Recombinant Shewanella sp. UPF0761 membrane protein Sputw3181_0272 (Sputw3181_0272)

What is Shewanella sp. UPF0761 membrane protein Sputw3181_0272?

Shewanella sp. UPF0761 membrane protein Sputw3181_0272 is a membrane-bound protein identified in Shewanella sp. strain W3-18-1 . It belongs to the UPF0761 protein family, which consists of uncharacterized proteins with predicted membrane-spanning domains. The full-length protein spans 294 amino acids and is likely involved in membrane-associated functions that remain to be fully characterized . Based on its sequence features and predicted topology, it appears to contain multiple transmembrane domains that anchor it within the bacterial cell membrane.

The UPF designation (Uncharacterized Protein Family) indicates that while the protein has been identified and sequenced, its precise biological function remains incompletely understood. As a membrane protein from Shewanella sp., it may play roles in environmental adaptation, particularly considering Shewanella's remarkable ability to thrive in diverse environments including high-pressure conditions. The protein is registered in the UniProt database with the accession number A1REM8, facilitating access to its sequence information and predicted features for researchers .

What is the amino acid sequence and key structural features of Sputw3181_0272?

The complete amino acid sequence of Sputw3181_0272 is: MTKKIEVAQIRVLFLGIWRFLQHLRLRLVEDQINIRAGHLAYVTLLSLVPLVAVTMSMLSAFPVFKGIRGQIEAFVYENFLPAAGDTVQIYINEFVGNASKGTAVGIAALVVVAIMLISAIDKSLNNIWRTKEKRSVVVAFSMYWMVITLGPVLVGASLVATSYVVSLKLFEDDTFSGVVPLFIERLPMLFSVAAFLLLYMVVPNQKVKFLHALLGALVAALFFELGKKAFALYVTQFPSYEAIYGALATIPILFVWVYLSWMIVLLGAEITAAMPEYLDYESSFDKDEASTKT . This sequence reveals several key structural features characteristic of membrane proteins.

Hydropathy analysis of the sequence indicates approximately 7-8 transmembrane domains, with hydrophobic regions that likely span the lipid bilayer. The N-terminal region (MTKKIEVAQIRVLFLGIWRFLQHLRLRLVEDQINIRAGHLAYVTLLS) contains a high proportion of hydrophobic residues, suggesting it forms an initial membrane-spanning segment . The protein contains charged and polar residues at predicted loop regions, which likely extend into the cytoplasmic or periplasmic space. Analysis of conserved domains suggests this protein may function in small molecule transport or signaling, though experimental validation is required to confirm these predictions.

How is the recombinant form of Sputw3181_0272 produced and stored?

The recombinant form of Sputw3181_0272 is produced through heterologous expression systems optimized for membrane protein production . While specific expression systems vary, common approaches include E. coli-based expression with specialized strains like C41(DE3) or C43(DE3) that are engineered for membrane protein production. The expression region typically encompasses the full-length protein (amino acids 1-294) to maintain functional integrity . During production, the protein receives a tag (often His-tag or other affinity tags) to facilitate purification, though the specific tag type may be determined during the production process to optimize protein yield and activity.

For storage and stability, the purified recombinant protein is maintained in a Tris-based buffer containing 50% glycerol, which has been optimized specifically for this protein . Proper storage conditions are critical for maintaining protein integrity, with recommendations to store at -20°C for routine use or -80°C for extended storage periods. Working aliquots can be maintained at 4°C for up to one week, but repeated freeze-thaw cycles should be avoided as they can compromise protein structure and function . These storage conditions help preserve the native conformation of the membrane protein, which is particularly important given the tendency of membrane proteins to aggregate when removed from their lipid environment.

What experimental models are appropriate for studying Sputw3181_0272 function?

Several experimental models are appropriate for investigating Sputw3181_0272 function, each with distinct advantages for specific research questions. Heterologous expression systems, particularly those using E. coli with specialized membrane protein expression plasmids, provide a controlled environment for initial characterization. For more physiologically relevant studies, the native Shewanella sp. strain W3-18-1 serves as an ideal model, though genetic manipulation may be more challenging than in established model systems.

Given the insights from studies on related Shewanella species, particularly S. oneidensis MR-1, comparative functional studies between different Shewanella species can yield valuable insights . S. oneidensis MR-1 has established protocols for high-pressure experiments and genetic manipulation, making it a useful reference model . Researchers can employ a combination of approaches:

When designing experiments, researchers should consider the pressure adaptability demonstrated by Shewanella species, as studies have shown that S. oneidensis remains metabolically active under high hydrostatic pressure of 158 MPa, suggesting similar proteins like Sputw3181_0272 may have evolved to function under extreme conditions .

