DHRS4 Human

What is the primary enzymatic function of DHRS4 in humans?

DHRS4 functions primarily as a reductase that catalyzes the conversion of all-trans-retinal and 9-cis retinal to their corresponding retinol forms. It can also catalyze the reverse reaction (oxidation of all-trans-retinol) when utilizing NADP as a cofactor, though with lower efficiency. Additionally, it reduces alkyl phenyl ketones and alpha-dicarbonyl compounds with aromatic rings, including pyrimidine-4-aldehyde, 3-benzoylpyridine, 4-benzoylpyridine, menadione, and 4-hexanoylpyridine. Notably, DHRS4 shows no activity toward aliphatic aldehydes and ketones .

How is DHRS4 structurally and functionally related to other SDR family members?

DHRS4 belongs to the short-chain dehydrogenases/reductases (SDR) family, sharing structural and functional similarities with DHRS3 and DHRS9. These proteins all participate in retinoid metabolism, with overlapping yet distinct substrate specificities. DHRS3 and DHRS4 show particularly high functional overlap, as both catalyze the reduction of all-trans retinal to all-trans retinol in the presence of NADPH . This functional redundancy suggests potential compensatory mechanisms within biological systems, which should be considered when designing gene knockout experiments.

What are the recommended methods for detecting DHRS4 expression in tissue samples?

For robust detection of DHRS4 expression, a multi-platform approach is recommended:

• Western blotting: Effective for quantifying protein levels in tissue lysates

• Immunofluorescence: Useful for visualizing cellular localization (primarily cytoplasmic in neurons)

• qRT-PCR: For mRNA expression analysis

When performing immunofluorescence, co-staining with neuronal markers like NeuN can help identify cell-specific expression patterns. For example, in SOD1^G93A ALS mouse models, DHRS4 showed increased cytoplasmic expression in neurons compared to wild-type mice . For protein extraction from spinal cord tissue, homogenization in RIPA buffer with protease inhibitors, followed by centrifugation at 12,000g for 15 minutes at 4°C, yields optimal results for subsequent Western blot analysis.

How can weighted gene co-expression network analysis (WGCNA) be applied to identify DHRS4-associated gene modules?

WGCNA is a powerful bioinformatics approach for identifying gene modules correlated with DHRS4 expression or disease progression. When applying WGCNA to DHRS4 research, consider the following methodological steps:

• Data preprocessing: Normalize expression data and filter low-quality samples

• Network construction: Select an appropriate soft-thresholding power to achieve scale-free topology

• Module identification: Use hierarchical clustering and dynamic tree cutting

• Correlation analysis: Correlate module eigengenes with clinical traits (e.g., DHRS4 expression, disease status)

In ALS research, WGCNA identified modules where DHRS4 was positively correlated with disease progression. The red module in GSE52946 data showed significant correlation with both DHRS4 expression and ALS patient status . This approach revealed DHRS4's potential association with immune-related pathways, such as neutrophil degranulation and macrophage activation.

What approaches should be used to validate DHRS4 as a potential biomarker in neurodegenerative diseases?

Validating DHRS4 as a biomarker requires a comprehensive multi-stage approach:

• Discovery phase:

  • Differential expression analysis across disease stages

  • ROC curve analysis to determine diagnostic potential

  • Correlation with clinical parameters

• Validation phase:

  • Cross-validation in multiple independent cohorts

  • Longitudinal studies tracking expression changes

  • Comparison with established biomarkers

• Functional validation:

  • Knockdown/overexpression studies

  • Pathway analysis of downstream effects

In ALS research, DHRS4 validation included analysis across multiple datasets (GSE52946, GSE10953, GSE43879, GSE46298) showing consistent upregulation with disease progression . Experimental validation using Western blotting and immunofluorescence confirmed these findings. ROC curve analysis demonstrated high diagnostic value (AUC>0.7) when combined with related genes (C1Q complex, C3, ITGB2) .

How can protein-protein interaction (PPI) networks be constructed to understand DHRS4's molecular context?

