Recombinant Peptidoglycan synthase ftsI homolog (ftsI)

What is FtsI and what is its role in bacterial cell division?

FtsI (Filamenting temperature-sensitive mutant I), also known as PBP3 (Penicillin-Binding Protein 3), is a transpeptidase required for synthesis of peptidoglycan in the division septum during bacterial cell division. It is a bitopic membrane protein with a short N-terminal cytoplasmic domain, a single transmembrane helix (TMH), and a large periplasmic region consisting of two domains—one of unknown function and the other containing the transpeptidase catalytic domain .

FtsI is part of the divisome, a complex of proteins that orchestrates bacterial cell division. It plays a crucial role in cross-linking the peptidoglycan cell wall at the division site, which is essential for proper septum formation and ultimately cell separation .

How does FtsI contribute to peptidoglycan synthesis during cell division?

FtsI functions as a transpeptidase that catalyzes the cross-linking of peptide side chains in peptidoglycan during septal cell wall synthesis. During cell division, FtsI works in concert with FtsW, which functions as a glycosyltransferase (GTase), while FtsI serves as the transpeptidase (TPase) . This coordinated action allows for the synthesis of new peptidoglycan at the division site.

The transpeptidase activity of FtsI is critical for constriction of the FtsZ cytokinetic ring, which provides the contractile force for membrane invagination during division. Without the active transpeptidase function of FtsI, FtsZ rings remain unconstricted at the midpoint of the cell for several generations . This indicates that peptidoglycan synthesis drives the progression of bacterial cell division through FtsI's transpeptidase activity.

What is the structural organization of FtsI?

FtsI is a membrane protein with distinct structural domains:

• A short N-terminal cytoplasmic domain

• A single transmembrane helix (TMH) spanning approximately residues 24-45 (based on various prediction programs)

• A large periplasmic region consisting of:

  • A pedestal domain (also called the domain of unknown function)

  • A transpeptidase catalytic domain at the C-terminus

The transmembrane helix is particularly important for FtsI localization to the division site. Multiple prediction programs (TMpred, HMMTOP, PHDhtm, TMHMM, and TopPred II) predict different but overlapping regions for the TMH, with most predictions placing it approximately between residues 21-45 . Mutations in specific residues within or near the TMH (R23, L39, Q46) affect FtsI's ability to localize to the septum without preventing insertion into the membrane .

How does FtsI interact with FtsW and other divisome proteins?

FtsI forms a complex with FtsW, with the interaction occurring primarily between the transmembrane regions of both proteins. Specifically, the transmembrane helix of FtsI interacts with transmembrane helices 8 and 9 of FtsW . This interaction is critical for the proper functioning of both proteins during cell division.

In the broader context, FtsI is part of the FtsQLBWI complex, where multiple proteins interact to form a functional divisome. AlphaFold2 modeling has provided insights into these interactions, showing that FtsI in its extended conformation interacts with the FtsLB subcomplex through its pedestal domain . The interaction between FtsW's extracellular loop 4 (ECL4) and the FtsI pedestal domain is particularly important, as it appears to modulate the activation mechanism of the FtsWI complex .

The FtsI protein can adopt different conformations, including an extended state and a compact state (similar to the RodA-PBP2 configuration), suggesting flexibility that may be important for its function during different stages of cell division .

What is the relationship between FtsW and FtsI in peptidoglycan synthesis?

FtsW and FtsI form a functional complex that coordinates two essential activities in peptidoglycan synthesis:

• FtsW functions as a glycosyltransferase (GTase) that polymerizes the glycan strands of peptidoglycan

• FtsI serves as the transpeptidase (TPase) that cross-links these strands

This division of labor allows for coordinated synthesis of peptidoglycan at the division site. FtsW is believed to signal the activation of FtsI, likely through its pedestal region . The complex localizes at the inner membrane, with FtsI protruding into the intermembrane space where peptidoglycan synthesis occurs .

The activities of this complex are regulated by other divisome components, including FtsN, which is thought to trigger a transition in the FtsLB complex from an "off" to an "on" state, thereby activating FtsWI . This regulatory mechanism ensures that peptidoglycan synthesis occurs at the right time and place during cell division.

How do the FtsQLB and FtsWI complexes interact during bacterial cell division?

The FtsQLB and FtsWI complexes form an intricate network of interactions crucial for coordinated cell division. The FtsQLBWI model predicted by AlphaFold2 shows that:

• FtsLB interacts with FtsQ through β-sheet augmentation, where the C-terminal tail of FtsB binds to the edge of the C-terminal β-sheet of FtsQ

• The C-terminal tail of FtsL contributes an additional strand to this β-sheet by binding to the FtsB β-strand, potentially aiding the FtsB-FtsQ interaction

• FtsLB is positioned to support FtsI in an extended conformation and is located properly relative to FtsWI to allow FtsI to adopt various conformational states

The constriction control domain (CCD) regions of both FtsL and FtsB are critical components for the transition from an "off" to an "on" state following accumulation of FtsN at the division site . This transition is thought to activate the FtsWI complex, triggering septal peptidoglycan synthesis.

Interestingly, AlphaFold2 predicts a monomeric form (Fts[QLBWI]₁) for this complex, while experimental evidence supports a heterotetramer (Fts[LB]₂) structure . The model is compatible with a diprotomeric configuration, suggesting that FtsLB could act as a central hub binding two FtsWI complexes and two FtsQ subunits into a single complex .

How do mutations in FtsI contribute to antibiotic resistance in bacteria?

Mutations in FtsI can lead to resistance against β-lactam antibiotics, which typically target the transpeptidase domain of FtsI. A systematic meta-analysis of H. influenzae isolates revealed specific mutations associated with ampicillin and cefotaxime resistance .

Key mutations contributing to resistance include:

• A502V

• N526K

• V547I

• N569S

These mutations are located in the transpeptidase domain of FtsI and can alter its interaction with antibiotics. Joint models of 44 high-quality non-synonymous mutations in FtsI showed that only a few mutations significantly contributed to resistance phenotypes, with A502V, N526K, and V547I being the most consistent significant substitutions in models predicting both minimum inhibitory concentration (MIC) values and resistance status .