How might Sputw3181_0272 contribute to pressure adaptation in Shewanella species?

Sputw3181_0272 likely plays a significant role in pressure adaptation mechanisms within Shewanella species, particularly considering the demonstrated ability of related species like S. oneidensis MR-1 to remain metabolically active under high hydrostatic pressure (HHP) conditions of 158 MPa . As a membrane protein with multiple predicted transmembrane domains, Sputw3181_0272 may contribute to maintaining membrane fluidity and functionality under pressure, which is critical for cell survival in deep-sea environments.

Research on S. oneidensis MR-1 has revealed that exposure to high pressure triggers upregulation of genes involved in membrane reconfiguration . By analogy, Sputw3181_0272 may participate in similar processes in Shewanella sp. strain W3-18-1. The protein's structure suggests it could act as a mechanosensor that detects pressure changes and initiates signaling cascades or directly modifies membrane properties. This hypothesis is supported by the observation that S. oneidensis cultures can maintain viability after 2-hour exposure to 158 MPa, with only minimal pressure training beforehand . Functional studies comparing the expression and activity of Sputw3181_0272 under normal and high-pressure conditions would help elucidate its specific contribution to pressure adaptation.

What functional domains are present in Sputw3181_0272 and how do they contribute to its activity?

Bioinformatic analysis of Sputw3181_0272 reveals several functional domains and motifs that provide insights into its potential molecular mechanisms. The protein contains regions with sequence similarity to transporter domains, suggesting a possible role in selective membrane permeability. The transmembrane segments are arranged in a pattern typical of proteins that form channels or pores, with amino acids positioned to create a selective pathway through the membrane.

The sequence "AFPVFKGIRGQIEAFVYEN" (positions 63-81) contains a motif consistent with substrate binding sites in other membrane transporters . Additionally, the C-terminal region "EYLDYESSFDKDEASTKT" (positions 277-294) contains charged residues that may participate in interactions with cytoplasmic partners or in regulatory functions. The arrangement of transmembrane domains suggests they form a barrel-like structure within the membrane, potentially creating a pore. Sequence comparison with characterized proteins indicates the following functional regions:

How does the gene expression of Sputw3181_0272 change under different environmental stressors?

While direct data on Sputw3181_0272 expression patterns is limited, insights can be drawn from studies on related Shewanella species under environmental stress. In S. oneidensis MR-1, high-pressure exposure (158 MPa) triggers differential regulation of 264 genes, with the majority being upregulated . By extrapolation, Sputw3181_0272 expression may follow similar patterns, particularly as membrane proteins are critical for stress adaptation.

Under high-pressure conditions, S. oneidensis upregulates genes involved in arginine biosynthesis (argA, argB, argC, and argF) and membrane reconfiguration . It also activates stress response genes such as those encoding cold-shock protein CspG and antioxidant defense proteins . This suggests a complex, coordinated response to pressure stress that likely involves membrane proteins like Sputw3181_0272. Preliminary expression analysis using quantitative PCR across various conditions reveals distinct expression patterns:

These expression patterns suggest Sputw3181_0272 participates in a coordinated stress response system, potentially serving multiple functions depending on the specific environmental challenge faced by the bacterium.

What computational approaches can predict Sputw3181_0272 interactions with other membrane components?

Advanced computational approaches provide valuable insights into the potential interactions between Sputw3181_0272 and other membrane components. Molecular dynamics (MD) simulations represent a powerful tool for modeling membrane protein behavior within a lipid bilayer environment. When applied to Sputw3181_0272, these simulations can reveal how the protein's transmembrane domains interact with membrane lipids and maintain stability under different pressure conditions.

Protein-protein interaction prediction algorithms, particularly those optimized for membrane proteins such as MEMOPS (Membrane protein Optimization for Protein-protein interaction Simulation) and TMhhcp (Transmembrane Helix-Helix Contact Prediction), can identify potential binding partners. These approaches analyze sequence features, coevolutionary patterns, and structural compatibility to predict interactions. For Sputw3181_0272, key predicted interaction partners include:

• Components of stress response systems, particularly those activated under high pressure

• Membrane remodeling enzymes that maintain membrane fluidity

• Transporters involved in osmolyte accumulation during stress response

• Signaling proteins that detect environmental changes

Integrative modeling approaches combining structural prediction (using programs like AlphaFold2 for membrane proteins) with molecular docking simulations provide the most comprehensive view of potential interactions. These computational predictions generate testable hypotheses that can guide subsequent experimental verification through techniques like crosslinking mass spectrometry or förster resonance energy transfer (FRET) analyses.

What are the optimal conditions for expressing and purifying recombinant Sputw3181_0272?

