Lung Epithelium Releases Growth Differentiation Factor 15 in Response to Pathogen-mediated Injury

GDF15 (growth differentiation factor 15) is a stress cytokine with several proposed roles, including support of stress erythropoiesis. Higher circulating GDF15 levels are prognostic of mortality during acute respiratory distress syndrome, but the cellular sources and downstream effects of GDF15 during pathogen-mediated lung injury are unclear. We quantified GDF15 in lower respiratory tract biospecimens and plasma from patients with acute respiratory failure. Publicly available data from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were reanalyzed. We used mouse models of hemorrhagic acute lung injury mediated by Pseudomonas aeruginosa exoproducts in wild-type mice and mice genetically deficient for Gdf15 or its putative receptor, Gfral. In critically ill humans, plasma levels of GDF15 correlated with lower respiratory tract levels and were higher in nonsurvivors. SARS-CoV-2 infection induced GDF15 expression in human lung epithelium, and lower respiratory tract GDF15 levels were higher in coronavirus disease (COVID-19) nonsurvivors. In mice, intratracheal P. aeruginosa type II secretion system exoproducts were sufficient to induce airspace and plasma release of GDF15, which was attenuated with epithelial-specific deletion of Gdf15. Mice with global Gdf15 deficiency had decreased airspace hemorrhage, an attenuated cytokine profile, and an altered lung transcriptional profile during injury induced by P. aeruginosa type II secretion system exoproducts, which was not recapitulated in mice deficient for Gfral. Airspace GDF15 reconstitution did not significantly modulate key lung cytokine levels but increased circulating erythrocyte counts. Lung epithelium releases GDF15 during pathogen injury, which is associated with plasma levels in humans and mice and can increase erythrocyte counts in mice, suggesting a novel lung-blood communication pathway.

The Florida Scoring System for stratifying children with suspected Sjögren's disease: a cross-sectional machine learning study

BACKGROUND

Childhood Sjögren's disease is a rare, underdiagnosed, and poorly-understood condition. By integrating machine learning models on a paediatric cohort in the USA, we aimed to develop a novel system (the Florida Scoring System) for stratifying symptomatic paediatric patients with suspected Sjögren's disease.

METHODS

This cross-sectional study was done in symptomatic patients who visited the Department of Pediatric Rheumatology at the University of Florida, FL, USA. Eligible patients were younger than 18 years or had symptom onset before 18 years of age. Patients with confirmed diagnosis of another autoimmune condition or infection with a clear aetiological microorganism were excluded. Eligible patients underwent comprehensive examinations to rule out or diagnose childhood Sjögren's disease. We used latent class analysis with clinical and laboratory variables to detect heterogeneous patient classes. Machine learning models, including random forest, gradient-boosted decision tree, partial least square discriminatory analysis, least absolute shrinkage and selection operator-penalised ordinal regression, artificial neural network, and super learner were used to predict patient classes and rank the importance of variables. Causal graph learning selected key features to build the final Florida Scoring System. The predictors for all models were the clinical and laboratory variables and the outcome was the definition of patient classes.

FINDINGS

Between Jan 16, 2018, and April 28, 2022, we screened 448 patients for inclusion. After excluding 205 patients due to symptom onset later than 18 years of age, we recruited 243 patients into our cohort. 26 patients were excluded because of confirmed diagnosis of a disorder other than Sjögren's disease, and 217 patients were included in the final analysis. Median age at diagnosis was 15 years (IQR 11-17). 155 (72%) of 216 patients were female and 61 (28%) were male, 167 (79%) of 212 were White, and 20 (9%) of 213 were Hispanic, Latino, or Spanish. The latent class analysis identified three distinct patient classes: class I (dryness dominant with positive tests, n=27), class II (high symptoms with negative tests, n=98), and class III (low symptoms with negative tests, n=92). Machine learning models accurately predicted patient class and ranked variable importance consistently. The causal graphical model discovered key features for constructing the Florida Scoring System.

INTERPRETATION

The Florida Scoring System is a paediatrician-friendly tool that can be used to assist classification and long-term monitoring of suspected childhood Sjögren's disease. The resulting stratification has important implications for clinical management, trial design, and pathobiological research. We found a highly symptomatic patient group with negative serology and diagnostic profiles, which warrants clinical attention. We further revealed that salivary gland ultrasonography can be a non-invasive alternative to minor salivary gland biopsy in children. The Florida Scoring System requires validation in larger prospective paediatric cohorts.

FUNDING

National Institute of Dental and Craniofacial Research, National Institute of Arthritis, Musculoskeletal and Skin Diseases, National Heart, Lung, and Blood Institute, and Sjögren's Foundation.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup

OBJECTIVE

Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS

A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS

Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

CellularPotts.jl: simulating multiscale cellular models in Julia

SUMMARY

CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia.

AVAILABILITY AND IMPLEMENTATION

The CellularPotts.jl package is released under the MIT license and is available at https://github.com/RobertGregg/CellularPotts.jl. An archived version of the code (v0.3.2) at time of submission can also be found at https://doi.org/10.5281/zenodo.10407783.

Early Evidence of Chronic Obstructive Pulmonary Disease Obscured by Race-Specific Prediction Equations

Rationale: The identification of early chronic obstructive pulmonary disease (COPD) is essential to appropriately counsel patients regarding smoking cessation, provide symptomatic treatment, and eventually develop disease-modifying treatments. Disease severity in COPD is defined using race-specific spirometry equations. These may disadvantage non-White individuals in diagnosis and care. Objectives: Determine the impact of race-specific equations on African American (AA) versus non-Hispanic White individuals. Methods: Cross-sectional analyses of the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease) cohort were conducted, comparing non-Hispanic White (n = 6,766) and AA (n = 3,366) participants for COPD manifestations. Measurements and Main Results: Spirometric classifications using race-specific, multiethnic, and "race-reversed" prediction equations (NHANES [National Health and Nutrition Examination Survey] and Global Lung Function Initiative "Other" and "Global") were compared, as were respiratory symptoms, 6-minute-walk distance, computed tomography imaging, respiratory exacerbations, and St. George's Respiratory Questionnaire. Application of different prediction equations to the cohort resulted in different classifications by stage, with NHANES and Global Lung Function Initiative race-specific equations being minimally different, but race-reversed equations moving AA participants to more severe stages and especially between the Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 0 and preserved ratio impaired spirometry groups. Classification using the established NHANES race-specific equations demonstrated that for each of GOLD stages 1-4, AA participants were younger, had fewer pack-years and more current smoking, but had more exacerbations, shorter 6-minute-walk distance, greater dyspnea, and worse BODE (body mass index, airway obstruction, dyspnea, and exercise capacity) scores and St. George's Respiratory Questionnaire scores. Differences were greatest in GOLD stages 1 and 2. Race-reversed equations reclassified 774 AA participants (43%) from GOLD stage 0 to preserved ratio impaired spirometry. Conclusions: Race-specific equations underestimated disease severity among AA participants. These effects were particularly evident in early disease and may result in late detection of COPD.