PPI network analysis for DHRS4 should follow these methodological steps:

• Data collection:

  • Extract DHRS4 co-expressed genes from relevant modules

  • Include known interactors from databases like STRING

• Network construction:

  • Upload gene lists to STRING database (http://www.string-db.org/)

  • Set appropriate confidence score thresholds (recommended: 0.4-0.7)

  • Include experimental and database interaction sources

• Network analysis:

  • Identify central nodes through centrality measures

  • Perform cluster analysis to identify functional modules

  • Conduct enrichment analysis of network components

In DHRS4 research, PPI analysis revealed interactions with complement cascade components (C1QA, C1QB, C1QC, C3) and integrin subunit beta 2 (ITGB2), suggesting immune-mediated functions . The network visualization showed these components occupying central positions, highlighting their potential importance in DHRS4-related pathways.

What evidence supports DHRS4 as a biomarker for ALS progression?

Multiple lines of evidence support DHRS4 as a potential biomarker for ALS progression:

• Transcriptomic evidence:

  • Significant upregulation in ALS patient spinal cord compared to healthy controls

  • Progressive increase in expression with disease advancement in SOD1^G93A mice

  • Consistent findings across multiple independent datasets (GSE52946, GSE10953, GSE43879, GSE46298)

• Proteomic evidence:

  • Increased protein expression verified by Western blotting

  • Enhanced immunoreactivity in motor neurons of ALS models

• Functional relevance:

  • Association with complement cascade activation

  • Correlation with immune cell infiltration

Experimental validation showed that DHRS4 expression increases from pre-onset (60-70 days) through onset (90-100 days) to progression (120-130 days) stages in SOD1^G93A mice, paralleling the clinical course of disease . This temporal pattern of expression makes DHRS4 particularly valuable as a progression rather than simply a diagnostic biomarker.

How does DHRS4 interact with the immune system in neurodegenerative conditions?

DHRS4's interaction with the immune system in neurodegenerative diseases involves multiple mechanisms:

Methodologically, these associations were established through immune infiltration analysis using the ImmuCellAI tool (http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/), followed by Spearman's correlation analysis between DHRS4 expression and immune cell abundance estimates . This suggests that monitoring DHRS4 expression may provide insights into neuroimmune interactions in disease states.

What experimental models are most appropriate for studying DHRS4 in neurodegeneration?

For studying DHRS4 in neurodegeneration, multiple complementary models are recommended:

• In vivo models:

  • SOD1^G93A transgenic mice (most validated for ALS studies)

  • Age-matched cohorts at pre-onset, onset, and progression stages

  • Conditional DHRS4 knockout/overexpression models

• In vitro models:

  • Primary motor neuron cultures

  • iPSC-derived motor neurons from patients and controls

  • Microfluidic chambers for axonal transport studies

• Ex vivo models:

  • Organotypic spinal cord slices

  • Patient-derived tissue samples

The SOD1^G93A mouse model provides several advantages for DHRS4 research, including well-characterized disease progression, similar pathology to human ALS, and the ability to study pre-symptomatic stages . When using this model, proper genotyping via PCR of tail DNA is essential, and standard housing conditions (12h light/dark cycle, 20°C–27°C, 40%–50% humidity) should be maintained to ensure reproducibility.

What are the key protein interaction partners of DHRS4 and how should they be studied?

DHRS4 interacts with several key protein partners that contribute to its biological functions:

To study these interactions effectively:

• Co-immunoprecipitation (Co-IP): Use anti-DHRS4 antibodies to pull down protein complexes

• Proximity ligation assay (PLA): For detecting protein-protein interactions in situ

• Bimolecular fluorescence complementation (BiFC): For visualizing direct interactions

• FRET/FLIM: For analyzing dynamic interactions in living cells

Validation should include both overexpression systems and endogenous protein detection to avoid artifacts. When studying the DHRS4-DHRS3 synergistic relationship, consider analyzing both proteins simultaneously, as they share functional overlap in retinoid metabolism .

How does DHRS4 contribute to retinoid metabolism and what methodologies are best for studying this pathway?