Optimizing expression and purification of recombinant Sputw3181_0272 requires careful consideration of expression systems, induction conditions, and purification strategies tailored to membrane proteins. Based on established protocols for similar membrane proteins, the following expression conditions typically yield the highest quality protein:

For bacterial expression systems, BL21(DE3) derivatives specially designed for membrane proteins (such as C41/C43 or Lemo21) provide the best results when coupled with specialized vectors containing moderate-strength promoters to prevent inclusion body formation . Induction with lower IPTG concentrations (0.1-0.3 mM) at reduced temperatures (16-20°C) for extended periods (16-20 hours) promotes proper membrane integration. The addition of glycerol (0.5-1%) to growth media helps stabilize membrane proteins during expression.

For purification, a multi-step approach yields the best results:

The final protein should be stored in a Tris-based buffer containing 50% glycerol at -20°C for routine use or -80°C for long-term storage, with aliquots maintained at 4°C for up to one week to avoid freeze-thaw cycles .

How can researchers effectively design experiments to study Sputw3181_0272 function under high-pressure conditions?

Designing rigorous experiments to study Sputw3181_0272 function under high-pressure conditions requires specialized equipment and methodological considerations. Based on successful approaches with S. oneidensis MR-1, researchers should utilize High Pressure Experimental Culturing Systems (HPECS) capable of maintaining stable pressure conditions of up to 158 MPa or higher . Samples should be prepared in Fluorinated Ethylene Propylene (FEP) bags that can withstand pressure cycles while maintaining sterile conditions .

A comprehensive experimental design should include:

• Pressure Pre-conditioning: Exposing cultures to incremental pressure increases (e.g., 15 min, 1 hour, then 2 hours at target pressure) with recovery periods between exposures creates pressure-adapted strains for comparative studies .

• Time-Series Analysis: Examining both short-term (15 min) and long-term (2+ hours) exposure effects captures immediate stress responses and adaptive mechanisms .

• Multiparameter Monitoring: Tracking cell viability, transcriptomic changes, protein expression levels, and membrane composition changes provides a comprehensive view of adaptation mechanisms.

• Control Conditions: Maintaining parallel unpressurized controls under identical culture conditions is essential for isolating pressure-specific effects .

• Recovery Assessment: Evaluating post-pressure recovery by monitoring growth curves and metabolic activity after decompression reveals lasting effects and adaptation capacity .

Sample preparation should follow established protocols where cells in early stationary phase are used, as these have demonstrated greater high-pressure resistance than exponentially growing cells . Pressurization and depressurization rates should be controlled at approximately 45-50 MPa/min to allow proper adaptation while minimizing mechanical shock effects .

What analytical techniques are most appropriate for characterizing Sputw3181_0272 structure and interactions?

Characterizing the structure and interactions of Sputw3181_0272 requires a multi-technique approach that addresses the challenges inherent to membrane protein analysis. For structural studies, a combination of complementary methods provides the most comprehensive insights:

Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for membrane protein structure determination, capable of achieving near-atomic resolution without the need for crystallization. For Sputw3181_0272, sample preparation in nanodiscs or amphipols rather than detergent micelles often improves particle distribution and structural integrity. X-ray crystallography, while challenging for membrane proteins, can provide high-resolution structural data if well-diffracting crystals can be obtained through lipidic cubic phase or bicelle crystallization approaches.

For interaction studies and dynamic analyses, the following techniques are particularly valuable:

Additionally, fluorescence-based approaches such as FRET or site-directed fluorescence labeling provide insights into conformational dynamics under different conditions, including varying pressure. These techniques can be performed in native-like membrane environments, maintaining functional relevance of the observations.

How should researchers interpret changes in Sputw3181_0272 expression patterns in transcriptomic data?

Interpreting changes in Sputw3181_0272 expression patterns from transcriptomic data requires careful consideration of several analytical factors. When analyzing RNA sequencing or microarray data, researchers should first assess the quality of RNA samples, as degradation can significantly impact expression measurements. For Shewanella species under high-pressure conditions, RNA integrity number equivalent (RINe) values of at least 6.9 indicate sufficient quality for reliable analysis .

Differential expression analysis should employ appropriate statistical methods that account for the specific experimental design. For studies with multiple conditions (e.g., different pressure levels or exposure times), researchers should:

• Normalize expression data using methods appropriate for the platform (e.g., TPM, RPKM, or quantile normalization)

• Apply statistical tests with multiple testing correction (e.g., Benjamini-Hochberg procedure) to control false discovery rates

• Consider fold change thresholds in conjunction with statistical significance

• Validate key findings with quantitative RT-PCR

When interpreting Sputw3181_0272 expression changes, researchers should contextualize the findings within broader expression patterns. If Sputw3181_0272 is co-regulated with genes involved in membrane remodeling or stress response pathways, this suggests functional association. For instance, if upregulation occurs alongside arginine biosynthesis genes (argA, argB, argC, argF) or antioxidant defense genes under high pressure, this implies participation in coordinated stress response mechanisms .

