In April 2023, an investigational gut microbiota therapeutic tailored to address CDI, SER-109 (trade name VOWST), was approved by the Food and Drug Administration for clinical use (Mullard, 2023). This was the second FDA-approved microbiome therapeutic followed by a rectally administered microbiota cocktail, RBX2660 (trade name Rebyota). These two biotherapeutics have been tailored specifically for the prevention of CDI, which is a leading cause of antibiotic-associated diarrhea that results in 15,000–30,000 deaths annually (McDonald et al., 2018). Although the exact microbial compositions isolated from the therapeutic cocktails were not fully defined or disclosed (except that VOWST comprises selectively purified Firmicutes spores (Sims et al., 2023)), systems analysis of C. difficile etiology provides a glimpse into candidate species with potential therapeutic relevance and the underlying mechanisms of gut microbe-based modulation of the infection.

Gut microbiota dysbiosis is correlated with the depletion of luminal secondary bile acids (BAs) such as deoxycholic and lithocholic acids, which render the intestine highly prone to CDI (Theriot et al., 2016). The human gut microbiome, particularly the phyla Firmicutes and Bacteroidetes, have been predicted to harbor the biosynthetic capacity to transform host-derived primary BAs into 12 of the 13 types of secondary BAs in vivo (Heinken et al., 2019). With regard to infection susceptibility, primary bile acids promote spore germination in C. difficile, whereas secondary BAs are inhibitory towards its vegetative growth and the TcdB toxin (Icho et al., 2023). Thus, the depletion of BA-converting species and other antipeptide-producing native gut flora during an antibiotic sweep creates an environment highly conducive to spore germination, overgrowth, and infection. In addition, secondary BAs activate orthogonal receptors that antagonize pro-inflammatory nuclear factor-κB (NF-κB) signaling (Collins et al., 2023). Therefore, the reduction in luminal secondary BA levels is thought to exacerbate gut inflammation and increase susceptibility towards spore-forming pathogenic agents, and antibiotic treatment frequently leads to relapses.

Systems biology has shed light on the therapeutic potential of gut microbiota editing for thwarting recurring CDI. In one study, authors identified native species of gut microbiota that contributed significantly to CDI resistance (Buffie et al., 2015). Importantly, species-level characterization of microbial abundance together with time-course modeling of microbial dynamics in response to antibiotic perturbation identified a group of bacteria that ameliorated infection upon engraftment (Figure 1B). Among these, the adoptive transfer of Clostridium scindens, a species that retains a complete secondary BA biosynthetic pathway (Funabashi et al., 2020), confers infection resistance (Buffie et al., 2015). Interestingly, subsequent analysis revealed that resistance to the C. difficile gene family was strongly linked to the enrichment of genes associated with secondary BA biosynthesis. The results were reproducible in a constraint-based microbial community modeling framework, where the simulation suggested that patients with inflammatory bowel disease (IBD) varied considerably in their metabolic capacity to produce secondary BAs compared with healthy controls (Heinken et al., 2019). Taken together, the systems analysis of C. difficile pathobiology helped identify a potent probiotic candidate as well as the underlying mechanism of action, ultimately allowing novel therapeutic options for difficult-to-treat diseases such as CDI (with the FDA-approved therapeutics Rebyota and VOWST).

Recent progress in etiological characterization of celiac disease (CD) driven by-multi-omics analysis is also worth notable mentions (Leonard et al., 2020; Leonard et al., 2021). CD is an autoimmune condition triggered by gluten consumption. While it has been proposed that genetic predisposition and environmental stimuli play key role in CD development, its exact pathogenesis and etiology remains incompletely understood. Recently the Celiac Disease Genomic, Environmental, Microbiome, and Metabolomic (CDGEMM) study was launched in an attempt to investigate potential link between gut microbiota and CD development. The metagenomic and metabolomic features in stools collected from a longitudinal cohort of infants at high risks of CD were analyzed to explore multivariate risk factors encompassing genetics, mode of delivery and feeding, antibiotic use to CD development, in the venture point of gut microbiome (Leonard et al., 2020). Interestingly, the study reported major taxonomic and functional shifts in the gut microbiota of CD-prone infants, providing insights on how environmental factors that shape gut microbiota composition, in turn, may adversely affect immunomodulation of the host (Leonard et al., 2020). More recently, a longitudinal study that enrolled 10 CD and 10 non-CD infants reported changes in the gut metagenomes, pathway enrichments and associated metabolomes that are specific to the onset of CD (Leonard et al., 2021). As such, the multi-omics driven investigation of the longitudinal changes in gut microbiome has yielded valuable insights into the emerging role of gut microbiota in the development the chronic autoimmune disease. The data-driven findings shed light onto potentially novel therapeutic targets for the treatment and prevention of CD. The efforts are currently ongoing, with more than 550 subjects enrolled in the study as of May 2023. The large-scale longitudinal investigations hold the promise of unveiling undercharacterized molecular mechanisms and bring forth innovative therapeutic modalities for addressing CD (Leonard et al., 2023).