What specific mutations in FtsI have been identified in multidrug-resistant Acinetobacter baumannii?

In multidrug-resistant A. baumannii, several specific mutations in FtsI have been identified:

• Pro508Leu - Observed after exposure to erythromycin

• Ala515Val and Ala579Thr - Observed after exposure to imipenem

These mutations occur in the periplasmic space where peptidoglycan synthesis takes place, specifically within or near the catalytic region of FtsI. Intraspecies multiple sequence alignment suggests that these mutations are uncommon among wild-type A. baumannii strains .

The mutations result in notable changes:

• Pro508Leu changes from a nonpolar to a hydrophobic residue

• Ala579Thr changes from a hydrophobic to a non-polar residue

• The spatial occupancy increases in all cases (Pro508Leu: 17 atoms to 22 atoms; Ala515Val: 13 atoms to 19 atoms; Ala579Thr: 13 atoms to 17 atoms)

While Ala515Val and Ala579Thr occur within alpha helices, Pro508Leu occurs in a loop structure connecting an alpha-helix and beta-sheet, potentially impacting either the conformation of the catalytic region or modifying the transpeptidase activity .

How can computational modeling help predict the impact of FtsI mutations on antibiotic resistance?

Computational modeling provides valuable insights into how mutations in FtsI might affect its structure and function, thereby contributing to antibiotic resistance. Several approaches have proven useful:

• Protein structure prediction: Tools like AlphaFold2 can generate high-quality models of FtsI and its complexes, allowing researchers to visualize the potential impact of mutations on protein structure and interactions .

• Mutant protein modeling: Programs like PyMol can be used to create mutant structures by replacing specific residues and selecting the lowest energy side chain conformations . This helps visualize how mutations might alter the protein's structure.

• Active site prediction: Tools such as DoGSitescorer can identify potential binding sites, with those having a druggable score ≥ 0.7 being particularly relevant . This helps determine if mutations occur near functionally important regions.

• Multimer prediction: Software like ChimeraX and Google Collab GPU can be used to construct multimeric forms of protein complexes like FtsWI, allowing researchers to study how mutations might affect protein-protein interactions .

• Hydrogen bond analysis: Computational models can reveal changes in hydrogen bonding patterns due to mutations, such as the loss of hydrogen bonds observed in Ala515Val and Ala579Thr mutations in A. baumannii FtsI .

By integrating these computational approaches with experimental data, researchers can develop more comprehensive models of how FtsI mutations contribute to antibiotic resistance and potentially design new strategies to overcome this resistance.

What techniques can be used to study FtsI localization in bacterial cells?

Several experimental techniques have proven effective for studying FtsI localization in bacterial cells:

• GFP fusion proteins: Creating fusion proteins between GFP and FtsI (or its domains) allows for visualization of FtsI localization in living cells. For example, researchers have shown that the transmembrane helix (TMH) of FtsI can direct GFP to the septal ring, confirming the importance of this domain for localization . For optimal results, expression levels must be carefully controlled using inducible promoters with appropriate inducer concentrations (e.g., 1 mM IPTG for gfp-ftsW) .

• Immunofluorescence microscopy: This technique has been used to show that FtsZ assembles early in the division cycle and that inhibition of FtsI affects FtsZ ring constriction . The method involves fixing cells, permeabilizing them, and using antibodies to detect the protein of interest.

• Site-directed mutagenesis: Introducing specific mutations in the FtsI gene, particularly in the transmembrane region, can help identify residues critical for localization. For example, alanine-scanning mutagenesis has revealed that one face of the TMH is particularly important for septal localization .

• Suppressor analysis: Identifying suppressors of localization-defective mutations can provide insights into protein-protein interactions involved in FtsI localization. Suppressors of TMH lesions that map back to the TMH support the hypothesis that this domain is the primary localization determinant .

• Depletion studies: Creating conditional mutants where FtsI expression can be controlled allows researchers to study the effects of FtsI depletion on cell division and the localization of other divisome components .

How can recombinant FtsI be expressed and purified for in vitro studies?

Efficient expression and purification of recombinant FtsI requires specific strategies to address the challenges associated with membrane proteins:

• Construct design:

  • For full-length FtsI, include the native signal sequence to ensure proper membrane insertion

  • For the periplasmic domain only, use a signal sequence or fusion tag that facilitates periplasmic export

  • Consider codon optimization for the expression host

  • Include appropriate affinity tags (His-tag, FLAG-tag) for purification

• Expression systems:

  • E. coli BL21(DE3) or derivatives are commonly used

  • Cold-shock expression (16-18°C) can improve folding of membrane proteins

  • Use IPTG-inducible promoters (like T7) with carefully optimized inducer concentrations

  • Consider specialized E. coli strains designed for membrane protein expression (C41, C43)

• Membrane protein extraction:

  • Use mild detergents like n-dodecyl-β-D-maltoside (DDM) or digitonin for extraction

  • Optimize detergent concentration to efficiently solubilize FtsI while maintaining its native structure

  • Consider native membrane extraction followed by detergent solubilization

• Purification strategy:

  • Immobilized metal affinity chromatography (IMAC) using His-tagged constructs

  • Size exclusion chromatography to remove aggregates and contaminants

  • Maintain detergent above critical micelle concentration throughout purification

  • Consider on-column detergent exchange if needed for downstream applications

• Quality control:

  • SDS-PAGE and Western blotting to confirm identity and purity

  • Size exclusion chromatography to assess oligomeric state

  • Circular dichroism to confirm proper folding

  • Activity assays to verify retention of transpeptidase function

For constructs focusing on specific domains, strategies may differ. For example, the periplasmic domain can be expressed as a soluble protein without the transmembrane region, potentially simplifying purification .

What methods are effective for studying the interaction between FtsI and FtsW?

