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Background Constraint-based modeling is a widely used and powerful methodology to assess the metabolic phenotypes and capabilities of an organism. The starting point and cornerstone of all such modeling is a genome-scale metabolic network reconstruction. The creation, further development, and application of such networks is a growing field of research thanks to a plethora of readily accessible computational tools. While the majority of studies are focused on single-species analyses, typically of a microbe, the computational study of communities of organisms is gaining attention. Similarly, reconstructions that are unified for a multi-cellular organism have gained in popularity. Consequently, the rapid generation of genome-scale metabolic reconstructed networks is crucial. While multiple web-based or stand-alone tools are available for automated network reconstruction, there is, however, currently no publicly available tool that allows the swift assembly of draft reconstructions of community metabolic networks and consolidated metabolic networks for a specified list of organisms. Results Here, we present AutoKEGGRec, an automated tool that creates first draft metabolic network reconstructions of single organisms, community reconstructions based on a list of organisms, and finally a consolidated reconstruction for a list of organisms or strains. AutoKEGGRec is developed in Matlab and works seamlessly with the COBRA Toolbox v3, and it is based on only using the KEGG database as external input. The generated first draft reconstructions are stored in SBML files and consist of all reactions for a KEGG organism ID and corresponding linked genes. This provides a comprehensive starting point for further refinement and curation using the host of COBRA toolbox functions or other preferred tools. Through the data structures created, the tool also facilitates a comparative analysis of metabolic content in any given number of organisms present in the KEGG database. Conclusion AutoKEGGRec provides a first step in a metabolic network reconstruction process, filling a gap for tools creating community and consolidated metabolic networks. Based only on KEGG data as external input, the generated reconstructions consist of data with a directly traceable foundation and pedigree. With AutoKEGGRec, this kind of modeling is made accessible to a wider part of the genome-scale metabolic analysis community.

Excessive workload may impair patient safety. However, little is known about emergency care providers' workload during the treatment of life-threatening cases including cardiopulmonary resuscitation (CPR). Therefore, we tested the hypothesis that subjective and physiological indicators of workload are associated with the patient's initial NACA score and that workload is particularly high during CPR. NASA task load index (NASA-tlx) and alarm codes were obtained for 216 sorties of pre-hospital emergency medical care. Furthermore, initial NACA scores of 140 patients were extracted from the physicians' protocols. The physiological workload indicators mean heart rate (HR) and permutation entropy (PeEn) were calculated for 51 sorties of primary care. General linear mixed models were used to analyze the association of NACA scores with subjective (NASA-tlx) and physiological (mean HR, PeEn) measures of workload. In contrast to the physiological variables PeEn (p = 0.10) and HR (p = 0.19), the mental (p<0.001) and temporal demands (p<0.001) as well as the effort (p<0.001) and frustration (p = 0.04) subscale of the NASA-tlx were significantly associated with initial NACA scores. Compared to NACA = I, an initial NACA score of VI (representing CPR) increased workload by a mean of 389.5% (p = 0.001) in the mental and 345.9% (p<0.001) in the temporal demands, effort by a mean of 446,8% (p = 0.002) and frustration by 190.0% (p = 0.03). In line with the increase in NASA-tlx, PeEn increased by 20.6% (p = 0.01) and HR by 6.4% (p = 0.57). Patients' initial NACA scores are associated with subjective workload. Workload was highest during CPR.

Approaches for systematizing information of relatedness between organisms is important in biology. Phylogenetic analyses based on sets of highly conserved genes are currently the basis for the Tree of Life. Genome-scale metabolic reconstructions contain high-quality information regarding the metabolic capability of an organism and are typically restricted to metabolically active enzyme-encoding genes. While there are many tools available to generate draft reconstructions, expert-level knowledge is still required to generate and manually curate high-quality genome-scale metabolic models and to fill gaps in their reaction networks. Here, we use the tool AutoKEGGRec to construct 975 genome-scale metabolic draft reconstructions encoded in the KEGG database without further curation. The organisms are selected across all three domains, and their metabolic networks serve as basis for generating phylogenetic trees. We find that using all reactions encoded, these metabolism-based comparisons give rise to a phylogenetic tree with close similarity to the Tree of Life. While this tree is quite robust to reasonable levels of noise in the metabolic reaction content of an organism, we find a significant heterogeneity in how much noise an organism may tolerate before it is incorrectly placed in the tree. Furthermore, by using the protein sequences for particular metabolic functions and pathway sets, such as central carbon-, nitrogen-, and sulfur-metabolism, as basis for the organism comparisons, we generate highly specific phylogenetic trees. We believe the generation of phylogenetic trees based on metabolic reaction content, in particular when focused on specific functions and pathways, could aid the identification of functionally important metabolic enzymes and be of value for genome-scale metabolic modellers and enzyme-engineers.

Abstract Background Assessment of the disease activity in inflammatory bowel disease (IBD) is essential for adequate treatment management and reliable noninvasive biomarkers for verification of mucosal healing are still needed. MicroRNAs (miRNAs) are differentially expressed in IBD and cancer. We aimed to evaluate the potential of circulating and fecal miRNAs as diagnostic biomarkers for IBD. Methods In this proof-of-principle study we used 2 independent patient cohorts. Testing cohort (n = 96) included serum and fecal samples from controls (n = 35) and IBD patients (n = 61) including 43 patients with Crohn′s disease (CD), 18 with ulcerative colitis (UC) with an active disease (n = 38), or in remission (n = 23). Validation cohort included fecal samples from patients with calprotectin/endoscopy-confirmed active disease (n = 30) or in remission (n = 15). Target-based approach (miR-16, miR-21, miR-155, and miR-223) has been used to evaluate miRNA expression. Results Sera samples from IBD patients showed higher level of miR-16, miR-21, and miR-223, but not miR-155, compared to controls and was higher in CD than in UC patients. Much stronger miRNA expression changes were observed in feces from IBD patients for all studied miRNAs with highest expression of miR-155 and miR-223 in testing and validation cohorts. MiRNA expression correlated with clinical remission, however, only fecal but not circulating miRNAs, correlated with surrogate parameters such as fecal calprotectin or C-reactive protein. Conclusions Our data provide a novel evidence for differential expression level of fecal miRNAs in IBD. We demonstrate that miRNAs in feces correlate with disease activity and may be considered as potential tool for the further biomarker research in IBD. 10.1093/ibd/izy046_video1 izy046.video1 5794822319001

Despite immunotherapy advancements for patients with advanced or metastatic non-small-cell lung cancer (NSCLC), pivotal first-line trials were limited to patients with an Eastern Cooperative Oncology Group performance status (ECOG PS) 0–1 and a median age of 65 years or younger. We aimed to compare the efficacy and safety of first-line atezolizumab monotherapy with single-agent chemotherapy in patients ineligible for platinum-based chemotherapy. This trial was a phase 3, open-label, randomised controlled study conducted at 91 sites in 23 countries across Asia, Europe, North America, and South America. Eligible patients had stage IIIB or IV NSCLC in whom platinum-doublet chemotherapy was deemed unsuitable by the investigator due to an ECOG PS 2 or 3, or alternatively, being 70 years or older with an ECOG PS 0–1 with substantial comorbidities or contraindications for platinum-doublet chemotherapy. Patients were randomised 2:1 by permuted-block randomisation (block size of six) to receive 1200 mg of atezolizumab given intravenously every 3 weeks or single-agent chemotherapy (vinorelbine [oral or intravenous] or gemcitabine [intravenous]; dosing per local label) at 3-weekly or 4-weekly cycles. The primary endpoint was overall survival assessed in the intention-to-treat population. Safety analyses were conducted in the safety-evaluable population, which included all randomised patients who received any amount of atezolizumab or chemotherapy. This trial is registered with ClinicalTrials.gov, NCT03191786. Between Sept 11, 2017, and Sept 23, 2019, 453 patients were enrolled and randomised to receive atezolizumab (n=302) or chemotherapy (n=151). Atezolizumab improved overall survival compared with chemotherapy (median overall survival 10·3 months [95% CI 9·4–11·9] vs 9·2 months [5·9–11·2]; stratified hazard ratio 0·78 [0·63–0·97], p=0·028), with a 2-year survival rate of 24% (95% CI 19·3–29·4) with atezolizumab compared with 12% (6·7–18·0) with chemotherapy. Compared with chemotherapy, atezolizumab was associated with stabilisation or improvement of patient-reported health-related quality-of-life functioning scales and symptoms and fewer grade 3–4 treatment-related adverse events (49 [16%] of 300 vs 49 [33%] of 147) and treatment-related deaths (three [1%] vs four [3%]). First-line treatment with atezolizumab monotherapy was associated with improved overall survival, a doubling of the 2-year survival rate, maintenance of quality of life, and a favourable safety profile compared with single-agent chemotherapy. These data support atezolizumab monotherapy as a potential first-line treatment option for patients with advanced NSCLC who are ineligible for platinum-based chemotherapy. F Hoffmann-La Roche and Genentech Inc, a member of the Roche group.

