Recombinant Bradyrhizobium japonicum UPF0311 protein blr7842 (blr7842)

What is the UPF0311 protein blr7842 in Bradyrhizobium japonicum?

UPF0311 protein blr7842 is an uncharacterized protein family member in Bradyrhizobium japonicum, a nitrogen-fixing bacterial symbiont of soybeans. As a UPF (Uncharacterized Protein Family) member, its precise biological function remains to be fully elucidated. The protein is encoded by the blr7842 gene locus in B. japonicum's genome. Based on sequence homology and structural prediction, it likely plays a role in cellular processes related to the bacterium's symbiotic relationship with leguminous plants, possibly in nitrogen fixation pathways or stress response mechanisms .

Why is recombinant expression of blr7842 important for research?

Recombinant expression of blr7842 allows researchers to produce sufficient quantities of the protein for structural and functional characterization. This approach is crucial because:

• Natural expression levels in B. japonicum are typically too low for extensive biochemical studies

• Recombinant systems enable the addition of purification tags for easier isolation

• Expression can be controlled and optimized to produce soluble, functional protein

• It facilitates site-directed mutagenesis studies to examine structure-function relationships

• Recombinant expression permits isotopic labeling for NMR studies and structural analysis

While B. japonicum proteins can be challenging to express recombinantly due to their specialized cellular environment, optimized expression systems have been developed to overcome these limitations .

What expression systems are most suitable for blr7842 production?

The selection of an appropriate expression system depends on research objectives and protein characteristics. For blr7842, several approaches can be considered:

For initial characterization, E. coli-based systems are typically employed due to their efficiency, though optimization may be necessary to prevent inclusion body formation . The PURE system provides advantages for specialized applications where high purity and control are required .

How can I optimize soluble expression of blr7842 when facing inclusion body formation?

When facing inclusion body challenges with blr7842 expression, a systematic approach to optimization is recommended:

• Temperature modulation: Lower the expression temperature to 15-18°C to slow protein synthesis and facilitate proper folding.

• Induction optimization: Reduce inducer concentration (e.g., 0.1-0.2 mM IPTG instead of 1 mM) and extend expression time.

• Co-expression with chaperones: Introduce plasmids encoding chaperone proteins like GroEL/GroES, DnaK/DnaJ/GrpE, or trigger factor to assist proper folding.

• Fusion partners: Express blr7842 as a fusion with solubility enhancers such as:

  • MBP (Maltose Binding Protein)

  • Thioredoxin

  • SUMO (Small Ubiquitin-like Modifier)

  • GST (Glutathione S-Transferase)

• Host strain selection: Test specialized strains like BL21(DE3)pLysS, Rosetta, or Origami for improved expression.

• Medium composition: Supplement with osmolytes (e.g., 1% glucose, 500 mM sorbitol) or amino acids that may stabilize protein structure.

These approaches can be combined and evaluated systematically in small-scale expression trials before scaling up . Recent systematic reviews indicate that the combination of multiple strategies often yields better results than single interventions for difficult-to-express proteins.

What structural prediction tools are most reliable for understanding blr7842 function?

For predicting and understanding the structure of UPF0311 protein blr7842, several computational approaches can be employed in a complementary manner:

• AlphaFold2: Currently provides the most accurate structural predictions with confidence scores (pLDDT) indicating reliability of each region. Similar to the approach used for other UPF proteins, AlphaFold2 can provide high-confidence structural models .

• Homology modeling: When sequence identity with known structures is >30%, tools like SWISS-MODEL, Phyre2, and MODELLER can provide reliable structural insights.

• Domain identification: InterPro, Pfam, and CDD databases help identify conserved domains that may indicate function.

• Structure-based function prediction: After obtaining a structural model, tools like ProFunc, COACH, and COFACTOR can predict binding sites and potential functions.

• Molecular dynamics simulations: MD simulations can provide insights into protein flexibility and potential conformational changes relevant to function.

