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Microorganisms with simplified genomes represent interesting cell chassis for systems and synthetic biology. However, genome reduction can lead to undesired traits, such as decreased growth rate and metabolic imbalances. To investigate the impact of genome reduction on Escherichia coli strain DGF-298, a strain in which ~ 36% of the genome has been removed, we reconstructed a strain-specific metabolic model ( i AC1061), investigated the regulation of gene expression using iModulon-based transcriptome analysis, and performed adaptive laboratory evolution to let the strain correct potential imbalances that arose during its simplification. The model notably predicted that the removal of all three key pathways for glycolaldehyde disposal in this microorganism would lead to a metabolic bottleneck through folate starvation. Glycolaldehyde is also known to cause self-generation of reactive oxygen species, as evidenced by the increased expression of oxidative stress resistance genes in the SoxS iModulon. The reintroduction of the aldA gene, responsible for one native glycolaldehyde disposal route, alleviated the constitutive oxidative stress response. Our results suggest that systems-level approaches and adaptive laboratory evolution have additive benefits when trying to repair and optimize genome-engineered strains., Importance: Genomic streamlining can be employed in model organisms to reduce complexity and enhance strain predictability. One of the most striking examples is the bacterial strain Escherichia coli DGF-298, notable for having over one-third of its genome deleted. However, such extensive genome modifications raise the question of how similar this simplified cell remains when compared with its parent, and what are the possible unintended consequences of this simplification. In this study, we used metabolic modeling along with iModulon-based transcriptomic analysis in different growth conditions to assess the impact of genome reduction on metabolism and gene regulation. We observed little impact of genomic reduction on the regulatory network of E. coli DGF-298 and identified a potential metabolic bottleneck leading to the constitutive activity of the SoxS iModulon. We then leveraged the model's predictions to successfully restore SoxS activity to the basal level.

Many pathogenic Gram-negative bacteria use repeats-in-toxin adhesins for colonization and biofilm formation. In the cholera agent Vibrio cholerae , flagellar-regulated hemagglutinin A (FrhA) enables these functions. Using bioinformatic analysis, a sugar-binding domain was identified in FrhA adjacent to a domain of unknown function. AlphaFold2 indicated the boundaries of both domains to be slightly shorter than previously predicted and assisted in the recognition of the unknown domain as a split immunoglobulin-like fold that can assist in projecting the sugar-binding domain toward its target. The AlphaFold2-predicted structure is in excellent agreement with the molecular envelope obtained from small-angle X-ray scattering analysis of a recombinant construct spanning the sugar-binding and unknown domains. This two-domain construct was probed by glycan micro-array screening and showed binding to mammalian fucosylated glycans, some of which are characteristic erythrocyte markers and intestinal cell epitopes. Isothermal titration calorimetry further showed the construct-bound l-fucose with a K d of 21 µM. Strikingly, this recombinant protein construct bound and lysed erythrocytes in a concentration-dependent manner, and its hemolytic activity was blocked by the addition of l-fucose. A protein ortholog construct from Aeromonas veronii was also produced and showed a similar glycan-binding pattern, binding affinity, erythrocyte-binding, and hemolytic activities. As demonstrated here with Hep-2 cells, fucose-based inhibitors of this sugar-binding domain can potentially be developed to block colonization by V. cholerae and other pathogenic bacteria that share this adhesin domain.IMPORTANCEThe bacterium, Vibrio cholerae , which causes cholera, uses an adhesion protein to stick to human cells and begin the infection process. One part of this adhesin protein binds to a particular sugar, fucose, on the surface of the target cells. This binding can lead to colonization and killing of the cells by the bacteria. Adding l-fucose to the bacteria before they bind to the human cells can prevent attachment and has promise as a preventative drug to protect against cholera., Competing Interests: The authors declare no conflict of interest.

