Polyphosphate kinase deletion increases laboratory productivity in cyanobacteria.

Identification and manipulation of cellular energy regulation mechanisms may be a strategy to increase productivity in photosynthetic organisms. This work tests the hypothesis that polyphosphate synthesis and degradation play a role in energy management by storing or dissipating energy in the form of ATP. A polyphosphate kinase (ppk) knock-out strain unable to synthesize polyphosphate was generated in the cyanobacterium Synechocystis sp. PCC 6803. This mutant strain demonstrated higher ATP levels and faster growth than the wildtype strain in high-carbon conditions and had a growth defect under multiple stress conditions. In a strain that combined ppk deletion with heterologous expression of ethylene-forming enzyme, higher ethylene productivity was observed than in the wildtype background. These results support the role of polyphosphate synthesis and degradation as an energy regulation mechanism and suggest that such mechanisms may be effective targets in biocontainment design.

Proximate biomass characterization of the high productivity marine microalga Picochlorum celeri TG2.

Microalgae are compelling renewable resources with applications including biofuels, bioplastics, nutrient supplements, and cosmetic products. Picochlorum celeri is an alga with high industrial interest due to exemplary outdoor areal biomass productivities in seawater. Detailed proximate analysis is needed in multiple environmental conditions to understand the dynamic biomass compositions of P. celeri, and how these compositions might be leveraged in biotechnological applications. In this study, biomass characterization of P. celeri was performed under nutrient-replete, nitrogen-restricted, and hyper-saline conditions. Nutrient-replete cultivation of P. celeri resulted in protein-rich biomass (∼50% ash-free dry weight) with smaller carbohydrate (∼12% ash-free dry weight) and lipid (∼11% ash-free dry weight) partitions. Gradual nitrogen depletion elicited a shift from proteins to carbohydrates (∼50% ash-free dry weight, day 3) as cells transitioned into the production of storage metabolites. Importantly, dilutions in nitrogen-restricted 40 parts per million (1.43 mM nitrogen) media generated high-carbohydrate (∼50% ash-free dry weight) biomass without substantially compromising biomass productivity (36 g ash-free dry weight m-2 day-1) despite decreased chlorophyll (∼2% ash-free dry weight) content. This strategy for increasing carbohydrate content allowed for the targeted production of polysaccharides, which could potentially be utilized to produce fuels, oligosaccharides, and bioplastics. Cultivation at 2X sea salts resulted in a shift towards carbohydrates from protein, with significantly increased levels of the amino acid proline, which putatively acts as an osmolyte. A detailed understanding of the biomass composition of P. celeri in nutrient-replete, nitrogen-restricted, and hyper saline conditions informs how this strain can be useful in the production of biotechnological products.

Risk Assessment of Industrial Microbes Using a Terrestrial Mesocosm Platform.

Industrial microbes and bio-derived products have emerged as an integral component of the bioeconomy, with an array of agricultural, bioenergy, and biomedical applications. However, the rapid development of microbial biotechnology raises concerns related to environmental escape of laboratory microbes, detection and tracking thereof, and resultant impact upon native ecosystems. Indeed, though wild-type and genetically modified microbes are actively deployed in industrial bioprocesses, an understanding of microbial interactivity and impact upon the environment is severely lacking. In particular, the persistence and sustained ecosystem impact of industrial microbes following laboratory release or unintentional laboratory escape remains largely unexplored. Herein, we investigate the applicability of soil-sorghum mesocosms for the ecological risk assessment of the industrial microbe, Saccharomyces cerevisiae. We developed and applied a suite of diagnostic and bioinformatic analyses, including digital droplet PCR, microscopy, and phylogenomic analyses to assess the impacts of a terrestrial ecosystem perturbation event over a 30-day time course. The platform enables reproducible, high-sensitivity tracking of S. cerevisiae in a complex soil microbiome and analysis of the impact upon abiotic soil characteristics and soil microbiome population dynamics and diversity. The resultant data indicate that even though S. cerevisiae is relatively short-lived in the soil, a single perturbation event can have sustained impact upon mesocosm soil composition and underlying microbial populations in our system, underscoring the necessity for more comprehensive risk assessment and development of mitigation and biocontainment strategies in industrial bioprocesses.

Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis.

The capability of cyanobacteria to produce sucrose from CO2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. We used targeted metabolomics, 13C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.

Heterologous expression of formate dehydrogenase enables photoformatotrophy in the emerging model microalga, Picochlorum renovo.

Rising global greenhouse gas emissions and the impacts of resultant climate change necessitate development and deployment of carbon capture and conversion technologies. Amongst the myriad of bio-based conversion approaches under evaluation, a formate bio-economy has recently been proposed, wherein CO2-derived formate serves as a substrate for concurrent carbon and energy delivery to microbial systems. To date, this approach has been explored in chemolithotrophic and heterotrophic organisms via native or engineered formatotrophy. However, utilization of this concept in phototrophic organisms has yet to be reported. Herein, we have taken the first steps to establish formate utilization in Picochlorum renovo, a recently characterized eukaryotic microalga with facile genetic tools and promising applied biotechnology traits. Plastidial heterologous expression of a formate dehydrogenase (FDH) enabled P. renovo growth on formate as a carbon and energy source. Further, FDH expression enhanced cultivation capacity on ambient CO2, underscoring the potential for bypass of conventional CO2 capture and concentration limitations. This work establishes a photoformatotrophic cultivation regime that leverages light energy-driven formate utilization. The resultant photosynthetic formate platform has widespread implications for applied phototrophic cultivation systems and the bio-economy at large.

FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis.

13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.

Improved Combinatorial Assembly and Barcode Sequencing for Gene-Sized DNA Constructs.

Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The incorporation of DNA barcodes to mark genes coupled with next-generation sequencing offers a solution to this challenge. We describe improvements for a key method in this space, CombiGEM, to broaden its application to assembling typical gene-sized DNA fragments and to reduce the cost of sequencing for prevalent small-scale projects. The expanded reach of the method beyond currently targeted small RNA genes promotes the discovery and incorporation of gene synergy in natural and engineered processes such as biocontainment, the production of desired compounds, and previously uncharacterized fundamental biological mechanisms.

Long-Acting Opioid Analgesics for Acute Pain: Pharmacokinetic Evidence Reviewed.

Long-acting injectable (LAI) opioid formulations mitigate the harm profiles and management challenges associated with providing effective analgesia for animals. A single dose of a long-acting opioid analgesic can provide up to 72 h of clinically relevant pain management. Yet, few of these new drugs have been translated to products for veterinary clinics. Regulatory pathways allow accelerated drug approvals for generic and biosimilar drugs. These pathways depend on rigorous evidence for drug safety and pharmacokinetic evidence demonstrating bioequivalence between the new and the legacy drug. This report reviews the animal PK data associated with lipid and polymer-bound buprenorphine LAI formulations. Buprenorphine is a widely used veterinary opioid analgesic. Because of its safety profile and regulatory status, buprenorphine is more accessible than morphine, methadone, and fentanyl. This review of PK studies coupled with the well-established safety profile of buprenorphine suggests that the accelerated approval pathways may be available for this new family of LAI veterinary pharmaceuticals.

Extracting patient-level data from the electronic health record: Expanding opportunities for health system research.

BACKGROUND: Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical data needed to address many important research questions. We hypothesized that we could automate longitudinal ILD cohort development using the EHR of a large, community-based healthcare system. STUDY DESIGN AND METHODS: We applied a previously validated algorithm to the EHR of a community-based healthcare system to identify ILD cases between 2012-2020. We then extracted disease-specific characteristics and outcomes using fully automated data-extraction algorithms and natural language processing of selected free-text. RESULTS: We identified a community cohort of 5,399 ILD patients (prevalence = 118 per 100,000). Pulmonary function tests (71%) and serologies (54%) were commonly used in the diagnostic evaluation, whereas lung biopsy was rare (5%). IPF was the most common ILD diagnosis (n = 972, 18%). Prednisone was the most commonly prescribed medication (911, 17%). Nintedanib and pirfenidone were rarely prescribed (n = 305, 5%). ILD patients were high-utilizers of inpatient (40%/year hospitalized) and outpatient care (80%/year with pulmonary visit), with sustained utilization throughout the post-diagnosis study period. DISCUSSION: We demonstrated the feasibility of robustly characterizing a variety of patient-level utilization and health services outcomes in a community-based EHR cohort. This represents a substantial methodological improvement by alleviating traditional constraints on the accuracy and clinical resolution of such ILD cohorts; we believe this approach will make community-based ILD research more efficient, effective, and scalable.

Factor structure and psychometric performance of the PCL-5 in a clinical sample of veterans seeking treatment in a VA PTSD outpatient clinic.

There is a clear need to improve the measurement of posttraumatic symptoms in veterans seeking trauma-focused treatment. We evaluated the structure and psychometric performance of the Posttraumatic Stress Disorder (PTSD) Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (PCL-5) in a naturalistic sample of veterans engaging in evidence-based therapy in a Veterans Affairs (VA) PTSD outpatient clinic. We collected data from 500 sequential patients at the beginning and end of treatment, evaluating the PCL-5 factor structure followed by psychometric analyses. Results align with recent research following rigorous methods and advanced statistical techniques in suggesting a unidimensional factor structure for the PCL-5, with indications for its items representing a general factor with no clear support for multiple factors or subscales aligned with specific symptom clusters. In addition, psychometric analyses suggest a wider range of potential cutoff scores predicting PTSD diagnosis that may be specific to this population or setting. Given the remaining research questions surrounding the PCL-5 and its structure and functioning specifically, as well as the evolving conceptualizations of PTSD as a psychological disorder, further research is needed to guide clinical practice and future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Assessing the Clinical Utility of a Wearable Device for Physiological Monitoring of Heart Rate Variability in Military Service Members with Traumatic Brain Injury.

Introduction: Autonomic dysfunction has been implicated as a consequence of traumatic brain injury (TBI). Heart rate variability (HRV) may be a viable measure of autonomic dysfunction that could enhance rehabilitative interventions for individuals with TBI. This pilot study sought to assess the feasibility and validity of using the Zeriscope™ platform system in a real-world clinical setting to measure HRV in active-duty service members with TBI who were participating in an intensive outpatient program. Methods: Twenty-five service members with a history of mild, moderate, or severe TBI were recruited from a military treatment facility. A baseline assessment was conducted in the cardiology clinic where point validity data were obtained by comparing a 5-min recording of a standard 12-lead electrocardiogram (ECG) output against the Zeriscope platform data. Results: Compared with the ECG device, the Zeriscope device had a concordance coefficient (rc) of 0.16, falling below the standard deemed to represent acceptable accuracy in HR measurement (i.e., 0.80). Follow-up analyses excluding outliers did not significantly improve the concordance coefficient to an acceptable standard for the total participant sample. System Usability Survey responses showed that participants rated the Zeriscope system as easy to use and something that most people would learn to use quickly. Conclusions: This study demonstrated promise in ambulatory HRV measurement in a representative military TBI sample. Future research should include further refinement of such ambulatory devices to meet the specifications required for use in a military active-duty TBI population.

Biocontainment of Genetically Engineered Algae.

Algae (including eukaryotic microalgae and cyanobacteria) have been genetically engineered to convert light and carbon dioxide to many industrially and commercially relevant chemicals including biofuels, materials, and nutritional products. At industrial scale, genetically engineered algae may be cultivated outdoors in open ponds or in closed photobioreactors. In either case, industry would need to address a potential risk of the release of the engineered algae into the natural environment, resulting in potential negative impacts to the environment. Genetic biocontainment strategies are therefore under development to reduce the probability that these engineered bacteria can survive outside of the laboratory or industrial setting. These include active strategies that aim to kill the escaped cells by expression of toxic proteins, and passive strategies that use knockouts of native genes to reduce fitness outside of the controlled environment of labs and industrial cultivation systems. Several biocontainment strategies have demonstrated escape frequencies below detection limits. However, they have typically done so in carefully controlled experiments which may fail to capture mechanisms of escape that may arise in the more complex natural environment. The selection of biocontainment strategies that can effectively kill cells outside the lab, while maintaining maximum productivity inside the lab and without the need for relatively expensive chemicals will benefit from further attention.

Validation of Respiratory Rate-Oxygenation Index in Patients With COVID-19-Related Respiratory Failure.

OBJECTIVES: The respiratory rate-oxygenation (ROX) index is a fraction of oxygen saturation, Fio2, and respiratory rate that has been validated to predict receipt of invasive mechanical ventilation in patients receiving high-flow nasal cannula (HFNC). This study aimed to validate ROX in a cohort of inpatients with COVID-19-related respiratory failure. DESIGN: Retrospective validation of the ROX index. We calculated sensitivity, specificity, positive predictive value, negative predictive value, and 95% CIs of ROX for invasive mechanical ventilation any time during hospitalization. SETTING: Twenty-one hospitals of Kaiser Permanente Northern California, an integrated healthcare delivery system. PATIENTS: We identified adults with positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction test within 3 weeks of, or during, hospitalization between February 1, 2020, and December 31, 2020. We calculated ROX at 12 hours after HFNC initiation. We grouped patients as low (≥ 4.88), intermediate (< 4.88 and ≥ 3.85), or high (< 3.85) risk using previously published thresholds. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 1,847 patients who had no limitation of life support. Of these, 525 (31.7%) received invasive mechanical ventilation any time during hospitalization and 511 died (27.7%). The sensitivity, specificity, positive predictive value, and negative predictive value of 12-hour ROX threshold (< 3.85) predicting invasive mechanical ventilation were 32.3% (95% CI, 28.5-36.3%), 89.8% (95% CI, 88.0-91.4%), 59.4% (95% CI, 53.8-64.9%), and 74.1% (95% CI, 71.8-76.3%), respectively. CONCLUSIONS: The 12-hour ROX index has a positive predictive value (59.4%) using threshold of less than 3.85 for COVID-19 patients needing invasive mechanical ventilation. Our health system has embedded ROX into the electronic health record to prioritize rounding during periods of inpatient surge.

