Precision engineering of biological function with large-scale measurements and machine learning.

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Method for reproducible automated bacterial cell culture and measurement.

Microbial cell culture is one of the most commonly performed protocols for synthetic biology, and laboratories are increasingly using 96-well plates and laboratory automation systems for cell culture. Here, we describe a method for reproducible microbial culture using laboratory automation systems, including automated liquid handling, automated plate sealing and de-sealing, automated incubation and measurement of growing cultures. We discuss the key considerations that, in our experience, are important for reproducibility and present statistical analyses of data from 150 automated microbial growth experiments performed over 27 months using our automated method.

Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power.

Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (landscape interpretable nonparametric model, https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights of the landscape, including its inherent dimensionality, a latent space of additive mutational effects, and metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic space.

Predicted coronavirus Nsp5 protease cleavage sites in the human proteome.

BACKGROUND: The coronavirus nonstructural protein 5 (Nsp5) is a cysteine protease required for processing the viral polyprotein and is therefore crucial for viral replication. Nsp5 from several coronaviruses have also been found to cleave host proteins, disrupting molecular pathways involved in innate immunity. Nsp5 from the recently emerged SARS-CoV-2 virus interacts with and can cleave human proteins, which may be relevant to the pathogenesis of COVID-19. Based on the continuing global pandemic, and emerging understanding of coronavirus Nsp5-human protein interactions, we set out to predict what human proteins are cleaved by the coronavirus Nsp5 protease using a bioinformatics approach. RESULTS: Using a previously developed neural network trained on coronavirus Nsp5 cleavage sites (NetCorona), we made predictions of Nsp5 cleavage sites in all human proteins. Structures of human proteins in the Protein Data Bank containing a predicted Nsp5 cleavage site were then examined, generating a list of 92 human proteins with a highly predicted and accessible cleavage site. Of those, 48 are expected to be found in the same cellular compartment as Nsp5. Analysis of this targeted list of proteins revealed molecular pathways susceptible to Nsp5 cleavage and therefore relevant to coronavirus infection, including pathways involved in mRNA processing, cytokine response, cytoskeleton organization, and apoptosis. CONCLUSIONS: This study combines predictions of Nsp5 cleavage sites in human proteins with protein structure information and protein network analysis. We predicted cleavage sites in proteins recently shown to be cleaved in vitro by SARS-CoV-2 Nsp5, and we discuss how other potentially cleaved proteins may be relevant to coronavirus mediated immune dysregulation. The data presented here will assist in the design of more targeted experiments, to determine the role of coronavirus Nsp5 cleavage of host proteins, which is relevant to understanding the molecular pathology of coronavirus infection.

The genotype-phenotype landscape of an allosteric protein.

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here, we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.

[The Guideline "Sedation for Gastrointestinal Endoscopy"]. / Leitlinien in der Praxis: Sedierung in der gastrointestinalen Endoskopie.

The guideline "Sedation for gastrointestinal endoscopy" (AWMF-register-no. 021/014) was published initially in 2008. Because of new and developing evidence, the guideline was updated in 2015. The aim of the guideline is to define the necessary structural, equipment and personnel requirements that contribute to minimizing the risk of sedation for endoscopy. In view of the high and increasing significance of gastrointestinal endoscopy, the guideline will remain highly relevant in the future. Essential aspects are the selection of sedatives/hypnotics, structural requirements, personnel requirements with regard to number, availability and training, management of complications and quality assurance. In this article, the development and evaluation of the evidence and its influence on the practical implementation, in particular for anaesthesia, are highlighted.

A Bayesian non-parametric mixed-effects model of microbial growth curves.

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

Measurements drive progress in directed evolution for precise engineering of biological systems.

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.

Synergistic Impacts of Organic Acids and pH on Growth of Pseudomonas aeruginosa: A Comparison of Parametric and Bayesian Non-parametric Methods to Model Growth.

