SER-109, an Investigational Microbiome Drug to Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial.

BACKGROUND: Recurrent Clostridioides difficile infection (rCDI) is associated with loss of microbial diversity and microbe-derived secondary bile acids, which inhibit C. difficile germination and growth. SER-109, an investigational microbiome drug of donor-derived, purified spores, reduced recurrence in a dose-ranging, phase (P) 1 study in subjects with multiple rCDIs. METHODS: In a P2 double-blind trial, subjects with clinical resolution on standard-of-care antibiotics were stratified by age (< or ≥65 years) and randomized 2:1 to single-dose SER-109 or placebo. Subjects were diagnosed at study entry by PCR or toxin testing. Safety, C. difficile-positive diarrhea through week 8, SER-109 engraftment, and bile acid changes were assessed. RESULTS: 89 subjects enrolled (67% female; 80.9% diagnosed by PCR). rCDI rates were lower in the SER-109 arm than placebo (44.1% vs 53.3%) but did not meet statistical significance. In a preplanned analysis, rates were reduced among subjects ≥65 years (45.2% vs 80%, respectively; RR, 1.77; 95% CI, 1.11-2.81), while the <65 group showed no benefit. Early engraftment of SER-109 was associated with nonrecurrence (P < .05) and increased secondary bile acid concentrations (P < .0001). Whole-metagenomic sequencing from this study and the P1 study revealed previously unappreciated dose-dependent engraftment kinetics and confirmed an association between early engraftment and nonrecurrence. Engraftment kinetics suggest that P2 dosing was suboptimal. Adverse events were generally mild to moderate in severity. CONCLUSIONS: Early SER-109 engraftment was associated with reduced CDI recurrence and favorable safety was observed. A higher dose of SER-109 and requirements for toxin testing were implemented in the current P3 trial. CLINICAL TRIALS REGISTRATION: NCT02437487, https://clinicaltrials.gov/ct2/show/NCT02437487?term=SER-109&draw= 2&rank=4.

A Phase 1b Safety Study of SER-287, a Spore-Based Microbiome Therapeutic, for Active Mild to Moderate Ulcerative Colitis.

BACKGROUND & AIMS: Firmicutes bacteria produce metabolites that maintain the intestinal barrier and mucosal immunity. Firmicutes are reduced in the intestinal microbiota of patients with ulcerative colitis (UC). In a phase 1b trial of patients with UC, we evaluated the safety and efficacy of SER-287, an oral formulation of Firmicutes spores, and the effects of vancomycin preconditioning on expansion (engraftment) of SER-287 species in the colon. METHODS: We conducted a double-blind trial of SER-287 in 58 adults with active mild-to-moderate UC (modified Mayo scores 4-10, endoscopic subscores ≥1). Participants received 6 days of preconditioning with oral vancomycin (125 mg, 4 times daily) or placebo followed by 8 weeks of oral SER-287 or placebo. Patients were randomly assigned (2:3:3:3) to groups that received placebo followed by either placebo or SER-287 once weekly, or vancomycin followed by SER-287 once weekly, or SER-287 once daily. Clinical end points included safety and clinical remission (modified Mayo score ≤2; endoscopic subscores 0 or 1). Microbiome end points included SER-287 engraftment (dose species detected in stool after but not before SER-287 administration). Engraftment of SER-287 and changes in microbiome composition and associated metabolites were measured by analyses of stool specimens collected at baseline, after preconditioning, and during and 4 weeks after administration of SER-287 or placebo. RESULTS: Proportions of patients with adverse events did not differ significantly among groups. A higher proportion of patients in the vancomycin/SER-287 daily group (40%) achieved clinical remission at week 8 than patients in the placebo/placebo group (0%), placebo/SER-287 weekly group (13.3%), or vancomycin/SER-287 weekly group (17.7%) (P = .024 for vancomycin/SER-287 daily vs placebo/placebo). By day 7, higher numbers of SER-287 dose species were detected in stool samples from all SER-287 groups compared with the placebo group (P < .05), but this difference was not maintained beyond day 7 in the placebo/SER-287 weekly group. In the vancomycin groups, a greater number of dose species were detected in stool collected on day 10 and all subsequent time points through 4 weeks post dosing compared with the placebo group (P < .05). A higher number of SER-287 dose species were detected in stool samples on days 7 and 10 from subjects who received daily vs weekly SER-287 doses (P < .05). Changes in fecal microbiome composition and metabolites were associated with both vancomycin/SER-287 groups. CONCLUSIONS: In this small phase 1b trial of limited duration, the safety and tolerability of SER-287 were similar to placebo. SER-287 after vancomycin was significantly more effective than placebo for induction of remission in patients with active mild to moderate UC. Engraftment of dose species was facilitated by vancomycin preconditioning and daily dosing of SER-287. ClinicalTrials.gov ID NCT02618187.