Quantification of Metabolic Products from Microbial Hosts in Complex Media Using Optically Diffracting Hydrogels.

We herein describe a highly versatile platform approach for the in situ and real-time screening of microbial biocatalysts for enhanced production of bioproducts using photonic crystal hydrogels. This approach was demonstrated by preparing optically diffracting films based on polymerized N-isopropylacrylamide that contracted in the presence of alcohols and organic acids. The hydrogel films were prepared in a microwell plate format, which allows for high-throughput screening, and characterized optically using a microwell plate reader. While demonstrating the ability to detect a broad range of relevant alcohols and organic acids, we showed that the response of the films correlated strongly with the octanol-water partition coefficient (log P) of the analyte. Differences in the secretion of ethanol and succinic acid from strains of Zymomonas mobilis and Actinobacillus succinogenes, respectively, were further detected via optical characterization of the films. These differences, which in some cases were as low as ∼3 g/L, were confirmed by high-performance liquid chromatography, thereby demonstrating the sensitivity of this approach. Our findings highlight the potential utility of this multiplexed approach for the detection of small organic analytes in complex biological media, which overcomes a major challenge in conventional optical sensing methods.

Computational Framework for Machine-Learning-Enabled 13C Fluxomics.

13C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

Buprenorphine blood concentrations: A biomarker for analgesia.

Opioids, the frontline drugs for postsurgical analgesia, have been linked to diversion and abuse with lethal consequences. The search for safe analgesics with less harm potential has been decades long. However, clinical trials for safe opioid and nonopioid analgesics have relied on subjective pain reports, which are biased by placebo effects that increase the complexity of trials to develop new therapies to manage pain. Research in opioid naïve animals and humans demonstrates that blood concentrations of opioids that effectively saturate the morphine opioid receptor are tightly linked with patient reports and quantitative sensory tests for analgesia. Opioid drug concentrations can predict clinical responses. This report reviews preclinical and clinical evidence correlating buprenorphine pharmacokinetics with analgesia. More than 30 years of data confirm buprenorphine blood concentrations can be an objective biomarker of analgesia for moderate to severe acute postoperative pain.

Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase (RubisCO) Is Essential for Growth of the Methanotroph Methylococcus capsulatus Strain Bath.

The ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) enzyme found in plants, algae, and an array of autotrophic bacteria is also encoded by a subset of methanotrophs, but its role in these microbes has largely remained elusive. In this study, we showed that CO2 was requisite for RubisCO-encoding Methylococcus capsulatus strain Bath growth in a bioreactor with continuous influent and effluent gas flow. RNA sequencing identified active transcription of several carboxylating enzymes, including key enzymes of the Calvin and serine cycles, that could mediate CO2 assimilation during cultivation with both CH4 and CO2 as carbon sources. Marker exchange mutagenesis of M. capsulatus Bath genes encoding key enzymes of potential CO2-assimilating metabolic pathways indicated that a complete serine cycle is not required, whereas RubisCO is essential for growth of this bacterium. 13CO2 tracer analysis showed that CH4 and CO2 enter overlapping anaplerotic pathways and implicated RubisCO as the primary enzyme mediating CO2 assimilation in M. capsulatus Bath. Notably, we quantified the relative abundance of 3-phosphoglycerate and ribulose-1,5-bisphosphate 13C isotopes, which supported that RubisCO-produced 3-phosphoglycerate is primarily converted to ribulose-1-5-bisphosphate via the oxidative pentose phosphate pathway in M. capsulatus Bath. Collectively, our data establish that RubisCO and CO2 play essential roles in M. capsulatus Bath metabolism. This study expands the known capacity of methanotrophs to fix CO2 via RubisCO, which may play a more pivotal role in the Earth's biogeochemical carbon cycling and greenhouse gas regulation than previously recognized. Further, M. capsulatus Bath and other CO2-assimilating methanotrophs represent excellent candidates for use in the bioconversion of biogas waste streams that consist of both CH4 and CO2. IMPORTANCE The importance of RubisCO and CO2 in M. capsulatus Bath metabolism is unclear. In this study, we demonstrated that both CO2 and RubisCO are essential for M. capsulatus Bath growth. 13CO2 tracing experiments supported that RubisCO mediates CO2 fixation and that a noncanonical Calvin cycle is active in this organism. Our study provides insights into the expanding knowledge of methanotroph metabolism and implicates dually CH4/CO2-utilizing bacteria as more important players in the biogeochemical carbon cycle than previously appreciated. In addition, M. capsulatus and other methanotrophs with CO2 assimilation capacity represent candidate organisms for the development of biotechnologies to mitigate the two most abundant greenhouse gases, CH4 and CO2.