Recombinant Mannheimia succiniciproducens Protein hfq (hfq)

What is the basic function of Hfq in Mannheimia succiniciproducens?

Hfq in M. succiniciproducens functions as an RNA chaperone protein that facilitates interactions between small non-coding RNAs (sRNAs) and their target messenger RNAs (mRNAs). By promoting these RNA-RNA interactions, Hfq affects translation efficiency and transcript turnover rates, contributing to complex post-transcriptional regulatory networks . While the specific functions of Hfq in M. succiniciproducens are still being elucidated, it likely plays key roles in stress response regulation and metabolic adaptation similar to its homologs in other bacteria, particularly given M. succiniciproducens' importance in industrial succinic acid production .

What genomic and proteomic data is available for M. succiniciproducens Hfq?

According to KEGG GENOME data, M. succiniciproducens MBEL55E has a circular chromosome of 2,314,078 nucleotides, encoding 2,384 protein genes and 79 RNA genes . The proteome of M. succiniciproducens has been analyzed using 2-DE (two-dimensional gel electrophoresis) and MS (mass spectrometry), establishing a proteome reference map that identified over 200 proteins . While specific genomic coordinates for the Hfq gene in M. succiniciproducens are not explicitly mentioned in the search results, Hfq is likely among the identified proteins given its widespread conservation across bacterial species . Comparative proteome profiling across different growth phases has revealed valuable information about physiological changes during growth, which could include Hfq-regulated pathways .

What are the optimal conditions for expressing recombinant M. succiniciproducens Hfq in E. coli?

For optimal expression of recombinant M. succiniciproducens Hfq in E. coli, researchers should consider the following methodological approach:

• Vector selection: A pT-based expression vector with NdeI and BamHI restriction sites has been successfully used for Hfq homologs, where the coding sequence is PCR-amplified with primers that generate an NdeI site 5′ of the hfq gene and a BamHI site 3′ of the gene .

• Expression strain: BL21(DE3) or similar E. coli strains designed for T7 promoter-driven expression systems are recommended.

• Induction conditions: Based on protocols for other Hfq proteins, induction with 0.5-1 mM IPTG at OD600 of 0.6-0.8, followed by growth at lower temperatures (16-25°C) for 4-16 hours can improve soluble protein yield .

• Verification: Western blot using anti-E. coli Hfq antibody can verify expression, as antibodies against E. coli Hfq have shown cross-reactivity with Hfq from related species .

The full-length, non-tagged Hfq can be purified using variants of previously described methods, which typically involve cell lysis, ammonium sulfate precipitation, and chromatography steps .

What purification strategy yields the highest purity for M. succiniciproducens Hfq?

A multi-step purification strategy for obtaining high-purity M. succiniciproducens Hfq should follow established protocols for Hfq purification with adaptations:

• Initial clarification: After cell lysis (sonication or French press), centrifugation at 15,000 × g for 30 minutes removes cell debris.

• Ammonium sulfate precipitation: Precipitation with ammonium sulfate (typically at 70-80% saturation) can concentrate Hfq and remove some contaminants.

• Chromatography steps:

  • Heat treatment (80°C for 10 minutes) exploits Hfq's heat stability

  • Anion exchange chromatography (Q Sepharose)

  • Hydrophobic interaction chromatography

  • Size exclusion chromatography for final polishing

• Assessment of purity: SDS-PAGE with Coomassie staining and isoelectric focusing can verify purity, with expected IEF profile comparable to that of native Hfq but lacking the Hfq-SPAM (small protein-associated molecules) forms M2 and M3 seen in cellular extracts .

The purified recombinant protein should be stored in a buffer containing 20 mM Tris-HCl (pH 8.0), 150 mM NaCl, 5% glycerol to maintain stability, with optional addition of 1 mM DTT to prevent oxidation of cysteine residues.

How can the activity of purified recombinant M. succiniciproducens Hfq be verified?

To verify the activity of purified recombinant M. succiniciproducens Hfq, several complementary approaches can be implemented:

• RNA binding assays:

  • Electrophoretic Mobility Shift Assay (EMSA): Incubate purified Hfq with in vitro-transcribed sRNAs (200 ng) and varying concentrations of recombinant Hfq (0.65 μM–5 μM) in reaction buffer (10 mM Tris-HCl, pH 7.4, 6% glycerol, 50 μg/ml BSA, and 0.75 μg Baker's yeast tRNA). After 30 minutes at room temperature, the samples should be electrophoresed on a 6% native polyacrylamide gel and stained with nucleic acid stain to visualize RNA band shifts indicative of Hfq binding .