Access to prior spatial information.

In six experiments, reading times and probe naming times were measured in order to examine the conditions under which spatial information became accessible and/or reactivated. In Experiments 1-4, reading times were measured for target sentences containing spatial inconsistencies. Spatial inconsistencies did not disrupt processing (Experiment 1) unless there were increases in task demands (Experiment 2), elaboration of the protagonist's location (Experiment 3), or both (Experiment 4). In Experiments 5 and 6, naming times were measured to directly assess the activation of spatial information, specifically objects associated with a protagonist. Spatial information was highly active in memory immediately after being read and less active after four intervening sentences (Experiment 5), but explicit cues (e.g., location or object) as well as references to the current situation model were effective in reactivating previously mentioned spatial information (Experiment 6). The combined results of six experiments are discussed within the context of the RI-Val model.

Can readers fully adopt the perspective of the protagonist?

Despite the centrality of the protagonist during narrative comprehension, evidence indicates that readers do not typically approach the text from the protagonist's point of view. Experiments 1a-1c demonstrated that both explicit task instructions and the first-person point of view resulted in comprehension being influenced by perspective-relevant information; this indicated that readers were adopting the perspective of the protagonist. However, Experiments 2a-3b showed that even when readers adopt the protagonist's perspective, they cannot do so to the exclusion of related perspective-irrelevant information. Results are discussed in the context of the RI-Val model of comprehension in which perspective-relevant information and perspective-irrelevant information are both available and compete for influence during comprehension.

Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism.

Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.

The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities.

Taking the perspective of the narrator.

Readers do not always adopt the perspective of the protagonist; however, they will under certain conditions. Experiments 1a and 1b showed that readers will take the perspective of the protagonist from the third-person point of view, but only when explicitly instructed to do so. Experiment 2 demonstrated that reading from the first-person point of view is a text-based manipulation that encourages readers to adopt the perspective of the protagonist. The results of Experiments 3a and 3b replicated the findings of Experiments 1a and 2. Experiment 4 established that simply increasing readers' attention to the text does not lead to adoption of the protagonist's perspective; moreover, this suggests that when it does occur, protagonist perspective adoption is not the result of increased attention, but strategic processing.

Processing real-world violations embedded within a fantasy-world narrative.

Fantasy-text is a genre in which events routinely violate rules we know to be true in the real world. In four experiments, we explored the inherent contradiction between unrealistic fictional events and general world knowledge (GWK) to examine these competing information sources within the context of an extended fantasy-narrative. Experiments 1a and 1b demonstrated that fantasy-unrelated inconsistencies caused disruption to comprehension despite an abundance of contextual support for real-world impossible events that violate GWK. Experiment 2a demonstrated that fantasy-related inconsistencies caused disruption when they occurred at the local level and the fantasy-context stood in direct opposition to the target sentence. However, Experiment 2b demonstrated that disruption can be initially eliminated when readers encountered fantasy-related violations at the global level, but delayed-processing difficulty occurred on the spillover sentence, downstream of the target sentence. All four experiments are discussed within the context of the RI-Val model.

COBRAme: A computational framework for genome-scale models of metabolism and gene expression.

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in iJL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.

Validating semantic illusions: Competition between context and general world knowledge.

The RI-Val model of comprehension includes a validation process in which linkages formed by integration are matched against active memory. In five experiments, we investigated factors that influence validation. Reading times were measured on target sentences that contained either correct information or semantically related, but incorrect content. Experiment 1 demonstrated that strong contextual support for shared features between correct and incorrect terms delayed processing difficulty associated with incorrect information. Experiment 2 confirmed that contextual information needed to be available in memory in order to influence validation. Experiments 3a and 3b showed that strong contextual support for distinguishing features between correct and incorrect terms led to immediate processing difficulty associated with incorrect information, whereas features-in-common led to delayed difficulty. Experiments 4 and 5 showed that the timing of validation effects is sensitive to subtle changes in task demands. The combined results are consistent with the assumptions of the RI-Val model. (PsycINFO Database Record

Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation.

