Recombinant Uncharacterized protein Rv2081c/MT2143 (Rv2081c, MT2143)

What is Recombinant Uncharacterized protein Rv2081c/MT2143, and what are its basic properties?

Recombinant Uncharacterized protein Rv2081c/MT2143 is a 146-amino acid protein from Mycobacterium tuberculosis that has not yet been functionally characterized. The protein is typically expressed in E. coli systems with an N-terminal His-tag for purification purposes. According to available data, the protein has the following properties:

For optimal storage, the protein should be stored at -20°C/-80°C upon receipt, with aliquoting recommended for multiple use to avoid repeated freeze-thaw cycles .

How does Rv2081c/MT2143 compare to other uncharacterized proteins in M. tuberculosis?

Rv2081c/MT2143 is one of several uncharacterized proteins in the M. tuberculosis genome. Comparative analysis with other uncharacterized proteins reveals:

• Sequence comparison: Unlike characterized proteins such as Rv2118c (a single-component homotetrameric m1A58 tRNA methyltransferase) or Rv2145c (involved in intracellular survival) , Rv2081c lacks clearly identified functional domains.

• Expression patterns: Similar to proteins like Rv0010c and Rv0011c, Rv2081c appears to be constitutively expressed in M. tuberculosis rather than specifically upregulated during infection .

• Conservation: Rv2081c/MT2143 shows conservation across mycobacterial species, suggesting a potentially important role in mycobacterial physiology. This pattern of conservation is similar to that observed in other membrane-associated proteins like Rv0010c .

What bioinformatic approaches should be used for initial characterization of Rv2081c/MT2143?

Initial bioinformatic characterization should include:

• Sequence homology analysis: Utilize BLAST, Pfam, and SMART databases to identify conserved domains and homologous proteins.

• Secondary structure prediction: Apply algorithms such as PSIPRED, JPred, and GOR IV to predict alpha helices, beta sheets, and random coils.

• Tertiary structure modeling: Employ I-TASSER, Phyre2, and AlphaFold2 to generate 3D structural models.

• Functional prediction: Use tools like InterProScan, ProtFun, and COFACTOR to predict potential functions.

• Subcellular localization prediction: Apply SignalP, TMHMM, and PSORTb to predict cellular localization.

Comprehensive bioinformatic analysis will provide preliminary insights into Rv2081c/MT2143's potential functions, which can guide subsequent experimental designs and hypothesis formulation.

What experimental design approach is most effective for optimizing Rv2081c/MT2143 expression?

A factorial design of experiments (DOE) approach is most effective for optimizing recombinant Rv2081c/MT2143 expression. This systematic method allows researchers to evaluate multiple parameters simultaneously and identify optimal conditions .

The recommended experimental design includes:

• Initial screening via Plackett-Burman design: Screen 6-12 variables that may affect expression:

  • Induction temperature (18-37°C)

  • IPTG concentration (0.1-1.0 mM)

  • Cell density at induction (OD600 0.4-1.0)

  • Post-induction time (4-24 hours)

  • Media composition (LB, TB, 2xYT)

  • Feed percentage (if using fed-batch)

  • Additives (glycerol, sucrose, amino acids)

• Optimization via Box-Behnken approach: After identifying significant variables, optimize using 3-level design:

  Parameter	Low (-1)	Middle (0)	High (+1)
  Temperature	18°C	25°C	30°C
  IPTG	0.1 mM	0.5 mM	1.0 mM
  Cell density	OD 0.4	OD 0.6	OD 0.8

• Response measurement: Evaluate protein yield and solubility through SDS-PAGE, Western blot, and activity assays.

• Statistical analysis: Use response surface methodology to identify optimal conditions and potential interactions between variables .

This approach has demonstrated effectiveness in optimizing expression of mycobacterial proteins, with studies showing up to 3.6-fold increases in expression levels compared to non-optimized conditions .

What expression system modifications can improve the solubility of Rv2081c/MT2143?