Several complementary approaches have proven effective for studying FtsI-FtsW interactions:

• Computational modeling:

  • AlphaFold2 multimer prediction has been successful in modeling the FtsWI complex structure

  • Careful validation of these models against experimental data is essential

  • Comparison with related structures, such as RodA-PBP2, can provide valuable insights

• Co-immunoprecipitation (Co-IP):

  • Use antibodies against one protein to pull down complexes

  • Western blotting can then detect the presence of interaction partners

  • Tagged versions of the proteins can facilitate this approach

• Bacterial two-hybrid (BACTH) assays:

  • Fusion of FtsI and FtsW to complementary fragments of adenylate cyclase

  • Interaction reconstitutes enzyme activity, producing a detectable signal

  • Allows mapping of interaction domains through truncation constructs

• Site-directed mutagenesis:

  • Introducing mutations at potential interaction interfaces (e.g., FtsI TMH or FtsW TM8/9)

  • Assessing the impact on complex formation and function

  • Has been used successfully to identify key residues in the FtsL-FtsW interface

• In vitro reconstitution:

  • Purification of both proteins and reconstitution in liposomes or nanodiscs

  • Assessment of complex formation by techniques like FRET or analytical ultracentrifugation

  • Functional assays to determine if the reconstituted complex retains enzymatic activity

• Crosslinking approaches:

  • Chemical or photo-crosslinking to capture transient interactions

  • Mass spectrometry analysis to identify crosslinked residues

  • Provides direct evidence of proximity between specific regions

These approaches have revealed that the interaction between FtsI and FtsW involves the transmembrane region of FtsI (specifically its TMH) and transmembrane helices 8 and 9 of FtsW . Additionally, there's an important interaction between FtsW's extracellular loop 4 (ECL4) and the FtsI pedestal domain, which appears to modulate the activation of the complex .

How does the constriction control domain (CCD) of FtsL and FtsB regulate FtsWI activation?

The constriction control domains (CCDs) of FtsL and FtsB play a crucial role in regulating FtsWI activation during bacterial cell division. Current research indicates:

• The CCDs of both FtsL and FtsB are critical components for the transition from an "off" to an "on" state following accumulation of FtsN at the division site .

• Mutations in the CCD regions that promote divisome activation (known as "gain-of-function" mutations) typically reverse the polarity of specific side chains, such as:

  • FtsB E56A/G/K/V and D59H/V

  • FtsL E88V/K, D93G, and H94Y

• In the AlphaFold2 model of the FtsQLBWI complex, these CCD residues appear to have stabilizing effects in their wild-type form, suggesting that the model may represent the "off" configuration of the CCD .

• The transition between states likely involves a conformational change hinged at the CCD region, which then affects the activation state of the FtsWI complex .

• This regulatory mechanism ensures that peptidoglycan synthesis by FtsWI is properly coordinated with other divisome components and only occurs at the appropriate time during cell division.

The exact molecular mechanism by which the CCD regions transmit signals to FtsWI remains an active area of research. The hinge-like nature of the CCD suggests that conformational changes in this region could propagate to FtsWI, altering its enzymatic activity or substrate accessibility.

What is the role of FtsI in coordinating peptidoglycan synthesis with outer membrane constriction?

FtsI plays a critical role in coordinating peptidoglycan (PG) synthesis with outer membrane (OM) constriction during bacterial cell division:

• The transpeptidase activity of FtsI is essential for constriction of the FtsZ cytokinetic ring and subsequent cell division . When FtsI is inactivated, FtsZ rings remain unconstricted at the midpoint of the cell .

• FtsI works in concert with FtsW, with the former functioning as a transpeptidase and the latter as a glycosyltransferase . This coordination ensures proper synthesis of septal peptidoglycan.

• The activities of the FtsWI complex are regulated by other systems, particularly the Tol system, which is involved in OM constriction:

  • The lipoprotein LpoB activates the transpeptidase (TPase) activity of PBP1B (another peptidoglycan synthase)

  • CpoB down-regulates this TPase activity

  • When the Tol system is energized (indicating OM proximity), TolQR-TolA counteracts CpoB, allowing increased TPase activity

• This regulatory mechanism allows the inner membrane-bound septal leading edge to sense and respond to the status of the trailing edge (the OM):

  • Increased cross-linking activity is permitted only when the OM is brought into close proximity

  • This synchronization helps maintain a constant distance between the OM and inner membrane (IM)

• Restricting transpeptidase activity based on Tol status may also promote better coordination of septal PG synthesis and septal cleavage by OM-controlled amidases .

This intricate regulatory network ensures that the multiple layers of the bacterial cell envelope constrict in a coordinated manner during cell division, maintaining cellular integrity throughout the process.

How can high-throughput screening be designed to identify inhibitors of FtsI for antibiotic development?

Designing effective high-throughput screening (HTS) for FtsI inhibitors requires multiple complementary approaches:

• Enzymatic activity assays:

  • Develop fluorogenic or chromogenic substrates that report on FtsI transpeptidase activity

  • Optimize reaction conditions (pH, salt, cofactors) for maximum signal-to-noise ratio

  • Include controls for detecting compounds that interfere with the detection method

  • Consider using reconstituted FtsWI complexes for more physiologically relevant screening

• Protein-protein interaction assays:

  • Design assays that measure FtsI-FtsW interaction using techniques like FRET, AlphaScreen, or split-luciferase complementation

  • Target specific interfaces, such as the interaction between FtsI's TMH and FtsW's TM8/9 domains

  • Compounds disrupting these interactions could serve as allosteric inhibitors

• Structure-based virtual screening:

  • Utilize computational models from AlphaFold2 or crystal structures

  • Identify druggable pockets using tools like DoGSiteScorer (sites with scores ≥0.7)

  • Perform in silico docking of compound libraries to these sites

  • Select top-scoring compounds for biochemical validation

• Cell-based phenotypic screens:

  • Develop bacterial reporter strains with fluorescent markers for cell division

  • Screen for compounds that induce filamentation (characteristic of FtsI inhibition)

  • Include counter-screens to eliminate compounds with general toxicity

  • Use microscopy-based high-content screening to measure multiple parameters simultaneously