A plasma peptidomic signature reveals extracellular matrix remodeling and predicts prognosis in alcohol-related hepatitis

Alcohol-related hepatitis (AH) is plagued with high mortality and difficulty in identifying at-risk patients. The extracellular matrix undergoes significant remodeling during inflammatory liver injury that can be detected in biological fluids and potentially used for mortality prediction. EDTA plasma samples were collected from AH patients (n= 62); Model for End-Stage Liver Disease (MELD) score defined AH severity as moderate (12-20; n=28) and severe (>20; n=34). The peptidome data was collected by high resolution, high mass accuracy UPLC-MS. Univariate and multivariate analyses identified differentially abundant peptides, which were used for Gene Ontology, parent protein matrisomal composition and protease involvement. Machine learning methods were used on patient-specific peptidome and clinical data to develop mortality predictors. Analysis of plasma peptides from AH patients and healthy controls identified over 1,600 significant peptide features corresponding to 130 proteins. These were enriched for ECM fragments in AH samples, likely related to turnover of hepatic-derived proteins. Analysis of moderate versus severe AH peptidomes showed a shift in abundance of peptides from collagen 1A1 and fibrinogen A proteins. The dominant proteases for the AH peptidome spectrum appear to be CAPN1 and MMP12. Increase in hepatic expression of these proteases was orthogonally-validated in RNA-seq data of livers from AH patients. Causal graphical modeling identified four peptides directly linked to 90-day mortality in >90% of the learned graphs. These peptides improved the accuracy of mortality prediction over MELD score and were used to create a clinically applicable mortality prediction assay. A signature based on plasma peptidome is a novel, non-invasive method for prognosis stratification in AH patients. Our results could also lead to new mechanistic and/or surrogate biomarkers to identify new AH mechanisms.

Lay summary

We used degraded proteins found the blood of alcohol-related hepatitis patients to identify new potential mechanisms of injury and to predict 90 day mortality.

LEF1 isoforms regulate cellular senescence and aging

The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high-throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. For the purpose of identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Our analysis identified lymphoid enhancer binding factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with idiopathic pulmonary fibrosis, an age-related disease with strong ties to cellular senescence, revealed a stark dysregulation of LEF1. Collectively, our results suggest that LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Defining trophoblast injury patterns in the transcriptomes of dysfunctional placentas

Trophoblast injury is central to clinically relevant placenta dysfunction. We hypothesized that the mRNA of primary human trophoblasts, exposed to distinct injuries in vitro, capture transcriptome patterns of placental biopsies obtained from common obstetrical syndromes. We deployed a CIBERSORTx deconvolution method to correlate trophoblastic RNAseq-based expression matrices with the transcriptome of omics-defined placental dysfunction patterns in vivo. We found distinct trophoblast injury patterns in placental biopsies from women with fetal growth restriction and a hypertensive disorder, or in biopsies clustered by their omics analysis. Our RNAseq data are useful for defining the contribution of trophoblast injuries to placental dysfunction syndromes.

Transcriptional changes of the aging lung

Aging is a natural process associated with declined organ function and higher susceptibility to developing chronic diseases. A systemic single-cell type-based study provides a unique opportunity to understand the mechanisms behind age-related pathologies. Here, we use single-cell gene expression analysis comparing healthy young and aged human lungs from nonsmoker donors to investigate age-related transcriptional changes. Our data suggest that aging has a heterogenous effect on lung cells, as some populations are more transcriptionally dynamic while others remain stable in aged individuals. We found that monocytes and alveolar macrophages were the most transcriptionally affected populations. These changes were related to inflammation and regulation of the immune response. Additionally, we calculated the LungAge score, which reveals the diversity of lung cell types during aging. Changes in DNA damage repair, fatty acid metabolism, and inflammation are essential for age prediction. Finally, we quantified the senescence score in aged lungs and found that the more biased cells toward senescence are immune and progenitor cells. Our study provides a comprehensive and systemic analysis of the molecular signatures of lung aging. Our LungAge signature can be used to predict molecular signatures of physiological aging and to detect common signatures of age-related lung diseases.

Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients

Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.

Prognostic Insights from Longitudinal Multicompartment Study of Host-Microbiota Interactions in Critically Ill Patients

Critical illness can disrupt the composition and function of the microbiome, yet comprehensive longitudinal studies are lacking. We conducted a longitudinal analysis of oral, lung, and gut microbiota in a large cohort of 479 mechanically ventilated patients with acute respiratory failure. Progressive dysbiosis emerged in all three body compartments, characterized by reduced alpha diversity, depletion of obligate anaerobe bacteria, and pathogen enrichment. Clinical variables, including chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, shaped dysbiosis. Notably, of the three body compartments, unsupervised clusters of lung microbiota diversity and composition independently predicted survival, transcending clinical predictors, organ dysfunction severity, and host-response sub-phenotypes. These independent associations of lung microbiota may serve as valuable biomarkers for prognostication and treatment decisions in critically ill patients. Insights into the dynamics of the microbiome during critical illness highlight the potential for microbiota-targeted interventions in precision medicine.

Integrated BATF transcriptional network regulates suppressive intratumoral regulatory T cells

Human regulatory T cells (Tregs) are crucial regulators of tissue repair, autoimmune diseases, and cancer. However, it is challenging to inhibit the suppressive function of Tregs for cancer therapy without affecting immune homeostasis. Identifying pathways that may distinguish tumor-restricted Tregs is important, yet the transcriptional programs that control intratumoral Treg gene expression, and that are distinct from Tregs in healthy tissues, remain largely unknown. We profiled single-cell transcriptomes of CD4+ T cells in tumors and peripheral blood from patients with head and neck squamous cell carcinomas (HNSCC) and those in nontumor tonsil tissues and peripheral blood from healthy donors. We identified a subpopulation of activated Tregs expressing multiple tumor necrosis factor receptor (TNFR) genes (TNFR+ Tregs) that is highly enriched in the tumor microenvironment (TME) compared with nontumor tissue and the periphery. TNFR+ Tregs are associated with worse prognosis in HNSCC and across multiple solid tumor types. Mechanistically, the transcription factor BATF is a central component of a gene regulatory network that governs key aspects of TNFR+ Tregs. CRISPR-Cas9-mediated BATF knockout in human activated Tregs in conjunction with bulk RNA sequencing, immunophenotyping, and in vitro functional assays corroborated the central role of BATF in limiting excessive activation and promoting the survival of human activated Tregs. Last, we identified a suite of surface molecules reflective of the BATF-driven transcriptional network on intratumoral Tregs in patients with HNSCC. These findings uncover a primary transcriptional regulator of highly suppressive intratumoral Tregs, highlighting potential opportunities for therapeutic intervention in cancer without affecting immune homeostasis.

Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes

BACKGROUND

Placental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes.

METHODS

Using our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters.

RESULTS

Pairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups.

CONCLUSIONS

Our results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions.

LEF1 isoforms regulate cellular senescence and aging

Background

The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies.

Methods

With the focus on identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples.

Results

We identified Lymphoid Enhancer Binding Factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with Idiopathic Pulmonary Fibrosis (IPF), an age-related disease with strong ties to cellular senescence, we demonstrated a stark dysregulation of LEF1.

Conclusions

Collectively, our results suggest that the LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

An intrinsically interpretable neural network architecture for sequence-to-function learning

MOTIVATION

Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs.

RESULTS

We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition.

AVAILABILITY AND IMPLEMENTATION

The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.

The plasma degradome reflects later development of NASH fibrosis after liver transplant

Although liver transplantation (LT) is an effective therapy for cirrhosis, the risk of post-LT NASH is alarmingly high and is associated with accelerated progression to fibrosis/cirrhosis, cardiovascular disease and decreased survival. Lack of risk stratification strategies hampers early intervention against development of post-LT NASH fibrosis. The liver undergoes significant remodeling during inflammatory injury. During such remodeling, degraded peptide fragments (i.e., 'degradome') of the ECM and other proteins increase in plasma, making it a useful diagnostic/prognostic tool in chronic liver disease. To investigate whether liver injury caused by post-LT NASH would yield a unique degradome profile that is predictive of severe post-LT NASH fibrosis, a retrospective analysis of 22 biobanked samples from the Starzl Transplantation Institute (12 with post-LT NASH after 5 years and 10 without) was performed. Total plasma peptides were isolated and analyzed by 1D-LC-MS/MS analysis using a Proxeon EASY-nLC 1000 UHPLC and nanoelectrospray ionization into an Orbitrap Elite mass spectrometer. Qualitative and quantitative peptide features data were developed from MSn datasets using PEAKS Studio X (v10). LC-MS/MS yielded ~ 2700 identifiable peptide features based on the results from Peaks Studio analysis. Several peptides were significantly altered in patients that later developed fibrosis and heatmap analysis of the top 25 most significantly changed peptides, most of which were ECM-derived, clustered the 2 patient groups well. Supervised modeling of the dataset indicated that a fraction of the total peptide signal (~ 15%) could explain the differences between the groups, indicating a strong potential for representative biomarker selection. A similar degradome profile was observed when the plasma degradome patterns were compared being obesity sensitive (C57Bl6/J) and insensitive (AJ) mouse strains. The plasma degradome profile of post-LT patients yielded stark difference based on later development of post-LT NASH fibrosis. This approach could yield new "fingerprints" that can serve as minimally-invasive biomarkers of negative outcomes post-LT.

The upper and lower respiratory tract microbiome in severe aspiration pneumonia

Uncertainty persists whether anaerobic bacteria represent important pathogens in aspiration pneumonia. In a nested case-control study of mechanically ventilated patients classified as macro-aspiration pneumonia (MAsP, n = 56), non-macro-aspiration pneumonia (NonMAsP, n = 91), and uninfected controls (n = 11), we profiled upper (URT) and lower respiratory tract (LRT) microbiota with bacterial 16S rRNA gene sequencing, measured plasma host-response biomarkers, analyzed bacterial communities by diversity and oxygen requirements, and performed unsupervised clustering with Dirichlet Multinomial Models (DMM). MAsP and NonMAsP patients had indistinguishable microbiota profiles by alpha diversity and oxygen requirements with similar host-response profiles and 60-day survival. Unsupervised DMM clusters revealed distinct bacterial clusters in the URT and LRT, with low-diversity clusters enriched for facultative anaerobes and typical pathogens, associated with higher plasma levels of SPD and sCD14 and worse 60-day survival. The predictive inter-patient variability in these bacterial profiles highlights the importance of microbiome study in patient sub-phenotyping and precision medicine approaches for severe pneumonia.

Respiratory Fungal Communities are Associated with Systemic Inflammation and Predict Survival in Patients with Acute Respiratory Failure

Rationale

Disruption of respiratory bacterial communities predicts poor clinical outcomes in critical illness; however, the role of respiratory fungal communities (mycobiome) is poorly understood.

Objectives

We investigated whether mycobiota variation in the respiratory tract is associated with host-response and clinical outcomes in critically ill patients.

Methods

To characterize the upper and lower respiratory tract mycobiota, we performed rRNA gene sequencing (internal transcribed spacer) of oral swabs and endotracheal aspirates (ETA) from 316 mechanically-ventilated patients. We examined associations of mycobiome profiles (diversity and composition) with clinical variables, host-response biomarkers, and outcomes.

Measurements and Main Results

ETA samples with >50% relative abundance for C. albicans (51%) were associated with elevated plasma IL-8 and pentraxin-3 (p=0.05), longer time-to-liberation from mechanical ventilation (p=0.04) and worse 30-day survival (adjusted hazards ratio (adjHR): 1.96 [1.04-3.81], p=0.05). Using unsupervised clustering, we derived two clusters in ETA samples, with Cluster 2 (39%) showing lower alpha diversity (p<0.001) and higher abundance of C. albicans (p<0.001). Cluster 2 was significantly associated with the prognostically adverse hyperinflammatory subphenotype (odds ratio 2.07 [1.03-4.18], p=0.04) and predicted worse survival (adjHR: 1.81 [1.03-3.19], p=0.03). C. albicans abundance in oral swabs was also associated with the hyperinflammatory subphenotype and mortality.

Conclusions

Variation in respiratory mycobiota was significantly associated with systemic inflammation and clinical outcomes. C. albicans abundance emerged as a negative predictor in both the upper and lower respiratory tract. The lung mycobiome may play an important role in the biological and clinical heterogeneity among critically ill patients and represent a potential therapeutic target for lung injury in critical illness.

Early events marking lung fibroblast transition to profibrotic state in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic Pulmonary Fibrosis (IPF) is an age-associated progressive lung disease with accumulation of scar tissue impairing gas exchange. Previous high-throughput studies elucidated the role of cellular heterogeneity and molecular pathways in advanced disease. However, critical pathogenic pathways occurring in the transition of fibroblasts from normal to profibrotic have been largely overlooked.

METHODS

We used single cell transcriptomics (scRNA-seq) from lungs of healthy controls and IPF patients (lower and upper lobes). We identified fibroblast subclusters, genes and pathways associated with early disease. Immunofluorescence assays validated the role of MOXD1 early in fibrosis.