DHRS4 plays a multifaceted role in retinoid metabolism:

• Primary function: Reduces all-trans-retinal and 9-cis retinal to their corresponding retinol forms

• Secondary function: Can oxidize all-trans-retinol with NADP as cofactor (lower efficiency)

• Pathway context: Works alongside DHRS3, DHRS9, and ALDH1A1 in retinoid interconversion

For studying DHRS4's role in retinoid metabolism, these methodologies are recommended:

• Enzymatic activity assays:

  • Spectrophotometric measurement of NAD(P)H consumption

  • HPLC analysis of retinoid conversion

  • LC-MS/MS for comprehensive retinoid profiling

• Cell-based assays:

  • Retinoid-responsive reporter gene assays

  • Metabolic labeling with deuterated retinoids

  • Live-cell imaging with fluorescent retinoid analogs

• In silico approaches:

  • Molecular docking to predict substrate binding

  • Pathway flux analysis for system-level understanding

When designing experiments, consider that DHRS4 shows substrate specificity for aromatic aldehydes and ketones but not aliphatic compounds . Control experiments should include both positive controls (known substrates like all-trans-retinal) and negative controls (aliphatic aldehydes).

How might single-cell transcriptomics advance our understanding of DHRS4 in tissue-specific contexts?

Single-cell transcriptomics offers powerful new approaches for DHRS4 research:

• Cell type-specific expression:

  • Identification of DHRS4-expressing cell populations

  • Characterization of expression heterogeneity within cell types

  • Correlation with cell state markers

• Methodological considerations:

  • Tissue dissociation protocols that preserve cellular integrity

  • FACS-based enrichment of neuronal populations

  • Computational analysis pipelines sensitive to low-abundance transcripts

• Integration with spatial transcriptomics:

  • Combined single-cell and spatial analysis to map DHRS4-expressing cells

  • Correlation with anatomical features and pathological hallmarks

Current evidence suggests DHRS4 is predominantly expressed in neuronal cytoplasm in the spinal cord . Single-cell approaches could reveal whether specific neuronal subtypes (e.g., motor neurons vs. interneurons) show differential expression or regulation, potentially explaining selective vulnerability in diseases like ALS.

What are the current challenges in developing therapeutic approaches targeting DHRS4 or its pathways?

Developing therapeutic approaches targeting DHRS4 faces several challenges:

• Target validation challenges:

  • Determining whether DHRS4 upregulation is causative or reactive in disease

  • Understanding compensatory mechanisms involving other SDR family members

  • Clarifying tissue-specific roles and potential off-target effects

• Methodological approaches:

  • Conditional knockout models to assess temporal requirements

  • Pharmacological inhibition with selective compounds

  • AAV-mediated gene therapy approaches

• Translational considerations:

  • Development of blood-based assays for monitoring DHRS4 activity

  • Identification of DHRS4 pathway modulators with BBB penetrance

  • Establishment of human iPSC-based screening platforms

The overlapping functions of DHRS4 with DHRS3 and other SDR family members present both a challenge and opportunity - while redundancy may limit efficacy of single-target approaches, it also suggests combination strategies targeting multiple pathway components might be more effective .

How can computational approaches be leveraged to predict novel functions and interactions of DHRS4?

Computational approaches offer powerful tools for predicting novel DHRS4 functions:

• Structure-based approaches:

  • Homology modeling based on SDR family crystal structures

  • Molecular dynamics simulations to identify conformational changes

  • Virtual screening for novel substrates and inhibitors

• Network-based predictions:

  • Network expansion beyond first-degree interactions

  • Inference of indirect functional associations through guilt-by-association

  • Identification of disease modules containing DHRS4

• Implementation methodology:

  • Begin with high-confidence protein structure prediction using AlphaFold2

  • Perform substrate docking using AutoDock Vina or similar tools

  • Validate predictions with focused biochemical assays

Current network analyses have already identified unexpected connections between DHRS4 and complement cascade components . Expanding these approaches could reveal additional non-canonical functions beyond retinoid metabolism, potentially explaining DHRS4's role in complex diseases like ALS where multiple pathways may be dysregulated simultaneously.