  • Fluorescence-based assays: Microscale thermophoresis can evaluate the binding affinity of the purified Hfq to fluorescently labeled RNA oligonucleotides .

• Functional complementation: Introduction of the purified M. succiniciproducens Hfq into Δhfq E. coli strains should rescue phenotypes associated with Hfq deficiency, such as growth defects under stress conditions or altered sRNA-mediated regulation .

• In vitro RNA annealing assay: Measure the ability of purified Hfq to facilitate the annealing of complementary RNA strands using fluorescence resonance energy transfer (FRET) or gel-based methods.

The expected RNA binding pattern would show increased band shifts proportional to Hfq concentration, with high-affinity binding to A/U-rich sequences typical of sRNA binding sites .

What techniques are most effective for studying M. succiniciproducens Hfq-RNA interactions?

For comprehensive characterization of M. succiniciproducens Hfq-RNA interactions, researchers should employ a combination of the following techniques:

• Biochemical approaches:

  • RNA co-immunoprecipitation (RIP) with tagged Hfq followed by RNA-seq to identify bound RNA species

  • EMSA to determine binding affinities and complex formation

  • Filter binding assays for quantitative assessment of Hfq-RNA interactions

  • RNA footprinting to map Hfq binding sites on target RNAs

• Biophysical methods:

  • Microscale thermophoresis for determining binding constants

  • Isothermal titration calorimetry (ITC) to measure thermodynamic parameters

  • Surface plasmon resonance (SPR) for real-time binding kinetics

  • Small-angle X-ray scattering (SAXS) to study the structural changes upon RNA binding

• Structural biology approaches:

  • Crystallography or cryo-EM to determine the 3D structure of Hfq-RNA complexes

  • NMR spectroscopy for mapping interaction interfaces in solution

• In vivo approaches:

  • Single-molecule localization microscopy (SMLM) to track Hfq-RNA interactions in living cells

  • RNA-seq analysis comparing wild-type and Δhfq strains to identify Hfq-dependent regulations

The choice of technique depends on the specific question being addressed, with combinatorial approaches providing the most comprehensive understanding of Hfq-RNA interactions.

What is known about the DNA-binding capability of M. succiniciproducens Hfq?

While specific studies on the DNA-binding capabilities of M. succiniciproducens Hfq are not directly presented in the search results, insights can be drawn from studies of Hfq proteins in other bacteria:

• Affinity comparison: Hfq proteins generally have a lower affinity for DNA compared to RNA. For E. coli Hfq, equilibrium dissociation constants (Kd) for DNA range from nM to μM, while for RNA they range from pM to nM .

• Sequence preferences: Hfq typically shows preference for A-tracts in DNA, though at higher concentrations it binds DNA in a sequence-nonspecific manner. Molecular imaging has shown Hfq binding along DNA in continuous stretches separated by naked DNA, suggesting a cooperative binding mechanism .

• Biological relevance: Despite lower affinity, Hfq has been shown to be one of the nucleoid-associated proteins (NAPs), with 10-20% of cellular Hfq associated with the nucleoid . It has been implicated in DNA-related processes including:

  • Regulation of ColE1 plasmid DNA replication

  • Influence on transposition processes

  • Potential involvement in transcription regulation

• Structural basis: The DNA-binding capability likely involves the conserved arginine residues positioned near the rim of the disc formed by the subunits' N-terminal domains, as demonstrated for other Hfq proteins .

If M. succiniciproducens Hfq follows patterns observed in other bacterial Hfq proteins, its DNA-binding function might contribute to gene regulation beyond its canonical role in RNA-mediated regulation.

How can M. succiniciproducens Hfq be used to identify novel sRNAs in related Pasteurellaceae?

M. succiniciproducens Hfq can serve as an effective tool for identifying novel sRNAs in related Pasteurellaceae through several complementary approaches:

• Hfq-based RNA immunoprecipitation (RIP) followed by sequencing:

  • Express tagged M. succiniciproducens Hfq (e.g., TAP-tagged) in the target organism

  • Perform immunoprecipitation to isolate Hfq-RNA complexes

  • Sequence the bound RNAs using RNA-seq or other high-throughput sequencing methods

  • Compare results with and without RNase A treatment to distinguish direct and indirect interactions

• Comparative genomics pipeline:

  • Use bioinformatic tools like RNAz (for conservation-based prediction), SIPHT (for transcriptional terminator-based prediction), and INFERNAL (for secondary structure-based prediction)

  • Search for intergenic regions that are evolutionarily conserved and likely to form stable secondary structures

