Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.

OBJECTIVE: The COVID-19 pandemic and subsequent government restrictions have had a major impact on healthcare services and disease transmission, particularly those associated with acute respiratory infection. This study examined non-identifiable routine electronic patient record data from a specialist children's hospital in England, UK, examining the effect of pandemic mitigation measures on seasonal respiratory infection rates compared with forecasts based on open-source, transferable machine learning models. METHODS: We performed a retrospective longitudinal study of respiratory disorder diagnoses between January 2010 and February 2022. All diagnoses were extracted from routine healthcare activity data and diagnosis rates were calculated for several diagnosis groups. To study changes in diagnoses, seasonal forecast models were fit to prerestriction period data and extrapolated. RESULTS: Based on 144 704 diagnoses from 31 002 patients, all but two diagnosis groups saw a marked reduction in diagnosis rates during restrictions. We observed 91%, 89%, 72% and 63% reductions in peak diagnoses of 'respiratory syncytial virus', 'influenza', 'acute nasopharyngitis' and 'acute bronchiolitis', respectively. The machine learning predictive model calculated that total diagnoses were reduced by up to 73% (z-score: -26) versus expected during restrictions and increased by up to 27% (z-score: 8) postrestrictions. CONCLUSIONS: We demonstrate the association between COVID-19 related restrictions and significant reductions in paediatric seasonal respiratory infections. Moreover, while many infection rates have returned to expected levels postrestrictions, others remain supressed or followed atypical winter trends. This study further demonstrates the applicability and efficacy of routine electronic record data and cross-domain time-series forecasting to model, monitor, analyse and address clinically important issues.

Multicentre randomised controlled trial: protocol for Plasma-Lyte Usage and Assessment of Kidney Transplant Outcomes in Children (PLUTO).

INTRODUCTION: Acute electrolyte and acid-base imbalance is experienced by many children following kidney transplantation. When severe, this can lead to complications including seizures, cerebral oedema and death. Relatively large volumes of intravenous fluid are administered to children perioperatively in order to establish perfusion to the donor kidney, the majority of which are from living and deceased adult donors. Hypotonic intravenous fluid is commonly used in the post-transplant period due to clinicians' concerns about the sodium, chloride and potassium content of isotonic alternatives when administered in large volumes.Plasma-Lyte 148 is an isotonic, balanced intravenous fluid that contains sodium, chloride, potassium and magnesium with concentrations equivalent to those of plasma. There is a physiological basis to expect that Plasma-Lyte 148 will reduce the incidence of clinically significant electrolyte and acid-base abnormalities in children following kidney transplantation compared with current practice.The aim of the Plasma-Lyte Usage and Assessment of Kidney Transplant Outcomes in Children (PLUTO) trial was to determine whether the incidence of clinically significantly abnormal plasma electrolyte levels in paediatric kidney transplant recipients will be different with the use of Plasma-Lyte 148 compared with intravenous fluid currently administered. METHODS AND ANALYSIS: PLUTO is a pragmatic, open-label, randomised controlled trial comparing Plasma-Lyte 148 to current care in paediatric kidney transplant recipients, conducted in nine UK paediatric kidney transplant centres.A total of 144 children receiving kidney transplants will be randomised to receive either Plasma-Lyte 148 (the intervention) intraoperatively and postoperatively, or current fluid. Apart from intravenous fluid composition, all participants will receive standard clinical transplant care.The primary outcome measure is acute hyponatraemia in the first 72 hours post-transplant, defined as laboratory plasma sodium concentration of <135 mmol/L. Secondary outcomes include symptoms of acute hyponatraemia, other electrolyte and acid-base imbalances and transplant kidney function.The primary outcome will be analysed using a logistic regression model adjusting for donor type (living vs deceased donor), patient weight (<20 kg vs ≥20 kg pretransplant) and transplant centre as a random effect. ETHICS AND DISSEMINATION: The trial received Health Research Authority approval on 20 January 2020. Findings will be presented to academic groups via national and international conferences and peer-reviewed journals. The patient and public involvement group will play an important part in disseminating the study findings to the public domain. TRIAL REGISTRATION NUMBERS: 2019-003025-22 and 16586164.

Impact of the COVID-19 pandemic on radiology appointments in a tertiary children's hospital: a retrospective study.

