Recombinant Scheffersomyces stipitis Altered inheritance of mitochondria protein 11 (AIM11)

What is AIM11 and what is its molecular structure?

AIM11 (Altered inheritance of mitochondria protein 11) is a mitochondrial protein found in Scheffersomyces stipitis. The full-length protein consists of 174 amino acids with the sequence: "MSVEAASNSSDQSGLKPFLAKYNFKLGEASDEYIARRKRQMVLFMSSAALTIFASRFAYKSTISRQYIPTLFQGNHAPPLSYNFATDAAVAVGTGTLLCGSVSSMVIFGSCWILDVSNFKEFGWKMKSMMGGYEKERELSKLPMDEESAYIQDGLNDILEGKYDFDEDGTEEGK" . Its UniProt accession number is A3LP48 . The protein is involved in mitochondrial inheritance processes, which are crucial for proper cellular function and division in yeast.

How does S. stipitis AIM11 differ from homologous proteins in other yeast species?

S. stipitis AIM11 functions within the context of a Crabtree negative yeast, which has fundamental metabolic differences compared to Crabtree positive yeasts like Saccharomyces cerevisiae . While both proteins may share structural similarities, their regulatory contexts differ significantly. In S. stipitis, mitochondrial function is particularly important as this organism maintains fully respiratory metabolism even under glucose excess conditions, unlike S. cerevisiae which shifts to fermentative metabolism under high glucose conditions . Comparative transcriptome analysis reveals different patterns of expression for mitochondrial genes between these species, suggesting that AIM11 may operate within distinct regulatory networks compared to its homologs in other yeasts .

What are the primary functional domains of S. stipitis AIM11?

Based on the available data, S. stipitis AIM11 contains regions critical for mitochondrial targeting and function. The protein sequence analysis suggests the presence of transmembrane domains that facilitate integration into mitochondrial membranes . While specific functional domains have not been explicitly identified in the search results, the amino acid sequence indicates hydrophobic regions consistent with membrane association, particularly in the segments containing glycine and leucine-rich motifs . Researchers studying this protein should consider performing domain prediction analyses and comparing with known mitochondrial proteins to better understand its functional architecture.

What are the optimal expression systems for recombinant S. stipitis AIM11?

The optimal expression system for recombinant S. stipitis AIM11 production is E. coli, as demonstrated by successful expression of the full-length protein (amino acids 1-174) with an N-terminal His-tag . For proper expression, researchers should consider the following methodology:

• Clone the AIM11 gene into an expression vector with an N-terminal His-tag

• Transform into a suitable E. coli strain optimized for protein expression

• Induce expression under conditions that promote proper protein folding

• Harvest cells and purify using nickel affinity chromatography

The resulting recombinant protein maintains the full sequence integrity while allowing for efficient purification through the His-tag . Alternative expression systems might include yeast expression systems for post-translational modifications, though the E. coli system has proven effective for basic structural and functional studies.

What are the recommended protocols for reconstitution and storage of recombinant AIM11?

For optimal handling of recombinant AIM11 protein, researchers should follow these evidence-based protocols:

• Reconstitution: Briefly centrifuge the vial containing lyophilized protein before opening. Reconstitute in deionized sterile water to a concentration of 0.1-1.0 mg/mL .

• Storage preparation: Add glycerol to a final concentration of 5-50% (with 50% being optimal) and aliquot for long-term storage .

• Storage conditions: Store aliquots at -20°C/-80°C to prevent protein degradation. Working aliquots can be stored at 4°C for up to one week .

• Handling precautions: Repeated freeze-thaw cycles should be avoided as they can compromise protein integrity. The reconstituted protein is maintained in a Tris/PBS-based buffer with 6% Trehalose at pH 8.0 .

This methodology ensures maximum protein stability and activity for experimental applications while minimizing degradation through careful storage practices.

What analytical techniques are most effective for studying AIM11 interactions with mitochondrial components?

