Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota

Characterizing the detection of inactivated Mycoplasma hyopneumoniae DNA in the respiratory tract of pigs

A positive Mycoplasma hyopneumoniae PCR result in a clinical specimen may eventually represent the mere detection of non-viable bacteria, complicating the diagnostic interpretation. Thus, the objective of this study was to evaluate the PCR detection of non-viable M. hyopneumoniae and its residual cell-free DNA in live pigs. Pigs were inoculated with either active or inactivated M. hyopneumoniae and were sampled for up to 14 days. Mycoplasma hyopneumoniae was not detected by PCR at any timepoint in pigs inoculated with the inactivated bacterium, suggesting that in healthy pigs, the non-viable M. hyopneumoniae DNA was rapidly sensed and cleared.

Transcriptional profiling and genetic analysis of a cystic fibrosis airway-relevant model shows asymmetric responses to growth in a polymicrobial community

Bacterial infections in the lungs of persons with cystic fibrosis are typically composed of multispecies biofilm-like communities, which modulate clinically relevant phenotypes that cannot be explained in the context of a single species culture. Most analyses to date provide a picture of the transcriptional responses of individual pathogens; however, there is relatively little data describing the transcriptional landscape of clinically relevant multispecies communities. Harnessing a previously described cystic fibrosis-relevant, polymicrobial community model consisting of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus sanguinis, and Prevotella melaninogenica, we performed an RNA-Seq analysis on the biofilm population to elucidate the transcriptional profiles of the community grown in artificial sputum medium (ASM) as compared to growth in monoculture, without mucin, and in fresh medium supplemented with tobramycin. We provide evidence that, although the transcriptional profile of P. aeruginosa is community agnostic, the transcriptomes of S. aureus and S. sanguinis are community aware. Furthermore, P. aeruginosa and P. melaninogenica are transcriptionally sensitive to the presence of mucin in ASM, whereas S. aureus and S. sanguinis largely do not alter their transcriptional profiles in the presence of mucin when grown in a community. Only P. aeruginosa shows a robust response to tobramycin. Genetic studies of mutants altered in community-specific growth provide complementary data regarding how these microbes adapt to a community context. IMPORTANCE Polymicrobial infections constitute the majority of infections in the cystic fibrosis (CF) airway, but their study has largely been neglected in a laboratory setting. Our lab previously reported a polymicrobial community that can help explain clinical outcomes in the lungs of persons with CF. Here, we obtained transcriptional profiles of the community versus monocultures to provide transcriptional information about how this model community responds to CF-related growth conditions and perturbations. Genetic studies provide complementary functional outputs to assess how the microbes adapt to life in a community.

Natural and anthropogenic carbon input affect microbial activity in salt marsh sediment

Salt marshes are dynamic, highly productive ecosystems positioned at the interface between terrestrial and marine systems. They are exposed to large quantities of both natural and anthropogenic carbon input, and their diverse sediment-hosted microbial communities play key roles in carbon cycling and remineralization. To better understand the effects of natural and anthropogenic carbon on sediment microbial ecology, several sediment cores were collected from Little Sippewissett Salt Marsh (LSSM) on Cape Cod, MA, USA and incubated with either Spartina alterniflora cordgrass or diesel fuel. Resulting shifts in microbial diversity and activity were assessed via bioorthogonal non-canonical amino acid tagging (BONCAT) combined with fluorescence-activated cell sorting (FACS) and 16S rRNA gene amplicon sequencing. Both Spartina and diesel amendments resulted in initial decreases of microbial diversity as well as clear, community-wide shifts in metabolic activity. Multi-stage degradative frameworks shaped by fermentation were inferred based on anabolically active lineages. In particular, the metabolically versatile Marinifilaceae were prominent under both treatments, as were the sulfate-reducing Desulfovibrionaceae, which may be attributable to their ability to utilize diverse forms of carbon under nutrient limited conditions. By identifying lineages most directly involved in the early stages of carbon processing, we offer potential targets for indicator species to assess ecosystem health and highlight key players for selective promotion of bioremediation or carbon sequestration pathways.

