Blood pressure variability and short-term outcomes after mechanical thrombectomy in patients with acute ischemic stroke.

OBJECTIVES: There is limited data evaluating effects of post-mechanical thrombectomy (MT) blood pressure (BP) control on short-term clinical outcomes in acute ischemic stroke (AIS) patients with large vessel occlusion (LVO). We aim to investigate the association of BP variations, after MT, with stroke early outcomes. MATERIALS AND METHODS: A retrospective study was conducted on AIS patients with LVO undergoing MT at a tertiary center over 3.5 years. Hourly BP data was recorded within the first 24- and 48-hours post-MT. BP variability was expressed as the interquartile range (IQR) of BP distribution. Short-term favorable outcome was defined as modified Rankin scale (mRS) 0-3, discharge to home or inpatient rehabilitation facility (IRF). RESULTS: Of the 95 enrolled subjects, 37(38.9%) had favorable outcomes at discharge and 8 (8.4%) died. After adjustment for confounders, an increase in IQR of systolic blood pressure (SBP) within the first 24 hours after MT revealed a significant inverse association with favorable outcomes (OR 0.43, 95% CI [0.19, 0.96], p = 0.039). Increased median MAP within the first 24 hours after MT correlated with favorable outcomes (OR 1.75, 95% CI [1.09, 2.83], p = 0.021). Subgroup analysis redemonstrated significant inverse association between increased SBP IQR and favorable outcomes (OR 0.48, 95% CI [0.21, 0.97], p = 0.042) among patients with successful revascularization. CONCLUSIONS: Post-MT high SBP variability was associated with worse short-term outcomes in AIS patients with LVO regardless of recanalization status. MAP values may be used as indicators for functional prognosis.

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth.

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.

Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen.

The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas (a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis (ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e.g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.

Serial Femtosecond Zero Dose Crystallography Captures a Water-Free Distal Heme Site in a Dye-Decolorising Peroxidase to Reveal a Catalytic Role for an Arginine in FeIV =O Formation.

Obtaining structures of intact redox states of metal centers derived from zero dose X-ray crystallography can advance our mechanistic understanding of metalloenzymes. In dye-decolorising heme peroxidases (DyPs), controversy exists regarding the mechanistic role of the distal heme residues aspartate and arginine in the heterolysis of peroxide to form the catalytic intermediate compound I (FeIV =O and a porphyrin cation radical). Using serial femtosecond X-ray crystallography (SFX), we have determined the pristine structures of the FeIII and FeIV =O redox states of a B-type DyP. These structures reveal a water-free distal heme site that, together with the presence of an asparagine, imply the use of the distal arginine as a catalytic base. A combination of mutagenesis and kinetic studies corroborate such a role. Our SFX approach thus provides unique insight into how the distal heme site of DyPs can be tuned to select aspartate or arginine for the rate enhancement of peroxide heterolysis.

Reducing sample consumption for serial crystallography using acoustic drop ejection.

Efficient sample delivery is an essential aspect of serial crystallography at both synchrotrons and X-ray free-electron lasers. Rastering fixed target chips through the X-ray beam is an efficient method for serial delivery from the perspectives of both sample consumption and beam time usage. Here, an approach for loading fixed targets using acoustic drop ejection is presented that does not compromise crystal quality, can reduce sample consumption by more than an order of magnitude and allows serial diffraction to be collected from a larger proportion of the crystals in the slurry.

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.

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

Model-driven discovery of underground metabolic functions in Escherichia coli.

Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli--aspC, argD, and gltA--are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.

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.

The Platelet Count and its Implications in Sickle Cell Disease Patients Admitted for Intensive Care.

BACKGROUND AND AIMS: In sickle cell disease (SCD) patients admitted for intensive care, evaluation of platelet counts in different types of sickle cell complications and its prognostic relevance are not well-studied. Illuminating these aspects were the objectives of this study. MATERIALS AND METHODS: A chart review of 136 adult SCD patients consecutively admitted to our Intensive Care Unit (ICU) was done. The prognosis on day 1 was assessed by Acute Physiology and Chronic Health Evaluation (APACHE II) and multiple organ dysfunction scores (MODS). Receiver operating characteristic (ROC) curves evaluated the ability of platelet counts, MODS, and APACHE II scores to predict survival. RESULTS: The most common types of crises were severe pain (n = 53), acute chest syndrome (n = 40), and infection (n = 18); 17 patients were nonsurvivors. Platelet counts varied widely (range, 19-838 × 109/L) with thrombocytopenia (n = 30) and thrombocytosis (n = 11). Counts correlated directly with leukocytes and reticulocytes; inversely with lactate dehydrogenase, APACHE, and MODS scores. Areas under ROC curve for platelets, MODS, and APACHE scores to predict survival were 0.73, 0.85, and 0.93, respectively. CONCLUSIONS: In severe sickle cell crisis thrombocytopenia is more common than thrombocytosis. In the ICU, day 1 platelet counts correlate inversely with prognostic scores and are significantly reduced in multi-organ failure and nonsurvivors. A platelet count above 175 × 109/L predicts patient survival with high specificity and positive predictive value but lacks sensitivity.

Anxiety and Depression: A Cross-sectional Survey among Parents of Children with Cancer.

