Using Genome-scale Models to Predict Biological Capabilities.

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Use of adaptive laboratory evolution to discover key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal medium.

Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.

Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction.

Growth is a fundamental process of life. Growth requirements are well-characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high-level behavior of the cell as a whole. Here, we construct an ME-Model for Escherichia coli--a genome-scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ~80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi-scale phenotypes, ranging from coarse-grained (growth rate, nutrient uptake, by-product secretion) to fine-grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth.

Interpersonal views of narcissism and authentic high self-esteem: it is not all about you.

Employing peer-rating methodology, this study examined relationship issues in narcissists versus individuals with authentic high self-esteem. Undergraduates (N = 147) were assigned to rate someone (a "target") they knew well who was most similar to a narcissistic prototype, an authentic self-esteem prototype, or a control person. Participants rating narcissistic targets reported significantly more interpersonal problems with the target and more avoidant and revenge behaviors directed toward them than did participants rating authentic self-esteem or control targets. Authentic high self-esteem was associated with positive social relationships. Large effect sizes suggested substantial interpersonal differences observed by peers interacting with narcissists compared to authentic high self-esteem individuals.

Tracking spatial information during reading: a cue-based process.

The goal of the present set of experiments was to examine whether a cue-based mechanism could account for how, and under what conditions, spatial information is tracked. In five experiments, reading times were measured for a target sentence that contradicted the earlier-described location of a protagonist. When the target sentence contained either one or two cues to earlier spatial information (Experiments 1a-1c), reading times were disrupted. When all cues were eliminated (Experiments 2a and 2b), reading time were disrupted only when readers were instructed to take the perspective of the protagonist. The combined results of all five experiments are consistent with a cue-based mechanism: Readers encode spatial information but do not update earlier-encoded spatial information except in response to specific text characteristics (i.e., cues to earlier spatial information) or task demands (e.g., an instruction to read from the perspective of the protagonist) that increase the accessibility of earlier-encoded spatial information.

Underdiagnosis of posttraumatic stress disorder in at risk youth.

Three studies examined the diagnosis of posttraumatic stress disorder (PTSD) in agencies treating at risk youth. Studies 1 and 2 (1999) found that baseline PTSD diagnosis was rare in a residential and an outpatient agency (2.3% and 5.4%, respectively) whereas trauma-focused interviews identified PTSD in 47.7% and 44.6% of these clients. Subsequent training efforts increased awareness of PTSD and recognition of unique issues in assessing at risk youth. Study 3 (2009) reexamined PTSD diagnosis rates in these agencies 10 years later and found that the residential agency had an increased rate of PTSD diagnosis (10.8%), whereas PTSD diagnosis remained rare in the outpatient agency (4.0%). Suggestions are offered for increased accuracy in the diagnosis of PTSD and complex PTSD with at risk youth.

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.

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.

Context and the activation of predictive inferences.

Previous research has demonstrated that the activation of predictive inferences can be affected both by the immediately preceding context and by the information contained in an earlier portion of a passage. Experiment 1 demonstrated that the same immediate context can result in the activation of different inferences when different character trait descriptions of the protagonist were presented earlier in the passage. Experiment 2 demonstrated that the trait descriptions produced activation of a specific inference; this occurred even though the immediately preceding context, in isolation, led to the activation of a different inference. The results of both experiments offer further support for the view that inference activation occurs as a result of the combined influence of the immediate context and the earlier portions of a text.