  • Filter candidates by requiring prediction by at least two different algorithms to increase accuracy

• Hfq-dependent transcriptome analysis:

  • Generate Hfq knockout strains in the target Pasteurellaceae

  • Compare RNA expression profiles between wild-type and Δhfq strains using RNA-seq

  • Identify transcripts with differential abundance that may represent Hfq-dependent sRNAs

• Validation of candidates:

  • Confirm expression using Northern blotting and/or RT-PCR

  • Verify Hfq binding using EMSA with recombinant Hfq protein

  • Determine transcript endpoints using RNA ligase-mediated (RLM)-rapid amplification of cDNA ends (RACE)

This approach has been successful in identifying sRNAs in other Pasteurellaceae, such as the iron-regulated HrrF in Haemophilus influenzae .

How can researchers determine the regulon of M. succiniciproducens Hfq?

Determining the comprehensive regulon of M. succiniciproducens Hfq requires a multi-faceted approach combining genomic, transcriptomic, and biochemical methods:

• Generation of Hfq mutant strains:

  • Create a clean deletion of the hfq gene in M. succiniciproducens

  • Develop complementation strains expressing wild-type Hfq from a plasmid (e.g., using a pT-based expression vector similar to pT-hfq constructed for other bacteria)

• Transcriptomic profiling:

  • Perform RNA-seq comparing wild-type, Δhfq, and complemented strains under various growth conditions (e.g., different growth phases, stress conditions)

  • Identify genes with differential expression patterns dependent on Hfq presence

  • Use computational approaches to identify potential sRNA binding sites in affected mRNAs

• Proteomic analysis:

  • Conduct comparative proteome profiling using 2-DE and MS as previously done for M. succiniciproducens

  • Identify proteins with altered abundance in the absence of Hfq

  • Compare proteomic and transcriptomic data to distinguish translational from transcriptional effects

• Direct binding assays:

  • Perform chromatin immunoprecipitation followed by sequencing (ChIP-seq) for identifying Hfq-bound DNA regions

  • Use RIP-seq to identify Hfq-bound RNAs

  • Validate key interactions with EMSA and other binding assays

• Pathway analysis:

  • Map affected genes to metabolic and regulatory pathways

  • Focus on pathways relevant to succinic acid production, stress response, and virulence factors

This comprehensive approach can provide insights into both direct and indirect regulatory effects of Hfq in M. succiniciproducens, potentially revealing targets for metabolic engineering to enhance succinic acid production.

What are the key considerations when designing experiments to study M. succiniciproducens Hfq in metabolic engineering applications?

When designing experiments to study M. succiniciproducens Hfq for metabolic engineering applications, researchers should consider several key factors:

• Growth conditions and monitoring:

  • Utilize defined media like MH5S (containing sucrose) or MH5G (containing glucose) to control carbon source effects

  • Monitor growth parameters across different phases, as Hfq levels and activity may vary with growth phase

  • Assess succinic acid production using HPLC or other appropriate analytical methods

• Genetic modification strategies:

  • Design precise mutations rather than complete deletions to modify specific Hfq functions

  • Consider tunable expression systems to modulate Hfq levels rather than binary presence/absence

  • Create point mutations in key RNA-binding residues to alter specific interactions while maintaining protein structure

• Target pathway selection:

  • Focus on pathways directly related to succinic acid production, such as the reductive TCA cycle

  • Consider the malate dehydrogenase (MDH) pathway, as MDH is a key enzyme for succinic acid production

  • Investigate potential interactions between Hfq and mRNAs encoding enzymes in the succinic acid production pathway

• Experimental controls:

  • Include appropriate controls for each modification (empty vector, point mutation controls)

  • Compare results with well-characterized strains with known succinic acid production efficiency

  • Use complementation studies to confirm that observed phenotypes are due to specific Hfq modifications

• Multi-omics integration:

  • Combine transcriptomic, proteomic, and metabolomic data to gain comprehensive understanding

  • Use systems biology approaches to model the effects of Hfq modulation on metabolic networks

  • Validate predictions with targeted biochemical assays

By carefully considering these factors, researchers can design robust experiments to elucidate the role of Hfq in M. succiniciproducens metabolism and potentially develop strategies to enhance succinic acid production through Hfq-mediated regulation.

What are the potential mechanisms by which M. succiniciproducens Hfq might contribute to optimized succinic acid production?