Different weak organic acids have significant potential as topical treatments for wounds infected by opportunistic pathogens that are recalcitrant to standard treatments. These acids have long been used as bacteriostatic compounds in the food industry, and in some cases are already being used in the clinic. The effects of different organic acids vary with pH, concentration, and the specific organic acid used, but no studies to date on any opportunistic pathogens have examined the detailed interactions between these key variables in a controlled and systematic way. We have therefore comprehensively evaluated the effects of several different weak organic acids on growth of the opportunistic pathogen Pseudomonas aeruginosa. We used a semi-automated plate reader to generate growth profiles for two different strains (model laboratory strain PAO1 and clinical isolate PA1054 from a hospital burns unit) in a range of organic acids at different concentrations and pH, with a high level of replication for a total of 162,960 data points. We then compared two different modeling approaches for the interpretation of this time-resolved dataset: parametric logistic regression (with or without a component to include lag phase) vs. non-parametric Gaussian process (GP) regression. Because GP makes no prior assumptions about the nature of the growth, this method proved to be superior in cases where growth did not follow a standard sigmoid functional form, as is common when bacteria grow under stress. Acetic, propionic and butyric acids were all more detrimental to growth than the other acids tested, and although PA1054 grew better than PAO1 under non-stress conditions, this difference largely disappeared as the levels of stress increased. As expected from knowledge of how organic acids behave, their effect was significantly enhanced in combination with low pH, with this interaction being greatest in the case of propionic acid. Our approach lends itself to the characterization of combinatorial interactions between stressors, especially in cases where their impacts on growth render logistic growth models unsuitable.

Transcriptional Regulation in Archaea: From Individual Genes to Global Regulatory Networks.

Archaea are major contributors to biogeochemical cycles, possess unique metabolic capabilities, and resist extreme stress. To regulate the expression of genes encoding these unique programs, archaeal cells use gene regulatory networks (GRNs) composed of transcription factor proteins and their target genes. Recent developments in genetics, genomics, and computational methods used with archaeal model organisms have enabled the mapping and prediction of global GRN structures. Experimental tests of these predictions have revealed the dynamical function of GRNs in response to environmental variation. Here, we review recent progress made in this area, from investigating the mechanisms of transcriptional regulation of individual genes to small-scale subnetworks and genome-wide global networks. At each level, archaeal GRNs consist of a hybrid of bacterial, eukaryotic, and uniquely archaeal mechanisms. We discuss this theme from the perspective of the role of individual transcription factors in genome-wide regulation, how these proteins interact to compile GRN topological structures, and how these topologies lead to emergent, high-level GRN functions. We conclude by discussing how systems biology approaches are a fruitful avenue for addressing remaining challenges, such as discovering gene function and the evolution of GRNs.

A transcription network of interlocking positive feedback loops maintains intracellular iron balance in archaea.

Iron is required for key metabolic processes but is toxic in excess. This circumstance forces organisms across the tree of life to tightly regulate iron homeostasis. In hypersaline lakes dominated by archaeal species, iron levels are extremely low and subject to environmental change; however, mechanisms regulating iron homeostasis in archaea remain unclear. In previous work, we demonstrated that two transcription factors (TFs), Idr1 and Idr2, collaboratively regulate aspects of iron homeostasis in the model species Halobacterium salinarum. Here we show that Idr1 and Idr2 are part of an extended regulatory network of four TFs of the bacterial DtxR family that maintains intracellular iron balance. We demonstrate that each TF directly regulates at least one of the other DtxR TFs at the level of transcription. Dynamical modeling revealed interlocking positive feedback loop architecture, which exhibits bistable or oscillatory network dynamics depending on iron availability. TF knockout mutant phenotypes are consistent with model predictions. Together, our results support that this network regulates iron homeostasis despite variation in extracellular iron levels, consistent with dynamical properties of interlocking feedback architecture in eukaryotes. These results suggest that archaea use bacterial-type TFs in a eukaryotic regulatory network topology to adapt to harsh environments.

Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis.