• Fragment-based approaches:

  • Screen libraries of low-molecular-weight fragments using NMR, SPR, or thermal shift assays

  • Identify fragments that bind to different regions of FtsI

  • Link or grow fragments to develop high-affinity inhibitors

• Data analysis and hit validation:

  • Implement robust statistical methods to identify true positives

  • Perform dose-response studies to determine potency

  • Test hits in orthogonal assays to confirm mechanism of action

  • Assess spectrum of activity across different bacterial species

  • Evaluate toxicity against mammalian cells

• Resistance mechanism prediction:

  • Use computational modeling to predict how known resistance mutations (e.g., A502V, N526K, V547I ) might affect inhibitor binding

  • Design inhibitors that maintain activity against common resistance mutations

  • Consider developing dual-target inhibitors that simultaneously inhibit FtsI and another essential protein

This multi-faceted approach can identify novel FtsI inhibitors with potential to overcome existing resistance mechanisms in pathogens.

What strategies can be used to create conditional FtsI mutants for functional studies?

Creating conditional FtsI mutants requires sophisticated genetic approaches to control protein expression or activity. Several effective strategies include:

• Inducible promoter systems:

  • Replace the native ftsI promoter with an inducible system like arabinose-inducible araBAD (PBAD) or IPTG-inducible lac or tac promoters

  • This allows for tight regulation of FtsI expression

  • For example, researchers have created arabinose-dependent strains where FtsI is only expressed in the presence of arabinose

• Degron-based approaches:

  • Fuse FtsI to a degron tag that targets the protein for degradation under specific conditions

  • Temperature-sensitive degrons allow protein degradation at elevated temperatures

  • Auxin-inducible degrons permit rapid protein depletion upon addition of auxin

• Temperature-sensitive (Ts) alleles:

  • Isolate or engineer ftsI temperature-sensitive mutations that render the protein non-functional at elevated temperatures

  • FtsI23Ts is an example of such a mutant used in research

  • These mutants allow for rapid inactivation of FtsI function by temperature shift

• Recombineering techniques:

  • Use lambda Red recombination system in specialized strains like DY330 for precise genetic manipulation

  • This approach allows for the introduction of point mutations or domain swaps in the chromosomal copy of ftsI

  • PCR fragments with homology arms directing recombination to specific genomic regions can be used

• Complementation systems:

  • Delete or inactivate the chromosomal ftsI gene while providing a functional copy on a plasmid

  • The plasmid-borne copy can contain various regulatory elements or mutations

  • This approach has been used to study the effects of specific mutations on FtsI function

• CRISPR-Cas9 methodology:

  • Use CRISPR-Cas9 to precisely edit the ftsI gene

  • Create knockouts, point mutations, or domain replacements

  • Combine with inducible systems for temporal control

When implementing these strategies, it's crucial to verify the conditional nature of the system by demonstrating that:

• The strain is viable under permissive conditions

• Growth is arrested or severely impaired under restrictive conditions

• The phenotype can be rescued by reverting to permissive conditions or by providing a wild-type copy of FtsI

How can CRISPR-Cas9 genome editing be optimized for introducing specific mutations in FtsI?

Optimizing CRISPR-Cas9 genome editing for FtsI mutations requires careful consideration of several factors:

• Guide RNA (gRNA) design:

  • Design gRNAs with high on-target efficiency and minimal off-target effects

  • Target sites as close as possible to the desired mutation site

  • Use tools like CHOPCHOP or E-CRISP specifically optimized for bacterial genomes

  • Avoid targeting sites containing known polymorphisms in the strain background

  • Create multiple gRNAs targeting the same region to increase success probability

• Repair template optimization:

  • Design single-stranded DNA oligonucleotides (ssODNs) or double-stranded DNA fragments as repair templates

  • Include 40-60 bp homology arms flanking the desired mutation

  • Introduce silent mutations in the PAM site or seed region to prevent re-cutting after successful editing

  • Consider incorporating selectable markers for easier screening, especially for mutations that don't confer obvious phenotypes

• Cas9 expression control:

  • Use inducible promoters to control Cas9 expression timing and level

  • Optimize induction conditions to minimize toxicity while maintaining editing efficiency

  • Consider temperature-sensitive plasmids for transient Cas9 expression

• Delivery methods:

  • For lab strains, standard transformation protocols may be sufficient

  • For clinical isolates or difficult-to-transform strains, consider electroporation or conjugation

  • Optimize transformation conditions to maximize efficiency for each strain

• Screening strategies:

  • Design PCR primers flanking the edited region

  • Use restriction enzyme digestion if the mutation creates or eliminates a restriction site

  • Consider mismatch cleavage assays like T7E1 for mutations without convenient restriction markers

  • Sanger sequencing to confirm successful editing

  • For resistance-conferring mutations, selective plating on appropriate antibiotics

• Strategies for essential genes:

  • FtsI is essential, so direct knockout is lethal

  • Use complementation with a wild-type copy during editing

  • Consider two-step approaches where resistance mutations are introduced first

  • Time Cas9 expression carefully to allow sufficient time for repair

• Validation approaches:

  • Confirm the mutation by sequencing

  • Verify phenotypic changes (e.g., antibiotic resistance, growth rates)

  • Check for off-target effects by sequencing potential off-target sites

  • Perform whole genome sequencing for critical strains to ensure no unintended mutations

These optimized strategies can successfully introduce specific mutations like A502V, N526K, or V547I into FtsI to study their effects on antibiotic resistance or protein function .

What techniques are available for creating fluorescently tagged FtsI constructs that maintain native function?