To improve Rv2081c/MT2143 solubility, consider these methodological approaches:

• Host strain selection:

  • BL21(DE3) derivatives like Rosetta (for rare codons)

  • SHuffle strains (enhances disulfide bond formation)

  • Arctic Express (low-temperature expression with chaperones)

  • Clear Coli (endotoxin-free for downstream applications)

• Vector modifications:

  • Fusion partners: Thioredoxin, SUMO, MBP, or GST tags

  • Codon optimization for E. coli expression

  • Inclusion of solubility-enhancing sequences

• Culture conditions optimization:

  • Reduced incubation temperature (16-25°C)

  • Additives: 5-10% glycerol, 0.1-1.0% glucose, 1-5 mM betaine

  • Osmotic shock treatment

  • Supplementation with 0.2-0.5% Triton X-100 for membrane proteins

• Co-expression strategies:

  • Molecular chaperones (GroEL/GroES, DnaK/DnaJ/GrpE)

  • Rare tRNA-encoding plasmids

  • Mycobacterial-specific factors

• Buffer optimization for purification:

  • Tris-NaCl buffer (100 mM NaCl, 20 mM Tris-HCl, pH 8.0)

  • Addition of 10% glycerol for stability

  • Low concentrations of non-ionic detergents if membrane-associated

Implementing these modifications systematically using a design of experiments approach can significantly improve soluble protein yield, as demonstrated for other challenging mycobacterial proteins .

What purification strategy is most effective for isolating high-purity Rv2081c/MT2143?

An effective purification strategy for Rv2081c/MT2143 involves multiple chromatographic steps:

• Initial capture via Ni-NTA affinity chromatography:

  • Equilibration buffer: 20 mM Tris-HCl, 100 mM NaCl, pH 8.0

  • Wash buffer: Equilibration buffer + 20-40 mM imidazole

  • Elution buffer: Equilibration buffer + 250-500 mM imidazole

  • Flow rate: 1 ml/min for optimal binding

• Intermediate purification via ion exchange chromatography:

  • Calculate theoretical pI from sequence to determine appropriate resin

  • For Rv2081c/MT2143: Use anion exchange (Q-Sepharose) at pH 8.5

  • Buffer: 20 mM Tris-HCl, pH 8.5

  • Elution: Linear gradient of 0-500 mM NaCl

• Polishing via size exclusion chromatography:

  • Column: Superdex 75 or Superdex 200

  • Buffer: 20 mM Tris-HCl, 150 mM NaCl, pH 8.0

  • Flow rate: 0.5 ml/min for optimal resolution

• Quality control assessments:

  • SDS-PAGE for purity (target >90%)

  • Western blot with anti-His antibody for identity confirmation

  • Mass spectrometry for accurate mass determination

  • Endotoxin testing via LAL assay (<0.1 EU/mg)

This multi-step approach typically yields protein with >90% purity as determined by SDS-PAGE, suitable for structural and functional studies .

What experimental approaches can determine the potential role of Rv2081c/MT2143 in M. tuberculosis pathogenesis?

To determine the potential role of Rv2081c/MT2143 in pathogenesis, employ these methodological approaches:

• Gene knockout and complementation studies:

  • Create a clean deletion mutant of Rv2081c in M. tuberculosis

  • Develop complemented strains with wildtype Rv2081c

  • Compare growth kinetics in standard media and stress conditions

  • Assess survival in macrophage infection models

  • Measure virulence in animal models (e.g., mouse infection)

• Heterologous expression in non-pathogenic mycobacteria:

  • Express Rv2081c in M. smegmatis (similar to approaches used for Rv2145c)

  • Assess changes in growth, survival in macrophages, and immune modulation

  • Evaluate effects on biofilm formation and stress resistance

• Host-pathogen interaction studies:

  • Expose macrophages to purified Rv2081c protein

  • Measure cytokine production (TNF-α, IL-10, IL-6, IL-12)

  • Assess activation of pattern recognition receptors (TLRs)

  • Analyze effects on phagosome maturation

  • Evaluate impact on macrophage polarization (M1/M2)

• Transcriptomic and proteomic analysis:

  • Compare gene expression profiles of wildtype vs. Rv2081c-deleted strains

  • Identify differentially regulated genes and pathways

  • Perform proteomics to assess changes in protein abundance

  • Analyze secretome alterations

These approaches have successfully elucidated the roles of other M. tuberculosis proteins like Rv2145c and Rv2231c in pathogenesis .