BONCAT-FACS-Seq reveals the active fraction of a biocrust community undergoing a wet-up event

Determining which microorganisms are active within soil communities remains a major technical endeavor in microbial ecology research. One promising method to accomplish this is coupling bioorthogonal non-canonical amino acid tagging (BONCAT) with fluorescence activated cell sorting (FACS) which sorts cells based on whether or not they are producing new proteins. Combined with shotgun metagenomic sequencing (Seq), we apply this method to profile the diversity and potential functional capabilities of both active and inactive microorganisms in a biocrust community after being resuscitated by a simulated rain event. We find that BONCAT-FACS-Seq is capable of discerning the pools of active and inactive microorganisms, especially within hours of applying the BONCAT probe. The active and inactive components of the biocrust community differed in species richness and composition at both 4 and 21 h after the wetting event. The active fraction of the biocrust community is marked by taxa commonly observed in other biocrust communities, many of which play important roles in species interactions and nutrient transformations. Among these, 11 families within the Firmicutes are enriched in the active fraction, supporting previous reports indicating that the Firmicutes are key early responders to biocrust wetting. We highlight the apparent inactivity of many Actinobacteria and Proteobacteria through 21 h after wetting, and note that members of the Chitinophagaceae, enriched in the active fraction, may play important ecological roles following wetting. Based on the enrichment of COGs in the active fraction, predation by phage and other bacterial members, as well as scavenging and recycling of labile nutrients, appear to be important ecological processes soon after wetting. To our knowledge, this is the first time BONCAT-FACS-Seq has been applied to biocrust samples, and therefore we discuss the potential advantages and shortcomings of coupling metagenomics to BONCAT to intact soil communities such as biocrust. In all, by pairing BONCAT-FACS and metagenomics, we are capable of highlighting the taxa and potential functions that typifies the microbes actively responding to a rain event.

Optimized Bioorthogonal Non-canonical Amino Acid Tagging to Identify Serotype-Specific Biomarkers in Verotoxigenic Escherichia coli

According to Canada's Food Report Card 2016, there are 4 million foodborne illnesses acquired each year in the nation alone. The leading causes of foodborne illness are pathogenic bacteria such as shigatoxigenic/verotoxigenic Escherichia coli (STEC/VTEC) and Listeria monocytogenes. Most current detection methods used to identify these bacterial pathogens are limited in their validity since they are not specific to detecting metabolically active organisms, potentially generating false-positive results from non-living or non-viable bacteria. Previously, our lab developed an optimized bioorthogonal non-canonical amino acid tagging (BONCAT) method which allows for the labeling of translationally active wild-type pathogenic bacteria. Incorporation of homopropargyl glycine (HPG) into the cellular surfaces of bacteria allows for protein tagging using the bioorthogonal alkyne handle to report on the presence of pathogenic bacteria. Here, we use proteomics to identify more than 400 proteins differentially detected by BONCAT between at least two of five different VTEC serotypes. These findings pave the way for future examination of these proteins as biomarkers in BONCAT-utilizing assays.

Differential toxicity of bioorthogonal non-canonical amino acids (BONCAT) in Escherichia coli

Single-cell methods allow studying the activity of single bacterial cells, potentially shedding light on regulatory mechanisms involved in services like biochemical cycling. Bioorthogonal non-canonical amino acid tagging (BONCAT) is a promising method for studying bacterial activity in natural communities, using the methionine analogues L-azidohomoalanine (AHA) and L-homopropargylglycine (HPG) to track protein production in single cells. Both AHA and HPG have been deemed non-toxic, but recent findings suggest that HPG affects bacterial metabolism. In this study we examined the effect of AHA and HPG on Escherichia coli with respect to acute toxicity and growth. E. coli exposed to 5.6-90 μM HPG showed no growth, and the growth rate was significantly reduced at >0.35 μM HPG, compared to the HPG-free control. In contrast, E. coli showed growth at concentrations up to 9 mM AHA. In assays where AHA or HPG were added during the exponential growth phase, the growth sustained but the growth rate was immediately reduced at the highest concentrations (90 μM HPG and 10 mM AHA). Prolonged incubations (20h) with apparently non-toxic concentrations suggest that the cells incorporating NCAAs fail to divide and do not contribute to the next generation resulting in the relative abundance of labelled cells to decrease over time. These results show that HPG and AHA have different impact on the growth of E. coli. Both concentration and incubation time affect the results and need to be considered when designing BONCAT experiments and evaluating results. Time course incubations are suggested as a possible way to obtain more reliable results.