Synthetic microbial consortia designed with careful consideration of strain-specific metabolism, potential impact on host physiology, and safety profiles have been demonstrated to be a viable and effective therapeutic modality for different disease types (Table 1). In a study investigating CDI treatment, researchers developed a formula of synthetic microbial consortia with a defined species composition, using microbial isolates obtained from a healthy donor stool. This synthetic regimen was effective in preventing recurrent CDI in affected patients for up to 6 months (Petrof et al., 2013). In another study, a group of bacterial species that stimulated the proliferation of interferon-γ-secretory CD8 T cells demonstrated a potent therapeutic effect against infectious and neoplastic diseases in murine models (Figure 1C) (Tanoue et al., 2019). In this study, synthetic consortia comprising low-abundance species of healthy human microbiota exhibited enhanced clearance of the infectious agent Listeria monocytogenes and potent anti-tumor effects without observable adverse effects (Tanoue et al., 2019). The study underscored the importance of underrepresented species in omics data, which nonetheless have broad implications in host physiology, emphasizing the need to revisit sequence-oriented microbiome analysis weighted heavily on the quantitative evaluation of individual components.

It is also worth noting that the application of systems biology extends to functional genetics, enabling the characterization of the aforementioned causal factors at nucleotide-level. For example, functional coding sequences of the complete secondary BA biosynthetic pathway of intractable gut microbes have been published recently (Funabashi et al., 2020; Sato et al., 2021), paving the way for functional expression and control of secondary BA biosynthetic pools for therapeutic applications (Figure 1D) (Funabashi et al., 2020; Koh et al., 2022). While the multitudes of systems biology toolkits have drastically expanded our ability to characterize host-microbe interactions in unprecedented detail, our ability to effectively manipulate and engineer gut commensals remains a challenge. Genetic manipulation of gut commensal species is important for validating data-driven hypotheses and serves as an enabling technology in the growing field of live microbial therapeutics. In this section, we describe a novel therapeutic modality driven by synthetic biology.

Assured safety, advanced genetic tractability, and relative ease of manipulation have led to the expanding use of probiotics as LBTs, which are live microbes engineered to deliver therapeutic payloads in vivo (Steidler et al., 2000). The development of titratable substrate-specific promoter arrays (Meyer et al., 2019) that facilitate complex logic operations (Nielsen et al., 2016; Jones et al., 2022) has streamlined the design and engineering of LBTs that perform real-time detection and therapeutic payload delivery in response to specific signals in vivo (Triassi et al., 2023). An LBT chassis can simultaneously diagnose, record, and self-regulate therapeutic payload delivery in response to host disease in vivo (Figure 2) (Zou et al., 2023). Such ‘smart LBT,’ for lack of a better term, is powered by versatile genetic circuitry that constitutes independent genetic components, each of which enables sensing of environmental cues, signal relay and processing, and actuation (Figure 2). Each genetic component is modularized as composite bioparts to enable reconfiguration of cellular functions in a “plug-and-play” manner; that is, as far as chassis strain is concerned, cells can be reprogrammed to execute various user-defined operations by re-wiring different input signals to output modules.

Biosensor modules, such as endogenous transcription factors (TFs), enzymes, or nucleic acids, regulate gene expression. The mode of action of a genetic biosensor typically entails a conformational change upon ligand binding, followed by the activation of orthogonal transcription machinery. Using a two-component signal transduction system (TCS) as an example, the membrane-bound sensor histidine kinase ThsS phosphorelays thiosulfate (S₂O₃ ²⁻)-induced signals at the receiving end of the DNA-binding regulator ThsR. Phosphorylated ThsR, in turn, activates its cognate promoter P_phsA and expression of the downstream reporter gene superfolder GFP (sfGFP) to transform the ligand-binding signal into luminescence readouts in a dose-dependent manner (Daeffler et al., 2017). To date, an array of genetic biosensors specific to the host and/or disease-specific biomarkers has been reported. These include biosensors for inflammation, infection, bleeding, cancer, diabetes, and availability of abiotic factors such as pH and O₂ (Table 2). The sensing modules confer stringent control over LBT phenotypes and augment the performance. In one instance, a refactoring of K5-type capsular polysaccharide expression with the lac promoter in EcN allowed programmable surface antigen display using isopropyl-b-D-thiogalactopyranoside (IPTG) induction. This rather simple tweak in transcription regulation led to prolonged in vivo survival by allowing programmable evasion of the host immune response, consequently augmenting the antitumor efficacy of LBT by ten-fold (Harimoto et al., 2022). The integration of more than one transcriptional regulator in a complex cellular response is characterized by multimodal transgene expression in response to diverse environmental cues (Taketani et al., 2020).

Complex cell programming is achieved through signal processing modules, which are important for informing LBTs about specific locations and timings to produce therapeutic responses at the required dosage. To this end, Boolean logic gates that compute input signals to guide appropriate responses have been implemented in genetic circuit designs. For instance, the biomarkers of gut inflammation thiosulfate/tetrathionate (S₄O₆ ²⁻) and nitrate (NO³⁻) are proxies for two distinct etiologies of IBD (Winter et al., 2010; Winter et al., 2013), and the means to detect each biomarker alone may be insufficient for IBD diagnosis. To address this issue, a genetic circuit incorporating an AND Boolean logic gate was constructed based on two TCSs and cognate promoter pairs, ThsSR with P _phsA , and NarXL with P _yeaR ; creating a system that requires the presence of two IBD biomarkers, thiosulfate and nitrate, to elicit an actuator response (Woo et al., 2020). The use of complex logic operations further enhances the specificity of genetic circuits by imparting multilevel control to the system output. However, designing genetic circuits with an increasing number of control elements is difficult and time consuming, which limits the implementation of complex cell programming (Kwok, 2010). Automated genetic circuit design software (Cello) streamlines the entire workflow (Nielsen et al., 2016; Jones et al., 2022). The software allowed the functional design of a genetic circuit using a library of NOR/NOT gates as complex as 10 regulators and 55 parts. In addition, the response functions of the sensors and gates can be extrapolated to quantitatively predict the behavior of the designed genetic sensor. A reliable circuit design algorithm that boasts 92% circuit output prediction accuracy was applied in the design of a gut commensal chassis for multimodal transgene expression (Taketani et al., 2020) as well as in an LBT to enable timely, predictable expression of therapeutic payloads in a condition-specific manner (Triassi et al., 2023).

Fine-tuning of transgene expression and the activity of encoded products are commonly sought to optimize strain performance. It should be noted that the functional expression of transgenes per the intended design may require extensive troubleshooting and ad hoc optimizations. For instance, owing to the highly subjective nature of composite elements within a genetic circuit, determining the optimal parameters for an expression system is confounded by multiple variables, such as the strength of the expression machinery (Chien et al., 2022; Choe et al., 2022; Song et al., 2022) and genetic and environmental contexts (Lou et al., 2012). In many instances, an optimal system output can be achieved by shuffling genetic parts (Choe et al., 2022) or their brute-force combinations (Song et al., 2022) and copy number variations (Isabella et al., 2018) to fine-tune the target transgene expression. Enhancing the activity of the encoded functions is also an integral part of the optimization process, particularly when transgene expression is not a bottleneck. To this end, the evolutionary (directed) engineering of target proteins at a specific scale, followed by biosensor-assisted screening, is effective in identifying target proteins with improved functionalities (Adolfsen et al., 2021). Furthermore, the characterization of highly expressed genomic regions within the chassis strains can be repurposed as a ‘landing pad’ for transgene expression cassettes, which confers higher genetic stability over the plasmid-based expression regimes (Park et al., 2020).