For uncharacterized proteins like blr7842, combining these approaches with experimental validation is essential. The confidence metrics (like pLDDT scores in AlphaFold predictions) should guide the reliability assessment of different protein regions .

How can I investigate the role of blr7842 in B. japonicum symbiotic interactions?

To investigate blr7842's role in symbiotic interactions, a multi-faceted approach combining genetic, biochemical, and ecological methods is recommended:

• Gene knockout/knockdown studies:

  • Create deletion mutants using homologous recombination

  • Employ CRISPR-Cas9 for precise genome editing

  • Use antisense RNA approaches for partial suppression

• Complementation assays:

  • Reintroduce the wild-type gene in mutant strains

  • Test variant alleles to identify critical residues

• Expression pattern analysis:

  • Monitor transcription under symbiotic vs. free-living conditions

  • Use reporter gene fusions (GFP, LacZ) to track expression in planta

• Protein localization:

  • Fluorescent protein tagging for microscopy

  • Subcellular fractionation followed by immunoblotting

• Nodulation and nitrogen fixation assays:

  • Quantify nodule formation, morphology, and distribution

  • Measure acetylene reduction as an indicator of nitrogenase activity

  • Assess plant growth parameters under controlled conditions

• Interaction studies:

  • Yeast two-hybrid or pull-down assays to identify protein partners

  • Co-immunoprecipitation from nodule extracts

The correlation between protein synthesis patterns and nitrogen fixation activity may be particularly revealing, as previous studies with B. japonicum bacteroids demonstrated shifting metabolic priorities during nodule development .

What is the optimal protocol for purifying recombinant blr7842 protein?

The optimal purification protocol for recombinant blr7842 depends on the expression system and fusion tags used. A comprehensive approach typically includes:

Step 1: Cell Lysis and Initial Clarification

• Buffer composition: 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10% glycerol, 1 mM DTT, protease inhibitors

• Lysis methods: Sonication (6 × 30s pulses), French press (15,000 psi), or enzymatic lysis with lysozyme (1 mg/ml)

• Clarification: Centrifugation at 15,000 × g for 30 minutes at 4°C

Step 2: Affinity Chromatography (assuming His-tagged protein)

• Equilibration buffer: 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole

• Wash buffer: Same as equilibration with 20-40 mM imidazole

• Elution buffer: Same as equilibration with 250-300 mM imidazole

• Flow rate: 1 ml/min for binding, 0.5 ml/min for elution

Step 3: Tag Removal (if necessary)

• Dialysis to remove imidazole

• Protease treatment (TEV, Factor Xa, or PreScission depending on construct)

• Reverse affinity chromatography to remove cleaved tag

Step 4: Secondary Purification

• Ion exchange chromatography: Based on predicted pI of blr7842

• Size exclusion chromatography: Final polishing step and buffer exchange

Step 5: Concentration and Storage

• Concentrate to 1-5 mg/ml using 10 kDa MWCO concentrators

• Flash-freeze in liquid nitrogen and store at -80°C in small aliquots

Throughout purification, monitor protein using SDS-PAGE, Western blotting, and activity assays if available. For difficult-to-express proteins like those from B. japonicum, maintaining protein solubility during purification is critical, often requiring optimization of buffer components and careful temperature control .

What are the most effective methods for assessing blr7842 function in vitro?