How can interactions between Rv2081c/MT2143 and host immune receptors be characterized?

Characterizing interactions between Rv2081c/MT2143 and host immune receptors requires a comprehensive approach:

• Receptor identification:

  • Screen purified Rv2081c using TLR reporter cell lines (HEK-Blue™)

  • Perform co-immunoprecipitation with macrophage lysates

  • Use surface plasmon resonance (SPR) to test direct binding with recombinant receptors

  • Apply CRISPR-Cas9 receptor knockout in macrophages to confirm specificity

• Signaling pathway analysis:

  • Examine MAPK, NF-κB, and STAT pathway activation via Western blotting

  • Monitor TLR-dependent gene expression using qRT-PCR

  • Assess receptor complex formation using proximity ligation assays

  • Measure calcium flux and reactive oxygen species production

• Functional consequences assessment:

  • Cytokine profiling via ELISA and multiplex assays

  • Flow cytometry analysis of surface markers (CD80, CD86, MHC-I/II)

  • Evaluate macrophage polarization markers

  • Measure bacterial survival in receptor-deficient cells

• Structural mapping of interaction domains:

  • Generate truncated protein variants to identify binding regions

  • Perform site-directed mutagenesis of predicted interaction sites

  • Use hydrogen-deuterium exchange mass spectrometry to map binding interfaces

  • Develop computational models of protein-receptor complexes

This methodological framework has successfully identified TLR4 as a receptor for other M. tuberculosis proteins like Rv2231c, which modulates host immune responses to promote bacterial survival .

What approaches can be used to assess the potential enzymatic activities of Rv2081c/MT2143?

To assess potential enzymatic activities of Rv2081c/MT2143, implement this systematic screening approach:

• Sequence-based activity prediction:

  • Search for catalytic motifs and domains using specialized databases

  • Compare with structurally characterized enzymes

  • Identify conserved catalytic residues across homologs

• High-throughput activity screening:

  • Design colorimetric assays for common enzymatic activities:

    • Hydrolase activity (esterase, protease, glycosidase)

    • Transferase activity (methyltransferase, kinase)

    • Oxidoreductase activity (dehydrogenase, oxidase)

  • Screen against substrate libraries in 96-well format

  • Monitor changes in pH, cofactor redox state, or substrate disappearance

• Metabolite analysis:

  • Incubate protein with mycobacterial lysates

  • Perform untargeted metabolomics to identify altered compounds

  • Use stable isotope labeling to track substrate conversion

  • Compare metabolite profiles between wildtype and knockout strains

• Structural analysis for activity prediction:

  • Identify potential active site pockets using computational tools

  • Dock potential substrates in silico

  • Perform molecular dynamics simulations with cofactors

  • Compare binding energies with known substrates of related enzymes

• Targeted activity assays based on predictions:

  • Design specific assays for the most likely activities

  • Include appropriate positive and negative controls

  • Test dependency on common cofactors (ATP, NAD(P)H, AdoMet)

  • Determine optimal pH, temperature, and metal ion requirements

Similar approaches successfully identified the tRNA methyltransferase activity of Rv2118c from M. tuberculosis, which was initially uncharacterized .

How can Rv2081c/MT2143 be analyzed in the context of M. tuberculosis persistence and dormancy?

To investigate Rv2081c/MT2143's role in persistence and dormancy, implement these methodological approaches:

• Expression analysis under dormancy-inducing conditions:

  • Monitor Rv2081c expression in the Wayne hypoxia model

  • Compare expression levels in nutrient starvation models

  • Examine regulation under nitric oxide stress

  • Measure protein levels during macrophage infection using reporter strains

• Genetic manipulation studies:

  • Create conditional knockdown strains of Rv2081c

  • Evaluate survival of knockdown strains under dormancy conditions

  • Perform complementation with wild-type and mutant variants

  • Analyze transcriptional changes in dormancy regulon genes

• Dormancy phenotype characterization:

  • Assess lipid body formation using Nile Red staining

  • Measure antibiotic tolerance profiles

  • Evaluate respiratory activity using resazurin reduction

  • Determine ATP levels during entrance/exit from dormancy

• In vivo persistence models:

  • Compare wild-type and Rv2081c-deficient strains in Cornell model of latent TB

  • Analyze bacterial loads in chronic mouse infection models

  • Assess reactivation potential following immunosuppression

  • Perform histopathological analysis of granulomas

• Integration with systems biology data:

  • Compare Rv2081c expression with known dormancy regulators (DosR regulon)

  • Perform network analysis to identify potential functional partners

  • Integrate transcriptomics, proteomics, and metabolomics data

  • Develop predictive models of Rv2081c's role in persistence

This comprehensive approach has elucidated roles of other initially uncharacterized proteins in M. tuberculosis dormancy and could reveal whether Rv2081c is involved in this critical aspect of tuberculosis pathogenesis .

What strategies can be employed to investigate the potential of Rv2081c/MT2143 as a drug target?

To evaluate Rv2081c/MT2143 as a potential drug target, implement this systematic approach:

• Target validation:

  • Determine essentiality using conditional knockdown or CRISPRi systems

  • Evaluate growth patterns in different media and stress conditions

  • Assess impact on virulence in cellular and animal infection models

  • Compare conservation across mycobacterial species and absence in humans

• Structural characterization for druggability assessment:

  • Obtain high-resolution crystal or cryo-EM structure

  • Identify potential binding pockets using computational tools (SiteMap, FTMap)

  • Assess druggability indices of identified pockets

  • Compare with known successful drug target structures

• High-throughput screening (HTS) assay development:

  • Design activity-based assays with Z' factor >0.5

  • Develop binding assays using thermal shift or surface plasmon resonance

  • Establish counter-screening assays to eliminate false positives

  • Validate with known inhibitors of similar protein classes if available

• In silico drug discovery approaches:

  • Perform virtual screening against compound libraries

  • Conduct fragment-based screening for initial hits

  • Design structure-based pharmacophore models

  • Use molecular dynamics simulations to identify transient binding sites

• Hit validation and optimization:

  • Confirm binding using orthogonal biophysical methods

  • Determine structure-activity relationships

  • Assess selectivity against human homologs

  • Evaluate cellular activity against M. tuberculosis

  • Measure cytotoxicity against mammalian cell lines

This approach has successfully identified drug candidates targeting mycobacterial proteins, including initially uncharacterized ones, and could establish whether Rv2081c represents a viable target for anti-tuberculosis drug development .

How can researchers design experiments to investigate the immunomodulatory potential of Rv2081c/MT2143?

To investigate the immunomodulatory potential of Rv2081c/MT2143, design experiments following these methodological guidelines:

• Innate immune response characterization:

  • Stimulate macrophages, dendritic cells, and neutrophils with purified protein

  • Measure cytokine production (TNF-α, IL-6, IL-12, IL-10, IL-1β) via ELISA

  • Assess pattern recognition receptor activation using reporter cell lines

  • Evaluate ROS/RNS production using fluorescent probes

  • Analyze phagocytosis efficiency and phagosome maturation

• Adaptive immunity investigation:

  • Perform T-cell epitope mapping using overlapping peptides

  • Assess antigen presentation via MHC-I and MHC-II pathways

  • Measure T-cell proliferation in response to Rv2081c stimulation

  • Determine T-helper polarization (Th1/Th2/Th17/Treg) profiles

  • Evaluate memory T-cell generation in recall response assays

• Systems immunology approach:

  • Conduct transcriptomic analysis of stimulated immune cells

  • Perform phosphoproteomic analysis of signaling pathways

  • Apply single-cell RNA sequencing to identify responding cell populations

  • Develop computational models of immune response networks

• In vivo immunological studies:

  • Evaluate adjuvant properties in vaccination models

  • Assess protection against challenge in animal models

  • Analyze granuloma formation and composition

  • Measure antibody responses and determine isotype profiles

• Clinical correlation studies:

  • Compare immune responses in different TB patient cohorts

  • Analyze Rv2081c-specific responses in latent vs. active TB

  • Investigate potential as diagnostic or prognostic biomarker

This experimental framework has successfully characterized immunomodulatory properties of M. tuberculosis proteins such as Rv2145c, which was found to promote M2 macrophage polarization and inhibit pro-inflammatory cytokine production .