SYNB1618 and its derivatives are LBTs designed for the treatment of phenylketonuria (PKU) and assimilate the aforementioned engineering and optimization principles, which are worthy of a detailed discussion (Figure 3). The original strain, SYNB1618, is an EcN-based LBT engineered to complement a disabling mutation in the host phenylalanine hydroxylase (PAH), which is the cause of the debilitating metabolic disease. SYNB1618 carries two synthetic phenylalanine degradation pathways. First, PheP and PAL, encoded by phenylalanine transporter, pheP and phenylalanine ammonia-lyase, stlA, respectively. Second, LAAD, encoded by membrane-associated L-amino acid deaminase pma (Figure 3A) (Isabella et al., 2018). To achieve high expression levels and impart transcriptional control in vivo, multiple copies of pheP/stlA were genome-integrated and expression-controlled in an IPTG- (P _tac ), L-arabinose (P _BAD ) or anaerobic-inducible (P _fnrS ) manner (Figure 3A). The in vivo analysis showed that SYNB1618 was effective in lowering blood phenylalanine levels in a mouse model of PKU, stabilized phenylalanine levels in healthy non-human primates (Isabella et al., 2018), and was safely tolerated by healthy individuals (Puurunen et al., 2021).

In a follow-up study aimed at further augmenting the phenylalanine-degrading capacity of LBT, PAL was no longer rate-limiting, hence calling for efforts to optimize the performance of PAL (Adolfsen et al., 2021). In this study, the authors used a biosensor-assisted screen based on an allosteric TF activated in the presence of trans-cinnamate (TCA), a product of PAL, in a concentration-dependent manner. The stlA mutant library was designed considering the enzyme phylogeny, co-evolution, and structural attributes to infer enzyme residues for targeted engineering. To avoid cross-talk between different TCA producer strains (which also house the TF-based biosensor) in the pooled library, an oil-emulsion-assisted microculture followed by a fluorescence-activated cell sorting (FACS) strategy was adopted to effectively isolate high TCA-producing mutants (Figure 3B) (Adolfsen et al., 2021). The authors identified a PAL mutant (stlA* or mPAL) carrying five mutations (S92G, H133M, I167K, L432I, and V470A) with V _max and K _M two to three folds higher compared to that of wild-type PAL. The SYNB1618-derivative that expresses stlA*, designated SYNB 1934, showed an approximately 2-fold increase in in vivo phenylalanine conversion capacity compared to SYNB1618 (Adolfsen et al., 2021). The PKU-targeting LBT series SYNB1618 and SYNB1934 have undergone phase I and II clinical trials (ClinicalTrials.gov ID: NCT04984525 and NCT04534842, respectively), and a phase III trial of SYNB 1934 (NCT05764239) is underway in June 2023.

More recently, an independent study revisited the transgene expression architecture of SYNB1618 with the final goal of optimizing its therapeutic gene expression and alleviating the potential genetic burdens incurred during the process. By leveraging the knowledge of genomic ‘hot spots’ that are conducive to strong gene expression and fine-tuning transgene expression using automated genetic circuit design, the authors constructed the final strain, SYN8784 (Figure 3C). With fewer genomic copies of stlA* and PheP, SYN8784 maintained higher TCA activity while also exhibiting enhanced strain fitness compared to SYNB1618, underscoring the importance of rationalized genetic circuit design informed by computer-aided design tools and augmentation of strain functions to reinforce system performance (Park et al., 2020; Triassi et al., 2023).

Together, these studies show that a synthetic-biology-powered chassis platform can be readily translated into real-life applications. The use of modularized bioparts with quantitatively measured and standardized characteristics confers enormous flexibility and predictability to the biological function design, facilitating the reconfiguration of cellular phenotypes in a user-defined manner. However, our insufficient understanding of complex biology limits our capacity to overcome certain biological constraints. The inherent biological constraints of probiotic strains, such as rapid clearance, low in vivo cell density, and inability to colonize the intestinal niche for an extended duration, represent hurdles in probiotic-based therapeutics (Zmora et al., 2018), especially for addressing chronic diseases (Inda et al., 2019). As a promising alternative, repurposing native gut microbes for therapeutic applications has gained traction in recent years. In the next section, we discuss systems and synthetic biology approaches for repurposing native gut resident species for therapeutic purposes.

An a priori understanding of the cellular systems precedes their rational design and engineering. In the case of gut commensals, factors such as the genetic determinants of strain fitness within the intestinal niche provide important engineering considerations for in vivo applications. However, conventional approaches to characterize genetic functions, such as gene knockout, are highly effective but limited in scale and require considerable labor and resources (Baba et al., 2006). Therefore, the vast majority of genetic elements in species belong to hypothetical genes that lack validation in literature, even in model gut commensal species.

