Recombinant Bovine Diacylglycerol O-acyltransferase 2-like protein 6 (DGAT2L6)

What is DGAT2L6 and how does it relate to the DGAT2 family?

DGAT2L6 (Diacylglycerol O-acyltransferase 2-like protein 6) is a member of the DGAT2 family of enzymes that catalyzes the final step in triacylglycerol (TAG) synthesis. The DGAT2 family enzymes catalyze the acyl-CoA-dependent formation of TAG using 1,2-diacyl-sn-glycerol (DAG) as an acyl acceptor, playing a crucial role in lipid metabolism . DGAT2L6 shares structural and functional similarities with DGAT2 but has distinct tissue distribution and potentially specialized functions in bovine lipid metabolism. Unlike some related enzymes that exhibit multiple activities (such as mammalian DGAT1, which can exhibit MGAT, WS, and retinol acyltransferase activities), DGAT2L6 appears to be more specialized in its catalytic function .

What metabolic pathways involve DGAT2L6 in bovine systems?

DGAT2L6, like other DGAT2 family members, participates in the acyl-CoA-dependent formation of TAG, a critical storage form of energy. Based on studies of related DGAT2 enzymes, DGAT2L6 likely functions in:

• Lipid droplet formation and accumulation in adipocytes

• TAG synthesis during adipocyte differentiation

• Integration with PPAR signaling pathways that regulate adipogenesis

• Interaction with other lipid metabolism enzymes like DGAT1, LPIN1, and GPAT4

While specific DGAT2L6 pathways are still being elucidated, research on DGAT2 shows it significantly affects TAG accumulation, adiponectin content, and lipid droplet formation in bovine preadipocytes .

What expression systems are optimal for recombinant bovine DGAT2L6 production?

For successful expression of recombinant bovine DGAT2L6, several expression systems can be employed:

Bacterial Expression Systems:

• E. coli systems using specialized strains (Rosetta, BL21(DE3)) that are designed for membrane protein expression

• Expression as fusion proteins with solubility enhancers (MBP, SUMO, GST)

Eukaryotic Expression Systems:

• Yeast systems (particularly S. cerevisiae strain H1246, which features disruptions of four genes encoding enzymes contributing to TAG production and has been extensively used in studies of recombinant DGAT enzymes)

• Insect cell systems (Sf9, High Five) using baculovirus vectors

• Mammalian expression systems (HEK293, CHO) for proper folding and post-translational modifications

Based on research with related proteins, adenoviral vectors have been successfully used for DGAT2 expression studies in bovine preadipocytes, with an optimal multiplicity of infection (MOI) of 100 and treatment with 5 μg/mL polybrene .

What purification strategy yields the highest purity and activity for DGAT2L6?

A multi-step purification strategy is recommended for obtaining high-purity, active DGAT2L6:

• Membrane Protein Extraction:

  • Detergent-based extraction (n-dodecyl-β-D-maltoside or CHAPS at 0.5-1%)

  • Careful optimization of detergent:protein ratios to maintain activity

• Initial Purification:

  • Immobilized metal affinity chromatography (IMAC) if His-tagged

  • Affinity chromatography using appropriate fusion tags

• Further Purification:

  • Ion exchange chromatography (IEX)

  • Size exclusion chromatography (SEC) for removing aggregates

• Activity Preservation:

  • Addition of glycerol (25-50%) in storage buffer

  • Inclusion of reducing agents (DTT or β-mercaptoethanol)

  • Optimization of pH (typically 7.4-8.0)

Commercial recombinant DGAT2L6 preparations are typically stored in Tris-based buffer with 50% glycerol for stability .

How can DGAT2L6 expression be verified and quantified?

Multiple complementary approaches should be used for verification and quantification:

Expression Verification:

• Western blotting using anti-DGAT2L6 or tag-specific antibodies

• RT-qPCR for mRNA expression levels (as demonstrated with DGAT2, where overexpression increased expression by >500 times compared to control)

• Enzyme activity assays measuring TAG formation

Quantification Methods:

• Bradford or BCA protein assays for total protein concentration

• Densitometry analysis of SDS-PAGE gels

• ELISA using specific antibodies

• Activity-based quantification using standard curves

For verification of successful gene delivery, GFP reporters can be used, as shown in DGAT2 studies where cell morphology and fluorescence expression were observed under a fluorescence microscope (BX53; Olympus) to confirm infection .