RESULTS

We identified four distinct fibroblast subgroups, including one marking the normal-to-profibrotic state transition. Our results show for the first time that global downregulation of ribosomal proteins and significant upregulation of the majority of copper-binding proteins, including MOXD1, mark the IPF transition. We find no significant differences in gene expression in IPF upper and lower lobe samples, which were selected to have low and high degree of fibrosis, respectively.

CONCLUSIONS

Early events during IPF onset in fibroblasts include dysregulation of ribosomal and copper-binding proteins. Fibroblasts in early stage IPF may have already acquired a profibrotic phenotype while hallmarks of advanced disease, including fibroblast foci and honeycomb formation, are still not evident. The new transitional fibroblasts we discover could prove very important for studying the role of fibroblast plasticity in disease progression and help develop early diagnosis tools and therapeutic interventions targeting earlier disease states.

Abstract 6537: Single cell transcriptomics uncovers cellular and molecular differences in PBMCs of responders and non-responders to the MUC1 cancer vaccine given in the preventative setting

Introduction: A single arm trial (NCT007773097) and a double-blind, placebo controlled randomized trial (NCT02134925) were conducted in patients with newly diagnosed advanced colonic adenomas to test the safety and immunogenicity of the MUC1 antigen vaccine and its potential to prevent new adenoma formation. These are the first trials of a non-viral cancer vaccine administered in the absence of cancer. In both trials, the vaccine was safe and strongly immunogenic in 43% and 25% of participants (Responders), respectively. The lack of robust response in a significant number of participants suggested, for the first time, that even in a premalignant setting, the immune system may have already been exposed to regulatory influences that, in the case of the vaccine, determine who does and who does not respond. We hypothesized that there could be molecular and cellular differences in the immune competence between vaccine responders and non-responders, and that they could be identified by studying their pre-vaccination peripheral blood mononuclear cells (PBMCs).

Methods: The two MUC1 vaccine trials are described in https://doi.org/10.1101/2022.10.05.22280474 and https://doi.org/10.1158%2F1940-6207.CAPR-12-0275. We performed single cell RNA-sequencing (scRNAseq) on banked pre-vaccination PBMCs from 16 Responders and 16 Non-Responders, determined by anti-MUC1 IgG response. Using differential gene expression (DGE), pathway enrichment, and network estimation analyses, we identified specific cell types, genes, and pathways that differ between responders and non-responders.

Results: Pre-vaccination PBMCs from Responders contained a significantly higher percentage of CD4+ naive T cells, while Non-Responders showed significantly higher percentage of CD8+ T effector memory (TEM) cells and a higher percentage of CD16+ monocytes. DGE and gene interaction network analysis showed a higher level of expression of T cell activation genes, such as Fos and Jun, in the CD4+ naive T cells in Responders. Further network analysis showed that these genes were directly connected to response. We also found pre-vaccination specific gene ontology (GO) pathways for translational and transcriptional activity enriched in all cell types in Responders compared to Non-Responders.

Conclusion: Our analyses identified candidate biomarkers that are predictive of a preventative cancer vaccine response. Thus, our results can be used for patient selection for vaccine administration. Furthermore, we identified cell type differences and transcriptional pathways that provide information of possible mechanisms of vaccine response.

Citation Format: Daniel Y. Yuan, Michelle L. McKeague, Matthew T. Dracz, Olivera J. Finn, Panayiotis V. Benos. Single cell transcriptomics uncovers cellular and molecular differences in PBMCs of responders and non-responders to the MUC1 cancer vaccine given in the preventative setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6537.

Foxm1 regulates cardiomyocyte proliferation in adult zebrafish after cardiac injury

The regenerative capacity of the mammalian heart is poor, with one potential reason being that adult cardiomyocytes cannot proliferate at sufficient levels to replace lost tissue. During development and neonatal stages, cardiomyocytes can successfully divide under injury conditions; however, as these cells mature their ability to proliferate is lost. Therefore, understanding the regulatory programs that can induce post-mitotic cardiomyocytes into a proliferative state is essential to enhance cardiac regeneration. Here, we report that the forkhead transcription factor Foxm1 is required for cardiomyocyte proliferation after injury through transcriptional regulation of cell cycle genes. Transcriptomic analysis of injured zebrafish hearts revealed that foxm1 expression is increased in border zone cardiomyocytes. Decreased cardiomyocyte proliferation and expression of cell cycle genes in foxm1 mutant hearts was observed, suggesting it is required for cell cycle checkpoints. Subsequent analysis of a candidate Foxm1 target gene, cenpf, revealed that this microtubule and kinetochore binding protein is also required for cardiac regeneration. Moreover, cenpf mutants show increased cardiomyocyte binucleation. Thus, foxm1 and cenpf are required for cardiomyocytes to complete mitosis during zebrafish cardiac regeneration.

The plasma degradome reflects later development of NASH fibrosis after liver transplant

Although liver transplantation (LT) is an effective therapy for cirrhosis, the risk of post-LT NASH is alarmingly high and is associated with accelerated progression to fibrosis/cirrhosis, cardiovascular disease, and decreased survival. Lack of risk stratification strategies hamper liver undergoes significant remodeling during inflammatory injury. During such remodeling, degraded peptide fragments (i.e., 'degradome') of the ECM and other proteins increase in plasma, making it a useful diagnostic/prognostic tool in chronic liver disease. To investigate whether inflammatory liver injury caused by post-LT NASH would yield a unique degradome profile, predictive of severe post-LT NASH fibrosis, we performed a retrospective analysis of 22 biobanked samples from the Starzl Transplantation Institute (12 with post-LT NASH after 5 years and 10 without). Total plasma peptides were isolated and analyzed by 1D-LC-MS/MS analysis using a Proxeon EASY-nLC 1000 UHPLC and nanoelectrospray ionization into an Orbitrap Elite mass spectrometer. Qualitative and quantitative peptide features data were developed from MSn datasets using PEAKS Studio X (v10). LC-MS/MS yielded ∼2700 identifiable peptide features based on the results from Peaks Studio analysis. Several peptides were significantly altered in patients that later developed fibrosis and heatmap analysis of the top 25 most significantly-changed peptides, most of which were ECM-derived, clustered the 2 patient groups well. Supervised modeling of the dataset indicated that a fraction of the total peptide signal (∼15%) could explain the differences between the groups, indicating a strong potential for representative biomarker selection. A similar degradome profile was observed when the plasma degradome patterns were compared being obesity sensitive (C57Bl6/J) and insensitive (AJ) mouse strains. Both The plasma degradome profile of post-LT patients yields stark difference based on later development of post-LT NASH fibrosis. This approach could yield new "fingerprints" that can serve as minimally-invasive biomarkers of negative outcomes post-LT.