Creating functional fluorescently tagged FtsI constructs requires careful design to preserve native protein activity while enabling visualization. Several effective approaches include:

• GFP fusion design strategies:

  • N-terminal GFP fusions (GFP-FtsI) are generally more successful than C-terminal fusions, as the C-terminus may be critical for function

  • Include flexible linker sequences between GFP and FtsI to minimize interference with folding and function

  • For example, researchers have successfully used the linker sequence YKEFNNNMR, where YK are the last two residues of GFP and MR are the first two residues of FtsI

  • Consider using monomeric fluorescent proteins to prevent artificial dimerization

• Expression control methods:

  • Maintain expression at near-native levels using weak promoters or tightly regulated inducible systems

  • Fine-tune inducer concentrations (e.g., 2.5 μM IPTG for gfp-ftsI) to achieve optimal signal-to-noise ratio without overexpression artifacts

  • Use the native ftsI promoter when possible to preserve natural expression patterns

• Genomic integration techniques:

  • Integrate the fusion construct at the native locus to maintain normal regulation

  • The lambda Red recombination system can be used for precise genomic integration

  • PCR fragments with homology arms directing recombination to specific genomic regions facilitate this approach

• Validation approaches:

  • Confirm that the fusion protein complements ftsI deletion or depletion

  • Verify normal growth rates and cell morphology

  • Compare localization patterns with immunofluorescence using anti-FtsI antibodies

  • Test sensitivity to β-lactam antibiotics that target FtsI

• Alternative tagging strategies:

  • Consider smaller tags like SNAP, CLIP, or HaloTag that allow for pulse-chase labeling

  • Split-GFP complementation can be used for studying protein-protein interactions

  • Photoactivatable or photoconvertible fluorescent proteins for super-resolution microscopy

  • Site-specific fluorophore incorporation using unnatural amino acid technology for minimal perturbation

• Advanced imaging considerations:

  • For single-molecule studies, consider photobleaching-resistant fluorophores

  • For FRET studies, use appropriate donor-acceptor pairs

  • For pulse-chase experiments, utilize photoconvertible fluorescent proteins

Examples of successfully constructed fluorescent FtsI fusions include GFP-FtsI expressed from plasmids like pDSW311 (ampicillin resistance) or pDSW360 (kanamycin resistance) . These constructs have been used effectively to study FtsI localization and dynamics during bacterial cell division.

What statistical approaches are most appropriate for analyzing mutations in FtsI associated with antibiotic resistance?

• Regression analysis techniques:

  • Logistic regression for binary resistance status (resistant vs. susceptible)

  • Linear regression for continuous measurements like minimum inhibitory concentration (MIC) values (using log₂-normalized values to achieve normal distribution)

  • Multiple regression models that incorporate all relevant FtsI variants to assess their combined effects

  • Include appropriate covariates to control for confounding factors

• Significance testing and effect size evaluation:

  • Report p-values to assess statistical significance (typically using threshold p < 0.05)

  • Calculate odds ratios for logistic regression to quantify the increased odds of resistance

  • Determine effect sizes and confidence intervals for MIC changes

  • For A502V, N526K, and V547I mutations, significant contributions have been observed in both MIC values and resistance status models

• Model performance assessment:

  • Use adjusted R² to evaluate how well FtsI mutations explain variability in MIC values (e.g., model-adjusted R² = 0.5603)

  • Calculate area under the ROC curve (AUC) for binary classification models

  • Perform cross-validation to assess model generalizability

  • Analyze residuals to check model assumptions

• Handling linkage disequilibrium:

  • Calculate linkage disequilibrium between mutations using measures like squared Pearson correlation coefficient

  • Identify highly correlated mutations (e.g., N569S and V547I)

  • Consider principal component analysis to address collinearity

  • Use stepwise regression or LASSO to select the most informative variants

• Genome-wide association study (GWAS) approaches:

  • Implement microbial GWAS to test associations with resistance status or MIC measurements

  • Filter variants observed in fewer than a defined threshold of isolates (e.g., 10) to avoid spurious associations

  • Correct for population structure using principal component analysis or mixed models

  • Apply multiple testing correction (e.g., Bonferroni or false discovery rate)

• Haplotype analysis:

  • Compute and analyze combinations of FtsI variants (haplotypes) using specialized software

  • Implement network analyses and visualization using algorithms like TCS

  • Assess whether certain combinations of mutations have synergistic effects on resistance

These statistical approaches have successfully identified key mutations like A502V, N526K, and V547I as significantly associated with antibiotic resistance, offering insight into resistance mechanisms and potential targets for new antimicrobial development .

How can molecular dynamics simulations be used to understand the effects of mutations on FtsI structure and function?

Molecular dynamics (MD) simulations provide powerful insights into how mutations affect FtsI structure and function at the atomic level:

These simulation approaches provide mechanistic insights into how specific mutations contribute to changes in FtsI function and antibiotic resistance, complementing experimental findings with atomic-level details.

What bioinformatic approaches can be used to identify conserved functional domains and predict the impact of novel FtsI mutations?

Multiple bioinformatic approaches can be employed to analyze FtsI sequence conservation and predict mutational impacts:

• Multiple sequence alignment (MSA) analysis:

  • Perform intraspecies MSA to identify conservation patterns within a single bacterial species

  • Conduct interspecies MSA across different bacterial taxa to detect universally conserved regions

  • For instance, MSA analysis of A. baumannii FtsI proteins revealed that Pro508, Ala515, and Ala579 are prevalent amino acids in wild-type strains

  • Compare with ESKAPE pathogens to identify residues that differ across species barriers

  • Use visualization tools like Jalview or WebLogo to highlight conservation patterns

• Domain and motif identification:

  • Apply tools like PFAM, SMART, or InterPro to identify known functional domains

  • FtsI contains a transpeptidase domain and a domain of unknown function (pedestal domain)

  • Search for conserved catalytic motifs, particularly in the transpeptidase domain

  • Identify transmembrane regions using prediction tools like TMpred, HMMTOP, PHDhtm, TMHMM, and TopPred II

• Structure-based analysis:

  • Use AlphaFold2 or other protein structure prediction tools to generate models

  • Assess the structural context of mutations (e.g., whether they occur in alpha-helices, beta-sheets, or loops)

  • For example, Ala515Val and Ala579Thr occur within alpha helices, while Pro508Leu occurs in a loop structure