What enzymatic assays are used to measure DGAT2L6 activity?

Several assays can be employed to measure DGAT2L6 activity:

1. Radioactive Substrate Assay:

• Using [14C]-labeled acyl-CoA and unlabeled DAG

• Separation of labeled TAG by thin-layer chromatography (TLC)

• Quantification via scintillation counting

2. Fluorescent Assay:

• Using fluorescent DAG analogs (NBD-DAG)

• Monitoring formation of fluorescent TAG products

• Analysis by HPLC with fluorescence detection

3. Coupled Enzymatic Assay:

• Monitoring CoA release during the acyltransferase reaction

• Coupling with enzymes that utilize free CoA

• Spectrophotometric measurement

4. Mass Spectrometry-Based Assays:

• Direct measurement of TAG formation using LC-MS/MS

• Allows analysis of acyl-chain specificity

• Provides detailed product characterization

How do substrate specificity and enzyme kinetics of DGAT2L6 compare to other DGAT family members?

DGAT2L6 shares the fundamental catalytic function of DGAT2 family enzymes but with distinct kinetic properties:

Substrate Preferences:

• DGAT2L6 likely has specific preferences for particular acyl-CoA chain lengths and saturations

• Unlike DGAT1, which exhibits broader substrate specificity, DGAT2 family members (including DGAT2L6) may show greater selectivity

Kinetic Parameters:

• Typical Km values for acyl-CoA substrates range from 5-50 μM

• Km values for DAG substrates typically range from 20-100 μM

• Vmax and catalytic efficiency (kcat/Km) vary based on specific substrates

Comparative Table of DGAT Family Enzymes:

What factors influence DGAT2L6 enzyme activity in experimental settings?

Several factors can significantly impact DGAT2L6 activity measurements:

1. Detergent Concentration:

• Too high: may disrupt enzyme structure

• Too low: insufficient substrate solubilization

• Optimal: typically 0.1-0.3% for most non-ionic detergents

2. pH and Temperature:

• Optimal pH: typically 7.0-8.0

• Optimal temperature: typically 30-37°C

• Deviations significantly reduce activity

3. Divalent Cations:

• Mg2+ (1-5 mM) often enhances activity

• Zn2+ and Cu2+ may inhibit at higher concentrations

4. Reducing Agents:

• DTT or β-mercaptoethanol (1-5 mM) help maintain thiol groups

• Absence may lead to oxidation and activity loss

5. Substrate Presentation:

• DAG solubilization method affects availability

• Phospholipid composition of assay vesicles

• Acyl-CoA:DAG ratio influences reaction kinetics

How does DGAT2L6 expression correlate with bovine adipocyte differentiation?

Based on studies of the related DGAT2 enzyme, DGAT2L6 expression likely increases during bovine adipocyte differentiation. Research has shown that DGAT2 overexpression significantly enhances:

• Adipocyte differentiation marker expression (PPARγ, C/EBPα, C/EBPβ, SREBF1, and FABP4)

• Triacylglycerol (TAG) synthesis and accumulation

• Lipid droplet formation

• Adiponectin (ADP) content

Conversely, DGAT2 knockdown decreased lipid, TAG, and ADP contents in adipocytes and downregulated adipocyte differentiation markers . Given its homology to DGAT2, DGAT2L6 likely follows similar patterns during adipogenesis.

What signaling pathways interact with DGAT2L6 in bovine cells?

Based on transcriptomic analyses of DGAT2-overexpressing cells, DGAT2L6 likely interacts with several key metabolic pathways:

• PPAR Signaling Pathway:

  • Central to adipocyte differentiation

  • Upregulates expression of lipogenic genes

  • Reciprocal relationship with DGAT2 family enzymes

• AMP-activated Protein Kinase (AMPK) Pathway:

  • Energy sensing pathway affecting lipid metabolism

  • May regulate DGAT2L6 activity through phosphorylation

• Fatty Acid Synthesis Pathway:

  • Involves ACACA, FASN, and SCD genes

  • Provides substrates for DGAT2L6-catalyzed reactions

• Cholesterol Metabolism Pathway:

  • Cross-talk between sterol and glycerolipid metabolism

  • Shared regulatory elements with TAG synthesis genes

How does modulating DGAT2L6 expression affect bovine lipid metabolism?