An intrinsically interpretable neural network architecture for sequence to function learning

Motivation

Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post-hoc analyses, and even then, we often cannot explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called tiSFM (totally interpretable sequence to function model). tiSFM improves upon the performance of standard multi-layer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multi-layer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs.

Results

tiSFM's model architecture makes use of convolutions with a fixed set of kernel weights representing known transcription factor (TF) binding site motifs. We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state- of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition.

Availability and implementation

The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv , implemented in Python.

Contact

atb44@pitt.edu.

Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

BACKGROUND

Chronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment.

METHODS

Blood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples.

RESULTS

The discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS).

CONCLUSIONS

The identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis.

Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems

BACKGROUND

Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size.

RESULTS

To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molecular and clinical data of various diseases, we demonstrated that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations. In the analyses, we also illustrate how identifying nonlinear causal relations and estimating their effect size help understand the complex disease pathobiology, which is not possible using other methods.

CONCLUSIONS

With these advantages, the application of DAG-deepVASE can help identify driver genes and therapeutic agents in biomedical studies and clinical trials.

Lipidomic signatures align with inflammatory patterns and outcomes in critical illness

Alterations in lipid metabolism have the potential to be markers as well as drivers of pathobiology of acute critical illness. Here, we took advantage of the temporal precision offered by trauma as a common cause of critical illness to identify the dynamic patterns in the circulating lipidome in critically ill humans. The major findings include an early loss of all classes of circulating lipids followed by a delayed and selective lipogenesis in patients destined to remain critically ill. The previously reported survival benefit of early thawed plasma administration was associated with preserved lipid levels that related to favorable changes in coagulation and inflammation biomarkers in causal modelling. Phosphatidylethanolamines (PE) were elevated in patients with persistent critical illness and PE levels were prognostic for worse outcomes not only in trauma but also severe COVID-19 patients. Here we show selective rise in systemic PE as a common prognostic feature of critical illness.

Causal Discovery in High-dimensional, Multicollinear Datasets

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets

High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation.

People critically ill with COVID-19 exhibit peripheral immune profiles predictive of mortality and reflective of SARS-CoV-2 lung viral burden

Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in people critically ill with COVID-19. Here, we present high-dimensional profiling of blood and respiratory samples from people with severe COVID-19 to examine the association between cell-linked molecular features and mortality outcomes. Peripheral transcriptional profiles by single-cell RNA sequencing (RNA-seq)-based deconvolution of immune states are associated with COVID-19 mortality. Further, persistently high levels of an interferon signaling module in monocytes over time lead to subsequent concerted upregulation of inflammatory cytokines. SARS-CoV-2-infected myeloid cells in the lower respiratory tract upregulate CXCL10, leading to a higher risk of death. Our analysis suggests a pivotal role for viral-infected myeloid cells and protracted interferon signaling in severe COVID-19.

Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis

BACKGROUND

Sarcoidosis is a multisystem granulomatous disease of unknown origin with a variable and often unpredictable course and pattern of organ involvement. In this study we sought to identify specific bronchoalveolar lavage (BAL) cell gene expression patterns indicative of distinct disease phenotypic traits.

METHODS

RNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well-characterised patients with pulmonary sarcoidosis enrolled in the multicentre Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Weighted gene co-expression network analysis and nonparametric statistics were used to analyse genome-wide BAL transcriptome. Validation of results was performed using a microarray expression dataset of an independent sarcoidosis cohort (Freiburg, Germany; n=50).

RESULTS

Our supervised analysis found associations between distinct transcriptional programmes and major pulmonary phenotypic manifestations of sarcoidosis including T-helper type 1 (Th1) and Th17 pathways associated with hilar lymphadenopathy, transforming growth factor-β1 (TGFB1) and mechanistic target of rapamycin (MTOR) signalling with parenchymal involvement, and interleukin (IL)-7 and IL-2 with airway involvement. Our unsupervised analysis revealed gene modules that uncovered four potential sarcoidosis endotypes including hilar lymphadenopathy with increased acute T-cell immune response; extraocular organ involvement with PI3K activation pathways; chronic and multiorgan disease with increased immune response pathways; and multiorgan involvement, with increased IL-1 and IL-18 immune and inflammatory responses. We validated the occurrence of these endotypes using gene expression, pulmonary function tests and cell differentials from Freiburg.

CONCLUSION

Taken together, our results identify BAL gene expression programmes that characterise major pulmonary sarcoidosis phenotypes and suggest the presence of distinct disease molecular endotypes.

Neurological Complications Acquired During Pediatric Critical Illness: Exploratory "Mixed Graphical Modeling" Analysis Using Serum Biomarker Levels

OBJECTIVES

Neurologic complications, consisting of the acute development of a neurologic disorder, that is, not present at admission but develops during the course of illness, can be difficult to detect in the PICU due to sedation, neuromuscular blockade, and young age. We evaluated the direct relationships of serum biomarkers and clinical variables to the development of neurologic complications. Analysis was performed using mixed graphical models, a machine learning approach that allows inference of cause-effect associations from continuous and discrete data.

DESIGN

Secondary analysis of a previous prospective observational study.

SETTING

PICU, single quaternary-care center.

PATIENTS

Individuals admitted to the PICU, younger than18 years old, with intravascular access via an indwelling catheter.

INTERVENTIONS

None.

MEASUREMENTS

About 101 patients were included in this analysis. Serum (days 1-7) was analyzed for glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and alpha-II spectrin breakdown product 150 utilizing enzyme-linked immunosorbent assays. Serum levels of neuron-specific enolase, myelin basic protein, and S100 calcium binding protein B used in these models were reported previously. Demographic data, use of selected clinical therapies, lengths of stay, and ancillary neurologic testing (head CT, brain MRI, and electroencephalogram) results were recorded. The Mixed Graphical Model-Fast-Causal Inference-Maximum algorithm was applied to the dataset.

MAIN RESULTS

About 13 of 101 patients developed a neurologic complication during their critical illness. The mixed graphical model identified peak levels of the neuronal biomarker neuron-specific enolase and ubiquitin C-terminal hydrolase-L1, and the astrocyte biomarker glial fibrillary acidic protein to be the direct causal determinants for the development of a neurologic complication; in contrast, clinical variables including age, sex, length of stay, and primary neurologic diagnosis were not direct causal determinants.

CONCLUSIONS

Graphical models that include biomarkers in addition to clinical data are promising methods to evaluate direct relationships in the development of neurologic complications in critically ill children. Future work is required to validate and refine these models further, to determine if they can be used to predict which patients are at risk for/or with early neurologic complications.