Attempts to functionally characterize the genetic functions of gut commensal have accelerated using high-throughput screening systems. These include large-scale expression libraries for gain-of-function analyses (Yaung et al., 2015), genome-wide transposon mutagenesis and sequencing (Tn-Seq) (Goodman et al., 2009; Wu et al., 2015; Liu et al., 2021), and CRISPR interference screening platforms for loss-of-function studies (Shin et al., 2023). Such high-throughput sequencing methodologies are an effective means to build and validate novel hypotheses on genotype-phenotype relationships and streamline the investigation of genotype-phenotype relationships in a context-specific manner. For example, a mutant population of B. thetaiotaomicron containing a 3.5 × 10⁴ transposon-insertion mutant library that influences 78% of the predicted coding regions at 5.5 insertions/kb resolution was used to survey the essentiality of B. thetaiotaomicron genome (Goodman et al., 2009). Transposon insertion sequencing (INSeq) performed in vitro and in vivo revealed the context-specific nature of gene essentiality, where enriched gene groups differed in functional categories that were a direct reflection of nutrition-replete or -deplete in vitro environments. Notably, INSeq also identified genetic determinants of in vivo fitness, including the gene cluster BT1957-49 associated with Vitamin B₁₂ synthesis and utilization, in which B. thetaiotaomicron mobilizes in response to changes in microbiota composition in vivo (Goodman et al., 2009). Similarly, specific phenotypes for most of the 516 B. thetaiotaomicron genes associated with carbon metabolism and chemical resistance systems were newly characterized via Tn-Seq analysis across a panel of different carbon sources and stressors (Liu et al., 2021).

High-throughput screening of genome-wide transcriptome changes, such as transcriptome sequencing (RNA-Seq), is a common strategy for analyzing condition-specific changes in mRNA transcript abundance. Variations in transcriptome sequencing technologies allow the characterization of distinct transcriptome landscapes across species that reflect unique ecological features and provide an important system-level inference that translates into engineering applications. For example, investigation of B. thetaiotaomicron transcriptome response in the presence of sub-components of porcine mucin, a glycoprotein that makes up the mucus lining of the mammalian intestine, has revealed a synchronized action of its PULs along with the regulatory extracytoplasmic sigma factors (ECF-σ). The deletion of genes that encode ECF-σs embedded within O-glycan-responsive PULs led to a significant in vivo deficit, elucidating yet another essential genetic determinant of in vivo fitness, along with the regulatory elements that impart transcriptional control (Martens et al., 2008). Transcriptome analysis has also identified two hybrid TCSs, BT3334 and BT0267, that activate cognate promoters in response to specific carbon substrates (Martens et al., 2008; Martens et al., 2011), which have been repurposed as inducible promoter bioparts in B. thetaiotaomicron (Mimee et al., 2015).

More recently, exploration of the primary transcriptome landscape of B. thetaiotaomicron genome-wide led to the discovery of more than 4 × 10³ transcription start sites corresponding to putative promoters with conserved motifs, conserved ribosome binding sites (RBSs) in the downstream vicinity, and putative riboswitches (Ryan et al., 2020). This discovery has important implications in synthetic biology due to the fact that Bacteroides retain a unique σ⁷⁰-like transcription factor σ^ABfr that recognizes an unconventional −33/−7 consensus motif (TTTG/TAnnTTTG), and different RBS strength rules that limit the use of standard synthetic biology tools (Mastropaolo et al., 2009). Hence, they add greatly to the pool of promoter and RBS bioparts for Bacteroides (Ryan et al., 2020). In addition, differential RNA sequencing readouts provide important inferences regarding the presence of noncoding cis-regulatory elements such as riboswitches, many of which assume important regulatory roles but are often omitted from standard genome annotations (Sharma et al., 2010). It is worth highlighting that GibS, a small RNA in B. thetaiotaomicron that is strongly induced in the presence of the mucin-constituent amino sugar, N-acetyl-D-glucosamine. Upon induction, GibS upregulated the expression of simple sugar metabolic enzymes and downregulated the expression of glucan-branching enzymes, providing evidence for cis-regulatory carbon catabolite repression in B. thetaiotaomicron (Ryan et al., 2020). As non-coding RNA domains are being harnessed as emerging gene expression machinery in synthetic biology, the accumulation of relevant datasets is expected to streamline novel biopart discovery, especially in non-model organisms, for which validated genetic parts are limited.