For studying AIM11 interactions with mitochondrial components, researchers should consider implementing a multi-technique approach:

• Protein-RNA interaction analysis: Techniques like those used to predict AIM11 interactions with various RNA transcripts (as shown in search result ) can be valuable. These include computational prediction methods followed by experimental validation through RNA immunoprecipitation .

• Systems biology approaches: As demonstrated in the comparative study of S. stipitis metabolism, integrating multiple analytical techniques provides a comprehensive understanding of protein function in its cellular context . This includes:

  • Transcriptomics (RNA-seq) to examine expression patterns under different conditions

  • Metabolomics to identify downstream effects of AIM11 function

  • C-13-based flux analysis to understand metabolic pathway alterations

• Mitochondrial isolation and fractionation: To specifically study AIM11's role in mitochondria, subcellular fractionation followed by immunoblotting or proteomic analysis would provide insights into its localization and binding partners.

The integration of these methodologies allows researchers to build a comprehensive picture of AIM11's interactions and functional significance within the mitochondrial network.

How does AIM11 contribute to mitochondrial inheritance in S. stipitis?

AIM11's role in mitochondrial inheritance appears to be connected to S. stipitis' unique respiratory metabolism. Based on comparative systems biology studies, S. stipitis maintains fully respiratory metabolism under both glucose-limited and glucose-excess conditions , suggesting that mitochondrial function and inheritance are critically important for this organism. While the search results don't provide direct experimental evidence of AIM11's specific mechanism, its name (Altered inheritance of mitochondria protein 11) indicates involvement in ensuring proper mitochondrial distribution during cell division.

The protein's amino acid sequence suggests membrane association , potentially contributing to mitochondrial membrane dynamics during inheritance. Researchers investigating this function should consider:

• Analyzing AIM11 knockout phenotypes in S. stipitis

• Fluorescently tagging AIM11 to observe its localization during cell division

• Comparing mitochondrial morphology and distribution in wildtype versus AIM11-deficient cells

These approaches would help elucidate the specific contribution of AIM11 to mitochondrial inheritance processes.

What is the significance of AIM11 in S. stipitis metabolism compared to other yeast species?

AIM11's significance in S. stipitis metabolism must be understood in the context of this yeast's unique metabolic characteristics. Unlike Saccharomyces cerevisiae (a Crabtree positive yeast), S. stipitis is Crabtree negative, meaning that fermentation is regulated by oxygen levels rather than sugar concentration . This fundamental difference reflects distinctive mitochondrial function and regulation.

Comparative transcriptome analysis between S. stipitis and S. cerevisiae reveals different patterns of expression for genes involved in central carbon metabolism . The transcription factor analysis suggests divergent regulation of glycolytic and gluconeogenic pathways between these species . AIM11, as a mitochondrial protein, likely contributes to maintaining this respiratory-focused metabolism, potentially through:

• Supporting mitochondrial biogenesis and inheritance during growth

• Facilitating mitochondrial membrane dynamics in response to metabolic demands

• Contributing to regulatory networks that maintain respiratory capacity under various conditions

These functions would be particularly important in S. stipitis compared to Crabtree positive yeasts that can more readily shift to fermentative metabolism.

How can recombinant AIM11 be utilized in studies of yeast mitochondrial function?

Recombinant AIM11 protein can serve as a valuable tool for investigating yeast mitochondrial function through several methodological approaches:

• Structure-function analysis: The availability of purified recombinant AIM11 allows researchers to perform structural studies using techniques like circular dichroism, X-ray crystallography, or NMR to understand functional domains.

• Protein-protein interaction studies: Using His-tagged AIM11 for pull-down assays or co-immunoprecipitation experiments can identify binding partners within the mitochondrial network.

• In vitro reconstitution experiments: Purified AIM11 can be incorporated into artificial membrane systems to study its effect on membrane properties, potentially revealing functional mechanisms.