  • Identify binding sites using tools like DoGSiteScorer (sites with druggable scores ≥0.7)

• Mutation impact prediction:

  • Apply algorithms like SIFT, PolyPhen-2, or PROVEAN to predict functional impacts of amino acid substitutions

  • Consider physicochemical property changes (e.g., Pro508Leu changes from nonpolar to hydrophobic)

  • Analyze spatial occupancy alterations (e.g., Pro508Leu increases from 17 to 22 atoms)

  • Evaluate potential changes in hydrogen bonding patterns

• Coevolution analysis:

  • Identify coevolving residue pairs using methods like direct coupling analysis (DCA) or mutual information

  • These pairs often represent physically interacting regions or functionally linked sites

  • Particularly useful for predicting contacts between FtsI and its binding partners like FtsW

• Network analysis of mutation patterns:

  • Construct haplotype networks to visualize common combinations of mutations

  • Identify mutation patterns associated with specific resistance profiles

  • Use tools like the pegas R package with algorithms such as TCS

• Integration with experimental data:

  • Combine bioinformatic predictions with laboratory data on mutation effects

  • Validate predictions using site-directed mutagenesis and functional assays

  • Correlate sequence variation with phenotypic data like antibiotic resistance

By integrating these approaches, researchers can gain comprehensive insights into FtsI structure-function relationships and make informed predictions about the impacts of novel mutations, particularly in the context of emerging antibiotic resistance.

How can understanding FtsI function contribute to the development of novel antibiotics?

Understanding FtsI function provides multiple avenues for novel antibiotic development:

• Structure-based drug design targeting the transpeptidase domain:

  • Utilize high-resolution structures or AlphaFold2 models of FtsI to identify druggable pockets

  • Design inhibitors that specifically target the transpeptidase active site

  • Account for known resistance mutations (A502V, N526K, V547I) in the design process

  • Develop compounds that maintain activity against resistant variants

• Targeting protein-protein interactions:

  • Disrupt critical interactions between FtsI and other divisome components

  • Focus on the interface between FtsI's TMH and FtsW's TM8/9

  • Target the interaction between FtsW's ECL4 and FtsI's pedestal domain, which appears to modulate activation

  • Develop peptide-based inhibitors that mimic interaction interfaces

• Allosteric inhibition strategies:

  • Identify allosteric sites that regulate FtsI function

  • Design compounds that lock FtsI in inactive conformations

  • Target sites distant from the active site that may be less susceptible to resistance mutations

  • Focus on regions involved in conformational changes between extended and compact states

• Dual-target approaches:

  • Develop molecules that simultaneously inhibit FtsI and other division proteins

  • Target the FtsWI complex as a functional unit rather than individual proteins

  • Create hybrid molecules that inhibit both transpeptidase and glycosyltransferase activities

  • This approach may increase the barrier to resistance development

• Exploiting regulatory mechanisms:

  • Target the regulatory pathway involving FtsQLB that controls FtsWI activation

  • Develop compounds that prevent the transition from "off" to "on" states

  • Focus on the constriction control domain (CCD) regions of FtsL and FtsB

  • This indirect approach may face less selective pressure for resistance

• Species-selective targeting:

  • Exploit structural differences in FtsI between bacterial species

  • Design narrow-spectrum antibiotics that target specific pathogens

  • Use interspecies MSA data to identify unique regions in target pathogens

  • This approach may reduce collateral damage to beneficial microbiota

• Combination therapies:

  • Develop FtsI inhibitors that synergize with existing antibiotics

  • Target different steps in peptidoglycan synthesis

  • Combine with molecules that affect outer membrane integrity to enhance penetration

  • This multi-pronged approach can overcome existing resistance mechanisms

These strategies represent promising directions for developing novel antibiotics targeting FtsI, potentially addressing the growing challenge of antimicrobial resistance.

What role does FtsI play in biofilm formation and how might this impact antimicrobial strategies?

FtsI's role in biofilm formation offers insights for novel antimicrobial approaches:

• FtsI's dual function in cell division and biofilm development:

  • As a critical peptidoglycan synthase, FtsI influences cell morphology and division, which indirectly affects biofilm architecture

  • Sub-inhibitory concentrations of β-lactams targeting FtsI can induce filamentous growth, altering biofilm structure

  • Cell wall remodeling enzymes, including transpeptidases like FtsI, contribute to the extracellular matrix structure in biofilms

  • Disruptions in peptidoglycan synthesis can trigger stress responses that modify biofilm formation genes

• FtsI inhibition impacts on biofilm formation:

  • β-lactam antibiotics targeting FtsI can paradoxically enhance biofilm formation at sub-inhibitory concentrations

  • Cell filamentation due to FtsI inhibition can increase initial surface attachment

  • Altered peptidoglycan cross-linking may affect cell-to-cell interactions within biofilms

  • Changes in cell envelope stress responses can upregulate biofilm-associated genes

• The relationship between antimicrobial resistance and biofilm formation:

  • Mutations in FtsI that confer β-lactam resistance (e.g., A502V, N526K, V547I) may simultaneously affect biofilm properties

  • Altered peptidoglycan cross-linking due to modified FtsI activity could change biofilm matrix composition

  • Resistant bacteria with FtsI mutations may exhibit different biofilm architectures or dispersal patterns

  • Biofilm growth may further enhance the selection of resistant FtsI variants

• Anti-biofilm strategies targeting FtsI:

  • Develop inhibitors that specifically disrupt FtsI's role in biofilm formation without triggering stress responses

  • Design molecules that enhance biofilm dispersal by modulating FtsI activity

  • Create combination therapies that simultaneously target FtsI and biofilm-specific proteins

  • Develop adjuvants that prevent biofilm induction when co-administered with β-lactams

• FtsI as a biofilm vulnerability target:

  • Target FtsI-dependent processes during key phases of biofilm development (attachment, maturation, or dispersal)

  • Exploit differences in FtsI activity between planktonic and biofilm states

  • Design approaches that enhance antibiotic penetration into biofilms while targeting FtsI

  • Identify potential synergies between FtsI inhibitors and biofilm matrix degrading enzymes

• Future research directions:

  • Investigate FtsI expression and localization patterns in biofilm-associated cells compared to planktonic cells

  • Characterize peptidoglycan composition and cross-linking in biofilms formed by FtsI mutant strains

  • Evaluate how FtsI inhibition affects biofilm formation in different bacterial species

  • Develop high-throughput screening methods to identify compounds that disrupt FtsI-dependent biofilm processes

Understanding the complex relationship between FtsI function and biofilm formation provides opportunities for developing targeted anti-biofilm strategies, potentially addressing the challenge of biofilm-associated infections that are notoriously difficult to eradicate with conventional antibiotics.

