Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition.

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas - Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.

A newly evolved rice-specific gene JAUP1 regulates jasmonate biosynthesis and signalling to promote root development and multi-stress tolerance.

Root architecture and function are critical for plants to secure water and nutrient supply from the soil, but environmental stresses alter root development. The phytohormone jasmonic acid (JA) regulates plant growth and responses to wounding and other stresses, but its role in root development for adaptation to environmental challenges had not been well investigated. We discovered a novel JA Upregulated Protein 1 gene (JAUP1) that has recently evolved in rice and is specific to modern rice accessions. JAUP1 regulates a self-perpetuating feed-forward loop to activate the expression of genes involved in JA biosynthesis and signalling that confers tolerance to abiotic stresses and regulates auxin-dependent root development. Ectopic expression of JAUP1 alleviates abscisic acid- and salt-mediated suppression of lateral root (LR) growth. JAUP1 is primarily expressed in the root cap and epidermal cells (EPCs) that protect the meristematic stem cells and emerging LRs. Wound-activated JA/JAUP1 signalling promotes crosstalk between the root cap of LR and parental root EPCs, as well as induces cell wall remodelling in EPCs overlaying the emerging LR, thereby facilitating LR emergence even under ABA-suppressive conditions. Elevated expression of JAUP1 in transgenic rice or natural rice accessions enhances abiotic stress tolerance and reduces grain yield loss under a limited water supply. We reveal a hitherto unappreciated role for wound-induced JA in LR development under abiotic stress and suggest that JAUP1 can be used in biotechnology and as a molecular marker for breeding rice adapted to extreme environmental challenges and for the conservation of water resources.

Acute hepatitis of unknown origin in children: Behind the statistics.

Since April 2022, the world has been witnessing a rapidly spreading outbreak of acute hepatitis of unknown origin in children < 16 years old that has affected several countries around the world. Most of the cases have presented with the clinical picture of severe hepatitis that has led to resorting to liver transplantation in several cases. Despite the numerous theories that have been suggested on the possible underlying etiologies of the outbreak, an association with hepatitis A-E viruses and a link to COVID-19 vaccines have been excluded. Adenovirus serotype 41 has been detected in numerous cases, which makes it the most likely underlying cause of the disease. Nevertheless, other hypotheses are being investigated to justify the severity of the clinical picture, which is not typical of this type of virus. This review aims to summarize the current knowledge about the outbreak, highlight the suggested working hypotheses, and report the public health measures undertaken to tackle the outbreak.

SARS-CoV-2 Omicron and its current known unknowns: A narrative review.

The emergence of the SARS-CoV-2 Omicron variant (B.1.1.529) has created great global distress. This variant of concern shows multiple sublineages, importantly B.1.1.529.1 (BA.1), BA.1 + R346K (BA.1.1), and B.1.1.529.2 (BA.2), each with unique properties. However, little is known about this new variant, specifically its sub-variants. A narrative review was conducted to summarise the latest findings on transmissibility, clinical manifestations, diagnosis, and efficacy of current vaccines and treatments. Omicron has shown two times higher transmission rates than Delta and above ten times more infectious than other variants over a similar period. With more than 30 mutations in the spike protein's receptor-binding domain, there is reduced detection by conventional RT-PCR and rapid antigen tests. Moreover, the two-dose vaccine effectiveness against Delta and Omicron variants was found to be approximately 21%, suggesting an urgent need for a booster dose to prevent the possibility of breakthrough infections. However, the current vaccines remain highly efficacious against severe disease, hospitalisation, and mortality. Japanese preliminary lab data elucidated that the Omicron sublineage BA.2 shows a higher illness severity than BA.1. To date, the clinical management of Omicron remains unchanged, except for monoclonal antibodies. Thus far, only Bebtelovimab could sufficiently treat all three sub-variants of Omicron. Further studies are warranted to understand the complexity of Omicron and its sub-variants. Such research is necessary to improve the management and prevention of Omicron infection.

A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells.

Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common 'digital cytometry' methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells ('mixture data') to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells ('signature matrix'), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

MOTIVATION: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. RESULTS: This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. AVAILABILITY AND IMPLEMENTATION: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Oncologists' responsibility, comfort, and knowledge managing hyperglycemia in patients with cancer undergoing chemotherapy: a cross sectional study.

PURPOSE: To assess oncologists' responsibility, comfort, and knowledge managing hyperglycemia in patients undergoing chemotherapy. METHODS: In this cross-sectional study, a questionnaire collected oncologists' perceptions about professionals responsible for managing hyperglycemia during chemotherapy; comfort (score range 12-120); and knowledge (score range 0-16). Descriptive statistics were calculated including Student t-tests and one-way ANOVA for mean score differences. Multivariable linear regression identified predictors of comfort and knowledge scores. RESULTS: Respondents (N = 229) were 67.7% men, 91.3% White and mean age 52.1 years. Oncologists perceived endocrinologists/diabetologists and primary care physicians as those responsible for managing hyperglycemia during chemotherapy, and most frequently referred to these clinicians. Reasons for referral included lack of time to manage hyperglycemia (62.4%), belief that patients would benefit from referral to an alternative provider clinician (54.1%), and not perceiving hyperglycemia management in their scope of practice (52.4%). The top-3 barriers to patient referral were long wait times for primary care (69.9%) and endocrinology (68.1%) visits, and patient's provider outside of the oncologist's institution (52.8%). The top-3 barriers to treating hyperglycemia were lack of knowledge about when to start insulin, how to adjust insulin, and what insulin type works best. Women (ß = 1.67, 95% CI: 0.16, 3.18) and oncologists in suburban areas (ß = 6.98, 95% CI: 2.53, 11.44) had higher comfort scores than their respective counterparts; oncologists working in practices with > 10 oncologists had lower comfort scores (ß = -2.75, 95% CI: -4.96, -0.53) than those in practices with ≤ 10. No significant predictors were identified for knowledge. CONCLUSION: Oncologists expected endocrinology or primary care clinicians to manage hyperglycemia during chemotherapy, but long wait times were among the top barriers cited when referring patients. New models that provide prompt and coordinated care are needed.

treeheatr: an R package for interpretable decision tree visualizations.

SUMMARY: treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students' understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. AVAILABILITY AND IMPLEMENTATION: The treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration.

A Potential Role of the CD47/SIRPalpha Axis in COVID-19 Pathogenesis.

The coronavirus SARS-CoV-2 is the cause of the ongoing COVID-19 pandemic. Most SARS-CoV-2 infections are mild or even asymptomatic. However, a small fraction of infected individuals develops severe, life-threatening disease, which is caused by an uncontrolled immune response resulting in hyperinflammation. However, the factors predisposing individuals to severe disease remain poorly understood. Here, we show that levels of CD47, which is known to mediate immune escape in cancer and virus-infected cells, are elevated in SARS-CoV-2-infected Caco-2 cells, Calu-3 cells, and air-liquid interface cultures of primary human bronchial epithelial cells. Moreover, SARS-CoV-2 infection increases SIRPalpha levels, the binding partner of CD47, on primary human monocytes. Systematic literature searches further indicated that known risk factors such as older age and diabetes are associated with increased CD47 levels. High CD47 levels contribute to vascular disease, vasoconstriction, and hypertension, conditions that may predispose SARS-CoV-2-infected individuals to COVID-19-related complications such as pulmonary hypertension, lung fibrosis, myocardial injury, stroke, and acute kidney injury. Hence, age-related and virus-induced CD47 expression is a candidate mechanism potentially contributing to severe COVID-19, as well as a therapeutic target, which may be addressed by antibodies and small molecules. Further research will be needed to investigate the potential involvement of CD47 and SIRPalpha in COVID-19 pathology. Our data should encourage other research groups to consider the potential relevance of the CD47/ SIRPalpha axis in their COVID-19 research.

Pitfalls in brain age analyses.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap." Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist's prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. RESULTS: We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual. AVAILABILITY AND IMPLEMENTATION: Detailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding.

SUMMARY: Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for case-control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR's utility for GWAS and continuous outcomes. AVAILABILITY AND IMPLEMENTATION: Available at: https://insilico.github.io/npdr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sexual and reproductive health challenges facing minority ethnic girls in Vietnam: a photovoice study.

This paper describes a study that examined the challenges faced by Vietnamese ethnic minority girls regarding their sexual and reproductive health. The study employed photovoice, a research method which treats photographs and the accompanying stories provided by participants as qualitative data. Twenty-six (26) minority ethnic girls took photographs of aspects of their lives as a way of documenting the challenges, difficulties and barriers that they faced in looking after their sexual and reproductive health. Findings indicated limited access to sexual health knowledge, the exclusion of young people from mainly adult-focused sexual and reproductive health services in minority ethnic communities and the prevalence of cultural beliefs and practices that negatively affected young people's sexual and reproductive health. The intersection of ethnicity, age and gender places Vietnamese ethnic minority girls at risk, as everyday practices informed by culture and tradition curtail their access to the limited sexual and reproductive health information and services available in their communities. Understanding these challenges is needed in developing appropriate policies, programmes and services aimed at enhancing the sexual and reproductive health of this segment of the population.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

UGT2B17 modifies drug response in chronic lymphocytic leukaemia.

BACKGROUND: High UGT2B17 is associated with poor prognosis in untreated chronic lymphocytic leukaemia (CLL) patients and its expression is induced in non-responders to fludarabine-containing regimens. We examined whether UGT2B17, the predominant lymphoid glucuronosyltransferase, affects leukaemic drug response and is involved in the metabolic inactivation of anti-leukaemic agents. METHODS: Functional enzymatic assays and patients' plasma samples were analysed by mass-spectrometry to evaluate drug inactivation by UGT2B17. Cytotoxicity assays and RNA sequencing were used to assess drug response and transcriptome changes associated with high UGT2B17 levels. RESULTS: High UGT2B17 in B-cell models led to reduced sensitivity to fludarabine, ibrutinib and idelalisib. UGT2B17 expression in leukaemic cells involved a non-canonical promoter and was induced by short-term treatment with these anti-leukaemics. Glucuronides of both fludarabine and ibrutinib were detected in CLL patients on respective treatment, however UGT2B17 conjugated fludarabine but not ibrutinib. AMP-activated protein kinase emerges as a pathway associated with high UGT2B17 in fludarabine-treated patients and drug-treated cell models. The expression changes linked to UGT2B17 exposed nuclear factor kappa B as a key regulatory hub. CONCLUSIONS: Data imply that UGT2B17 represents a mechanism altering drug response in CLL through direct inactivation but would also involve additional mechanisms for drugs not inactivated by UGT2B17.

STatistical Inference Relief (STIR) feature selection.

MOTIVATION: Relief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a parameterized model with an underlying probability distribution for the estimator, making it difficult to determine the statistical significance of Relief-based attribute estimates. Thus, a statistical inferential formalism is needed to avoid imposing arbitrary thresholds to select the most important features. We reconceptualize the Relief-based feature selection algorithm to create a new family of STatistical Inference Relief (STIR) estimators that retains the ability to identify interactions while incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Specifically, we develop a pseudo t-test version of Relief-based algorithms for case-control data. RESULTS: We demonstrate the statistical power and control of type I error of the STIR family of feature selection methods on a panel of simulated data that exhibits properties reflected in real gene expression data, including main effects and network interaction effects. We compare the performance of STIR when the adaptive radius method is used as the nearest neighbor constructor with STIR when the fixed-k nearest neighbor constructor is used. We apply STIR to real RNA-Seq data from a study of major depressive disorder and discuss STIR's straightforward extension to genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: Code and data available at http://insilico.utulsa.edu/software/STIR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.