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.

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.

Disentangling Predictors of COPD Mortality with Probabilistic Graphical Models.

Background-Research question: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in COPD patients can be important for disease management strategies. Although scores for all-cause mortality have been developed previously, there is limited research on factors that may directly affect COPD-specific mortality. Study design-Methods: used probabilistic (causal) graphs to analyze clinical baseline COPDGene data, including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data (from year-5). Results: We identified factors linked to all-cause and COPD-specific mortality. Although many were similar, there were differences in certain comorbidities (all-cause mortality model only) and forced vital capacity (COPD-specific mortality model only). Using our results, we developed VAPORED , a 7-variable COPD-specific mortality risk score, which we validated using the ECLIPSE 3-yr mortality data. We showed that the new model is more accurate than the existing ADO, BODE, and updated BODE indices. Additionally, we identified biological signatures linked to all-cause mortality, including a plasma cell mediated component. Finally, we developed a web page to help clinicians calculate mortality risk using VAPORED, ADO, and BODE indices. Interpretation: Given the importance of predicting COPD-specific and all-cause mortality risk in COPD patients, we showed that probabilistic graphs can identify the features most directly affecting them, and be used to build new, more accurate models of mortality risk. Novel biological features affecting mortality were also identified. This is an important step towards improving our identification of high-risk patients and potential biological mechanisms that drive COPD mortality.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.