Plasma 1,3-β-d-glucan levels predict adverse clinical outcomes in critical illness

BACKGROUNDThe fungal cell wall constituent 1,3-β-d-glucan (BDG) is a pathogen-associated molecular pattern that can stimulate innate immunity. We hypothesized that BDG from colonizing fungi in critically ill patients may translocate into the systemic circulation and be associated with host inflammation and outcomes.METHODSWe enrolled 453 mechanically ventilated patients with acute respiratory failure (ARF) without invasive fungal infection and measured BDG, innate immunity, and epithelial permeability biomarkers in serially collected plasma samples.RESULTSCompared with healthy controls, patients with ARF had significantly higher BDG levels (median [IQR], 26 pg/mL [15-49 pg/mL], P < 0.001), whereas patients with ARF with high BDG levels (≥40 pg/mL, 31%) had higher odds for assignment to the prognostically adverse hyperinflammatory subphenotype (OR [CI], 2.88 [1.83-4.54], P < 0.001). Baseline BDG levels were predictive of fewer ventilator-free days and worse 30-day survival (adjusted P < 0.05). Integrative analyses of fungal colonization and epithelial barrier disruption suggested that BDG may translocate from either the lung or gut compartment. We validated the associations between plasma BDG and host inflammatory responses in 97 hospitalized patients with COVID-19.CONCLUSIONBDG measurements offered prognostic information in critically ill patients without fungal infections. Further research in the mechanisms of translocation and innate immunity recognition and stimulation may offer new therapeutic opportunities in critical illness.FUNDINGUniversity of Pittsburgh Clinical and Translational Science Institute, COVID-19 Pilot Award and NIH grants (K23 HL139987, U01 HL098962, P01 HL114453, R01 HL097376, K24 HL123342, U01 HL137159, R01 LM012087, K08HK144820, F32 HL142172, K23 GM122069).

Myofibroblast transcriptome indicates SFRP2hi fibroblast progenitors in systemic sclerosis skin

Skin and lung fibrosis in systemic sclerosis (SSc) is driven by myofibroblasts, alpha-smooth muscle actin expressing cells. The number of myofibroblasts in SSc skin correlates with the modified Rodnan skin score, the most widely used clinical measure of skin disease severity. Murine fibrosis models indicate that myofibroblasts can arise from a variety of different cell types, but their origin in SSc skin has remained uncertain. Utilizing single cell RNA-sequencing, we define different dermal fibroblast populations and transcriptome changes, comparing SSc to healthy dermal fibroblasts. Here, we show that SSc dermal myofibroblasts arise in two steps from an SFRP2^hi/DPP4-expressing progenitor fibroblast population. In the first step, SSc fibroblasts show globally upregulated expression of transcriptome markers, such as PRSS23 and THBS1. A subset of these cells shows markers indicating that they are proliferating. Only a fraction of SFRP2^hi SSc fibroblasts differentiate into myofibroblasts, as shown by expression of additional markers, SFRP4 and FNDC1. Bioinformatics analysis of the SSc fibroblast transcriptomes implicated upstream transcription factors, including FOSL2, RUNX1, STAT1, FOXP1, IRF7 and CREB3L1, as well as SMAD3, driving SSc myofibroblast differentiation.

Myofibroblasts drive fibrosis in systemic sclerosis (SSc), but the cellular progenitors are unknown. Utilizing single cell RNA-sequencing, the authors show that SSc dermal myofibroblasts arise in a two-step process from SFRP2/DPP4-expressing progenitors and implicate upstream transcription factors.

Compensatory hepatic adaptation accompanies permanent absence of intrahepatic biliary network due to YAP1 loss in liver progenitors

Yes-associated protein 1 (YAP1) regulates cell plasticity during liver injury, regeneration, and cancer, but its role in liver development is unknown. We detect YAP1 activity in biliary cells and in cells at the hepatobiliary bifurcation in single-cell RNA sequencing analysis of developing livers. Deletion of Yap1 in hepatoblasts does not impair Notch-driven SOX9+ ductal plate formation but does prevent the formation of the abutting second layer of SOX9+ ductal cells, blocking the formation of a patent intrahepatic biliary tree. Intriguingly, these mice survive for 8 months with severe cholestatic injury and without hepatocyte-to-biliary transdifferentiation. Ductular reaction in the perihilar region suggests extrahepatic biliary proliferation, likely seeking the missing intrahepatic biliary network. Long-term survival of these mice occurs through hepatocyte adaptation via reduced metabolic and synthetic function, including altered bile acid metabolism and transport. Overall, we show YAP1 as a key regulator of bile duct development while highlighting a profound adaptive capability of hepatocytes.

A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data

Genome sequencing technologies have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand mechanisms of disease and predict the effects of medical interventions, high-throughput data must be integrated with demographic, phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods must infer relationships between these data types. We recently proposed a pipeline (CausalMGM) to achieve this. CausalMGM uses probabilistic graphical models to infer the relationships between variables in the data; however, CausalMGM's graphical structure learning algorithm can only handle small datasets efficiently. We propose a new methodology (piPref-Div) that selects the most informative variables for CausalMGM, enabling it to scale. We validate the efficacy of piPref-Div against other feature selection methods and demonstrate how the use of the full pipeline improves breast cancer outcome prediction and provides biologically interpretable views of gene expression data.

Bifurcated monocyte states are predictive of mortality in severe COVID-19

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection presents with varied clinical manifestations1, ranging from mild symptoms to acute respiratory distress syndrome (ARDS) with high mortality2,3. Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in severe COVID-19. We performed high-dimensional cellular and molecular profiling of blood and respiratory samples from critically ill COVID-19 patients to define immune cell genomic states that are predictive of outcome in severe COVID-19 disease. Critically ill patients admitted to the intensive care unit (ICU) manifested increased frequencies of inflammatory monocytes and plasmablasts that were also associated with ARDS not due to COVID-19. Single-cell RNAseq (scRNAseq)-based deconvolution of genomic states of peripheral immune cells revealed distinct gene modules that were associated with COVID-19 outcome. Notably, monocytes exhibited bifurcated genomic states, with expression of a cytokine gene module exemplified by CCL4 (MIP-1β) associated with survival and an interferon signaling module associated with death. These gene modules were correlated with higher levels of MIP-1β and CXCL10 levels in plasma, respectively. Monocytes expressing genes reflective of these divergent modules were also detectable in endotracheal aspirates. Machine learning algorithms identified the distinctive monocyte modules as part of a multivariate peripheral immune system state that was predictive of COVID-19 mortality. Follow-up analysis of the monocyte modules on ICU day 5 was consistent with bifurcated states that correlated with distinct inflammatory cytokines. Our data suggests a pivotal role for monocytes and their specific inflammatory genomic states in contributing to mortality in life-threatening COVID-19 disease and may facilitate discovery of new diagnostics and therapeutics.