How might FTSI research contribute to synthetic biology applications beyond antibiotic development?

FtsI research offers diverse applications in synthetic biology beyond antimicrobial development:

• Engineered cell division systems:

  • Create bacteria with controllable cell size by modulating FtsI activity

  • Engineer elongated cells for enhanced productivity in biomanufacturing

  • Develop strains with accelerated division rates for faster bioproduction

  • Design bacteria that divide asymmetrically for specialized functions

• Synthetic cell shape engineering:

  • Modify FtsI and its interaction partners to create bacteria with novel morphologies

  • Engineer branched or filamentous bacteria for increased surface area in environmental applications

  • Create miniaturized cells for specialized delivery systems

  • Design bacteria with controlled division planes for three-dimensional structures

• Peptidoglycan engineering applications:

  • Leverage FtsI's transpeptidase activity to incorporate non-canonical amino acids into cell walls

  • Create modified peptidoglycan with enhanced stability for industrial applications

  • Design bacteria with cell walls resistant to extreme environmental conditions

  • Develop custom peptidoglycan structures with novel material properties

• Bio-containment strategies:

  • Engineer dependence on synthetic FtsI variants for environmental containment

  • Create kill switches based on inducible FtsI disruption

  • Develop auxotrophic strains requiring specific molecules for FtsI function

  • Design genetic circuits that couple essential production to proper FtsI activation

• Biosensing and bioremediation applications:

  • Create biosensors where environmental stimuli trigger changes in FtsI localization or activity

  • Engineer bacteria that modify their division patterns in response to pollutants

  • Develop strains with enhanced surface-to-volume ratios for bioremediation

  • Design systems where biofilm formation is coupled to sensing of specific compounds

• Cell-free synthetic biology applications:

  • Utilize purified FtsI and other divisome components to create synthetic peptidoglycan

  • Develop cell-free systems for screening peptidoglycan-targeting compounds

  • Create minimal systems for studying divisome assembly and function

  • Design synthetic vesicles with peptidoglycan-like outer shells for specialized applications

• Protein engineering platforms:

  • Use knowledge of FtsI structure-function relationships to design novel enzymes

  • Develop scaffold proteins based on FtsI's modular architecture

  • Create chimeric proteins incorporating FtsI domains with novel functionalities

  • Engineer protein-protein interaction systems based on FtsI's natural interaction network

These innovative applications demonstrate how fundamental research on FtsI can contribute to diverse fields within synthetic biology, potentially creating new solutions for biotechnology, biomanufacturing, environmental remediation, and biomedical applications beyond traditional antibiotic development.

How do FtsI homologs differ across bacterial species and what implications does this have for broad-spectrum antibiotic development?

FtsI homologs show important variations across bacterial species, presenting both challenges and opportunities for antibiotic development:

• Sequence and structural divergence:

  • Interspecies multiple sequence alignment (MSA) reveals variable conservation patterns across bacterial species

  • While the catalytic transpeptidase domain shows higher conservation, other regions display significant variability

  • In positions corresponding to A. baumannii FtsI residues 515, 579, and 508, ESKAPE pathogens show different prevalent amino acids:

    • Position 515: Leu and Tyr are common (vs. Ala in A. baumannii)

    • Position 579: Leu and Glu are prevalent (vs. Ala in A. baumannii)

    • Position 508: Lys and Ala are frequent (vs. Pro in A. baumannii)

  • These variations could affect inhibitor binding and specificity

• Functional conservation despite sequence divergence:

  • Despite sequence differences, the core transpeptidase function is preserved across species

  • The catalytic mechanism involving serine-based nucleophilic attack is highly conserved

  • Similar organization into transmembrane, pedestal, and transpeptidase domains is maintained

  • Key protein-protein interactions, particularly with FtsW, are preserved across diverse bacteria

• Species-specific protein interactions:

  • Interfaces between FtsI and other divisome proteins may vary between species

  • The exact composition of the divisome can differ across bacterial taxa

  • Regulatory mechanisms controlling FtsI activation might be species-specific

  • These differences could be exploited for selective targeting

• Implications for broad-spectrum antibiotic development:

  • Target highly conserved catalytic residues for broad-spectrum activity

  • Focus on the transpeptidase domain's active site, which shows the highest conservation

  • Design flexible inhibitors that can accommodate minor species variations

  • Consider chemical scaffolds that mimic the universal peptidoglycan substrate

• Narrow-spectrum targeting opportunities:

  • Exploit unique structural features of specific pathogens' FtsI proteins

  • Target species-specific regions outside the catalytic domain

  • Design inhibitors that interact with both conserved and variable regions

  • Develop pathogen-specific drugs that spare beneficial microbiota

• Resistance prediction across species:

  • Analyze how common resistance mutations manifest in different bacterial species

  • Monitor cross-species transfer of resistance mechanisms

  • Identify species-specific resistance barriers that might affect drug design

  • Key mutations like A502V, N526K, and V547I may have varying effects across species

• Model selection for drug development:

  • Choose representative species that cover the structural diversity of FtsI

  • Include both Gram-positive and Gram-negative bacteria in screening cascades

  • Consider species-specific differences in cell envelope permeability

  • Validate findings across multiple bacterial species to ensure broad applicability

Understanding these interspecies differences enables more sophisticated antibiotic development strategies, potentially leading to both broad-spectrum antibiotics targeting conserved features and narrow-spectrum drugs exploiting species-specific characteristics.