• Antibody development: Recombinant AIM11 serves as an antigen for generating specific antibodies, enabling immunolocalization studies and western blots to track endogenous protein.

• Comparative studies: Using recombinant AIM11 from S. stipitis alongside homologs from other yeast species enables direct comparison of functional properties related to respiratory versus fermentative metabolism .

These applications collectively contribute to understanding the molecular mechanisms underlying differences in mitochondrial function between Crabtree positive and negative yeasts.

What are the challenges in studying AIM11's role in the respiratory metabolism of S. stipitis?

Researching AIM11's specific role in S. stipitis respiratory metabolism presents several methodological challenges that researchers should consider:

• Genetic manipulation complexity: Unlike S. cerevisiae, genetic tools for S. stipitis are less developed, making precise gene editing more challenging. Researchers must optimize transformation protocols and selection markers specifically for this organism.

• Physiological condition standardization: S. stipitis metabolism is highly sensitive to oxygen levels , requiring precise control of aeration during experiments. Standardized cultivation protocols using controlled bioreactors rather than shake flasks are recommended for reproducible results.

• Mitochondrial isolation difficulties: Purifying intact mitochondria from S. stipitis while maintaining AIM11 associations requires careful buffer optimization to preserve protein-membrane interactions.

• Functional redundancy: The mitochondrial inheritance system likely involves multiple proteins with overlapping functions, potentially masking phenotypes in single-gene studies of AIM11.

• Integration of multi-omics data: As demonstrated by the systems biology approach , understanding AIM11 function requires integration of transcriptomics, metabolomics, and fluxomics data, which presents computational and experimental design challenges.

Addressing these challenges requires interdisciplinary approaches combining molecular biology, biochemistry, and systems biology methodologies.

How do RNA-protein interactions influence AIM11 function in mitochondrial processes?

The potential RNA-protein interactions of AIM11, as suggested by prediction data , may play significant roles in its mitochondrial functions, though current evidence indicates relatively weak interaction scores. Research into these interactions should consider:

• Target validation methodology: Researchers should validate predicted RNA interactions using techniques such as RNA immunoprecipitation followed by sequencing (RIP-seq) or CLIP-seq (crosslinking immunoprecipitation).

• Functional significance assessment: For validated interactions, researchers should investigate whether the RNA-protein binding:

  • Affects AIM11 localization to specific mitochondrial subcompartments

  • Modulates AIM11 protein activity or conformation

  • Influences mitochondrial gene expression

• Comparative analysis: The pattern of RNA interactions might differ between AIM11 homologs in different yeast species, potentially contributing to the distinct metabolic characteristics of Crabtree positive versus negative yeasts .

The prediction data shows potential interactions with various RNA transcripts including tRNAs and mRNAs , suggesting possible roles in translation regulation or RNA trafficking that merit further investigation.

What experimental approaches can reveal AIM11's role in the unique Crabtree-negative phenotype of S. stipitis?

To elucidate AIM11's contribution to S. stipitis' Crabtree-negative phenotype, researchers should implement these methodological approaches:

• Conditional expression systems: Develop regulatable promoters for AIM11 to create strains with variable expression levels, allowing observation of dose-dependent effects on respiratory metabolism.

• Heterologous expression studies: Express S. stipitis AIM11 in S. cerevisiae and assess its impact on the Crabtree effect, potentially identifying transferable elements of respiratory regulation.

• Domain swap experiments: Create chimeric proteins combining domains from S. stipitis AIM11 and homologs from Crabtree-positive yeasts to identify regions responsible for metabolic phenotype differences.

• Metabolic flux analysis: Perform C-13-based flux analysis comparing wildtype and AIM11-modified S. stipitis strains under varying glucose and oxygen conditions, as exemplified in the systems biology study .