Respiratory Tract Dysbiosis Is Associated with Worse Outcomes in Mechanically Ventilated Patients

Rationale: Host inflammatory responses have been strongly associated with adverse outcomes in critically ill patients, but the biologic underpinnings of such heterogeneous responses have not been defined.Objectives: We examined whether respiratory tract microbiome profiles are associated with host inflammation and clinical outcomes of acute respiratory failure.Methods: We collected oral swabs, endotracheal aspirates (ETAs), and plasma samples from mechanically ventilated patients. We performed 16S ribosomal RNA gene sequencing to characterize upper and lower respiratory tract microbiota and classified patients into host-response subphenotypes on the basis of clinical variables and plasma biomarkers of innate immunity and inflammation. We derived diversity metrics and composition clusters with Dirichlet multinomial models and examined our data for associations with subphenotypes and clinical outcomes.Measurements and Main Results: Oral and ETA microbial communities from 301 mechanically ventilated subjects had substantial heterogeneity in α and β diversity. Dirichlet multinomial models revealed a cluster with low α diversity and enrichment for pathogens (e.g., high Staphylococcus or Pseudomonadaceae relative abundance) in 35% of ETA samples, associated with a hyperinflammatory subphenotype, worse 30-day survival, and longer time to liberation from mechanical ventilation (adjusted P < 0.05), compared with patients with higher α diversity and relative abundance of typical oral microbiota. Patients with evidence of dysbiosis (low α diversity and low relative abundance of "protective" oral-origin commensal bacteria) in both oral and ETA samples (17%, combined dysbiosis) had significantly worse 30-day survival and longer time to liberation from mechanical ventilation than patients without dysbiosis (55%; adjusted P < 0.05).Conclusions: Respiratory tract dysbiosis may represent an important, modifiable contributor to patient-level heterogeneity in systemic inflammatory responses and clinical outcomes.

Human ex vivo lung perfusion: a novel model to study human lung diseases

Experimental animal models to predict physiological responses to injury and stress in humans have inherent limitations. Therefore, the development of preclinical human models is of paramount importance. Ex vivo lung perfusion (EVLP) has typically been used to recondition donor lungs before transplantation. However, this technique has recently advanced into a model to emulate lung mechanics and physiology during injury. In the present study, we propose that the EVLP of diseased human lungs is a well-suited preclinical model for translational research on chronic lung diseases. Throughout this paper, we demonstrate this technique's feasibility in pulmonary arterial hypertension (PAH), idiopathic pulmonary fibrosis (IPF), emphysema, and non-disease donor lungs not suitable for transplantation. In this study, we aimed to perfuse the lungs for 6 h with the EVLP system. This facilitated a robust and continuous assessment of airway mechanics, pulmonary hemodynamics, gas exchange, and biochemical parameters. We then collected at different time points tissue biopsies of lung parenchyma to isolate RNA and DNA to identify each disease's unique gene expression. Thus, demonstrating that EVLP could successfully serve as a clinically relevant experimental model to derive essential insights into pulmonary pathophysiology and various human lung diseases.

Lipidomic Signatures Align with Inflammatory Patterns and Outcomes in Critical Illness

Alterations in lipid metabolism have the potential to be markers as well as drivers of the pathobiology of acute critical illness. Here, we took advantage of the temporal precision offered by trauma as a common cause of critical illness to identify the dynamic patterns in the circulating lipidome in critically ill humans. The major findings include an early loss of all classes of circulating lipids followed by a delayed and selective lipogenesis in patients destined to remain critically ill. Early in the clinical course, Fresh Frozen Plasma administration led to improved survival in association with preserved lipid levels that related to favorable changes in coagulation and inflammation biomarkers. Late over-representation of phosphatidylethanolamines with critical illness led to the validation of a Lipid Reprogramming Score that was prognostic not only in trauma but also severe COVID-19 patients. Our lipidomic findings provide a new paradigm for the lipid response underlying critical illness.

Topographic heterogeneity of lung microbiota in end-stage idiopathic pulmonary fibrosis: the Microbiome in Lung Explants-2 (MiLEs-2) study

BACKGROUND

Lung microbiota profiles in patients with early idiopathic pulmonary fibrosis (IPF) have been associated with disease progression; however, the topographic heterogeneity of lung microbiota and their roles in advanced IPF are unknown.

METHODS

We performed a retrospective, case-control study of explanted lung tissue obtained at the time of lung transplantation or rapid autopsy from patients with IPF and other chronic lung diseases (connective tissue disease-associated interstitial lung disease (CTD-ILD), cystic fibrosis (CF), COPD and donor lungs unsuitable for transplant from Center for Organ Recovery and Education (CORE)). We sampled subpleural tissue and airway-based specimens (bronchial washings and airway tissue) and quantified bacterial load and profiled communities by amplification and sequencing of the 16S rRNA gene.

FINDINGS

Explants from 62 patients with IPF, 15 patients with CTD-ILD, 20 patients with CF, 20 patients with COPD and 20 CORE patients were included. Airway-based samples had higher bacterial load compared with distal parenchymal tissue. IPF basilar tissue had much lower bacterial load compared with CF and CORE lungs (p<0.001). No microbial community differences were found between parenchymal tissue samples from different IPF lobes. Dirichlet multinomial models revealed an IPF cluster (29%) with distinct composition, high bacterial load and low alpha diversity, exhibiting higher odds for acute exacerbation or death.

INTERPRETATION

IPF explants had low biomass in the distal parenchyma of all three lobes with higher bacterial load in the airways. The discovery of a distinct subgroup of patients with IPF with higher bacterial load and worse clinical outcomes supports investigation of personalised medicine approaches for microbiome-targeted interventions.

miR-1207-5p Can Contribute to Dysregulation of Inflammatory Response in COVID-19 via Targeting SARS-CoV-2 RNA

The present study focuses on the role of human miRNAs in SARS-CoV-2 infection. An extensive analysis of human miRNA binding sites on the viral genome led to the identification of miR-1207-5p as potential regulator of the viral Spike protein. It is known that exogenous RNA can compete for miRNA targets of endogenous mRNAs leading to their overexpression. Our results suggest that SARS-CoV-2 virus can act as an exogenous competing RNA, facilitating the over-expression of its endogenous targets. Transcriptomic analysis of human alveolar and bronchial epithelial cells confirmed that the CSF1 gene, a known target of miR-1207-5p, is over-expressed following SARS-CoV-2 infection. CSF1 enhances macrophage recruitment and activation and its overexpression may contribute to the acute inflammatory response observed in severe COVID-19. In summary, our results indicate that dysregulation of miR-1207-5p-target genes during SARS-CoV-2 infection may contribute to uncontrolled inflammation in most severe COVID-19 cases.

Causal network perturbations for instance-specific analysis of single cell and disease samples

MOTIVATION

Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.

RESULTS

ssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNA-seq data, where the correct cell timing is recovered; and two TCGA datasets, where ssNPA patient clusters have significant survival differences. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches.