Based on DGAT2 studies, modulation of DGAT2L6 would likely produce the following effects:

Overexpression Effects:

• Increased TAG synthesis and accumulation

• Enhanced lipid droplet formation

• Upregulation of adipogenic markers (PPARγ, C/EBPα)

• Elevated expression of lipid metabolism genes (DGAT1, LPIN1, GPAT4)

• Increased fatty acid synthesis gene expression (ACACA, FASN, SCD)

Knockdown Effects:

• Decreased TAG content and lipid droplet formation

• Downregulation of C/EBPβ, MGAT1, LPIN1, AGPAT4, and ACACA

• Potentially compensatory upregulation of SREBF1, FABP4, and other lipogenic genes

These metabolic changes highlight the potential importance of DGAT2L6 as a therapeutic target for modulating bovine adiposity and fat quality in agricultural applications.

How can CRISPR-Cas9 be optimized for DGAT2L6 gene editing in bovine cell models?

For effective CRISPR-Cas9 editing of bovine DGAT2L6:

gRNA Design Strategy:

• Target conserved catalytic domains for knockout studies

• Use multiple prediction tools to identify gRNAs with high on-target and low off-target scores

• Ensure bovine-specific sequence targeting by accounting for polymorphisms

Delivery Optimization:

• For bovine preadipocytes: nucleofection often provides higher efficiency than lipofection

• Ribonucleoprotein (RNP) complex delivery reduces off-target effects

• Lentiviral delivery for stable editing in difficult-to-transfect cells

Verification Protocol:

• Primary verification: T7 Endonuclease I assay or heteroduplex mobility assay

• Secondary confirmation: Sanger sequencing of the target region

• Functional validation: Western blot and enzyme activity assays

Efficiency Enhancement:

• Use high-fidelity Cas9 variants (eSpCas9, SpCas9-HF1)

• Include HDR templates for precise mutations or tagging

• Optimize culture conditions post-editing for clone survival

What methods are most effective for studying DGAT2L6 protein-protein interactions?

Several complementary approaches can be employed:

Proximity-Based Methods:

• BioID or TurboID: Fusion of a biotin ligase to DGAT2L6 to biotinylate proximal proteins

• APEX2: Peroxidase-based labeling of proteins in proximity to DGAT2L6

• Split-BioID: For detecting specific interaction partners

Traditional Interaction Methods:

• Co-immunoprecipitation (Co-IP) with specific antibodies

• Pull-down assays using tagged recombinant DGAT2L6

• Yeast two-hybrid screening using membrane-based systems

Advanced Biophysical Approaches:

• Förster resonance energy transfer (FRET) between DGAT2L6 and potential partners

• Bioluminescence resonance energy transfer (BRET)

• Surface plasmon resonance (SPR) for binding kinetics

Proteomic Integration:

• Crosslinking mass spectrometry (XL-MS)

• Protein correlation profiling

• Quantitative interactomics with SILAC or TMT labeling

How can metabolic flux analysis be applied to study DGAT2L6 function?

Metabolic flux analysis provides insights into DGAT2L6's role in lipid metabolism pathways:

Stable Isotope Labeling Approaches:

• [13C]-labeled fatty acids or glycerol to track substrate incorporation into TAG

• [2H]-labeled water to measure de novo lipogenesis rates

• Multiple isotope incorporation for pathway elucidation

Analytical Methods:

• Gas chromatography-mass spectrometry (GC-MS)

• Liquid chromatography-tandem mass spectrometry (LC-MS/MS)

• Nuclear magnetic resonance (NMR) spectroscopy

Computational Modeling:

• Flux balance analysis (FBA) incorporating DGAT2L6 reactions

• Metabolic control analysis (MCA) to determine flux control coefficients

• Kinetic modeling of TAG synthesis pathways

Experimental Design Considerations:

• Time-course experiments to capture dynamic changes

• Pulse-chase designs to determine TAG turnover rates

• Comparative analysis between wild-type and DGAT2L6-modulated cells

This approach can quantitatively determine how DGAT2L6 affects the distribution of carbon flux through competing lipid synthesis pathways.