Determining the function of an uncharacterized protein like blr7842 requires a systematic approach combining multiple biochemical and biophysical techniques:

• Enzymatic activity screening:

  • Test against substrate libraries relevant to bacterial metabolism

  • Employ coupled enzyme assays to detect product formation

  • Use colorimetric or fluorometric readouts for high-throughput screening

• Protein-protein interaction studies:

  • Pull-down assays with potential interacting partners

  • Surface Plasmon Resonance (SPR) for binding kinetics

  • Isothermal Titration Calorimetry (ITC) for thermodynamic parameters

  • Microscale Thermophoresis (MST) for solution-based interaction analysis

• Structural characterization:

  • Circular Dichroism (CD) spectroscopy for secondary structure composition

  • Small-Angle X-ray Scattering (SAXS) for envelope structure

  • X-ray crystallography or NMR for high-resolution structure

  • Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) for conformational dynamics

• Ligand binding assays:

  • Differential Scanning Fluorimetry (DSF) for thermal stability shifts

  • Fluorescence-based binding assays using intrinsic tryptophan fluorescence

  • NMR-based screening of fragment libraries

• Functional complementation:

  • Expression in bacteria with mutations in related pathways

  • Rescue experiments in bacterial or yeast systems

For proteins involved in nitrogen fixation or symbiosis, specific assays may include examination of interactions with plant-derived signals, redox partners, or components of the nitrogen fixation machinery . The synergistic use of these methods increases the likelihood of functional assignment.

How can I set up a ribosome display system for directed evolution of blr7842?

Setting up a ribosome display system for directed evolution of blr7842 requires careful design and optimization, especially when using the PURE system approach:

Step 1: Library Construction

• Design blr7842 gene variants through error-prone PCR, DNA shuffling, or site-directed mutagenesis

• Add T7 promoter and ribosome binding site at 5' end

• Remove stop codon and add spacer sequence (≥100 nucleotides) at 3' end

• Construct a library with 10^8-10^10 variants

Step 2: In vitro Transcription

• Linearize template DNA

• Perform transcription using T7 RNA polymerase

• Purify mRNA using commercial kits or phenol-chloroform extraction

Step 3: Translation Using PURE System

• Prepare PURE system components as described in published protocols

• Optimize magnesium concentration (typically 8-12 mM)

• Conduct translation at 37°C for 30-60 minutes

• Cool reaction to 4°C to stabilize ribosome-mRNA-protein complexes

Step 4: Selection

• Immobilize target ligand on surface (magnetic beads or microtiter plates)

• Incubate with ribosome complexes at 4°C

• Wash thoroughly to remove non-binders

• Elute bound complexes with EDTA (≥10 mM)

Step 5: Recovery and Amplification

• Extract mRNA from selected complexes

• Perform reverse transcription

• Amplify cDNA by PCR

• Use for next round of selection or analysis

The PURE system offers advantages for ribosome display due to its defined composition and reduced RNase activity, potentially increasing mRNA recovery rates by 12,000-fold compared to crude extracts . For blr7842, this approach could be particularly valuable for evolving variants with enhanced stability, solubility, or novel functions related to plant-microbe interactions.

How should I interpret contradictory results between in vitro and in planta studies of blr7842?

When facing contradictory results between in vitro and in planta studies of blr7842, a systematic analysis approach is necessary:

• Examine protein context:

  • In vitro studies typically use purified proteins lacking the cellular environment

  • In planta studies involve complex interactions with plant factors and bacterial components

  • Consider if post-translational modifications present in planta might be absent in vitro

• Evaluate experimental conditions:

  • pH, redox state, and ion concentrations often differ between in vitro and in planta environments

  • Temperature fluctuations in planta versus controlled in vitro conditions

  • Presence of metabolites that may act as cofactors or inhibitors

• Statistical robustness assessment:

  • Evaluate sample sizes and statistical power of both approaches

  • Consider biological versus technical replication strategies

  • Apply appropriate statistical tests for each data type

• Reconciliation strategies:

  • Design intermediate experiments bridging the gap (e.g., ex planta nodule extracts)

  • Use genetic approaches to test hypotheses in both contexts

  • Develop more sophisticated in vitro systems that better mimic the in planta environment

The developmental regulation of protein synthesis observed in B. japonicum bacteroids suggests that the timing of sampling in planta studies is critical for meaningful comparison with in vitro results.

What bioinformatic approaches can help identify potential functions of blr7842?