Oxygen release and irreversible cation migration are the main causes of voltage fade in Li-rich transition metal oxide cathode. But their correlation is not very clear and voltage decay is still a bottleneck. Herein, we modulate the oxygen anionic redox chemistry by constructing Li 2 ZrO 3 slabs into Li 2 MnO 3 domain in Li 1.21 Ni 0.28 Mn 0.51 O 2 , which induces the lattice strain, tunes the chemical environment for redox-active oxygen and enlarges the gap between metallic and anionic bands. This modulation expands the region in which lattice oxygen contributes capacity by oxidation to oxygen holes and relieves the charge transfer from anionic band to antibonding metal–oxygen band under a deep delithiation. This restrains cation reduction, metal–oxygen bond fracture, and the formation of localized O 2 molecule, which fundamentally inhibits lattice oxygen escape and cation migration. The modulated cathode demonstrates a low voltage decay rate (0.45 millivolt per cycle) and a long cyclic stability. Voltage fade is a critical issue for Li-rich transition metal oxide cathode. Here, the authors modulate the oxygen anionic redox chemistry and enlarges the gap between metallic and anionic states by constructing Li 2 ZrO 3 slabs into Li 2 MnO 3 domain in Li 1.21 Ni 0.28 Mn 0.51 O 2 which fundamentally suppresses the voltage decay.

Clonal haematopoiesis, which is highly prevalent in older individuals, arises from somatic mutations that endow a proliferative advantage to haematopoietic cells. Clonal haematopoiesis increases the risk of myocardial infarction and stroke independently of traditional risk factors 1 . Among the common genetic variants that give rise to clonal haematopoiesis, the JAK2 V617F ( JAK2 VF ) mutation, which increases JAK–STAT signalling, occurs at a younger age and imparts the strongest risk of premature coronary heart disease 1 , 2 . Here we show increased proliferation of macrophages and prominent formation of necrotic cores in atherosclerotic lesions in mice that express Jak2 VF selectively in macrophages, and in chimeric mice that model clonal haematopoiesis. Deletion of the essential inflammasome components caspase 1 and 11, or of the pyroptosis executioner gasdermin D, reversed these adverse changes. Jak2 VF lesions showed increased expression of AIM2, oxidative DNA damage and DNA replication stress, and Aim2 deficiency reduced atherosclerosis. Single-cell RNA sequencing analysis of Jak2 VF lesions revealed a landscape that was enriched for inflammatory myeloid cells, which were suppressed by deletion of Gsdmd . Inhibition of the inflammasome product interleukin-1β reduced macrophage proliferation and necrotic formation while increasing the thickness of fibrous caps, indicating that it stabilized plaques. Our findings suggest that increased proliferation and glycolytic metabolism in Jak2 VF macrophages lead to DNA replication stress and activation of the AIM2 inflammasome, thereby aggravating atherosclerosis. Precise application of therapies that target interleukin-1β or specific inflammasomes according to clonal haematopoiesis status could substantially reduce cardiovascular risk. Accelerated atherosclerosis in a mouse model of clonal haematopoiesis is prevented by genetic interruption of AIM2 inflammasome activation or by inhibition of interleukin-1β.

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination. A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP). In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model i ML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.

Macrophages and dendritic cells (DCs) are key components of cellular immunity and are thought to originate and renew from hematopoietic stem cells (HSCs). However, some macrophages develop in the embryo before the appearance of definitive HSCs. We thus reinvestigated macrophage development. We found that the transcription factor Myb was required for development of HSCs and all CD11b high monocytes and macrophages, but was dispensable for yolk sac (YS) macrophages and for the development of YS-derived F4/80 bright macrophages in several tissues, such as liver Kupffer cells, epidermal Langerhans cells, and microglia— cell populations that all can persist in adult mice independently of HSCs. These results define a lineage of tissue macrophages that derive from the YS and are genetically distinct from HSC progeny.

Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.