Gene regulatory networks (GRNs) are critical for dynamic transcriptional responses to environmental stress. However, the mechanisms by which GRN regulation adjusts physiology to enable stress survival remain unclear. Here we investigate the functions of transcription factors (TFs) within the global GRN of the stress-tolerant archaeal microorganism Halobacterium salinarum. We measured growth phenotypes of a panel of TF deletion mutants in high temporal resolution under heat shock, oxidative stress, and low-salinity conditions. To quantitate the noncanonical functional forms of the growth trajectories observed for these mutants, we developed a novel modeling framework based on Gaussian process regression and functional analysis of variance (FANOVA). We employ unique statistical tests to determine the significance of differential growth relative to the growth of the control strain. This analysis recapitulated known TF functions, revealed novel functions, and identified surprising secondary functions for characterized TFs. Strikingly, we observed that the majority of the TFs studied were required for growth under multiple stress conditions, pinpointing regulatory connections between the conditions tested. Correlations between quantitative phenotype trajectories of mutants are predictive of TF-TF connections within the GRN. These phenotypes are strongly concordant with predictions from statistical GRN models inferred from gene expression data alone. With genome-wide and targeted data sets, we provide detailed functional validation of novel TFs required for extreme oxidative stress and heat shock survival. Together, results presented in this study suggest that many TFs function under multiple conditions, thereby revealing high interconnectivity within the GRN and identifying the specific TFs required for communication between networks responding to disparate stressors. IMPORTANCE To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

Effect of Xenon Anesthesia Compared to Sevoflurane and Total Intravenous Anesthesia for Coronary Artery Bypass Graft Surgery on Postoperative Cardiac Troponin Release: An International, Multicenter, Phase 3, Single-blinded, Randomized Noninferiority Trial.

BACKGROUND: Ischemic myocardial damage accompanying coronary artery bypass graft surgery remains a clinical challenge. We investigated whether xenon anesthesia could limit myocardial damage in coronary artery bypass graft surgery patients, as has been reported for animal ischemia models. METHODS: In 17 university hospitals in France, Germany, Italy, and The Netherlands, low-risk elective, on-pump coronary artery bypass graft surgery patients were randomized to receive xenon, sevoflurane, or propofol-based total intravenous anesthesia for anesthesia maintenance. The primary outcome was the cardiac troponin I concentration in the blood 24 h postsurgery. The noninferiority margin for the mean difference in cardiac troponin I release between the xenon and sevoflurane groups was less than 0.15 ng/ml. Secondary outcomes were the safety and feasibility of xenon anesthesia. RESULTS: The first patient included at each center received xenon anesthesia for practical reasons. For all other patients, anesthesia maintenance was randomized (intention-to-treat: n = 492; per-protocol/without major protocol deviation: n = 446). Median 24-h postoperative cardiac troponin I concentrations (ng/ml [interquartile range]) were 1.14 [0.76 to 2.10] with xenon, 1.30 [0.78 to 2.67] with sevoflurane, and 1.48 [0.94 to 2.78] with total intravenous anesthesia [per-protocol]). The mean difference in cardiac troponin I release between xenon and sevoflurane was -0.09 ng/ml (95% CI, -0.30 to 0.11; per-protocol: P = 0.02). Postoperative cardiac troponin I release was significantly less with xenon than with total intravenous anesthesia (intention-to-treat: P = 0.05; per-protocol: P = 0.02). Perioperative variables and postoperative outcomes were comparable across all groups, with no safety concerns. CONCLUSIONS: In postoperative cardiac troponin I release, xenon was noninferior to sevoflurane in low-risk, on-pump coronary artery bypass graft surgery patients. Only with xenon was cardiac troponin I release less than with total intravenous anesthesia. Xenon anesthesia appeared safe and feasible.

Neuron-Specific Enolase Predicts Poor Outcome After Cardiac Arrest and Targeted Temperature Management: A Multicenter Study on 1,053 Patients.