Vibrio cholerae, the causative agent of the disease cholera, is responsible for multiple pandemics. V. cholerae binds to and colonizes the gastrointestinal tract within the human host, as well as various surfaces in the marine environment (e.g., zooplankton) during interepidemic periods. A large adhesin, the Flagellar Regulated Hemagglutinin A (FrhA), enhances binding to erythrocytes and epithelial cells and enhances intestinal colonization. We identified a peptide-binding domain (PBD) within FrhA that mediates hemagglutination, binding to epithelial cells, intestinal colonization, and facilitates biofilm formation. Intriguingly, this domain is also found in the ice-binding protein of the Antarctic bacterium Marinomonas primoryensis , where it mediates binding to diatoms. Peptide inhibitors of the M. primoryensis PBD inhibit V. cholerae binding to human cells as well as to diatoms and inhibit biofilm formation. Moreover, the M. primoryensis PBD inserted into FrhA allows V. cholerae to bind human cells and colonize the intestine and also enhances biofilm formation, demonstrating the interchangeability of the PBD from these bacteria. Importantly, peptide inhibitors of PBD reduce V. cholerae intestinal colonization in infant mice. These studies demonstrate how V. cholerae uses a PBD shared with a diatom-binding Antarctic bacterium to facilitate intestinal colonization in humans and biofilm formation in the environment.

We investigate the flow over smooth (non-ribletted) shark skin denticles in an open-channel flow using direct numerical simulation (DNS) and two Reynolds averaged Navier-Stokes (RANS) closures. Large peaks in pressure and viscous drag are observed at the denticle crown edges, where they are exposed to high-speed fluid which penetrates between individual denticles, increasing shear and turbulence. Strong lift forces lead to a positive spanwise torque acting on individual denticles, potentially encouraging bristling if the denticles were not fixed. However, DNS predicts that denticles ultimately increase drag by 58% compared to a flat plate. Good predictions of drag distributions are obtained by RANS models, although an underestimation of turbulent kinetic energy production leads to an underprediction of drag. Nevertheless, RANS methods correctly predict trends in the drag data and the regions contributing most to viscous and pressure drag. Subsequently, RANS models are used to investigate the dependence of drag on the flow blockage ratio (boundary layer to roughness height ratio), finding that the drag increase due to denticles is halved when the blockage ratio δ / h is increased from 14 to 45. Our results provide an integrated understanding of the drag over non-ribletted denticles, enabling existing diverse drag data to be explained., (© 2023 The Authors.)

Here we discuss the structure and regulation of the Vibrio flagellum and its role in the virulence of pathogenic species. We will cover some of the novel insights into the structure of this nanomachine that have recently been enabled by cryoelectron tomography. We will also highlight the recent genetic studies that have increased our understanding in flagellar synthesis specifically at the bacterial cell pole, temporal regulation of flagellar genes, and how the flagellum enables directional motility through Run-Reverse-Flick cycles., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

In silico genome mining provides easy access to secondary metabolite biosynthetic gene clusters (BGCs) encoding the biosynthesis of many bioactive compounds, which are the basis for many important drugs used in human medicine. However, the association between BGCs and other functions encoded in the genomes of producers have remained elusive. Here, we present a systems biology workflow that integrates genome mining with a detailed pangenome analysis for detecting genes associated with a particular BGC. We analyzed 3,889 enterobacterial genomes and found 13,266 BGCs, represented by 252 distinct BGC families and 347 additional singletons. A pangenome analysis revealed 88 genes putatively associated with a specific BGC coding for the colon cancer-related colibactin that code for diverse metabolic and regulatory functions . The presented workflow opens up the possibility to discover novel secondary metabolites, better understand their physiological roles, and provides a guide to identify and analyze BGC associated gene sets., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Metastatic cutaneous SCC carries a poor prognosis with five-year survival of 25%-57%. The aim of this study is to examine the outcomes following surgery with adjuvant therapy for management of metastatic cSCC in a UK-based population. This is a retrospective review of patients with metastatic cSCC of the head and neck who underwent primary surgery at a regional center during a six-year period. Overall and disease specific survival were calculated using Kaplan-Meier and log-rank tests. Results were reported as hazard ratios (HR) with 95% confidence intervals. Forty-five patients met the inclusion criteria. The mean time to discovery of metastases was 9.3 months (range, 0-40 months). Only two patients (4%) had discovery of metastases after two years, with none after 3.3 years. The overall five5-year survival was 31% (95% CI 15%to 48%) with two-year survival at 48% (95% CI 31%to 63%). The median OS survival was 722 days (95% CI 607to 1359). Patients aged >80 years had a decreased OS. This is the largest UK based study documenting the overall and disease specific survival associated with metastatic cutaneous SCC of the head and neck. Our overall survival is comparable to similar studies, but remains poor. Total number of involved nodes, and lymph node ratio were not statistically significant., (Crown Copyright © 2021. Published by Elsevier Ltd. All rights reserved.)