CONTEXT: Parents of children with cancer are experiencing high levels of psychological distress. Elevated levels of depression and anxiety following the disclosure of diagnosis affect many aspects of parents' health. AIMS: The aim of this study was to assess anxiety and depression of parents of Iranian children with cancer. SETTINGS AND DESIGN: This descriptive-correlational study was undertaken among 148 parents of children with cancer admitted to a pediatric hospital affiliated to Tabriz University of Medical Sciences, Tabriz/Iran. SUBJECTS AND METHODS: Participants were selected using convenience sampling method. Hospital Anxiety and Depression Scale was used to evaluate patients' levels of anxiety and depression. STATISTICAL ANALYSIS USED: The data were analyzed using SPSS version 13.0. RESULTS: The study findings showed that the mean anxiety and depression scores were 9.63 ± 3.69 and 8.66 ± 4.59 (range score: 0-21), respectively. Additionally, 41.2% (n = 61) and 32.4% (n = 48) of participants had clinical symptoms of anxiety and depression, respectively. CONCLUSIONS: Parents of children with cancer experienced high levels of anxiety and depression. Effective interventions are essential to improve the mental health of parents of children with cancer. These interventions may include mental health screening, psychological counseling, and training programs to cope with the problems caused by the child's disease.

Determining the control circuitry of redox metabolism at the genome-scale.

Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2) (p<1e-6) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network.

solveME: fast and reliable solution of nonlinear ME 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.

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways.

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)--in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.

Evolution of Escherichia coli to 42 °C and subsequent genetic engineering reveals adaptive mechanisms and novel mutations.

Adaptive laboratory evolution (ALE) has emerged as a valuable method by which to investigate microbial adaptation to a desired environment. Here, we performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655 grown in glucose minimal media. Tightly controlled experimental conditions allowed selection based on exponential-phase growth rate, yielding strains that uniformly converged toward a similar phenotype along distinct genetic paths. Adapted strains possessed as few as 6 and as many as 55 mutations, and of the 144 genes that mutated in total, 14 arose independently across two or more strains. This mutational recurrence pointed to the key genetic targets underlying the evolved fitness increase. Genome engineering was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains reaffirmed the impact of the key mutations on the growth rate at 42 °C. Interestingly, most of the identified key gene targets differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. Additionally, transcriptomic analysis of the ancestral and evolved strains revealed a general trend for restoration of the global expression state back toward preheat stressed levels. This restorative effect was previously documented following evolution to metabolic perturbations, and thus may represent a general feature of ALE experiments. The widespread evolved expression shifts were enabled by a comparatively scant number of regulatory mutations, providing a net fitness benefit but causing suboptimal expression levels for certain genes, such as those governing flagellar formation, which then became targets for additional ameliorating mutations. Overall, the results of this study provide insight into the adaptation process and yield lessons important for the future implementation of ALE as a tool for scientific research and engineering.

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.

Minimal metabolic pathway structure is consistent with associated biomolecular interactions.

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.

GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data.

MOTIVATION: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. RESULTS: GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. AVAILABILITY: GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ CONTACTS: brianjamesschmidt@gmail.com

Early post-operative relief of pain and shivering using diclofenac suppository versus intravenous pethidine in spinal anesthesia.

BACKGROUND: Pain and shivering are two challenging components in the post operative period. Many drugs were used for prevention and treatment of them. The aim of this study was to compare the effects of prophylactic prescription of diclofenac suppository versus intravenous (IV) pethidine in spinal anesthesia. MATERIALS AND METHODS: We conducted a multi central, prospective, double-blind, randomized clinical trial on a total of 180 patients who were scheduled for surgery under spinal anesthesia including 60 patients in three groups. Patients were randomly allocated to receive 100 mg sodium diclofenac suppository or 30 mg IV pethidine or placebo. Categorical and continuous variables were analyzed by Chi-square test, t-test, Mann-Whitney and ANOVA or Kruskal-Wallis tests. RESULTS: There was no statistical difference with regard to patient characteristics and hemodynamic indices among the three groups. Nine (15%), 10 (16.65%) and 24 (40%) of patients in diclofenac, pethidine and control groups reported pain and 2, 2, 7 patients received treatment due to it, respectively (P = 0.01). Prevalence of shivering in pethidine group and diclofenac group was the same and both of them were different from the control group (P < 0.001). Pruritus was repetitive in the pethidine group and was statistically significant (P = 0.036) but, post-operative nausea and vomiting was not significantly different among groups. CONCLUSION: A single dose of sodium diclofenac suppository can provide satisfactory analgesia immediately after surgery and decrease shivering without remarkable complications. This investigation highlights the role of pre-operative administration of a single dose of rectal diclofenac as a sole analgesic for early post-operative period.

Pushed to extremes: distinct effects of high temperature versus pressure on the structure of STEP.

Protein function hinges on small shifts of three-dimensional structure. Elevating temperature or pressure may provide experimentally accessible insights into such shifts, but the effects of these distinct perturbations on protein structures have not been compared in atomic detail. To quantitatively explore these two axes, we report the first pair of structures at physiological temperature versus. high pressure for the same protein, STEP (PTPN5). We show that these perturbations have distinct and surprising effects on protein volume, patterns of ordered solvent, and local backbone and side-chain conformations. This includes interactions between key catalytic loops only at physiological temperature, and a distinct conformational ensemble for another active-site loop only at high pressure. Strikingly, in torsional space, physiological temperature shifts STEP toward previously reported active-like states, while high pressure shifts it toward a previously uncharted region. Altogether, our work indicates that temperature and pressure are complementary, powerful, fundamental macromolecular perturbations.