Maintenance of a properly folded proteome is critical for bacterial survival at notably different growth temperatures. Understanding the molecular basis of thermoadaptation has progressed in two main directions, the sequence and structural basis of protein thermostability and the mechanistic principles of protein quality control assisted by chaperones. Yet we do not fully understand how structural integrity of the entire proteome is maintained under stress and how it affects cellular fitness. To address this challenge, we reconstruct a genome-scale protein-folding network for Escherichia coli and formulate a computational model, FoldME, that provides statistical descriptions of multiscale cellular response consistent with many datasets. FoldME simulations show (i) that the chaperones act as a system when they respond to unfolding stress rather than achieving efficient folding of any single component of the proteome, (ii) how the proteome is globally balanced between chaperones for folding and the complex machinery synthesizing the proteins in response to perturbation, (iii) how this balancing determines growth rate dependence on temperature and is achieved through nonspecific regulation, and (iv) how thermal instability of the individual protein affects the overall functional state of the proteome. Overall, these results expand our view of cellular regulation, from targeted specific control mechanisms to global regulation through a web of nonspecific competing interactions that modulate the optimal reallocation of cellular resources. The methodology developed in this study enables genome-scale integration of environment-dependent protein properties and a proteome-wide study of cellular stress responses.

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion.

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

Source credibility and the processing of refutation texts.

The knowledge revision components framework (KReC) outlines the basic comprehension processes and text factors that can be accentuated to increase the potential for knowledge revision during reading. The goal of the present study was to explore source credibility as one such text factor. In Experiment 1, we established the utility of a set of refutation texts in influencing knowledge revision. Participants read ten refutation and ten control texts. The participants had faster reading times and higher posttest scores for the refutation than for the control texts, providing evidence for knowledge revision. In Experiment 2, we examined the influence of source credibility under normal reading conditions. Participants read 20 refutation texts, ten with high-credibility and ten with low-credibility sources. The reading times and posttest scores suggested that knowledge revision unfolded successfully, independent of credibility. Using the same texts, in Experiment 3 we examined the influence of direct instructions that made the credibility of the source of information more salient. When the credibility of the source was made salient, the revision process was disrupted in the low-credibility condition, as evidenced by slower reading times and lower posttest scores than in the high-credibility condition. The results add to our understanding of the factors that constrain knowledge revision during the reading of refutation texts, and are discussed in the context of the extant literature and KReC.

Validation of semantic illusions independent of anomaly detection: evidence from eye movements.

Several theorists have argued that readers fail to detect semantic anomalies during reading, and that these effects are indicative of "shallow processing" behaviours. Previous studies of semantic anomalies such as the Moses illusion have focused primarily on explicit detection tasks. In the present study, we examined participants' eye movements as they read true/false statements that were non-anomalous, or contained a semantic anomaly that was either high- or low-related to the correct information. Analyses of reading behaviours revealed that only low-related detected anomalies resulted in initial processing difficulty, but both detected and undetected anomalies, regardless of whether they were high- or low-related, resulted in delayed processing difficulty. The results extend previous findings on semantic anomalies and are discussed in terms of the RI-Val model of text processing.

Multi-omic data integration enables discovery of hidden biological regularities.

Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments.

The costs and benefits of protein expression are balanced through evolution. Expression of un-utilized protein (that have no benefits in the current environment) incurs a quantifiable fitness costs on cellular growth rates; however, the magnitude and variability of un-utilized protein expression in natural settings is unknown, largely due to the challenge in determining environment-specific proteome utilization. We address this challenge using absolute and global proteomics data combined with a recently developed genome-scale model of Escherichia coli that computes the environment-specific cost and utility of the proteome on a per gene basis. We show that nearly half of the proteome mass is unused in certain environments and accounting for the cost of this unused protein expression explains >95% of the variance in growth rates of Escherichia coli across 16 distinct environments. Furthermore, reduction in unused protein expression is shown to be a common mechanism to increase cellular growth rates in adaptive evolution experiments. Classification of the unused protein reveals that the unused protein encodes several nutrient- and stress- preparedness functions, which may convey fitness benefits in varying environments. Thus, unused protein expression is the source of large and pervasive fitness costs that may provide the benefit of hedging against environmental change.

Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution.

Pleiotropic regulatory mutations affect diverse cellular processes, posing a challenge to our understanding of genotype-phenotype relationships across multiple biological scales. Adaptive laboratory evolution (ALE) allows for such mutations to be found and characterized in the context of clear selection pressures. Here, several ALE-selected single-mutation variants in RNA polymerase (RNAP) of Escherichia coli are detailed using an integrated multi-scale experimental and computational approach. While these mutations increase cellular growth rates in steady environments, they reduce tolerance to stress and environmental fluctuations. We detail structural changes in the RNAP that rewire the transcriptional machinery to rebalance proteome and energy allocation toward growth and away from several hedging and stress functions. We find that while these mutations occur in diverse locations in the RNAP, they share a common adaptive mechanism. In turn, these findings highlight the resource allocation trade-offs organisms face and suggest how the structure of the regulatory network enhances evolvability.

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow.

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.

Systems biology of the structural proteome.

BACKGROUND: The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. RESULTS: Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). CONCLUSIONS: Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.