How does bovine DGAT2L6 compare to orthologs in other species?

Bovine DGAT2L6 shares structural and functional characteristics with orthologs across species, but with notable differences:

Cross-Species Comparison:

• Human DGAT2L6: ~80-85% sequence identity, similar domain organization

• Mouse DGAT2L6: ~75-80% sequence identity, more divergent C-terminal region

• Porcine DGAT2L6: ~90% sequence identity, highly conserved functional domains

Functional Conservation:

• Core catalytic domains are typically highly conserved

• Species-specific differences in regulatory regions likely reflect adaptation to different metabolic requirements

• Substrate specificity may vary across species due to differences in available lipid substrates

Expression Pattern Divergence:

• Tissue expression profiles often differ between species

• Developmental regulation shows species-specific patterns

• Response to nutritional status varies across species

What evolutionary insights can be gained from DGAT2L6 sequence analysis?

Evolutionary analysis of DGAT2L6 reveals:

Phylogenetic Relationships:

• DGAT2L6 evolved from gene duplication events within the DGAT2 family

• More rapid evolution compared to the core DGAT2 gene suggests functional specialization

• Selective pressure analysis indicates regions under positive selection

Conserved Motifs:

• Key catalytic residues show high conservation across diverse species

• Transmembrane domains exhibit higher sequence variability while maintaining hydrophobicity

• Regulatory regions show greatest divergence, suggesting species-specific control mechanisms

Evolutionary Timeline:

• Emergence coincides with the evolution of complex adipose tissue in vertebrates

• Ruminant-specific adaptations correlate with specialized fat metabolism requirements

• Gene duplication events correlate with increasing metabolic complexity

How has DGAT2L6 function diverged in ruminant species?

DGAT2L6 shows specific adaptations in ruminants:

Ruminant-Specific Features:

• Potential specialization for processing unique fatty acid profiles derived from ruminal fermentation

• Adaptation to handle higher proportions of saturated fatty acids

• Possible role in metabolizing branch-chain fatty acids unique to ruminants

Functional Divergence:

• Expression patterns that align with ruminant-specific adipose depot development

• Potential involvement in developing marbling characteristics in beef cattle

• Regulatory elements responsive to ruminant-specific hormonal patterns

Agricultural Implications:

• Variations in DGAT2L6 sequences between cattle breeds correlate with meat quality traits

• Expression levels may predict intramuscular fat deposition

• Could serve as a genetic marker for selective breeding programs in cattle

What are common challenges in recombinant DGAT2L6 expression?

Researchers frequently encounter several challenges when expressing recombinant DGAT2L6:

Low Expression Yields:

• Hydrophobic transmembrane domains cause protein aggregation

• Potential toxicity to host cells due to membrane disruption

• Codon usage bias in heterologous expression systems

Solution Strategies:

• Use rare codon-optimized expression hosts

• Lower induction temperature (16-20°C)

• Co-express with molecular chaperones

• Utilize fusion partners that enhance solubility

Protein Misfolding:

• Improper disulfide bond formation

• Incorrect membrane insertion

• Aggregation in inclusion bodies

Purification Difficulties:

• Detergent selection critical for extraction without denaturation

• Multiple purification steps reduce yield

• Potential loss of activity during purification

Troubleshooting Table:

How can non-specific activity be distinguished from DGAT2L6-specific activity?

Distinguishing specific from non-specific activity requires multiple controls:

Negative Controls:

• Heat-inactivated enzyme preparations

• Preparations from cells expressing empty vector

• Enzyme preparation with site-directed mutations in catalytic residues

Competitive Inhibition:

• Use of DGAT2 family-specific inhibitors

• Substrate competition experiments

• Antibody-based inhibition of activity

Specificity Verification:

• Substrate specificity profiling compared to known DGAT profiles

• Kinetic parameter determination (Km, Vmax)

• Response to known activators and inhibitors of DGAT enzymes

Authentication Methods:

• Activity correlation with protein levels (Western blot)

• Activity recovery after immunodepletion

• Mass spectrometry-based activity-based protein profiling

What strategies can optimize solubility and stability of recombinant DGAT2L6?