To identify potential functions of the uncharacterized blr7842 protein, an integrated bioinformatic workflow combining multiple approaches is recommended:

• Sequence-based analysis:

  • PSI-BLAST and HHpred for remote homology detection

  • Conservation analysis across bacterial species

  • Identification of functional motifs and domains

  • Analysis of genomic context and gene neighborhood

• Structure-based prediction:

  • AlphaFold2 or RoseTTAFold for structural modeling

  • Structural alignment with known protein structures

  • Binding site and pocket prediction

  • Electrostatic surface analysis

• Systems biology integration:

  • Co-expression network analysis

  • Protein-protein interaction prediction

  • Metabolic pathway mapping

  • Analysis of gene expression under various conditions

• Comparative genomics:

  • Phylogenetic profiling across bacterial species

  • Synteny analysis to identify conserved gene clusters

  • Presence/absence patterns in different Bradyrhizobium strains and other nitrogen-fixing bacteria

• Machine learning approaches:

  • Function prediction using ensemble methods

  • Feature extraction from sequence and structure

  • Integration of heterogeneous data sources

For UPF proteins like blr7842, these computational predictions should guide experimental design rather than being considered definitive. The presence of repeated sequences in B. japonicum and their potential roles in symbiotic gene regulation should be considered when analyzing genomic context. Data from protein synthesis patterns in bacteroids may provide additional context for functional predictions.

How can I assess the impact of blr7842 mutations on B. japonicum nitrogen fixation efficiency?

To comprehensively assess the impact of blr7842 mutations on nitrogen fixation efficiency, a multi-level experimental approach is necessary:

• Bacteroid-level assays:

  • Acetylene reduction assay (ARA) to measure nitrogenase activity

  • Hydrogen evolution measurements

  • ATP/ADP ratio determination as indicator of energetic status

  • Protein synthesis rate comparison (using radioactive amino acid incorporation as described in search result 2)

  • Leghemoglobin content analysis in infected nodules

• Nodule-level evaluations:

  • Nodule number, size, and distribution quantification

  • Histological examination of nodule structure

  • Electron microscopy of bacteroid morphology and arrangement

  • In situ localization of key symbiotic proteins

  • Metabolomic profiling of nodule contents

• Plant-level measurements:

  • Total plant nitrogen content (Kjeldahl method)

  • Plant biomass determination

  • Chlorophyll content and photosynthetic efficiency

  • Ureide content in xylem sap (for tropical legumes)

  • Comparative growth under N-limited versus N-sufficient conditions

• Field-scale assessments:

  • Multi-location trials with different soil types

  • Performance across different soybean varieties

  • Persistence of inoculant strains in soil over time

  • Competition with indigenous rhizobia populations

The correlation between protein synthesis patterns and nitrogen fixation activity observed in B. japonicum bacteroids suggests that examining this relationship in blr7842 mutants could be particularly informative.

How can knowledge about blr7842 contribute to improving agricultural sustainability?

Understanding the function and regulation of blr7842 in B. japonicum could contribute to sustainable agriculture through several applications:

• Enhanced biological nitrogen fixation:

  • If blr7842 plays a role in symbiotic efficiency, optimizing its expression or activity could enhance nitrogen fixation capabilities

  • Engineered B. japonicum strains with improved blr7842 functionality could reduce the need for synthetic nitrogen fertilizers

  • Selection of natural B. japonicum isolates with optimal blr7842 variants for specific agricultural environments

• Expanded host range applications:

  • Understanding how blr7842 contributes to host specificity might allow engineering of strains that can nodulate additional legume crops

  • Development of more versatile inoculants capable of benefiting multiple crops in rotation systems

• Improved stress tolerance:

  • If blr7842 is involved in stress responses, modifications could enhance rhizobial survival under challenging field conditions

  • Development of inoculants better adapted to climate change impacts including drought, heat, and soil acidification

• Precision agriculture integration:

  • Designer inoculants with optimized blr7842 variants targeted to specific soil conditions

  • Integration with crop genomics for matching optimal rhizobial strains to specific soybean varieties

• Biomonitoring applications:

  • Using blr7842 as a marker for tracking inoculant persistence and performance in field conditions

  • Development of molecular tools to assess symbiotic potential of soil

The USDA-ARS research on novel B. japonicum isolates demonstrates the practical agricultural value of identifying and characterizing superior nitrogen-fixing strains . Understanding the molecular details of proteins like blr7842 provides the foundation for rational improvement strategies rather than relying solely on empirical selection approaches.

What are the challenges in translating blr7842 research findings from laboratory to field applications?

Translating laboratory findings about blr7842 to field applications faces several significant challenges that must be addressed systematically:

• Environmental complexity:

  • Laboratory conditions poorly mimic soil heterogeneity and fluctuating field environments

  • Interactions with diverse soil microbiomes can alter gene expression and protein function

  • Solution: Conduct intermediate studies in controlled soil mesocosms before field trials

• Scale and stability issues:

  • Maintaining genetic stability of engineered strains in field conditions

  • Ensuring consistent expression of blr7842 variants under variable field conditions

  • Solution: Develop chromosomal integration rather than plasmid-based systems for stable expression

• Competition with indigenous strains:

  • Laboratory-optimized strains often show reduced competitive ability against adapted soil populations

  • Solution: Incorporate competitive fitness assessments early in development pipeline

• Regulatory and biosafety considerations:

  • Genetically modified B. japonicum strains face regulatory hurdles

  • Need to demonstrate environmental safety and genetic containment

  • Solution: Focus on natural variants or non-GMO approaches when possible

• Host plant genotype interactions:

  • Variation in plant genotypes can significantly affect symbiotic performance

  • Solution: Test across diverse germplasm and consider plant breeding for enhanced symbiotic capacity

• Practical delivery systems:

  • Maintaining viability in inoculant formulations

  • Ensuring effective nodulation when applied in farming systems

  • Solution: Develop improved formulations with extended shelf life and field stability

The research on nitrogen-fixing bacterial isolates by USDA-ARS scientists illustrates the importance of testing across multiple soybean varieties to ensure broad applicability . Their approach of isolating and characterizing naturally occurring strains represents one strategy to overcome regulatory hurdles while still achieving agricultural benefits.

How can I design experiments to validate computational predictions about blr7842 function?

To rigorously validate computational predictions about blr7842 function, a structured experimental approach is needed:

• Prediction classification and prioritization:

  • Categorize predictions based on confidence scores and supporting evidence

  • Prioritize testing predictions with strongest computational support

  • Design experiments to test multiple predictions simultaneously when possible

• Structure-based validation:

  • For predicted binding sites: Conduct site-directed mutagenesis of key residues

  • For structural features: Verify using biophysical methods (CD, SAXS, limited proteolysis)

  • For conformational changes: Apply HDX-MS or FRET-based sensors

• Interaction validation:

  • For predicted protein partners: Conduct co-immunoprecipitation, Y2H, or BACTH assays

  • For ligand binding: Employ thermal shift assays, ITC, or SPR

  • For DNA/RNA interactions: Use EMSAs or RNA immunoprecipitation

• Functional validation:

  • For enzymatic activity: Design activity assays with predicted substrates

  • For regulatory roles: Assess expression patterns in response to predicted stimuli

  • For pathway involvement: Perform metabolic profiling in mutant strains

• In vivo significance:

  • Create knockout, knockdown, and complementation strains

  • Develop reporter fusions to monitor in vivo activity

  • Perform competition assays to assess fitness contributions

When validating computational predictions, it's essential to design experiments that can falsify the hypothesis, not just support it. The systematic approach used in protein synthesis studies of B. japonicum bacteroids provides a model for designing well-controlled experiments with appropriate quantification .