• Transcription factor interaction mapping: Based on the identified differences in transcription factor networks between S. stipitis and S. cerevisiae , examine AIM11's potential interactions with these regulatory networks through techniques like ChIP-seq or yeast two-hybrid screening.

These experimental strategies collectively address the complex interplay between mitochondrial function and metabolic regulation that underlies the Crabtree-negative phenotype.

How can researchers interpret AIM11 protein-RNA interaction prediction data?

When analyzing protein-RNA interaction prediction data for AIM11, such as that presented in search result , researchers should employ the following methodological approach:

• Functional clustering: Group predicted RNA partners by their cellular functions to identify potential biological pathways. For instance, the presence of multiple tRNA genes in the prediction list suggests possible involvement in translation processes.

• Experimental validation design: Design RNA immunoprecipitation experiments targeting the highest-scoring candidates, incorporating appropriate controls to distinguish specific from non-specific binding.

This interpretative framework helps researchers prioritize experimental efforts while maintaining awareness of the predictive nature of the data.

What considerations are important when comparing S. stipitis AIM11 function across different experimental conditions?

When comparing S. stipitis AIM11 function across different experimental conditions, researchers should address these methodological considerations:

• Oxygen level standardization: Since S. stipitis metabolism is highly responsive to oxygen availability , precise control and monitoring of dissolved oxygen is essential. Researchers should implement:

  • Continuous measurement of dissolved oxygen using calibrated probes

  • Standardized aeration rates across experiments

  • Detailed reporting of oxygen transfer coefficients

• Growth phase normalization: AIM11 expression and function may vary with growth phase. Comparative experiments should either:

  • Sample at identical growth phases determined by standardized metrics (OD600, cell count)

  • Conduct time-course sampling to capture dynamic changes

• Media composition effects: The comparative systems biology study demonstrated that S. stipitis metabolism responds differently to nutrient conditions compared to S. cerevisiae . Researchers should:

  • Maintain consistent media composition across experiments

  • Consider how carbon source concentration affects respiratory metabolism

  • Account for potential trace element effects on mitochondrial function

• Statistical analysis approach: When comparing AIM11 function across conditions, appropriate statistical methods should be applied to distinguish biological significance from experimental variation, particularly for systems biology datasets that contain inherent biological variability .

These considerations ensure that observed differences in AIM11 function can be reliably attributed to the experimental variables under investigation rather than uncontrolled factors.

How should researchers integrate transcriptomic and metabolomic data when studying AIM11's impact on S. stipitis metabolism?

To effectively integrate transcriptomic and metabolomic data when investigating AIM11's impact on S. stipitis metabolism, researchers should implement this methodological framework, inspired by the systems biology approach described in the search results :

• Experimental design integration:

  • Collect samples for transcriptomics and metabolomics from the same cultures

  • Include both wildtype and AIM11-modified strains

  • Sample under multiple conditions (oxygen levels, carbon sources) relevant to respiratory metabolism

• Data processing alignment:

  • Normalize both datasets appropriately for cross-comparison

  • Apply consistent statistical thresholds for significance

  • Use time-matched samples when examining dynamic responses

• Pathway-focused analysis:

  • Map both transcripts and metabolites to central carbon metabolism pathways

  • Focus on respiratory chain components, TCA cycle, and mitochondrial functions

  • Identify points of convergence where both transcript and metabolite levels change

• Computational integration tools:

  • Implement correlation networks connecting transcripts and metabolites

  • Apply flux balance analysis constrained by transcriptomic data

  • Use machine learning approaches to identify patterns across multi-omics datasets

• Validation experiments:

  • Design targeted experiments to test hypotheses generated from integrated analysis

  • Measure enzyme activities for key pathways identified in the analysis

  • Perform flux analysis using C-13-labeled substrates to confirm predicted metabolic changes

This integrated approach enables researchers to develop a comprehensive understanding of how AIM11 impacts the unique respiratory metabolism of S. stipitis across multiple biological levels.