Using click chemistry to study microbial ecology and evolution

Technological advances have largely driven the revolution in our understanding of the structure and function of microbial communities. Culturing, long the primary tool to probe microbial life, was supplanted by sequencing and other -omics approaches, which allowed detailed quantitative insights into species composition, metabolic potential, transcriptional activity, secretory responses and more. Although the ability to characterize "who's there" has never been easier or cheaper, it remains technically challenging and expensive to understand what the diverse species and strains that comprise microbial communities are doing in situ, and how these behaviors change through time. Our aim in this brief review is to introduce a developing toolkit based on click chemistry that can accelerate and reduce the expense of functional analyses of the ecology and evolution of microbial communities. After first outlining the history of technological development in this field, we will discuss key applications to date using diverse labels, including BONCAT, and then end with a selective (biased) view of areas where click-chemistry and BONCAT-based approaches stand to have a significant impact on our understanding of microbial communities.

Translatome analyses by bio-orthogonal non-canonical amino acid labeling reveal that MR1-activated MAIT cells induce an M1 phenotype and antiviral programming in antigen-presenting monocytes

MAIT cells are multifunctional innate-like effector cells recognizing bacterial-derived vitamin B metabolites presented by the non-polymorphic MHC class I related protein 1 (MR1). However, our understanding of MR1-mediated responses of MAIT cells upon their interaction with other immune cells is still incomplete. Here, we performed the first translatome study of primary human MAIT cells interacting with THP-1 monocytes in a bicellular system. We analyzed the interaction between MAIT and THP-1 cells in the presence of the activating 5-OP-RU or the inhibitory Ac-6-FP MR1-ligand. Using bio-orthogonal non-canonical amino acid tagging (BONCAT) we were able to enrich selectively those proteins that were newly translated during MR1-dependent cellular interaction. Subsequently, newly translated proteins were measured cell-type-specifically by ultrasensitive proteomics to decipher the coinciding immune responses in both cell types. This strategy identified over 2,000 MAIT and 3,000 THP-1 active protein translations following MR1 ligand stimulations. Translation in both cell types was found to be increased by 5-OP-RU, which correlated with their conjugation frequency and CD3 polarization at MAIT cell immunological synapses in the presence of 5-OP-RU. In contrast, Ac-6-FP only regulated a few protein translations, including GSK3B, indicating an anergic phenotype. In addition to known effector responses, 5-OP-RU-induced protein translations uncovered type I and type II Interferon-driven protein expression profiles in both MAIT and THP-1 cells. Interestingly, the translatome of THP-1 cells suggested that activated MAIT cells can impact M1/M2 polarization in these cells. Indeed, gene and surface expression of CXCL10, IL-1β, CD80, and CD206 confirmed an M1-like phenotype of macrophages being induced in the presence of 5-OP-RU-activated MAIT cells. Furthermore, we validated that the Interferon-driven translatome was accompanied by the induction of an antiviral phenotype in THP-1 cells, which were found able to suppress viral replication following conjugation with MR1-activated MAIT cells. In conclusion, BONCAT translatomics extended our knowledge of MAIT cell immune responses at the protein level and discovered that MR1-activated MAIT cells are sufficient to induce M1 polarization and an anti-viral program of macrophages.