Gut commensals equipped with different features in synthetic biology have been shown to be viable means for therapeutic payload delivery. For example, pioneering research groups engineered Bacteroides ovatus to produce anti-inflammatory peptides such as murine interleukin-2 (MuIL2), human keratinocyte growth factor-2 (hKGF-2), and human transforming growth factor-β (hTGF-β) using a xylanase-inducible promoter in pre-clinical models of gut inflammation (Table 2) (Farrar et al., 2005; Hamady et al., 2010; Hamady et al., 2011). The robust heterologous expression system of Bacteroides species is often used as a “surrogate cell” to produce potent proteins of intractable commensal origin in the gut for the evaluation of host physiology. For example, a gene encoding tryptophan decarboxylase in the native gut resident R. gnavus was heterologously expressed in B. thetaiotaomicron to assess the biological implications of this relatively rare pathway that converts tryptophan into tryptamine in vivo setting. Gnotobiotic mice colonized with tryptamine-producing B. thetaiotaomicron (TrpD ⁺) showed an increase in colonic secretion and a shortened gut transit time (Bhattarai et al., 2018). In a follow-up study, the introduction of B. thetaiotaomicron TrpD⁺ decreased weight loss in a colitis mouse model, primarily through tryptamine-induced activation of host serotonin receptor 4 (5-HT4R), which promoted goblet cell differentiation (Bhattarai et al., 2020). Similarly, to evaluate the impact of microbial bile acid metabolism in the colon, genes from Ruminococcus gnavus encoding a subset of the 3β-hydroxydeoxycholic acid (isoDCA) biosynthetic pathway, hydroxysteroid dehydrogenases, were heterologously expressed in model Bacteroides species (Campbell et al., 2020). Synthetic bile acid metabolism was performed by co-culturing engineered Bacteroides strains with C. scindens, which encodes the remaining subset of the isoDCA pathway. The introduction of synthetic consortia into the mouse model increased the colonic isoDCA concentration, with a concomitant elevation in the levels of colonic CD4⁺ regulatory T cells (Table 1).

Another avenue of strain engineering is based on a mathematical approach, first to understand biological phenotypes, and second to simulate optimal strain engineering designs that yield the desired outcomes. As mentioned in Section 2, in silico simulations using the GEM provide important inferences regarding the implicit dynamics of metabolic interactions within or between the host and microbes. For example, the first GEM reconstruction of B. thetaiotaomicron iAH991 was built with the extension of a mouse GEM compartment to simulate host-microbe interactions in response to different dietary regimes. While the multi-species GEM captured metabolite exchanges that were comparable with published metabolomics data, a stand-alone simulation of iAH991 for B. thetaiotaomicron monoculture phenotypes was incongruent with the experimental values (Heinken et al., 2013). Accumulating knowledge on gut commensal genetics and biochemistry has streamlined the reconstruction of strain-specific GEMs with improved prediction accuracy. Nearly a decade after its reconstruction, iAH991 was revisited by incorporating up-to-date strain-specific data and underwent extensive curation of model properties, resulting in an expanded GEM iKS1119 that showed remarkably improved in silico prediction accuracy (Kim et al., 2021). Subsequently, GEM-guided metabolic engineering was applied to B. thetaiotaomicron to produce non-native butyrate, a short-chain fatty acid (SCFA) with proposed immunomodulatory properties. While the strain failed to produce butyrate in the wild-type background, a round of metabolic engineering informed by the Optknock simulation redirected the cellular metabolic flux toward non-native butyrate biosynthesis, albeit at a much lower concentration than the native SCFA products (Kim et al., 2021).

Taken together, these studies demonstrate that prominent gut commensal species can be engineered to functionally express exogenous compounds at a sufficient dosage to elicit the desired changes in vivo. Furthermore, the development of in silico simulation platforms aids in the rational design and engineering to optimize the production of non-native metabolites. Leveraging this capacity, Novome Biotechnologies Inc. developed a B. thetaiotaomicron-based proprietary strain, NB1000S, designed to degrade oxalate for the treatment of enteric hyperoxaluria, and a porphyrin-based prebiotic, NB 2000P, that facilitates engraftment and tunes in vivo abundance of NB1000S. The combination product NOV-001 yielded promising results in phase 1 clinical trial assessing safety, tolerability, and strain colonization pharmacodynamics (NCT04909723), paving the way toward the clinical use of commensal-based LBTs that have hitherto been prospective.