We describe how we are creating a new and comprehensive R library solving the problem of exact sample size determination of RCTs. A crucial prerequisite for the trial protocol is a priori sample sizes that bound the test size below a target (often 5%) and the test power above a target (often 80%). Approximate formulas are available for binary trials but the target test size and power are often violated by standard methods for even quite large sample sizes. Moreover, adjusting standard tests to take account of their size bias can reduce power substantially. This has been well known for several decades. Exact and quasi-exact tests are now available and can be computed in a few seconds for a single data set. However, calculating the exact power and size of such tests requires computing them for all possible outcomes. Searching for minimum samples sizes that achieve a given target requires doing this for a wide range of sample sizes. This becomes computationally infeasible very quickly; to compute required sample sizes for a target size of 5% and power of 80% would, on a standard computer, take several months. Computation time increases as the size and clinically relevant difference decreases. After having presented the main operative challenges to creating this library, mainly due to the need of summarizing a very large amount of information, we put forward our innovative solutions to deal with this complex problem from a statistical viewpoint. The described library will be released in open source., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Mesoplasma florum, a fast-growing near-minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein-coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species-specific constraints to produce the functional iJL208 genome-scale model (GEM) of metabolism. Genome-wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI-syn3.0 and its parent JCVI-syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping-stone toward model-driven whole-genome engineering., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles., Competing Interests: The authors have declared that no competing interests exist.

We investigate the influence of smooth and ribletted shark skin on a turbulent boundary layer flow. Through laser Doppler anemometry (LDA) the role of riblets in combination with the shark skin denticle is established for the first time. Our results show that smooth denticles behave like a typical rough surface when exposed to an attached boundary layer. Drag is increased for the full range of tested dimensionless denticle widths, w + ≈ 25-80, where w + is the denticle width, w , scaled by the friction velocity, u τ , and the kinematic viscosity, ν . However, when riblets are added to the denticle crown we demonstrate there is a significant reduction in drag, relative to the smooth denticles. We obtain a modest maximum drag reduction of 2% for the ribletted denticles when compared to the flat plate, but when compared to the smooth denticles the difference in drag is in excess of 20% for w + ≈ 80. This study enables a new conclusion that riblets have evolved as a mechanism to reduce or eliminate the skin friction increase due to the presence of scales (denticles). The combination of scales and riblets is hydrodynamically efficient in terms of skin-friction drag, while also acting to maintain flow attachment, and providing the other advantages associated with scales, e.g. anti-fouling, abrasion resistance, and defence against parasites., (Creative Commons Attribution license.)

Group sequential single arm designs are common in phase II trials as well as attribute testing and acceptance sampling. After the trial is completed, especially if the recommendation is to proceed to further testing, there is interest in full inference on treatment efficacy. For a binary response, there is the potential to construct exact upper and lower confidence limits, the first published method for which is Jennison and Turnbull (1983). We place their method within the modern theory of exact confidence limits and provide a new general result that ensures that the exact limits are consistent with the test result, an issue that has been largely ignored in the literature. Amongst methods based on the minimal sufficient statistic, we propose two exact methods that out-perform Jennison and Turnbull's method across 10 selected designs. One of these we prefer and recommend for practical and theoretical reasons. We also investigate a method based on inverting Fisher's combination test, as well as a pure tie-breaking variant of it. For the range of designs considered, neither of these methods result in large enough improvements in efficiency to justify violation of the sufficiency principle. For any nonadaptive sequential design, an R-package is provided to select a method and compute the inference from a given realization., (© 2021 John Wiley & Sons, Ltd.)