Detection and Quantification of Antimicrobial-Resistant Cells in Aquatic Environments by Bioorthogonal Noncanonical Amino Acid Tagging

Aquatic environments are important reservoirs of antibiotic wastes, antibiotic resistance genes, and bacteria, enabling the persistence and proliferation of antibiotic resistance in different bacterial populations. To prevent the spread of antibiotic resistance, effective approaches to detect antimicrobial susceptibility in aquatic environments are highly desired. In this work, we adopt a metabolism-based bioorthogonal noncanonical amino acid tagging (BONCAT) method to detect, visualize, and quantify active antimicrobial-resistant bacteria in water samples by exploiting the differences in bacterial metabolic responses to antibiotics. The BONCAT approach can be applied to rapidly detect bacterial resistance to multiple antibiotics within 20 min of incubation, regardless of whether they act on proteins or DNA. In addition, the combination of BONCAT with the microscope enables the intuitive characterization of antibiotic-resistant bacteria in mixed systems at single-cell resolution. Furthermore, BONCAT coupled with flow cytometry exhibits good performance in determining bacterial resistance ratios to chloramphenicol and population heterogeneity in hospital wastewater samples. In addition, this approach is also effective in detecting antibiotic-resistant bacteria in natural water samples. Therefore, such a simple, fast, and efficient BONCAT-based approach will be valuable in monitoring the increase and spread of antibiotic resistance within natural and engineered aquatic environments.

Extra benefit of microalgae in raw piggery wastewater treatment: pathogen reduction

BACKGROUND

Monitoring microbial communities especially focused on pathogens in newly developed wastewater treatment systems is recommended for public health. Thus, we investigated the microbial community shift in a pilot-scale microalgal treatment system for piggery wastewater.

RESULTS

Microalgae showed reasonable removal efficiencies for COD and ammonia, resulting in higher transparency of the final effluent. Metagenome and microbial diversity analyses showed that heterotrophic microalgal cultivation barely changed the bacterial community; however, the mixotrophic microalgal cultivation induced a sudden change. In addition, an evaluation of risk groups (RGs) of bacteria showed that raw piggery wastewater included abundant pathogens, and the microalgal treatment of the raw piggery wastewater decreased the RG2 pathogens by 63%. However, co-cultivation of microalgae and the most dominant RG2 pathogen, Oligella, showed no direct effects between them.

CONCLUSIONS

Thus, a microbial interaction network was constructed to elucidate algae-bacteria interrelationships, and the decrease in Oligella was indirectly connected with microalgal growth via Brevundimonas, Sphingopyxis, and Stenotrophomonas. In a validation test, 3 among 4 connecting bacterial strains exhibited inhibition zones against Oligella. Therefore, we showed that microalgal wastewater treatment causes a decrease in RG2 bacteria, which is an indirect impact of microalgae associated with bacteria. Video abstract.

Therapeutic peptides: current applications and future directions

Peptide drug development has made great progress in the last decade thanks to new production, modification, and analytic technologies. Peptides have been produced and modified using both chemical and biological methods, together with novel design and delivery strategies, which have helped to overcome the inherent drawbacks of peptides and have allowed the continued advancement of this field. A wide variety of natural and modified peptides have been obtained and studied, covering multiple therapeutic areas. This review summarizes the efforts and achievements in peptide drug discovery, production, and modification, and their current applications. We also discuss the value and challenges associated with future developments in therapeutic peptides.

Microbial Community Response to Polysaccharide Amendment in Anoxic Hydrothermal Sediments of the Guaymas Basin

Organic-rich, hydrothermal sediments of the Guaymas Basin are inhabited by diverse microbial communities including many uncultured lineages with unknown metabolic potential. Here we investigated the short-term effect of polysaccharide amendment on a sediment microbial community to identify taxa involved in the initial stage of macromolecule degradation. We incubated anoxic sediment with cellulose, chitin, laminarin, and starch and analyzed the total and active microbial communities using bioorthogonal non-canonical amino acid tagging (BONCAT) combined with fluorescence-activated cell sorting (FACS) and 16S rRNA gene amplicon sequencing. Our results show a response of an initially minor but diverse population of Clostridia particularly after amendment with the lower molecular weight polymers starch and laminarin. Thus, Clostridia may readily become key contributors to the heterotrophic community in Guaymas Basin sediments when substrate availability and temperature range permit their metabolic activity and growth, which expands our appreciation of the potential diversity and niche differentiation of heterotrophs in hydrothermally influenced sediments. BONCAT-FACS, although challenging in its application to complex samples, detected metabolic responses prior to growth and thus can provide complementary insight into a microbial community's metabolic potential and succession pattern. As a primary application of BONCAT-FACS on a diverse deep-sea sediment community, our study highlights important considerations and demonstrates inherent limitations associated with this experimental approach.