In this retrospective observational study, we evaluated the impact of the COVID-19 pandemic in London on paediatric radiology activity, as a surrogate of overall hospital activity. We showed a large reduction in overall outpatient imaging activity: 49 250 records occurred in the 371 days post COVID-19 period compared with an expected 67 806 records pre COVID-19 period, representing 18 556 'missed' records. Governmental restrictions were associated with reductions in activity, with the largest reduction in activity during tiers 3 and 4 restrictions. Rescheduling such missed outpatients' appointments represents considerable resource planning and the associated clinical impact on paediatric healthcare remains to be determined.

HNF1B Mutations Are Associated With a Gitelman-like Tubulopathy That Develops During Childhood.

BACKGROUND: Mutations in the transcription factor hepatocyte nuclear factor 1B (HNF1B) are the most common inherited cause of renal malformations, yet also associated with renal tubular dysfunction, most prominently magnesium wasting with hypomagnesemia. The presence of hypomagnesemia has been proposed to help select appropriate patients for genetic testing. Yet, in a large cohort, hypomagnesemia was discriminatory only in adult, but not in pediatric patients. We therefore investigated whether hypomagnesemia and other biochemical changes develop with age. METHODS: We performed a retrospective analysis of clinical, biochemical, and genetic results of pediatric patients with renal malformations tested for HNF1B mutations, separated into 4 age groups. Values were excluded if concurrent estimated glomerular filtration rate (eGFR) was <30 ml/min per 1.73 m2, or after transplantation. RESULTS: A total of 199 patients underwent HNF1B genetic testing and mutations were identified in 52 (mut+). The eGFRs were comparable between mut+ and mut- in any age group. Although median plasma magnesium concentrations differed significantly between mut+ and mut- patients in all age groups, overt hypomagnesemia was not present until the second half of childhood in the mut+ group. There was also a significant difference in median potassium concentrations in late childhood with lower values in the mut+ cohort. CONCLUSIONS: The abnormal tubular electrolyte handling associated with HNF1B mutations develops with age and is not restricted to magnesium, but consistent with a more generalized dysfunction of the distal convoluted tubule, reminiscent of Gitelman syndrome. The absence of these abnormalities in early childhood should not preclude HNF1B mutations from diagnostic considerations.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

In Silico Analysis of the Small Molecule Content of Outer Membrane Vesicles Produced by Bacteroides thetaiotaomicron Indicates an Extensive Metabolic Link between Microbe and Host.

The interactions between the gut microbiota and its host are of central importance to the health of the host. Outer membrane vesicles (OMVs) are produced ubiquitously by Gram-negative bacteria including the gut commensal Bacteroides thetaiotaomicron. These vesicles can interact with the host in various ways but until now their complement of small molecules has not been investigated in this context. Using an untargeted high-coverage metabolomic approach we have measured the small molecule content of these vesicles in contrasting in vitro conditions to establish what role these metabolites could perform when packed into these vesicles. B. thetaiotaomicron packs OMVs with a highly conserved core set of small molecules which are strikingly enriched with mouse-digestible metabolites and with metabolites previously shown to be associated with colonization of the murine GIT. By use of an expanded genome-scale metabolic model of B. thetaiotaomicron and a potential host (the mouse) we have established many possible metabolic pathways between the two organisms that were previously unknown, and have found several putative novel metabolic functions for mouse that are supported by gene annotations, but that do not currently appear in existing mouse metabolic networks. The lipidome of these OMVs bears no relation to the mouse lipidome, so the purpose of this particular composition of lipids remains unclear. We conclude from this analysis that through intimate symbiotic evolution OMVs produced by B. thetaiotaomicron are likely to have been adopted as a conduit for small molecules bound for the mammalian host in vivo.

Deciphering the metabolic response of Mycobacterium tuberculosis to nitrogen stress.