Vibrio cholerae causes the gastrointestinal illness cholera, which spreads throughout the globe in large pandemics. The current pandemic is caused by O1 El Tor biotype strains, whereas previous pandemics were caused by O1 classical biotype strains. El Tor V. cholerae is noted for its ability to acquire exogenous DNA through chitin-induced natural transformation, which has been exploited for genetic manipulation of El Tor strains in the laboratory. In contrast, the prototypical classical strain O395 lacks this ability, which was suspected to be due to a mutation in the regulatory gene hapR HapR and the regulator TfoX control expression of a third competence regulator, QstR. We found that artificial induction of both TfoX and QstR in the presence of HapR in O395 was required for efficient DNA uptake. However, natural transformation in the classical strain is still orders of magnitude below that of an El Tor strain. O395 expressing HapR could also undergo natural transformation after growth on chitin, which could be increased by artificial induction of TfoX and/or QstR. A plasmid that expresses both TfoX and QstR was created that allowed for consistent DNA uptake in O395 carrying a hapR plasmid. This technique was also used to facilitate cotransformation into O395 of unmarked DNA (Δ lacZ , Δ flaA , Δ flgG ) for multiplex genome editing by natural transformation (MuGENT). These results demonstrate that the classical biotype O395 strain is functionally capable of DNA uptake, which allows for the rapid genetic manipulation of its genome. IMPORTANCE Natural transformation (uptake of exogenous DNA) in Vibrio cholerae has contributed to the evolution of these human pathogens. Classical biotype V. cholerae strains were responsible for the first six cholera pandemics but were replaced by El Tor biotype V. cholerae in the current pandemic. This study demonstrates that classical V. cholerae is functionally capable of natural transformation, but inactivation of the transformation regulator HapR and inherent levels of transformation that are lower than those of El Tor V. cholerae suggest that the classical biotype may be less able to utilize natural transformation for horizontal gene transfer., (Copyright © 2021 American Society for Microbiology.)

New cloning vectors have been developed with features to enhance quick allelic exchange in gram-negative bacteria. The conditionally replicative R6K and transfer origins facilitate conjugation and chromosomal integration into a variety of bacterial species, whereas the sacB gene provides counterselection for allelic exchange. The vectors have incorporated the lacZ alpha fragment with an enhanced multicloning site for easy blue/white screening and priming sites identified for efficient in vivo  assembly or other DNA assembly cloning techniques. Different antibiotic resistance markers allow versatility for use with different bacteria, and transformation into an Escherichia coli strain capable of conjugation enables a quick method for allelic exchange. As a proof of principle, the authors used these vectors to inactivate genes in Vibrio cholerae and Salmonella typhimurium .

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses., (© 2020. Springer Nature Limited.)

Enzyme turnover numbers ( k cat s) are essential for a quantitative understanding of cells. Because k cat s are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k cat s using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k cat s are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k cat s predict unseen proteomics data with much higher precision than in vitro k cat s. The results demonstrate that in vivo k cat s can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Response to acid stress is critical for Escherichia coli to successfully complete its life-cycle by passing through the stomach to colonize the digestive tract. To develop a fundamental understanding of this response, we established a molecular mechanistic description of acid stress mitigation responses in E. coli and integrated them with a genome-scale model of its metabolism and macromolecular expression (ME-model). We considered three known mechanisms of acid stress mitigation: 1) change in membrane lipid fatty acid composition, 2) change in periplasmic protein stability over external pH and periplasmic chaperone protection mechanisms, and 3) change in the activities of membrane proteins. After integrating these mechanisms into an established ME-model, we could simulate their responses in the context of other cellular processes. We validated these simulations using RNA sequencing data obtained from five E. coli strains grown under external pH ranging from 5.5 to 7.0. We found: i) that for the differentially expressed genes accounted for in the ME-model, 80% of the upregulated genes were correctly predicted by the ME-model, and ii) that these genes are mainly involved in translation processes (45% of genes), membrane proteins and related processes (18% of genes), amino acid metabolism (12% of genes), and cofactor and prosthetic group biosynthesis (8% of genes). We also demonstrated several intervention strategies on acid tolerance that can be simulated by the ME-model. We thus established a quantitative framework that describes, on a genome-scale, the acid stress mitigation response of E. coli that has both scientific and practical uses., Competing Interests: The authors have declared that no competing interests exist.