What are the potential applications of understanding AIM11 function for metabolic engineering of S. stipitis?

Understanding AIM11 function could enable several metabolic engineering applications in S. stipitis, particularly relevant to biofuel and biochemical production:

• Enhanced pentose fermentation: Given S. stipitis' ability to ferment pentose sugars , manipulating AIM11 to optimize mitochondrial function could potentially improve pentose utilization efficiency under oxygen-limited conditions.

• Respiratory capacity engineering: If AIM11 plays a key role in maintaining respiratory metabolism, its controlled expression could be used to fine-tune the balance between respiratory and fermentative metabolism, potentially increasing yield of specific products.

• Stress tolerance improvement: Mitochondrial function is critical for cellular stress responses. Engineering AIM11 could potentially enhance S. stipitis tolerance to stressors encountered in industrial fermentations, including:

  • Inhibitory compounds in lignocellulosic hydrolysates

  • Elevated temperatures

  • Fluctuating oxygen levels

• Hybrid strain development: Transferring S. stipitis AIM11 or engineered variants to other yeast platforms could potentially confer beneficial respiratory characteristics to industrial strains.

These applications would require detailed understanding of AIM11's specific functions in mitochondrial processes and its regulatory networks, building upon the comparative systems biology approaches described in the research .

How might future structural studies of AIM11 inform our understanding of its function?

Future structural studies of AIM11 would significantly advance our understanding of its function through several methodological avenues:

• Structure determination approaches:

  • X-ray crystallography of purified recombinant AIM11 to resolve atomic-level structure

  • Cryo-electron microscopy to visualize AIM11 in membrane environments

  • NMR spectroscopy for dynamics studies of specific domains

• Functional insights from structural data:

  • Identification of binding pockets for potential small molecule interactions

  • Mapping of protein-protein interaction surfaces

  • Recognition of structural motifs shared with other mitochondrial proteins

• Structure-guided experimental design:

  • Site-directed mutagenesis targeting specific structural features

  • Design of domain-specific antibodies for localization studies

  • Development of small-molecule modulators of AIM11 function

• Computational extensions:

  • Molecular dynamics simulations to understand AIM11 behavior in membranes

  • Protein-RNA docking to evaluate predicted interactions

  • Comparative modeling with homologs from other yeast species to identify functionally important differences

The availability of recombinant AIM11 protein provides the starting material for such structural studies, which would bridge current gaps in understanding how this protein contributes to S. stipitis' unique mitochondrial functions and metabolism.

What emerging technologies might advance our understanding of AIM11's role in mitochondrial networks?

Several cutting-edge technologies offer promising approaches for elucidating AIM11's role in mitochondrial networks:

• Spatial transcriptomics and proteomics:

  • Single-cell spatial profiling to map AIM11 distribution relative to other mitochondrial components

  • Proximity labeling techniques (BioID, APEX) to identify proteins in close proximity to AIM11

  • Super-resolution microscopy to visualize AIM11 within mitochondrial substructures

• CRISPR-based technologies:

  • CRISPRi/CRISPRa for tunable repression/activation of AIM11 expression

  • Base editing for introducing specific mutations without double-strand breaks

  • CRISPR screens to identify genetic interactions with AIM11

• Real-time monitoring approaches:

  • FRET-based biosensors to monitor AIM11 interactions in living cells

  • Microfluidics combined with fluorescence microscopy for single-cell dynamics

  • Multi-parameter imaging to correlate AIM11 activity with mitochondrial function

• Systems-level integration methods:

  • Multi-omics data integration platforms building on existing systems biology approaches

  • Machine learning algorithms to identify patterns in large-scale datasets

  • Genome-scale metabolic models incorporating mitochondrial functionality

These technologies, particularly when combined, would provide unprecedented insights into how AIM11 functions within the complex and dynamic mitochondrial networks of S. stipitis, potentially revealing mechanisms underlying its unique respiratory metabolism.