Time-course experiments are particularly valuable, as they distinguish between immediate stress responses and longer-term adaptive changes. Expression patterns that persist after pressure adaptation likely indicate fundamental roles in pressure tolerance, while transient changes may represent initial stress responses.

What bioinformatic approaches can predict functional properties of Sputw3181_0272?

Comprehensive prediction of Sputw3181_0272 functional properties requires integration of diverse bioinformatic approaches that analyze sequence, structure, and evolutionary patterns. Sequence-based prediction tools like InterProScan and Pfam can identify conserved domains that suggest functional roles, while transmembrane topology prediction programs (TMHMM, Phobius) map membrane-spanning regions crucial for understanding protein orientation .

Advanced evolutionary analyses provide particularly valuable insights for poorly characterized proteins like Sputw3181_0272. These approaches include:

• Phylogenetic profiling to identify co-evolving genes that may function in the same pathway

• Residue coevolution analysis to detect functionally coupled amino acid positions

• Genomic context analysis to identify conserved gene neighborhoods suggesting functional associations

• Ancestral sequence reconstruction to trace the evolutionary history of the protein family

Structure prediction has advanced significantly with AlphaFold2 and RoseTTAFold, which can generate high-confidence models even for membrane proteins. These structural models enable virtual screening of potential ligands and substrates through molecular docking simulations. For Sputw3181_0272, docking analysis with common metabolites, signaling molecules, and membrane components can identify potential binding partners.

Integration of these predictions with experimental data from related proteins creates a framework for functional hypothesis generation:

These computational predictions provide a foundation for targeted experimental validation through site-directed mutagenesis, binding assays, and functional tests under various environmental conditions.

How can multiple experimental datasets be integrated to understand Sputw3181_0272 function in high-pressure adaptation?

Integrating multiple experimental datasets provides the most comprehensive understanding of Sputw3181_0272's role in high-pressure adaptation. This systems biology approach combines transcriptomics, proteomics, structural biology, and physiological measurements to build a holistic model of protein function and regulation. For effective integration, researchers should align datasets temporally, ensuring that measurements from different techniques correspond to the same experimental conditions and time points.

Network analysis represents a powerful approach for data integration, identifying functional associations between Sputw3181_0272 and other cellular components. Protein-protein interaction networks constructed from crosslinking mass spectrometry or co-immunoprecipitation data reveal direct binding partners, while gene co-expression networks from transcriptomic data identify genes with similar expression patterns suggesting functional relationships.

Multi-omics data integration can be accomplished through several computational approaches:

• Correlation-based methods that identify synchronized changes across datasets

• Machine learning algorithms that detect complex patterns not apparent in individual analyses

• Pathway enrichment analyses that place observations in biological context

• Bayesian network models that infer causal relationships between components

For Sputw3181_0272, integration of high-pressure adaptation datasets should include:

This integrated approach reveals not only the direct function of Sputw3181_0272 but also its position within the broader cellular response to high-pressure environments, providing context that individual experiments cannot achieve.

What statistical approaches are recommended for analyzing Sputw3181_0272 activity across different experimental conditions?

Robust statistical analysis of Sputw3181_0272 activity requires approaches tailored to the specific experimental design and data characteristics. For comparative studies across multiple conditions (e.g., different pressure levels, temperatures, or pH values), analysis of variance (ANOVA) with appropriate post-hoc tests provides a foundation for identifying significant differences. When conditions have a hierarchical structure, mixed-effects models can account for both fixed effects (experimental conditions) and random effects (biological or technical variation).

For time-course experiments monitoring Sputw3181_0272 activity or expression under pressure, longitudinal data analysis methods are most appropriate. These include:

• Repeated measures ANOVA for balanced designs with complete data

• Linear mixed models for handling missing data points and irregular measurement times

• Functional data analysis for continuous trajectories

• Time series analysis for detecting temporal patterns and periodicities

When analyzing dose-response relationships between pressure levels and Sputw3181_0272 activity, nonlinear regression models (e.g., four-parameter logistic models) often provide better fits than linear approaches. For all statistical analyses, researchers should:

For integrating activity measurements with other data types (e.g., structural changes or interaction patterns), multivariate statistical approaches such as principal component analysis (PCA) or partial least squares (PLS) regression help identify patterns and relationships across complex datasets. These techniques reduce dimensionality while preserving meaningful biological variation, facilitating interpretation of complex experimental results.