M. succiniciproducens Hfq could contribute to optimized succinic acid production through several potential regulatory mechanisms:

• Regulation of key metabolic enzymes:

  • Hfq may regulate the expression of malate dehydrogenase (MDH), a key enzyme for succinic acid production that catalyzes the conversion of oxaloacetate to malate in the reductive TCA cycle

  • Studies have shown that MDH from different organisms exhibit varying levels of activity and substrate inhibition. For example, Corynebacterium glutamicum MDH (CgMDH) shows higher specific activity and less substrate inhibition than M. succiniciproducens MDH (MsMDH)

  • Hfq could regulate the translation efficiency or stability of MDH mRNA, potentially influencing enzyme levels and activity

• Stress response coordination:

  • Succinic acid production creates acidic stress conditions

  • Hfq-dependent sRNAs often regulate stress response pathways

  • By modulating stress responses, Hfq could enhance M. succiniciproducens tolerance to acidic conditions and maintain productivity

• Carbon source utilization optimization:

  • M. succiniciproducens utilizes various carbon sources through specific transporters like the phosphotransferase system (PTS)

  • Hfq could regulate the expression of sugar transporters and metabolic enzymes to optimize carbon flux

  • This regulation might involve sRNAs that respond to carbon availability signals

• Energy metabolism coordination:

  • Succinic acid production is linked to cellular redox balance and energy status

  • Hfq-dependent regulation might help balance energy demands with biosynthetic needs

  • This could involve regulation of genes in electron transport chain or fermentative pathways

• quorum sensing and population-level regulation:

  • Hfq often regulates quorum sensing systems in bacteria

  • Population density-dependent regulation might optimize collective metabolic activities for succinic acid production

Understanding these mechanisms could lead to targeted genetic modifications to enhance succinic acid production in industrial strains.

How might modifications to M. succiniciproducens Hfq enhance its regulatory functions?

Strategic modifications to M. succiniciproducens Hfq could enhance its regulatory functions in several ways:

• Surface modifications for altered RNA binding specificity:

  • Mutations in the proximal face (typically U-rich RNA binding) could alter affinity for specific sRNAs

  • Modifications to the distal face (A-rich binding site) might change mRNA target preferences

  • Rim mutations could affect the protein's ability to facilitate RNA-RNA interactions

• C-terminal domain engineering:

  • The C-terminal domain varies substantially among bacterial species and affects RNA binding

  • Truncation or modification of this domain could alter RNA binding dynamics and partner selection

  • Chimeric C-terminal domains combining regions from different bacterial Hfq proteins might create novel functionalities

• Expression level optimization:

  • Modifying promoter strength to increase or decrease Hfq levels

  • Creating conditional expression systems that respond to metabolic signals relevant to succinic acid production

  • Fine-tuning Hfq abundance to balance competition between different RNA partners

• Stability engineering:

  • Mutations that affect protein stability could modify the half-life of Hfq

  • Post-translational modification sites could be added or removed to affect regulatory dynamics

  • Creating temperature-sensitive variants for conditional regulation

• Partner protein interaction modification:

  • Mutations affecting interaction with RNase E or other RNA processing enzymes

  • Alterations that modify binding to transcription factors or other regulatory proteins

  • Chimeric proteins combining Hfq with other RNA-binding domains for enhanced functionality

These modifications should be designed based on structural models and comparative analysis with well-characterized Hfq proteins, followed by rigorous functional testing to verify the intended effects on regulatory networks.

What are common pitfalls in studying M. succiniciproducens Hfq and how can they be avoided?

Researchers studying M. succiniciproducens Hfq should be aware of several common pitfalls and implement appropriate strategies to avoid them:

• Protein solubility and purification challenges:

  • Pitfall: Recombinant Hfq may form inclusion bodies or aggregate during purification

  • Solution: Optimize expression conditions (lower temperature, reduced induction), use solubility tags, or implement refolding protocols; purify under native conditions with gentle detergents when necessary

• RNA contamination in protein preparations:

  • Pitfall: Purified Hfq often contains tightly bound bacterial RNAs that can interfere with binding studies

  • Solution: Include high-salt washes, RNase treatment steps, and validate protein purity using both SDS-PAGE and techniques that detect nucleic acids

• Non-specific RNA interactions in vitro:

  • Pitfall: At high concentrations, Hfq can bind non-specifically to RNA, leading to false positives

  • Solution: Include appropriate controls (mutant Hfq, competitor RNAs), use physiologically relevant protein concentrations, and validate interactions using multiple techniques

• Phenotype attribution errors:

  • Pitfall: Phenotypes of Δhfq strains may result from pleiotropic effects rather than specific regulatory interactions

  • Solution: Use complementation studies, point mutants affecting specific functions, and direct biochemical validation of predicted interactions

• Limited transferability of findings from model organisms:

  • Pitfall: Assuming M. succiniciproducens Hfq functions identically to E. coli Hfq

  • Solution: Directly characterize M. succiniciproducens Hfq rather than relying solely on inference from other species; validate predicted sRNA-mRNA interactions in M. succiniciproducens specifically

• Technical challenges in RNA-protein interaction studies:

  • Pitfall: RNA degradation during experimental procedures

  • Solution: Use RNase inhibitors, DEPC-treated solutions, and consider crosslinking approaches to stabilize interactions prior to analysis

• Overlooking growth phase-dependent effects:

  • Pitfall: Hfq activity and abundance can vary with growth phase, potentially masking regulatory effects

  • Solution: Analyze Hfq function across multiple growth phases and stress conditions; consider the dynamic nature of Hfq-RNA interactions

By anticipating these challenges and implementing appropriate experimental controls and validation steps, researchers can generate more reliable insights into M. succiniciproducens Hfq function.

What is the relationship between M. succiniciproducens Hfq and the bacterium's metabolism of various carbon sources?

The relationship between M. succiniciproducens Hfq and carbon metabolism likely involves complex regulatory networks:

• Sugar utilization systems:

  • M. succiniciproducens efficiently utilizes various carbon sources including sucrose through specific phosphotransferase systems (PTS)

  • The bacterium uses a sucrose PTS (encoded by MS0784), sucrose 6-phosphate hydrolase (encoded by MS0909), and a fructose PTS (encoded by MS2178) for sucrose transport and utilization

  • Hfq likely regulates the expression of these transport and metabolic genes through sRNA-mediated mechanisms

• Inducible gene regulation:

  • Studies show that the sucrose PTS in M. succiniciproducens is inducible by sucrose, while sucrose 6-phosphate hydrolase is expressed constitutively

  • Hfq may be involved in this carbon source-dependent gene regulation, potentially through sRNAs that respond to metabolic signals

• Carbon catabolite repression:

  • In many bacteria, Hfq participates in carbon catabolite repression systems that prioritize the use of preferred carbon sources

  • Similar regulatory systems in M. succiniciproducens would optimize carbon source utilization in complex media

• Metabolic flux optimization:

  • As a key regulator of gene expression, Hfq likely helps coordinate carbon flux through central metabolic pathways

  • This coordination would be particularly important for optimizing succinic acid production, which requires balanced carbon flow through the reductive TCA cycle

• Adaptation to carbon limitation:

  • Hfq-dependent sRNAs often regulate stress responses, including adaptation to nutrient limitation

  • These regulatory mechanisms would help M. succiniciproducens adjust its metabolism during carbon source transitions or limitations

Understanding these relationships could lead to metabolic engineering strategies that optimize carbon utilization for enhanced succinic acid production in industrial applications.

How might computational approaches be used to predict M. succiniciproducens Hfq-sRNA-mRNA regulatory networks?

Computational approaches offer powerful tools for predicting M. succiniciproducens Hfq-sRNA-mRNA regulatory networks, which can guide experimental validation and discovery:

• Integrative bioinformatic pipeline for sRNA identification:

  • Combine multiple algorithms with different prediction approaches: RNAz (evolutionary conservation), SIPHT (transcriptional terminator-based), and INFERNAL (secondary structure/motif-based)

  • Filter candidates by requiring prediction by at least two different algorithms

  • Search for intergenic regions that are evolutionarily conserved among Pasteurellaceae

  • Identify potential Hfq binding sites (A/U-rich sequences) within predicted sRNAs

• Target mRNA prediction:

  • Use RNA secondary structure prediction tools (e.g., RNAfold) to identify sRNA regions with single-stranded unpaired regions rich in adenosine and uridine, which are proposed Hfq binding sites

  • Implement algorithms that predict base-pairing interactions between sRNAs and potential target mRNAs

  • Consider accessibility of binding sites and energy of interaction

• Network construction and analysis:

  • Build regulatory network models integrating predicted sRNA-mRNA interactions

  • Apply machine learning approaches trained on validated Hfq-dependent interactions from related bacteria

  • Use graph theory to identify regulatory hubs and motifs within the network

• Integration with experimental data:

  • Incorporate RNA-seq data comparing wild-type and Δhfq strains to identify genes likely regulated by Hfq

  • Include proteomics data to distinguish translational from transcriptional effects

  • Use Hfq-binding data (e.g., from RIP-seq) to refine computational predictions

• Comparative genomics across Pasteurellaceae:

  • Identify conserved sRNAs and regulatory patterns within the family

  • Leverage known Hfq-dependent regulations in related species like Haemophilus influenzae