AVAILABILITY AND IMPLEMENTATION

http://www.benoslab.pitt.edu/Software/ssnpa/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs

MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex.

In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis.

Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one.

Regional Molecular Signature of the Symptomatic Atherosclerotic Carotid Plaque

BACKGROUND

Many studies have explored molecular markers of carotid plaque development and vulnerability to rupture, usually having examined whole carotid plaques. However, there are regional differences in plaque morphology and known shear-related mechanisms in areas surrounding the lipid core.

OBJECTIVE

To determine whether there are regional differences in protein expression along the long axis of the carotid plaque and how that might produce gaps in our understanding of the carotid plaque molecular signature.

METHODS

Levels of 7 inflammatory cytokines (IL-1β, IL-6, IL-8, IL-10, IL-12 p70, IFN-γ, and TNF-α) and caspase-3 were analyzed in prebifurcation, bifurcation, and postbifurcation segments of internal carotid plaques surgically removed from symptomatic and asymptomatic patients. Expression profiles of miRNAs and mRNAs were determined with microarrays for the rupture-prone postbifurcation segment for comparison with published whole plaque results.

RESULTS

Expression levels of all proteins examined, except IL-10, were lowest in the prebifurcation segment and significantly higher in the postbifurcation segment. Patient group differences in protein expression were observed for the prebifurcation segment; however, no significant differences were observed in the postbifurcation segment between symptomatic and asymptomatic patients. Expression profiles from postbifurcation carotid plaques identified 4 novel high priority miRNAs differentially expressed between patient groups (miR-214, miR-484, miR-942, and miR-1287) and 3 high-confidence miRNA:mRNA targets, including miR-214:APOD, miR-484:DACH1, and miR-942:GPR56.

CONCLUSION

The results demonstrate regional differences in protein expression for the first time and show that focus on the rupture-prone postbifurcation region leads to prioritization for further study of novel miRNA gene regulation mechanisms.

The evolution of radiographic edema in ARDS and its association with clinical outcomes: A prospective cohort study in adult patients

PURPOSE

To assess the longitudinal evolution of radiographic edema using chest X-rays (CXR) in patients with Acute Respiratory Distress Syndrome (ARDS) and to examine its association with prognostic biomarkers, ARDS subphenotypes and outcomes.

MATERIALS AND METHODS

We quantified radiographic edema on CXRs from patients with ARDS or cardiogenic pulmonary edema (controls) using the Radiographic Assessment of Lung Edema (RALE) score on day of intubation and up to 10 days after. We measured baseline plasma biomarkers and recorded clinical variables.

RESULTS

The RALE score had good inter-rater agreement (r = 0.83, p < 0.0001) applied on 488 CXRs from 129 patients, with higher RALE scores in patients with ARDS (n = 108) compared to controls (n = 21, p = 0.01). Baseline RALE scores were positively correlated with levels of the receptor for end-glycation end products (RAGE) in ARDS patients (p < 0.05). Baseline RALE scores were not predictive of 30- or 90-day survival. Persistently elevated RALE scores were associated with prolonged need for mechanical ventilation (p = 0.002).

CONCLUSIONS

The RALE score is easily implementable with high inter-rater reliability. Longitudinal RALE scoring appears to be a reproducible approach to track the evolution of radiographic edema in patients with ARDS and can potentially predict prolonged need for mechanical ventilation.

Host-Response Subphenotypes Offer Prognostic Enrichment in Patients With or at Risk for Acute Respiratory Distress Syndrome

OBJECTIVES

Classification of patients with acute respiratory distress syndrome into hyper- and hypoinflammatory subphenotypes using plasma biomarkers may facilitate more effective targeted therapy. We examined whether established subphenotypes are present not only in patients with acute respiratory distress syndrome but also in patients at risk for acute respiratory distress syndrome (ARFA) and then assessed the prognostic information of baseline subphenotyping on the evolution of host-response biomarkers and clinical outcomes.

DESIGN

Prospective, observational cohort study.

SETTING

Medical ICU at a tertiary academic medical center.

PATIENTS

Mechanically ventilated patients with acute respiratory distress syndrome or ARFA.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We performed longitudinal measurements of 10 plasma biomarkers of host injury and inflammation. We applied unsupervised latent class analysis methods utilizing baseline clinical and biomarker variables and demonstrated that two-class models (hyper- vs hypoinflammatory subphenotypes) offered improved fit compared with one-class models in both patients with acute respiratory distress syndrome and ARFA. Baseline assignment to the hyperinflammatory subphenotype (39/104 [38%] acute respiratory distress syndrome and 30/108 [28%] ARFA patients) was associated with higher severity of illness by Sequential Organ Failure Assessment scores and incidence of acute kidney injury in patients with acute respiratory distress syndrome, as well as higher 30-day mortality and longer duration of mechanical ventilation in ARFA patients (p < 0.0001). Hyperinflammatory patients exhibited persistent elevation of biomarkers of innate immunity for up to 2 weeks postintubation.

CONCLUSIONS

Our results suggest that two distinct subphenotypes are present not only in patients with established acute respiratory distress syndrome but also in patients at risk for its development. Hyperinflammatory classification at baseline is associated with higher severity of illness, worse clinical outcomes, and trajectories of persistently elevated biomarkers of host injury and inflammation during acute critical illness compared with hypoinflammatory patients. Our findings provide strong rationale for examining treatment effect modifications by subphenotypes in randomized clinical trials to inform precision therapeutic approaches in critical care.

Transcriptional regulatory model of fibrosis progression in the human lung

To develop a systems biology model of fibrosis progression within the human lung we performed RNA sequencing and microRNA analysis on 95 samples obtained from 10 idiopathic pulmonary fibrosis (IPF) and 6 control lungs. Extent of fibrosis in each sample was assessed by microCT-measured alveolar surface density (ASD) and confirmed by histology. Regulatory gene expression networks were identified using linear mixed-effect models and dynamic regulatory events miner (DREM). Differential gene expression analysis identified a core set of genes increased or decreased before fibrosis was histologically evident that continued to change with advanced fibrosis. DREM generated a systems biology model (www.sb.cs.cmu.edu/IPFReg) that identified progressively divergent gene expression tracks with microRNAs and transcription factors that specifically regulate mild or advanced fibrosis. We confirmed model predictions by demonstrating that expression of POU2AF1, previously unassociated with lung fibrosis but proposed by the model as regulator, is increased in B lymphocytes in IPF lungs and that POU2AF1-knockout mice were protected from bleomycin-induced lung fibrosis. Our results reveal distinct regulation of gene expression changes in IPF tissue that remained structurally normal compared with moderate or advanced fibrosis and suggest distinct regulatory mechanisms for each stage.

COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.

METHODS

Four key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.

RESULTS

Using smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.

CONCLUSIONS

A substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.