What statistical approaches are most appropriate for analyzing protein-protein interaction data for Rv2081c/MT2143?

For analyzing protein-protein interaction (PPI) data involving Rv2081c/MT2143, implement these statistical approaches:

• Pull-down and co-immunoprecipitation data analysis:

  • Apply Student's t-test or ANOVA for comparing band intensities

  • Use fold-enrichment calculations (target protein/control) with appropriate normalization

  • Implement Bayesian statistics to estimate probability of true interactions

  • Set stringent cutoffs (typically >2-fold enrichment, p<0.05)

• Yeast two-hybrid screening analysis:

  • Apply hypergeometric distribution to assess enrichment of functional categories

  • Use permutation tests to determine significance of interaction networks

  • Implement multiple testing correction (Benjamini-Hochberg or Bonferroni)

  • Calculate confidence scores based on reporter strength and reproducibility

• Mass spectrometry-based interactomics:

  • Use significance analysis of interactome (SAINT) algorithm

  • Apply CompPASS scoring for comparative proteomics

  • Implement MaxQuant and Perseus for label-free quantification

  • Set false discovery rate thresholds (typically 1% FDR)

  • Use volcano plots to visualize significant interaction partners

• Network analysis:

  • Calculate centrality measures (degree, betweenness, closeness)

  • Identify significantly enriched network modules using MCODE or MCL

  • Perform Gene Ontology enrichment analysis of interaction partners

  • Use random network models as controls for network property comparison

  • Implement Cytoscape for visualization and topological analysis

• Integration with orthogonal datasets:

  • Apply Bayesian data integration methods

  • Use correlation analysis with transcriptomic profiles

  • Implement machine learning for prediction of functional associations

  • Calculate weighted integration scores across multiple experimental platforms

These statistical approaches have successfully identified functional interactions for other initially uncharacterized mycobacterial proteins, revealing their roles in specific biological pathways .

How should researchers address contradictory results when studying Rv2081c/MT2143 function?

When facing contradictory results in Rv2081c/MT2143 functional studies, implement this systematic troubleshooting approach:

• Methodological reconciliation:

  • Compare experimental conditions in detail (protein preparation, buffer composition, cell types)

  • Assess protein quality (purity, tag interference, proper folding, aggregation state)

  • Examine expression systems used (E. coli vs. mycobacterial hosts)

  • Evaluate assay sensitivity and specificity across different studies

• Statistical reassessment:

  • Recalculate statistical power to detect meaningful differences

  • Compare effect sizes rather than just p-values

  • Implement meta-analysis techniques if multiple datasets exist

  • Consider Bayesian approaches to integrate contradictory evidence

• Biological context evaluation:

  • Investigate strain-specific differences in Rv2081c sequence or regulation

  • Consider cell type-specific effects and microenvironmental factors

  • Assess post-translational modifications that may alter function

  • Examine potential moonlighting functions in different contexts

• Experimental validation strategy:

  • Design decisive experiments with appropriate positive and negative controls

  • Use orthogonal methods to test the same hypothesis

  • Implement dose-response studies rather than single concentrations

  • Develop targeted genetic approaches (point mutations of key residues)

  • Conduct time-course experiments to capture dynamic effects

• Collaborative resolution approach:

  • Initiate direct collaboration with laboratories reporting contradictory results

  • Exchange reagents, protocols, and samples for direct comparison

  • Perform blinded analyses to minimize bias

  • Consider joint publication of reconciliation efforts

This framework has successfully resolved contradictory findings regarding other M. tuberculosis proteins, such as those initially thought to have opposing effects on host immune responses .

What bioinformatic tools should be used to predict the structural features of Rv2081c/MT2143?

For comprehensive structural prediction of Rv2081c/MT2143, implement this multi-layered bioinformatic approach:

This comprehensive approach has enabled successful structural characterization of other initially uncharacterized mycobacterial proteins, providing insights into their potential functions and interaction capabilities .