportunities for rational drug design approaches.

The water displacement method is particularly promising for drug design targeting GMPR-II in chronic myelogenous leukemia. The water finding probability, relative interaction energy, entropy, and topologies of three key water sites (W2, W3, and W10) have been found to be thermodynamically acceptable for this approach . Designing compounds that can displace these specific water molecules while mimicking their properties could lead to effective inhibitors of GMPR-II.

What is GMPR normalization method and when is it used?

GMPR (Geometric Mean of Pairwise Ratios) is a robust normalization method developed specifically for zero-inflated sequencing data, such as microbiome sequencing data . Normalization is a critical first step in microbiome sequencing data analysis, used to account for variable library sizes that can confound comparisons between samples. The GMPR method is particularly useful when dealing with datasets that contain a large number of zero counts (zero-inflation), which is common in microbiome studies.

How does GMPR address zero-inflation in microbiome sequencing data?

GMPR addresses zero-inflation through a strategic approach that leverages pairwise comparisons between samples rather than requiring OTUs (Operational Taxonomic Units) to be present across all samples . While severe zero-inflation means that only a small number of OTUs might be shared across all samples, for every pair of samples, they usually share many OTUs (e.g., 83 OTUs on average for COMBO sample pairs ).

The method works by:

• Conducting pairwise comparisons between samples, focusing only on the common OTUs observed in both samples

• For each pair of samples, calculating the abundance ratio based on these common OTUs

• Synthesizing these pairwise abundance ratios using a geometric mean to obtain the final size factor

This pair analysis strategy utilizes far more information than other methods like RLE and TMM, which are restricted to a small subset of OTUs .

What are the basic principles behind GMPR calculation?

The GMPR normalization method follows these calculation steps :

• For each pair of samples, identify the common OTUs (those with non-zero counts in both samples)

• Calculate the ratio of counts for each common OTU between the two samples

• Compute the geometric mean of these ratios to obtain a pairwise size factor

• For each sample, calculate the geometric mean of all its pairwise size factors with other samples to obtain the final size factor

This approach is robust because it utilizes information from all OTUs that are present in at least two samples, and the geometric mean is less sensitive to extreme values than the arithmetic mean.

How does GMPR compare to other normalization methods for microbiome data?

GMPR has been extensively compared to other normalization methods through simulation studies and analyses of real datasets . The comparison results are summarized in the following table:

In paired Wilcoxon signed rank tests, GMPR achieved significantly better ranking than other methods (p-value < 0.05) .

How robust is GMPR to differential and outlier OTUs?

GMPR has demonstrated high robustness to both differential and outlier OTUs in simulation studies . In "fixed" perturbation scenarios (where a fixed percentage of OTUs are differentially abundant), GMPR maintained excellent performance even when the perturbation was strong. In "random" perturbation scenarios (where sample-specific outlier OTUs are introduced), GMPR again outperformed other methods, especially when the perturbation was strong.

This robustness is attributed to GMPR's pairwise comparison strategy, which utilizes far more information than methods restricted to a small subset of OTUs. The performance advantage of GMPR becomes more pronounced as the zero percentage increases in the data, making it particularly valuable for highly sparse microbiome datasets .

What impact does GMPR have on differential abundance analysis?

GMPR normalization significantly improves the performance of differential abundance analysis (DAA) in microbiome studies . Simulation studies have shown that the robustness of GMPR translates into better false positive control (reduced Type I error) and higher statistical power (reduced Type II error).

This improved performance in DAA is observed because GMPR more accurately recovers the "true" library size in the presence of differentially abundant OTUs or sample-specific outlier OTUs. This leads to more reliable normalization factors, which in turn yield more accurate estimates of differential abundance .

What methodologies are used to study GMPR enzyme activity in human tissues?

Studying GMPR enzyme activity in human tissues involves multiple methodological approaches:

• Oxidative histochemistry to detect cytochrome c oxidase-deficient fibers and ragged red fibers in skeletal muscle

• Molecular studies to identify the presence of multiple mtDNA deletions

• Diagnostic exome sequencing to identify variants in the GMPR gene

• Western blotting to assess GMPR protein levels in patient tissues

• Cell culture studies using proliferating and quiescent cells to examine the effects of GMPR deficiency

• Molecular dynamics simulations to study the structural and functional characteristics of GMPR enzyme

What are the methodological steps to implement GMPR normalization?

Implementing GMPR normalization involves the following methodological steps:

• Data preparation: Organize your microbiome sequencing data as a count matrix where rows represent OTUs and columns represent samples.

• Pairwise ratio calculation:

  • For each pair of samples, identify the set of common OTUs that have non-zero counts in both samples

  • For each common OTU, calculate the ratio of counts between samples

  • Compute the geometric mean of these ratios to obtain the pairwise size factor

• Size factor determination:

  • For each sample, calculate the geometric mean of all its pairwise size factors

  • Optionally, divide all raw size factors by their median to center them around 1

• Normalization application:

  • Divide the original counts in each sample by its corresponding size factor

  • The resulting normalized counts can be used for downstream analyses

Researchers can implement GMPR using the R package available at https://github.com/jchen1981/GMPR .

What are emerging areas of GMPR enzyme research?

Future research on GMPR enzyme could focus on:

• Further characterization of the relationship between GMPR variants and mitochondrial disorders

• Development of potential therapeutic approaches targeting GMPR for leukemia and other diseases

• Investigation of the regulatory mechanisms controlling GMPR expression in different tissues

• Exploration of GMPR's role in blood cell development, particularly in relation to the observed association with reticulocyte counts

• Detailed structural studies to fully understand the catalytic mechanism and water dynamics at binding sites

How might GMPR normalization methods evolve?

The GMPR normalization method could be extended in several ways:

• Application to other types of zero-inflated data beyond microbiome sequencing

• Development of computationally efficient implementations for very large datasets

• Integration with other bioinformatics tools for comprehensive microbiome analysis

• Adaptation of the method for single-cell sequencing data which also suffers from zero-inflation

• Comparative studies with newer normalization methods as they emerge

Both areas of GMPR research offer rich opportunities for further investigation, with potential impacts on understanding human biology and improving analytical approaches for complex biological data.