What model systems are most appropriate for studying FtsI function in different research contexts?

Different research questions about FtsI require specialized model systems:

• Laboratory strains of E. coli:

  • Advantages: Well-characterized genetics, extensive toolkits, ease of manipulation

  • Applications: Basic mechanism studies, protein localization, genetic interaction mapping

  • Specific strains: DY330 for lambda Red recombineering , BL21(DE3) for protein expression

  • Key techniques: GFP fusions, temperature-sensitive mutants, depletion strains

• Bacillus subtilis (Gram-positive model):

  • Advantages: Different cell wall architecture, natural competence for transformation

  • Applications: Comparative studies between Gram-positive and Gram-negative systems

  • Techniques: Fluorescent protein fusions to study localization patterns

  • Key insights: Differences in divisome assembly and regulation compared to E. coli

• Pathogenic clinical isolates:

  • Advantages: Direct relevance to infectious disease, natural diversity of FtsI variants

  • Applications: Antibiotic resistance studies, species-specific function analysis

  • Examples: H. influenzae isolates , A. baumannii , ESKAPE pathogens

  • Approaches: Genome-wide association studies, systematic meta-analysis of clinical samples

• Reconstituted in vitro systems:

  • Advantages: Precise control of components, direct biochemical measurements

  • Applications: Enzymatic activity assays, inhibitor screening, structural studies

  • Components: Purified recombinant FtsI, synthetic peptidoglycan substrates

  • Advanced systems: Reconstituted FtsWI complexes in liposomes or nanodiscs

• Cell-free expression systems:

  • Advantages: Rapid protein production, avoids toxicity issues

  • Applications: Structure-function studies, protein engineering, inhibitor screening

  • Types: E. coli extracts, PURE system, wheat germ extracts for eukaryotic-like folding

  • Applications: Expression of toxic or difficult-to-express FtsI variants

• Minimal cells and synthetic biology platforms:

  • Advantages: Reduced complexity, controlled genetic background

  • Applications: Essential function analysis, minimal divisome determination

  • Examples: JCVI-syn3.0 or other genome-minimized bacteria

  • Approaches: Bottom-up reconstruction of division machinery with defined components

• Computational models:

  • Advantages: High-throughput, atomic-level detail, testable predictions

  • Applications: Structure prediction, dynamics simulation, resistance mechanism modeling

  • Tools: AlphaFold2 for structure prediction , molecular dynamics for dynamics

  • Integration: Combine with experimental validation of key predictions

• Specialized microscopy systems:

  • Advantages: Real-time visualization of FtsI dynamics and localization

  • Applications: Division process studies, protein-protein interaction analysis

  • Techniques: Super-resolution microscopy (PALM/STORM), single-molecule tracking

  • Setups: Microfluidic devices for long-term imaging under controlled conditions

The choice of model system should align with specific research questions:

• For basic mechanism studies: E. coli or B. subtilis

• For antibiotic development: Clinical isolates and reconstituted systems

• For structural biology: Purified proteins and computational models

• For systems biology: Minimal cells and comprehensive genetic libraries

Integrating multiple model systems provides complementary insights into FtsI function across different contexts and scales of analysis.

How have computational modeling and AI-based structure prediction advanced our understanding of FtsI structure and function?

Computational modeling and AI approaches have revolutionized FtsI research:

• AlphaFold2 contributions to structural understanding:

  • Predicted high-quality models of FtsI in isolation and in complex with partner proteins

  • Successfully modeled the FtsQLBWI complex, revealing detailed interaction interfaces

  • Identified key interaction surfaces, such as between FtsI's TMH and FtsW's TM8/9

  • Provided structural insights into the constriction control domain (CCD) regions of FtsL and FtsB

• Validation and comparison with experimental data:

  • AlphaFold2 models show good correspondence with available partial structures and prior computational models

  • Predicted FtsLB component agrees well with the previously published "Y-model" of this subcomplex

  • FtsI-FtsW interactions in the model align with mutational data on key interface residues

  • Successful prediction of the interaction between FtsQ and FtsB through β-sheet augmentation

• Multi-state conformational insights:

  • Computational models suggest FtsI can adopt both extended and compact configurations

  • AlphaFold2 model appears to capture the "off" configuration of the CCD region

  • Homology modeling based on RodA-PBP2 structure has provided insights into the compact conformation

  • These multi-state models help explain how FtsI might transition between different functional states

• Mutational impact analysis:

  • Computational modeling has revealed how mutations affect protein structure and function

  • For A. baumannii FtsI, models showed how mutations like Ala515Val and Ala579Thr disrupt hydrogen bonds with nearby residues Ala512 and Gly574, respectively

  • Predictions of spatial occupancy changes for mutations (Pro508Leu: 17→22 atoms; Ala515Val: 13→19 atoms; Ala579Thr: 13→17 atoms)

  • Integration with experimental data on resistance-associated mutations like A502V, N526K, and V547I

• Binding site and druggability prediction:

  • Tools like DoGSiteScorer have identified potential binding sites with druggable scores ≥0.7

  • Computational analysis has revealed proximity of resistance-associated mutations to binding sites

  • Virtual screening and molecular docking enable in silico evaluation of potential inhibitors

  • Machine learning approaches can predict compound activity against wild-type and mutant FtsI

• Integration of multiple computational approaches:

  • Combined use of structure prediction, molecular dynamics, binding site analysis, and virtual screening

  • Integration of sequence analysis (MSA) with structural modeling for evolutionary insights

  • Correlation of computational predictions with experimental phenotypic data

  • Iterative refinement of models based on experimental validation

• Limitations and ongoing challenges:

  • Uncertainty in predicting oligomeric states (AlphaFold2 predicts monomeric forms while experimental evidence suggests heterotetramer structures)

  • Difficulty in modeling dynamic conformational changes during the division cycle

  • Challenges in accurately predicting effects of mutations on protein-protein interfaces

  • Need for experimental validation of computational predictions