A key component to the success of Mycobacterium tuberculosis as a pathogen is the ability to sense and adapt metabolically to the diverse range of conditions encountered in vivo, such as oxygen tension, environmental pH and nutrient availability. Although nitrogen is an essential nutrient for every organism, little is known about the genes and pathways responsible for nitrogen assimilation in M. tuberculosis. In this study we have used transcriptomics and chromatin immunoprecipitation and high-throughput sequencing to address this. In response to nitrogen starvation, a total of 185 genes were significantly differentially expressed (96 up-regulated and 89 down regulated; 5% genome) highlighting several significant areas of metabolic change during nitrogen limitation such as nitrate/nitrite metabolism, aspartate metabolism and changes in cell wall biosynthesis. We identify GlnR as a regulator involved in the nitrogen response, controlling the expression of at least 33 genes in response to nitrogen limitation. We identify a consensus GlnR binding site and relate its location to known transcriptional start sites. We also show that the GlnR response regulator plays a very different role in M. tuberculosis to that in non-pathogenic mycobacteria, controlling genes involved in nitric oxide detoxification and intracellular survival instead of genes involved in nitrogen scavenging.

Analysis of metabolic evolution in bacteria using whole-genome metabolic models.

Recent advances in the automation of metabolic model reconstruction have led to the availability of draft-quality metabolic models (predicted reaction complements) for multiple bacterial species. These reaction complements can be considered as trait representations and can be used for ancestral state reconstruction to infer the most likely metabolic complements of common ancestors of all bacteria with generated metabolic models. We present here an ancestral state reconstruction for 141 extant bacteria and analyze the reaction gains and losses for these bacteria with respect to their lifestyles and pathogenic nature. A simulated annealing approach is used to look at coordinated metabolic gains and losses in two bacteria. The main losses of Onion yellows phytoplasma OY-M, an obligate intracellular pathogen, are shown (as expected) to be in cell wall biosynthesis. The metabolic gains made by Clostridium difficile CD196 in adapting to its current habitat in the human colon is also analyzed. Our analysis shows that the capability to utilize N-Acetyl-neuraminic acid as a carbon source has been gained, rather than having been present in the Clostridium ancestor, as has the capability to synthesize phthiocerol dimycocerosate, which could potentially aid the evasion of the host immune response. We have shown that the availability of large numbers of metabolic models, along with conventional approaches, has enabled a systematic method to analyze metabolic evolution in the bacterial domain.

Deciphering the response of Mycobacterium smegmatis to nitrogen stress using bipartite active modules.

BACKGROUND: The ability to adapt to environments with fluctuating nutrient availability is vital for bacterial survival. Although essential for growth, few nitrogen metabolism genes have been identified or fully characterised in mycobacteria and nitrogen stress survival mechanisms are unknown. RESULTS: A global transcriptional analysis of the mycobacterial response to nitrogen stress, showed a significant change in the differential expression of 16% of the Mycobacterium smegmatis genome. Gene expression changes were mapped onto the metabolic network using Active Modules for Bipartite Networks (AMBIENT) to identify metabolic pathways showing coordinated transcriptional responses to the stress. AMBIENT revealed several key features of the metabolic response not identified by KEGG enrichment alone. Down regulated reactions were associated with the general reduction in cellular metabolism as a consequence of reduced growth rate. Up-regulated modules highlighted metabolic changes in nitrogen assimilation and scavenging, as well as reactions involved in hydrogen peroxide metabolism, carbon scavenging and energy generation. CONCLUSIONS: Application of an Active Modules algorithm to transcriptomic data identified key metabolic reactions and pathways altered in response to nitrogen stress, which are central to survival under nitrogen limiting environments.

AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.

BACKGROUND: With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner. RESULTS: Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment. CONCLUSIONS: ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient.

The Analysis of Multiple Genome Comparisons in Genus Escherichia and Its Application to the Discovery of Uncharacterised Metabolic Genes in Uropathogenic Escherichia coli CFT073.

A survey of a complete gene synteny comparison has been carried out between twenty fully sequenced strains from the genus Escherichia with the aim of finding yet uncharacterised genes implicated in the metabolism of uropathogenic strains of E. coli (UPEC). Several sets of adjacent colinear genes have been identified which are present in all four UPEC included in this study (CFT073, F11, UTI89, and 536), annotated with putative metabolic functions, but are not found in any other strains considered. An operon closely homologous to that encoding the L-sorbose degradation pathway in Klebsiella pneumoniae has been identified in E. coli CFT073; this operon is present in all of the UPEC considered, but only in 7 of the other 16 strains. The operon's function has been confirmed by cloning the genes into E. coli DH5alpha and testing for growth on L-sorbose. The functional genomic approach combining in silico and in vitro work presented here can be used as a basis for the discovery of other uncharacterised genes contributing to bacterial survival in specific environments.