Background: This study explored the prevalence of burnout syndrome among advanced practice providers (APPs = physician assistants (PAs) and advanced practice nurses (APNs)) and physician assistant students in training to become APPs. While previous research has focused on understanding burnout in a myriad of health professions, burnout among APPs and APP trainees has been underexplored. As such, this work serves as a primer for establishing benchmark levels of burnout in these specific healthcare provider/trainee populations., Methods: This study distributed a modified Maslach Burnout Inventory (MBI) to a sample of 297 APPs and 1200 PA students. Mean differences in burnout scores were compared against a national reference sample of healthcare professionals using one sample t tests and linear regression explored relationships among demographics and burnout dimension scores., Results: APPs ( n  = 124) and APP trainees ( n  = 230) who responded to the survey displayed average levels of burnout, though both populations expressed significantly lower personal accomplishment scores than the national reference sample. No significant differences were detected between APNs and practicing PAs ( p  = 0.761). Increased age was negatively associated with depersonalization scores suggesting that APP trainees and younger APPs are at higher risk of developing severe burnout and may need additional support in their training and early careers. Furthermore, the prevalence of burnout between APPs and APP trainees was found to be comparable, suggesting that burnout from training may persist into practice., Conclusions: A small proportion of APPs and APP trainees may be at risk of developing severe burnout. Individuals in these "at risk" populations may need additional support during training and perhaps later on in practice., Competing Interests: Conflict of InterestThe authors declare that there are no conflicts of interest., (© International Association of Medical Science Educators 2019.)

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli , called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis., Competing Interests: The authors declare no conflict of interest.

Applied statisticians and pharmaceutical researchers are frequently involved in the design and analysis of clinical trials where at least one of the outcomes is binary. Treatments are judged by the probability of a positive binary response. A typical example is the noninferiority trial, where it is tested whether a new experimental treatment is practically not inferior to an active comparator with a prespecified margin δ. Except for the special case of δ = 0, no exact conditional test is available although approximate conditional methods (also called second-order methods) can be applied. However, in some situations, the approximation can be poor and the logical argument for approximate conditioning is not compelling. The alternative is to consider an unconditional approach. Standard methods like the pooled z-test are already unconditional although approximate. In this article, we review and illustrate unconditional methods with a heavy emphasis on modern methods that can deliver exact, or near exact, results. For noninferiority trials based on either rate difference or rate ratio, our recommendation is to use the so-called E-procedure, based on either the score or likelihood ratio statistic. This test is effectively exact, computationally efficient, and respects monotonicity constraints in practice. We support our assertions with a numerical study, and we illustrate the concepts developed in theory with a clinical example in pulmonary oncology; R code to conduct all these analyses is available from the authors., (© 2019 John Wiley & Sons, Ltd.)

Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat)., Competing Interests: The authors have declared that no competing interests exist.

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., Competing Interests: The authors have declared that no competing interests exist.

Background: Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype., Results: We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics ("inertia") alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation., Conclusions: Overall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.

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., Competing Interests: The authors have declared that no competing interests exist.

A country's total fertility rate (TFR) depends on many factors. Attributing changes in TFR to changes of policy is difficult, as they could easily be correlated with changes in the unmeasured drivers of TFR. A case in point is Australia where both pronatalist effort and TFR increased in lock step from 2001 to 2008 and then decreased. The global financial crisis or other unobserved confounders might explain both the reducing TFR and pronatalist incentives after 2008. Therefore, it is difficult to estimate causal effects of policy using econometric techniques. The aim of this study is to instead look at the structure of the population to identify which subgroups most influence TFR. Specifically, we build a stochastic model relating TFR to the fertility rates of various subgroups and calculate elasticity of TFR with respect to each rate. For each subgroup, the ratio of its elasticity to its group size is used to evaluate the subgroup's potential cost effectiveness as a pronatalist target. In addition, we measure the historical stability of group fertility rates, which measures propensity to change. Groups with a high effectiveness ratio and also high propensity to change are natural policy targets. We applied this new method to Australian data on fertility rates broken down by parity, age and marital status. The results show that targeting parity 3+ is more cost-effective than lower parities. This study contributes to the literature on pronatalist policies by investigating the targeting of policies, and generates important implications for formulating cost-effective policies.

Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN-probably the best characterized TRN-several questions remain. Here, we address three questions: ( i ) How complete is our knowledge of the E. coli TRN; ( ii ) how well can we predict gene expression using this TRN; and ( iii ) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism's TRN from disparate data types., Competing Interests: The authors declare no conflict of interest.

Adaptive designs encompass all trials allowing various types of design modifications over the course of the trial. A key requirement for confirmatory adaptive designs to be accepted by regulators is the strong control of the family-wise error rate. This can be achieved by combining the p-values for each arm and stage to account for adaptations (including but not limited to treatment selection), sample size adaptation and multiple stages. While the theory for this is novel and well-established, in practice, these methods can perform poorly, especially for unbalanced designs and for small to moderate sample sizes. The problem is that standard stagewise tests have inflated type I error rate, especially but not only when the baseline success rate is close to the boundary and this is carried over to the adaptive tests, seriously inflating the family-wise error rate. We propose to fix this problem by feeding the adaptive test with second-order accurate p-values, in particular bootstrap p-values. Secondly, an adjusted version of the Simes procedure for testing intersection hypotheses that reduces the built-in conservatism is suggested. Numerical work and simulations show that unlike their standard counterparts the new approach preserves the overall error rate, at or below the nominal level across the board, irrespective of the baseline rate, stagewise sample sizes or allocation ratio. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Adaptive laboratory evolution (ALE) experiments are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the observed distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications. IMPORTANCE Evolution and natural selection inexorably lead to an organism's improved fitness in a given environment, whether in a laboratory or natural setting. However, despite the frequent natural occurrence of complex and dynamic growth environments, laboratory evolution experiments typically maintain simple, static culturing environments so as to reduce selection pressure complexity. In this study, we investigated the adaptive strategies underlying evolution to fluctuating environments by evolving Escherichia coli to conditions of frequently switching growth substrate. Characterization of evolved strains via a number of different data types revealed the various genetic and phenotypic changes implemented in pursuit of growth optimality and how these differed across the different growth substrates and switching protocols. This work not only helps to establish general principles of adaptation to complex environments but also suggests strategies for experimental design to achieve desired evolutionary outcomes., (Copyright © 2017 American Society for Microbiology.)

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the "generalist" (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and "hedging" against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σ S . Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.

Background: Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints., Results: Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints., Conclusions: Just as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.

Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

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.

For stratified 2 × 2 tables, standard approximate confidence limits can perform poorly from a strict frequentist perspective, even for moderate-sized samples, yet they are routinely used. In this paper, I show how to use importance sampling to compute highly accurate limits in reasonable time. The methodology is very general and simple to implement, and orders of magnitude are faster than existing alternatives., (Copyright © 2013 John Wiley & Sons, Ltd.)

Introduction: Population demographics and disease epidemiology is resulting in more elderly patients presenting with regional metastases from cutaneous malignancy of the head and neck region. Surgery remains the most appropriate primary treatment option., Patients and Methods: We analysed consecutive patients aged 80 and over who developed regional metastases from cutaneous cancers of head and neck and underwent a neck dissection over a two-and-a-half-year period. Data were obtained from the cancer database and patients' notes. A Kaplan-Meier survival graph was constructed., Results: Our study demonstrated a low postoperative morbidity but one patient died from medical complications with in the first 30 days post surgery. The median survival time following surgery is nearly two years., Conclusions: We continue to advocate primary surgery for cutaneous metastatic malignancy from the head and neck area but patients need multidisciplinary team discussions, thorough assessment and counselling.