Multi-Omics Study of Keystone Species in a Cystic Fibrosis Microbiome

Ecological networking and in vitro studies predict that anaerobic, mucus-degrading bacteria are keystone species in cystic fibrosis (CF) microbiomes. The metabolic byproducts from these bacteria facilitate the colonization and growth of CF pathogens like Pseudomonas aeruginosa. Here, a multi-omics study informed the control of putative anaerobic keystone species during a transition in antibiotic therapy of a CF patient. A quantitative metagenomics approach combining sequence data with epifluorescence microscopy showed that during periods of rapid lung function loss, the patient's lung microbiome was dominated by the anaerobic, mucus-degrading bacteria belonging to Streptococcus, Veillonella, and Prevotella genera. Untargeted metabolomics and community cultures identified high rates of fermentation in these sputa, with the accumulation of lactic acid, citric acid, and acetic acid. P. aeruginosa utilized these fermentation products for growth, as indicated by quantitative transcriptomics data. Transcription levels of P. aeruginosa genes for the utilization of fermentation products were proportional to the abundance of anaerobic bacteria. Clindamycin therapy targeting Gram-positive anaerobes rapidly suppressed anaerobic bacteria and the accumulation of fermentation products. Clindamycin also lowered the abundance and transcription of P. aeruginosa, even though this patient's strain was resistant to this antibiotic. The treatment stabilized the patient's lung function and improved respiratory health for two months, lengthening by a factor of four the between-hospitalization time for this patient. Killing anaerobes indirectly limited the growth of P. aeruginosa by disrupting the cross-feeding of fermentation products. This case study supports the hypothesis that facultative anaerobes operated as keystone species in this CF microbiome. Personalized multi-omics may become a viable approach for routine clinical diagnostics in the future, providing critical information to inform treatment decisions.

Model Systems to Study the Chronic, Polymicrobial Infections in Cystic Fibrosis: Current Approaches and Exploring Future Directions

A recent workshop titled “Developing Models to Study Polymicrobial Infections,” sponsored by the Dartmouth Cystic Fibrosis Center (DartCF), explored the development of new models to study the polymicrobial infections associated with the airways of persons with cystic fibrosis (CF). The workshop gathered 35+ investigators over two virtual sessions. Here, we present the findings of this workshop, summarize some of the challenges involved with developing such models, and suggest three frameworks to tackle this complex problem. The frameworks proposed here, we believe, could be generally useful in developing new model systems for other infectious diseases. Developing and validating new approaches to study the complex polymicrobial communities in the CF airway could open windows to new therapeutics to treat these recalcitrant infections, as well as uncovering organizing principles applicable to chronic polymicrobial infections more generally.

Tracking de novo protein synthesis in the activated sludge microbiome using BONCAT-FACS

In order to ensure stable performance of engineered biotechnologies that rely on mixed microbial community systems, it is important to identify process-specific microbial traits and study their in-situ activity and responses to changing environmental conditions and system operational parameters. We used BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT) in combination with Fluorescence-Activated Cell Sorting (FACS) and 16S rRNA gene amplicon sequencing to identify translationally active cells in activated sludge. We found that only a subset of the activated sludge microbiome is translationally active during the aerobic treatment phase of a full-scale sequencing batch reactor designed to enhance biological phosphorus removal from municipal wastewater. Relative abundance of amplicon sequence variants was not a reliable predictor of species activity. BONCAT-positive and -negative cells revealed a broad range of population-wide and taxa-specific translational heterogeneity. BONCAT-FACS in combination with amplicon sequencing can provide new insights into the ecophysiology of highly dynamic microbiomes in activated sludge systems.