Several approaches can improve DGAT2L6 solubility and stability:

Buffer Optimization:

• pH screening (typically 7.0-8.0 works best)

• Ionic strength adjustment (150-300 mM NaCl)

• Addition of glycerol (20-50%)

• Inclusion of reducing agents (1-5 mM DTT or TCEP)

Detergent Selection:

• Mild non-ionic detergents (DDM, LMNG, Brij-35)

• Detergent concentration optimization

• Mixed micelle systems with cholate or CHS

Stabilizing Additives:

• Lipids that mimic native environment (phosphatidylcholine)

• Osmolytes (sucrose, trehalose)

• Specific metal ions (determined empirically)

Engineering Approaches:

• Removal of non-essential hydrophobic regions

• Addition of solubility-enhancing tags (MBP, SUMO)

• Targeted surface mutations to increase hydrophilicity

• Consensus-based stability engineering

How should RNA-seq data for DGAT2L6 expression studies be analyzed?

RNA-seq analysis for DGAT2L6 studies requires specific considerations:

Preprocessing Pipeline:

• Quality control with FastQC

• Adapter trimming with Trimmomatic or Cutadapt

• Alignment to bovine reference genome using STAR or HISAT2

• Quantification with featureCounts or RSEM

Normalization Methods:

• TPM (Transcripts Per Million) for within-sample comparisons

• DESeq2 or EdgeR normalization for differential expression analysis

• Consider surrogate variable analysis for batch effect correction

Pathway Integration:

• Gene Set Enrichment Analysis (GSEA) with lipid metabolism gene sets

• Weighted Gene Co-expression Network Analysis (WGCNA)

• Integration with metabolomic data when available

Visualization Approaches:

• Heatmaps of DGAT2L6 with co-expressed genes

• Principal Component Analysis (PCA) plots

• Volcano plots highlighting lipid metabolism genes

Based on DGAT2 studies, RNA-seq analysis has successfully identified differentially expressed genes in overexpression experiments, revealing 208 DEGs (106 upregulated and 102 downregulated) that were mainly enriched in PPAR signaling and AMPK pathways, cholesterol metabolism, and fatty acid biosynthesis .

What statistical approaches are most appropriate for DGAT2L6 activity comparisons?

Statistical analysis should be tailored to the experimental design:

For Simple Comparisons:

• Student's t-test for two-group comparisons with normal distribution

• Mann-Whitney U test for non-parametric comparisons

• One-way ANOVA with appropriate post-hoc tests (Tukey, Bonferroni) for multiple groups

For Complex Designs:

• Two-way ANOVA for factorial designs (e.g., genotype × treatment)

• Mixed-effects models for repeated measures

• ANCOVA when controlling for covariates

Specialized Analyses:

• Enzyme kinetics: non-linear regression for Michaelis-Menten parameters

• Dose-response: four-parameter logistic regression

• Time-course: repeated measures ANOVA or growth curve modeling

Statistical Power Considerations:

• Minimum sample size of n=3-5 biological replicates

• Technical replicates to account for assay variability

• Power analysis based on expected effect size

In DGAT2 studies, statistical significance was typically established at p < 0.05, with appropriate multiple testing corrections when analyzing numerous genes or metabolites .

How can contradictory findings about DGAT2L6 function be reconciled?

Contradictory findings can be addressed through several approaches:

Methodological Reconciliation:

• Compare experimental conditions (cell types, assay conditions)

• Evaluate reagent specificity (antibodies, substrates)

• Assess expression systems and protein modifications

Biological Context Consideration:

• Developmental timing differences

• Tissue-specific effects

• Compensatory mechanisms and redundancy

Comprehensive Analysis Approach:

• Perform meta-analysis of available data

• Design experiments to directly test contradictory findings

• Incorporate multiple methodologies to address the same question

• Utilize systems biology approaches to place findings in pathway context

Reconciliation Framework:

• Develop testable hypotheses to explain discrepancies

• Consider partial redundancy with other DGAT family members

• Evaluate potential post-translational regulation mechanisms

• Assess context-dependent protein-protein interactions

When data appear contradictory, it is crucial to consider the possibility that DGAT2L6 may have context-dependent functions or be subject to complex regulatory mechanisms that vary across experimental systems.