Clinical trials data often come in the form of low-dimensional tables of small counts. Standard approximate tests such as score and likelihood ratio tests are imperfect in several respects. First, they can give quite different answers from the same data. Second, the actual type-1 error can differ significantly from nominal, even for quite large sample sizes. Third, exact inferences based on these can be strongly nonmonotonic functions of the null parameter and lead to confidence sets that are discontiguous. There are two modern approaches to small sample inference. One is to use so-called higher order asymptotics (Reid, 2003, Annal of Statistics 31, 1695-1731) to provide an explicit adjustment to the likelihood ratio statistic. The theory for this is complex but the statistic is quick to compute. The second approach is to perform an exact calculation of significance assuming the nuisance parameters equal their null estimate (Lee and Young, 2005, Statistic and Probability Letters 71, 143-153), which is a kind of parametric bootstrap. The purpose of this article is to explain and evaluate these two methods, for testing whether a difference in probabilities p(2) - p(1) exceeds a prechosen noninferiority margin δ(0) . On the basis of an extensive numerical study, we recommend bootstrap P-values as superior to all other alternatives. First, they produce practically identical answers regardless of the basic test statistic chosen. Second, they have excellent size accuracy and higher power. Third, they vary much less erratically with the null parameter value δ(0) ., (© 2009, The International Biometric Society.)

Preoperative elevation of serum C-reactive protein (CRP) has been demonstrated as a prognostic indicator in oesophageal, gastric and colorectal cancer. This study was designed to establish if elevated preoperative levels of serum CRP could predict the prognosis of patients treated with primary surgery for oral squamous cell carcinoma (SCC). Sixty patients with oral SCC who were treated by primary surgery and microvascular free flap reconstruction, were included in the study. The relation between preoperative levels of serum CRP, clinicopathological features and patient prognosis was determined. This study showed using bivariate analysis (p=0.003) and multivariate analysis (p<0.001) that a raised preoperative CRP was associated with worse overall survival. Tumour size and stage when combined with CRP levels increases the predictive power of this indicator.

We consider the problem of testing for a difference in the probability of success from matched binary pairs. Starting with three standard inexact tests, the nuisance parameter is first estimated and then the residual dependence is eliminated by maximization, producing what I call an E+M P-value. The E+M P-value based on McNemar's statistic is shown numerically to dominate previous suggestions, including partially maximized P-values as described in Berger and Sidik (2003, Statistical Methods in Medical Research 12, 91-108). The latter method, however, may have computational advantages for large samples.

Assessing the therapeutic noninferiority of one medical treatment compared with another is often based on the difference in response rates from a matched binary pairs design. This paper develops a new exact unconditional test for noninferiority that is more powerful than available alternatives. There are two new elements presented in this paper. First, we introduce the likelihood ratio statistic as an alternative to the previously proposed score statistic of Nam (Biometrics 1997; 53:1422-1430). Second, we eliminate the nuisance parameter by estimation followed by maximization as an alternative to the partial maximization of Berger and Boos (Am. Stat. Assoc. 1994; 89:1012-1016) or traditional full maximization. Based on an extensive numerical study, we recommend tests based on the score statistic, the nuisance parameter being controlled by estimation followed by maximization., (2008 John Wiley & Sons, Ltd)

We compare various one-sided confidence limits for the odds ratio in a 2 x 2 table. The first group of limits relies on first-order asymptotic approximations and includes limits based on the (signed) likelihood ratio, score and Wald statistics. The second group of limits is based on the conditional tilted hypergeometric distribution, with and without mid-P correction. All these limits have poor unconditional coverage properties and so we apply the general transformation of Buehler (J. Am. Statist. Assoc. 1957; 52:482-493) to obtain limits which are unconditionally exact. The performance of these competing exact limits is assessed across a range of sample sizes and parameter values by looking at their mean size. The results indicate that Buehler limits generated from the conditional likelihood have the best performance, with a slight preference for the mid-P version. This confidence limit has not been proposed before and is recommended for general use, especially when the underlying probabilities are not extreme., (Copyright 2007 John Wiley & Sons, Ltd.)

This paper examines exact one-sided confidence limits for the risk ratio in a 2 x 2 table with structural zero. Starting with four approximate lower and upper limits, we adjust each using the algorithm of Buehler (1957) to arrive at lower (upper) limits that have exact coverage properties and are as large (small) as possible subject to coverage, as well as an ordering, constraint. Different Buehler limits are compared by their mean size, since all are exact in their coverage. Buehler limits based on the signed root likelihood ratio statistic are found to have the best performance and recommended for practical use., ((c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)