Nutritional immunity: the impact of metals on lung immune cells and the airway microbiome during chronic respiratory disease

Nutritional immunity is the sequestration of bioavailable trace metals such as iron, zinc and copper by the host to limit pathogenicity by invading microorganisms. As one of the most conserved activities of the innate immune system, limiting the availability of free trace metals by cells of the immune system serves not only to conceal these vital nutrients from invading bacteria but also operates to tightly regulate host immune cell responses and function. In the setting of chronic lung disease, the regulation of trace metals by the host is often disrupted, leading to the altered availability of these nutrients to commensal and invading opportunistic pathogenic microbes. Similarly, alterations in the uptake, secretion, turnover and redox activity of these vitally important metals has significant repercussions for immune cell function including the response to and resolution of infection. This review will discuss the intricate role of nutritional immunity in host immune cells of the lung and how changes in this fundamental process as a result of chronic lung disease may alter the airway microbiome, disease progression and the response to infection.

An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

Pseudomonas aeruginosa biofilms and their partners in crime

Pseudomonas aeruginosa biofilms and the capacity of the bacterium to coexist and interact with a broad range of microorganisms have a substantial clinical impact. This review focuses on the main traits of P. aeruginosa biofilms, such as the structural composition and regulatory networks involved, placing particular emphasis on the clinical challenges they represent in terms of antimicrobial susceptibility and biofilm infection clearance. Furthermore, the ability of P. aeruginosa to grow together with other microorganisms is a significant pathogenic attribute with clinical relevance; hence, the main microbial interactions of Pseudomonas are especially highlighted and detailed throughout this review. This article also explores the infections caused by single and polymicrobial biofilms of P. aeruginosa and the current models used to recreate them under laboratory conditions. Finally, the antimicrobial and antibiofilm strategies developed against P. aeruginosa mono and multispecies biofilms are detailed at the end of this review.

Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota

Changes in bacterial diversity in the human gut have been associated with many conditions, despite not always reflecting changes in bacterial activity. Methods linking bacterial identity to function are needed for improved understanding of how bacterial communities adapt and respond to their environment, including the gut. Here, we optimized bioorthogonal non-canonical amino acid tagging (BONCAT) for the gut microbiota and combined it with fluorescently activated cell sorting and sequencing (FACS-Seq) to identify the translationally active members of the community. We then used this novel technique to compare with other bulk community measurements of activity and viability: relative nucleic acid content and membrane damage. The translationally active bacteria represent about half of the gut microbiota, and are not distinct from the whole community. The high nucleic acid content bacteria also represent half of the gut microbiota, but are distinct from the whole community and correlate with the damaged subset. Perturbing the community with xenobiotics previously shown to alter bacterial activity but not diversity resulted in stronger changes in the distinct physiological fractions than in the whole community. BONCAT is a suitable method to probe the translationally active members of the gut microbiota, and combined with FACS-Seq, allows for their identification. The high nucleic acid content bacteria are not necessarily the protein-producing bacteria in the community; thus, further work is needed to understand the relationship between nucleic acid content and bacterial metabolism in the human gut. Considering physiologically distinct subsets of the gut microbiota may be more informative than whole-community profiling.

Using Genetic Code Expansion for Protein Biochemical Studies

Protein identification has gone beyond simply using protein/peptide tags and labeling canonical amino acids. Genetic code expansion has allowed residue- or site-specific incorporation of non-canonical amino acids into proteins. By taking advantage of the unique properties of non-canonical amino acids, we can identify spatiotemporal-specific protein states within living cells. Insertion of more than one non-canonical amino acid allows for selective labeling that can aid in the identification of weak or transient protein-protein interactions. This review will discuss recent studies applying genetic code expansion for protein labeling and identifying protein-protein interactions and offer considerations for future work in expanding genetic code expansion methods.