OBJECTIVE: Outcome prediction after cardiac arrest is important to decide on continuation or withdrawal of intensive care. Neuron-specific enolase is an easily available, observer-independent prognostic biomarker. Recent studies have yielded conflicting results on its prognostic value after targeted temperature management. DESIGN, SETTING, AND PATIENTS: We analyzed neuron-specific enolase serum concentrations 3 days after nontraumatic in-hospital cardiac arrest and out-of-hospital cardiac arrest and outcome of patients from five hospitals in Germany, Austria, and Italy. Patients were treated at 33°C for 24 hours. Cerebral Performance Category was evaluated upon ICU discharge. We performed case reviews of good outcome patients with neuron-specific enolase greater than 90 µg/L and poor outcome patients with neuron-specific enolase less than or equal to 17 µg/L (upper limit of normal). MEASUREMENTS AND MAIN RESULTS: A neuron-specific enolase serum concentration greater than 90 µg/L predicted Cerebral Performance Category 4-5 with a positive predictive value of 99%, false positive rate of 0.5%, and a sensitivity of 48%. All three patients with neuron-specific enolase greater than 90 µg/L and Cerebral Performance Category 1-2 had confounders for neuron-specific enolase elevation. An neuron-specific enolase serum concentration less than or equal to 17 µg/L excluded Cerebral Performance Category 4-5 with a negative predictive value of 92%. The majority of 14 patients with neuron-specific enolase less than or equal to 17 µg/L who died had a cause of death other than hypoxic-ischemic encephalopathy. Specificity and sensitivity for prediction of poor outcome were independent of age, sex, and initial rhythm but higher for out-of-hospital cardiac arrest than for in-hospital cardiac arrest patients. CONCLUSION: High neuron-specific enolase serum concentrations reliably predicted poor outcome at ICU discharge. Prediction accuracy differed and was better for out-of-hospital cardiac arrest than for in-hospital cardiac arrest patients. Our "in-the-field" data indicate 90 µg/L as a threshold associated with almost no false positives at acceptable sensitivity. Confounders of neuron-specific enolase elevation should be actively considered: neuron-specific enolase-producing tumors, acute brain diseases, and hemolysis. We strongly recommend routine hemolysis quantification. Neuron-specific enolase serum concentrations less than or equal to 17 µg/L argue against hypoxic-ischemic encephalopathy incompatible with reawakening.

Detecting differential growth of microbial populations with Gaussian process regression.

Microbial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions for which growth has a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit nonstandard growth curves. Rather than assume specific functional forms for experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high-resolution time-series growth data enables accurate quantification of population growth and allows explicit control of effects from other covariates such as genetic background. This framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model and develop statistical tests to quantify the differential effects of environmental perturbations on microbial growth across a large compendium of genotypes in archaea and yeast. This method accurately identifies known transcriptional regulators and implicates novel regulators of growth under standard and stress conditions in the model archaeal organism Halobacterium salinarum For yeast, our method correctly identifies known phenotypes for a diversity of genetic backgrounds under cyclohexamide stress and also detects previously unidentified oxidative stress sensitivity across a subset of strains. Together, these results demonstrate that the GP models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

Additives used to reduce perioperative opioid consumption 1: Alpha2-agonists.

Because of their significant side effects, especially in obese patients, the routine perioperative use of opioids has been questioned recently. Alpha2-agonists are drugs with a considerable analgesic potency with the potential to reduce opioid consumption. Alpha2-agonists bind to alpha2-adrenergic receptors in the CNS and peripherally. They inhibit the central sympathetic outflow, resulting in an attenuation of blood pressure and heart rate and in a sparing effect on anaesthetics and analgesics. In the postoperative period alpha2-agonists provide an analgesic effect without respiratory depression and other known opioid side effects. Intraoperatively, a complete replacement of the synthetic opioid fentanyl by the alpha2-agonist dexmedetomidine has been demonstrated. Although alpha2-agonists have a sedative action, recovery times are not prolonged compared to those of opioids. Cardiovascular side effects such as bradycardia and hypotension have to be observed and treated.