Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes.

There are currently limited Food and Drug Administration (FDA)-approved drugs and vaccines for the treatment or prevention of Coronavirus Disease 2019 (COVID-19). Enhanced understanding of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and pathogenesis is critical for the development of therapeutics. To provide insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2, we performed single-cell (sc) RNA sequencing (RNA-seq) of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface (ALI) cultures over a time course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target at the onset of infection, which we confirmed by electron and immunofluorescence microscopy. Over the course of infection, the cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III interferons (IFNs) and interleukin (IL)-6 but not IL-1. This results in expression of interferon-stimulated genes (ISGs) in both infected and bystander cells. This provides a detailed characterization of genes, cell types, and cell state changes associated with SARS-CoV-2 infection in the human airway

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Molecular Assembly in the Endocytic Pathway

Proteins assembled into cellular pathways often possess non-catalytic, protein-interaction domains. Src-homology 3 (SH3) domains are protein-interaction domains that spatiotemporally connect molecules through transient binding interactions, recognizing linear peptide motifs and localizing proteins to various sub-cellular structures. In the endocytic pathway, there are many SH3-domain-containing proteins and several endocytic proteins contain multiple SH3 domains. I sought to interrogate the degeneracy in the number of SH3 domains within endocytosis and within endocytic proteins and to clarify the influence of each SH3 domain on the assembly and dynamics of the endocytic molecular machinery. To this end, in collaboration with Ronan Fernandez, I created a comprehensive library of endogenous, single SH3 domain deletions in the fission yeast Schizosaccharomyces pombe and used quantitative fluorescence microscopy to measure the effects of these deletions in vivo. I found that endocytic SH3 domains restrict, enhance, or have minor or redundant effects on actin assembly in endocytosis. I also found that some SH3 domains influence the cell’s ability to regulate the number of endocytic events. These observations are consistent with simulated perturbations to reaction steps in the Arp2/3 activation pathway, supporting the explanation that SH3 domains are regulators of Arp2/3-mediated actin nucleation in endocytosis. To investigate the endocytic localization dependence of SH3-domain containing proteins on their SH3 domain(s), in collaboration with Ronan Fernandez, we created a library of single SH3 domain deletions within strains where each SH3 domain’s native protein was also tagged with a fluorescent reporter. Analysis of the localization of these proteins and their fluorescent distribution in live cells reveals that most SH3 domains influence their protein’s localization and assembly dynamics into endocytic structures. Furthermore, several SH3 domains are required for robust localization of their protein to endocytic structures while being dispensable for their protein’s expression. Thus, endocytic SH3 domains may influence the assembly dynamics of SH3-domain-containing proteins into endocytic structures in addition to playing other assembly and regulatory roles within endocytic structures. Given that SH3 domains participate in a large number of interactions in the endocytic protein-interaction network, relative to other modular domains, a plausible answer to how endocytic proteins are recruited may be through SH3 domain-mediated interactions. Yet, one challenge to the use of SH3 domains in synthetic biology is that it is poorly understood how distinct sets of SH3 domains interact with distinct sets of proteins, given the potential overlap between SH3 domain-mediated interactions. To address how SH3 domains assemble proteins into distinct pathways, I proposed that SH3 domains achieve binding specificity through domain-mediated specificity, where binding preferences emerge from unique biophysical properties, and/or through contextual specificity, where binding preferences emerge through unique molecular and cellular environments. I hypothesized that SH3 domains primarily exhibit contextual specificity, which implies that individual SH3 domains are interchangeable. To determine the interchangeability of SH3 domains in a single context, I replaced native endocytic SH3 domains with non-native SH3 domains from other proteins and organisms. Contrary to my suppositions, my findings support the hypothesis that SH3 domains achieve interaction specificity primarily through domain-mediated specificity. However, my results do not entirely rule out contextually-mediated interaction specificity. Collectively, I describe a range of influences and activities that individual SH3 domains have on molecular assembly during endocytosis. The quantitative measurements of molecular assembly during endocytosis described in this dissertation, especially in the background of single deletions of each SH3 domain in endocytosis, reveal that SH3 domains have a variety of influences on actin assembly, endocytosis and the cell’s regulation of the endocytic rate. In particular, SH3 domains appear to play assembly and regulatory roles during endocytosis, perhaps by mediating interactions in the Arp2/3 activation pathway and by influencing the assembly dynamics of SH3 domain-containing proteins and actin accessory factors in the cell. These results add nuance to the purported role of SH3 domains in inducing phase-separated structures that promote local actin assembly in the cell. By providing precise quantitative descriptions into molecular assembly during endocytosis under a variety of perturbations to SH3 domains, this dissertation may inform future synthetic manipulations of endocytosis, especially by deleting or inserting SH3 domains as interchangeable parts in molecular circuits to predictably modulate the activity of the endocytic pathway and govern biological processes relevant to human health

Applications of artificial intelligence and machine learning in heart failure

: Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur