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Fibrotic interstitial lung diseases (ILDs) result from excessive deposition of extracellular matrix (ECM) proteins in the lung, causing irreversible damage to the lung architecture. Clinical management of ILDs differs depending on the diagnosis, but differentiation between subtypes can be difficult and better clinical biomarkers are needed. In this study, we use a 166-gene NanoString assay to investigate whether there are ILD subtype-specific transcripts in whole blood. We identified one transcript, killer cell lectin like receptor 1 (KLRF1), as differentially expressed between idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-associated ILD (SSc-ILD), and identified two transcripts (VCAN, LTK) associated with IPF expression against other ILD subtypes. These findings were validated by examining their expression in ILD lung, with KLRF1 expression significantly higher in SSc-ILD compared to IPF and hypersensitivity pneumonitis (HP) samples. Taken together, this pilot study provides support for the use of the peripheral transcriptome in identifying diagnostic biomarkers of ILD with biological relevance. [ABSTRACT FROM AUTHOR]

Rationale: The factors that lead to poor pulmonary exacerbation (PEx) outcomes in individuals with cystic fibrosis (CF) are still being investigated; however, delayed diagnosis and treatment are likely contributory. Identifying individuals at imminent risk of PEx could enable closer monitoring and/or earlier initiation of therapies to improve outcomes. Objectives: The goal of this study was to develop blood-based biomarkers that associate with imminent PEx risk in individuals with CF. Methods: We examined the whole-blood transcriptome and 55 inflammatory proteins from plasma and serum on 72 blood samples from 53 individuals with CF. Biomarker candidate genes and proteins were selected from 14 individuals with CF with paired stable and PEx visits (cohort 1). The biomarker candidates were then estimated and tested to classify individuals with CF who would experience a PEx within 4 months of a stable clinic visit or not (cohort 2). Results: A 16-gene panel and 9-protein panel were identified that could distinguish paired stable and PEx visits (area under the receiver operating characteristic curve [AUC] ± standard error = 0.83 ± 0.28 and 0.92 ± 0.18, respectively). These two panels also demonstrated strong performance in classifying individuals with CF who would experience a PEx within 4 months of a clinically stable visit or not (16-gene panel: AUC = 0.88; 9-protein panel: AUC = 0.83). In comparison, serum calprotectin and clinical variables (i.e., sex, precent predicted forced expiratory volume in 1 s, and the number of IV antibiotics in the preceding year) had AUCs of 0.75 and 0.71, respectively. Conclusions: Blood-based mRNA and protein biomarkers demonstrated strong performance in classifying individuals with CF at risk of imminent PEx. If the findings from this study can be validated, there is the potential to use blood biomarkers to enable more personalized disease activity monitoring in CF. [ABSTRACT FROM AUTHOR]

Previously incarcerated youth reintegrating into their communities face many challenges. This study provided visual entry into the lives of three youth through the use of Photovoice methodology. Participants included three males, two Latinos and one White non-Latino, who had successfully been reentered into their communities for at least four months. During the eight-week program, participants photographed images of importance to them in their homes and communities. These were discussed and ultimately shared through a community photo exhibit. The resulting themes, related to their perceived supports and challenges to successful reentry, are presented with implications for supporting youth in their community reintegration.

Over the past few decades, diversity and multiculturalism have received considerable attention in the field of psychology. While there have been notable efforts to ensure these important areas are addressed in undergraduate psychology training, little is known about this undertaking. The present study examined how diversity and multiculturalism were addressed in the course titles and course descriptions of 200 undergraduate, psychology programs across North America and their corresponding general education programs. Analyses revealed that while most undergraduate psychology programs offered diversity or multicultural courses, very few programs required these courses. Moreover, when these courses were offered, they typically examined diversity or multiculturalism in a singular fashion, giving very little attention to the intersectionality that exists among cultural factors. With respect to the institutions' general education programs, we found that while some institutions had a multicultural awareness requirement, few offered or required psychology-based diversity or multicultural courses of their psychology students. Implications of these finding are considered, and several recommendations and resources for improving undergraduate psychology programs are provided.

Understanding the epidemiology of long COVID and emerging variants has significant public-health implications as physical interventions and restrictions that help limit viral spread are eased globally. Here, we provide rationales for the necessity of updating current vaccines to improve protection against omicron and emerging variants, as well as more research into understanding the epidemiology and mechanisms of long COVID. [ABSTRACT FROM AUTHOR]

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual vaccine effectiveness suggests that a better mechanistic understanding of vaccine-induced immune responses could improve vaccine design and efficacy. We have previously shown that protective antibody levels could be elicited in a subset of recipients with only a single dose of the hepatitis B virus (HBV) vaccine and that a wide range of antibody levels were elicited after three doses. The immune mechanisms responsible for this vaccine response variability is unclear. Using single cell RNA sequencing of sorted innate immune cell subsets, we identified two distinct myeloid dendritic cell subsets (NDRG1-expressing mDC2 and CDKN1C-expressing mDC4), the ratio of which at baseline (pre-vaccination) correlated with the immune response to a single dose of HBV vaccine. Our results suggest that the participants in our vaccine study were in one of two different dendritic cell dispositional states at baseline – an NDRG2-mDC2 state in which the vaccine elicited an antibody response after a single immunization or a CDKN1C-mDC4 state in which the vaccine required two or three doses for induction of antibody responses. To explore this correlation further, genes expressed in these mDC subsets were used for feature selection prior to the construction of predictive models using supervised canonical correlation machine learning. The resulting models showed an improved correlation with serum antibody titers in response to full vaccination. Taken together, these results suggest that the propensity of circulating dendritic cells toward either activation or suppression, their "dispositional endotype" at pre-vaccination baseline, could dictate response to vaccination. [ABSTRACT FROM AUTHOR]

The Expanded Program for Immunization Consortium – Human Immunology Project Consortium study aims to employ systems biology to identify and characterize vaccine-induced biomarkers that predict immunogenicity in newborns. Key to this effort is the establishment of the Data Management Core (DMC) to provide reliable data and bioinformatic infrastructure for centralized curation, storage, and analysis of multiple de-identified "omic" datasets. The DMC established a cloud-based architecture using Amazon Web Services to track, store, and share data according to National Institutes of Health standards. The DMC tracks biological samples during collection, shipping, and processing while capturing sample metadata and associated clinical data. Multi-omic datasets are stored in access-controlled Amazon Simple Storage Service (S3) for data security and file version control. All data undergo quality control processes at the generating site followed by DMC validation for quality assurance. The DMC maintains a controlled computing environment for data analysis and integration. Upon publication, the DMC deposits finalized datasets to public repositories. The DMC architecture provides resources and scientific expertise to accelerate translational discovery. Robust operations allow rapid sharing of results across the project team. Maintenance of data quality standards and public data deposition will further benefit the scientific community. [ABSTRACT FROM AUTHOR]

Background: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. Methods: We applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres. Results: Using both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response. Conclusion: This study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets. [ABSTRACT FROM AUTHOR]

Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches—NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design. [ABSTRACT FROM AUTHOR]

The global pandemic of COVID-19 cases caused by infection with SARS-CoV-2 is ongoing, with no approved antiviral intervention. We describe here the effects of treatment with interferon (IFN)-α2b in a cohort of confirmed COVID-19 cases in Wuhan, China. In this uncontrolled, exploratory study, 77 adults hospitalized with confirmed COVID-19 were treated with either nebulized IFN-α2b (5 mU b.i.d.), arbidol (200 mg t.i.d.) or a combination of IFN-α2b plus arbidol. Serial SARS-CoV-2 testing along with hematological measurements, including cell counts, blood biochemistry and serum cytokine levels, and temperature and blood oxygen saturation levels, were recorded for each patient during their hospital stay. Treatment with IFN-α2b with or without arbidol significantly reduced the duration of detectable virus in the upper respiratory tract and in parallel reduced duration of elevated blood levels for the inflammatory markers IL-6 and CRP. These findings suggest that IFN-α2b should be further investigated as a therapy in COVID-19 cases. [ABSTRACT FROM AUTHOR]

The vaccine that keeps on giving: Bacille Calmette-Guérin (BCG) is a tuberculosis vaccine that is frequently given to infants in countries with a high incidence of tuberculosis infection. However, emerging evidence suggests that this vaccine may have additional beneficial effects for the infants. Following on previous observations of reduced neonatal mortality in BCG-vaccinated infants, Brook et al. performed a systematic study of mouse models and human infants in multiple countries. The authors demonstrated that BCG vaccine given during the neonatal period protected the infants from death due to sepsis and showed that this could be attributed to a rapid increase in neutrophils driven by the vaccine. Death from sepsis in the neonatal period remains a serious threat for millions. Within 3 days of administration, bacille Calmette-Guérin (BCG) vaccination can reduce mortality from neonatal sepsis in human newborns, but the underlying mechanism for this rapid protection is unknown. We found that BCG was also protective in a mouse model of neonatal polymicrobial sepsis, where it induced granulocyte colony-stimulating factor (G-CSF) within hours of administration. This was necessary and sufficient to drive emergency granulopoiesis (EG), resulting in a marked increase in neutrophils. This increase in neutrophils was directly and quantitatively responsible for protection from sepsis. Rapid induction of EG after BCG administration also occurred in three independent cohorts of human neonates. [ABSTRACT FROM AUTHOR]

Motivation In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present D ata I ntegration A nalysis for B iomarker discovery using L atent c O mponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. Results Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. Availability and implementation DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters' choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes.Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets.Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression.Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD.Trial Registration: The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960. [ABSTRACT FROM AUTHOR]

Background: Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarkers. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic cellular heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, specific cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can further complicate the sample collection process, however, and result in additional costs. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. Results: We present 'Enumerateblood', a freely-available and open source R package that exposes a multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles, outperforming other current methods when applied to Gene ST data. Conclusions: 'Enumerateblood' significantly improves our ability to study disease pathobiology from whole blood gene expression assayed on the popular Affymetrix Gene ST platform by allowing a more complete study of the various components of this complex tissue without the need for additional data collection. Future use of the model may allow for novel insights to be generated from the ~400 Affymetrix Gene ST blood gene expression datasets currently available on the Gene Expression Omnibus (GEO) website. [ABSTRACT FROM AUTHOR]

Background: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. Results: The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. Conclusions: The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues. [ABSTRACT FROM AUTHOR]

Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell type-specific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, in silico deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissue-specificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay. [ABSTRACT FROM AUTHOR]

Background: The molecular profile of circulating blood can reflect physiological and pathological events occurring in other tissues and organs of the body and delivers a comprehensive view of the status of the immune system. Blood has been useful in studying the pathobiology of many diseases. It is accessible and easily collected making it ideally suited to the development of diagnostic biomarker tests. The blood transcriptome has a high complement of globin RNA that could potentially saturate next-generation sequencing platforms, masking lower abundance transcripts. Methods to deplete globin mRNA are available, but their effect has not been comprehensively studied in peripheral whole blood RNA-Seq data. In this study we aimed to assess technical variability associated with globin depletion in addition to assessing general technical variability in RNA-Seq from whole blood derived samples. Results: We compared technical and biological replicates having undergone globin depletion or not and found that the experimental globin depletion protocol employed removed approximately 80% of globin transcripts, improved the correlation of technical replicates, allowed for reliable detection of thousands of additional transcripts and generally increased transcript abundance measures. Differential expression analysis revealed thousands of genes significantly up-regulated as a result of globin depletion. In addition, globin depletion resulted in the down-regulation of genes involved in both iron and zinc metal ion bonding. Conclusions: Globin depletion appears to meaningfully improve the quality of peripheral whole blood RNA-Seq data, and may improve our ability to detect true biological variation. Some concerns remain, however. Key amongst them the significant reduction in RNA yields following globin depletion. More generally, our investigation of technical and biological variation with and without globin depletion finds that high-throughput sequencing by RNA-Seq is highly reproducible within a large dynamic range of detection and provides an accurate estimation of RNA concentration in peripheral whole blood. High-throughput sequencing is thus a promising technology for whole blood transcriptomics and biomarker discovery. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets. Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression. Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD. Trial registration The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960 Electronic supplementary material The online version of this article (doi:10.1186/s12931-017-0558-1) contains supplementary material, which is available to authorized users.

Acute cardiac allograft rejection is a serious complication of heart transplantation. Investigating molecular processes in whole blood via microarrays is a promising avenue of research in transplantation, particularly due to the non-invasive nature of blood sampling. However, whole blood is a complex tissue and the consequent heterogeneity in composition amongst samples is ignored in traditional microarray analysis. This complicates the biological interpretation of microarray data. Here we have applied a statistical deconvolution approach, cell-specific significance analysis of microarrays (csSAM), to whole blood samples from subjects either undergoing acute heart allograft rejection (AR) or not (NR). We identified eight differentially expressed probe-sets significantly correlated to monocytes (mapping to 6 genes, all down-regulated in ARs versus NRs) at a false discovery rate (FDR) ⩽ 15%. None of the genes identified are present in a biomarker panel of acute heart rejection previously published by our group and discovered in the same data. [ABSTRACT FROM AUTHOR]

Background: Methacholine challenge is commonly used within the asthma diagnostic algorithm. Methacholine challenge has recently been shown to induce airway remodelling in asthma via bronchoconstriction, without additional airway inflammation. We evaluated the effect of methacholine-induced bronchoconstriction on the peripheral whole-blood transcriptome. Methods: Fourteen males with adult-onset, occupational asthma, 26-77 years of age, underwent methacholine inhalation challenges. The concentration of methacholine eliciting a ≥20% fall in FEV1 (PC20) was determined. Blood was collected immediately prior to and two hours after challenge. Complete blood counts and leukocyte differentials were obtained. Transcriptome analysis was performed using Affymetrix GeneChip® Human Gene 1.0 ST arrays. Data were analyzed using robust LIMMA and SAM. The cell-specific Significance Analysis of Microarrays (csSAM) algorithm was used to deconvolute the gene expression data according to cell type. Results: Microarray pathway analysis indicated that inflammatory processes were differentially affected. CsSAM identified 1,559 transcripts differentially expressed (all down-regulated) between pre- and post-methacholine in eosinophils at a false discovery cutoff of 10%. Notable changes included the GOLGA5 and METTL2B genes and the protein ubiquitination and CCR3 pathways. Conclusions: We demonstrated significant changes in the peripheral blood eosinophil-specific transcriptome of asthmatics two hours after methacholine challenge. CCR3 and protein ubiquitination pathways are both significantly down-regulated. [ABSTRACT FROM AUTHOR]

Transplantation of exogenous cells is one approach to spinal cord repair that could potentially enhance the growth and myelination of endogenous axons. Here, we asked whether skin-derived precursors (SKPs), a neural crest-like precursor that can be isolated and expanded from mammalian skin, could be used to repair the injured rat spinal cord. To ask this question, we isolated and expanded genetically tagged murine SKPs and either transplanted them directly into the contused rat spinal cord or differentiated them into Schwann cells (SCs), and performed similar transplantations with the isolated, expanded SKP-derived SCs. Neuroanatomical analysis of these transplants 12 weeks after transplantation revealed that both cell types survived well within the injured spinal cord, reduced the size of the contusion cavity, myelinated endogenous host axons, and recruited endogenous SCs into the injured cord. However, SKP-derived SCs also provided a bridge across the lesion site, increased the size of the spared tissue rim, myelinated spared axons within the tissue rim, reduced reactive gliosis, and provided an environment that was highly conducive to axonal growth. Importantly, SKP-derived SCs provided enhanced locomotor recovery relative to both SKPs and forebrain subventricular zone neurospheres, and had no impact on mechanical or heat sensitivity thresholds. Thus, SKP-derived SCs provide an accessible, potentially autologous source of cells for transplantation into and treatment of the injured spinal cord. [ABSTRACT FROM AUTHOR]

Corrigendum: Interferon- 2b Treatment for COVID-19. Interferon, COVID-19, viral shedding, IL-6, inflammation, ARDS. [Extracted from the article]

BACKGROUND. Patients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. METHODS. We performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes. RESULTS. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness. CONCLUSION. Our longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill. [ABSTRACT FROM AUTHOR]

Rationale: Individuals with allergic asthma respond differently, but reproducibly, to allergen inhalation challenge. Some individuals develop an isolated early response (early responders) (ERs), whereas others go on to develop a late response (dual responders) (DRs). It is not understood why late responses do not develop in all sensitized individuals.Objectives: The aim of this study was to identify blood biomarkers that can discriminate ERs and DRs using cellular frequencies and gene and metabolite expression from whole blood.Methods: Thirty-two individuals participated in the allergen inhalation challenge as part of the AllerGen Clinical Investigator Collaborative. Fifteen participants were classified as ERs and 17 as DRs. Blood samples were collected before (pre) and 2 hours after (post) the allergen challenge. Cell counts were obtained using a hematolyzer, gene transcript relative levels using RNA sequencing, and metabolite concentrations using tandem mass spectrometry. An integrative ensemble algorithm that was based on canonical correlation analysis was used to classify ERs and DRs using all three data sets, adjusting for age and sex. The objective of this algorithm was to identify a correlated subset of molecules from each data set that best discriminated ERs from DRs. Gene set enrichment analysis was performed using Enrichr (Chen et al., BMC Bioinformatics 2013;128).Measurements and Main Results: The pre-challenge multisignature classifier (error = 30%) outperformed the post-challenge multisignature classifier (error = 50%) in separating ERs from DRs. The cells selected in the prechallenge multisignature panel included eosinophils, lymphocytes, and neutrophils. The selected metabolites were enriched for glycerophospholipids. The subset of gene transcripts in the multisignature panel was enriched for the T-cell receptor and costimulatory signaling pathway (P = 3.4 × 10(-6)) (Wikipathways) and positive regulation of antigen receptor-mediated signaling pathway (P = 5.7 × 10(-4)) (GO Ontology).Conclusions: This study provides a systems perspective on the deregulated molecular processes between early and dual responses in whole blood. The integrative biomarker analysis suggests that a molecular signature that is predictive of the late-phase response can be identified. The variability in the onset of the late response may explain the poor predictive performance of the postchallenge multiomic biomarker signature. Replication of the prechallenge biomarker signature in additional independent samples is required to validate this panel. [ABSTRACT FROM AUTHOR]

Nasal allergen challenge (NAC) is a human model of allergic rhinitis (AR) that delivers standardized allergens locally to the nasal mucosa allowing clinical symptoms and biospecimens such as peripheral blood to be collected. Although many studies have focused on local inflammatory sites, peripheral blood, an important mediator and a component of the systemic immune response, has not been well studied in the setting of AR. We sought to investigate immune gene signatures in peripheral blood collected after NAC under the setting of AR. Clinical symptoms and peripheral blood samples from AR subjects were collected during NAC. Fuzzy c-means clustering method was used to identify immune gene expression patterns in blood over time points (before NAC and 1, 2, and 6 h after NAC).We identified and validated seven clusters of differentially expressed immune genes after NAC onset. Clusters 2, 3, and 4 were associated with neutrophil and lymphocyte frequencies and neutrophil/lymphocyte ratio after the allergen challenge. The patterns of the clusters and immune cell frequencies were associated with the clinical symptoms of the AR subjects and were significantly different from healthy nonallergic subjects who had also undergone NAC. Our approach identified dynamic signatures of immune gene expression in blood as a systemic immune response associated with clinical symptoms after NAC. The immune gene signatures may allow cross-sectional investigation of the pathophysiology of AR and may also be useful as a potential objective measurement for diagnosis and treatment of AR combined with the NAC model. [ABSTRACT FROM AUTHOR]

In both high- and low-income countries, HIV-negative children born to HIV-positive mothers (HIV exposed, uninfected [HEU]) are more susceptible to severe infection than HIV-unexposed, uninfected (HUU) children, with altered innate immunity hypothesized to be a cause. Both the gut microbiome and systemic innate immunity differ across biogeographically distinct settings, and the two are known to influence each other. And although the gut microbiome is influenced by HIV infection and may contribute to altered immunity, the biogeography of immune-microbiome correlations among HEU children have not been investigated. To address this, we compared the innate response and the stool microbiome of 2-y-old HEU and HUU children from Belgium, Canada, and South Africa to test the hypothesis that region-specific immune alterations directly correlate to differences in their stool microbiomes. We did not detect a universal immune or microbiome signature underlying differences between HEU versus HUU that was applicable to all children. But as hypothesized, population-specific differences in stool microbiomes were readily detected and included reduced abundances of short-chain fatty acid–producing bacteria in Canadian HEU children. Furthermore, we did not identify innate immune-microbiome associations that distinguished HEU from HUU children in any population. These findings suggest that maternal HIV infection is independently associated with differences in both innate immunity and the stool microbiome in a biogeographical population-specific way. [ABSTRACT FROM AUTHOR]

Objective: Gene expression (transcriptomics) studies have revealed potential mechanisms of interstitial lung disease (ILD), yet sample sizes of studies are often limited and between-subtype comparisons are scarce. The aim of this study was to identify and validate consensus transcriptomic signatures of ILD subtypes., Methods: We performed a systematic review and meta-analysis of fibrotic ILD transcriptomics studies using an individual participant data approach, and included studies examining bulk transcriptomics of human adult ILD samples and excluding those focusing on individual cell populations. Patient-level data and expression matrices were extracted from 43 studies and integrated using a multivariable integrative algorithm to develop ILD classification models., Results: Using 1459 samples from 24 studies, we identified transcriptomic signatures for idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis (HP), idiopathic nonspecific interstitial pneumonia (NSIP), and systemic sclerosis-associated ILD (SSc-ILD) against control samples, which were validated on 308 samples from 8 studies (area under receiver operating curve [AUC]=0.99 [95% CI: 0.99-1.00], HP AUC=0.91 [0.84-0.99], NSIP AUC=0.94 [0.88-0.99], SSc-ILD AUC=0.98 [0.93-1.00]). Significantly, meta-analysis allowed, for the first time, identification of robust lung transcriptomics signatures to discriminate IPF (AUC=0.71 [0.63-0.79]) and HP (AUC=0.76 [0.63-0.89]) from other fibrotic ILDs, and unsupervised learning algorithms identified putative molecular endotypes of ILD associated with decreased forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (D LCO ) % predicted. Transcriptomics signatures were reflective of both cell-specific and disease-specific changes in gene expression., Conclusion: We present the first systematic review and largest meta-analysis of fibrotic ILD transcriptomics to date, identifying reproducible transcriptomic signatures with clinical relevance., (Copyright ©The authors 2025.)

Hospitalized COVID-19 patients exhibit diverse immune responses during acute infection, which are associated with a wide range of clinical outcomes. However, understanding these immune heterogeneities and their links to various clinical complications, especially long COVID, remains a challenge. In this study, we performed unsupervised subtyping of longitudinal multi-omics immunophenotyping in over 1,000 hospitalized patients, identifying two critical subtypes linked to mortality or mechanical ventilation with prolonged hospital stay and three severe subtypes associated with timely acute recovery. We confirmed that unresolved systemic inflammation and T-cell dysfunctions were hallmarks of increased severity and further distinguished patients with similar acute respiratory severity by their distinct immune profiles, which correlated with differences in demographic and clinical complications. Notably, one critical subtype (SubF) was uniquely characterized by early excessive inflammation, insufficient anticoagulation, and fatty acid dysregulation, alongside higher incidences of hematologic, cardiac, and renal complications, and an elevated risk of long COVID. Among the severe subtypes, significant differences in viral clearance and early antiviral responses were observed, with one subtype (SubC) showing strong early T-cell cytotoxicity but a poor humoral response, slower viral clearance, and greater risks of chronic organ dysfunction and long COVID. These findings provide crucial insights into the complex and context-dependent nature of COVID-19 immune responses, highlighting the importance of personalized therapeutic strategies to improve both acute and long-term outcomes.