Transcriptomic evaluation of skin tape-strips in children with allergic asthma uncovers epidermal barrier dysfunction and asthma-associated biomarkers abnormalities.

INTRODUCTION: Tape-strips, a minimally invasive method validated for the evaluation of several skin diseases, may help identify asthma-specific biomarkers in the skin of children with allergic asthma. METHODS: Skin tape-strips were obtained and analyzed with RNA-Seq from children with moderate allergic asthma (MAA) (n = 11, mean age 7.00; SD = 1.67), severe allergic asthma (SAA) (n = 9, mean age 9.11; SD = 2.37), and healthy controls (HCs) (n = 12, mean age 7.36; SD = 2.03). Differentially expressed genes (DEGs) were identified by fold change ≥2 with a false discovery rate <0.05. Transcriptomic biomarkers were analyzed for their accuracy in distinguishing asthma from HCs, their relationships with asthma-related outcomes (exacerbation rate, lung function-FEV1, IOS-R5-20, and lung inflammation-FeNO), and their links to skin (barrier and immune response) and lung (remodeling, metabolism, aging) pathogenetic pathways. RESULTS: RNA-Seq captured 1113 in MAA and 2117 DEGs in SAA. Epidermal transcriptomic biomarkers for terminal differentiation (FLG/filaggrin), cell adhesion (CDH19, JAM2), lipid biosynthesis/metabolism (ACOT2, LOXL2) were significantly downregulated. Gene set variation analysis revealed enrichment of Th1/IFNγ pathways (p < .01). MAA and SAA shared downregulation of G-protein-coupled receptor (OR4A16, TAS1R3), upregulation of TGF-ß/ErbB signaling-related (ACVR1B, EGFR, ID1/2), and upregulation of mitochondrial-related (HIGD2A, VDAC3, NDUFB9) genes. Skin transcriptomic biomarkers correlated with the annualized exacerbation rate and with lung function parameters. A two-gene classifier (TSSC4-FAM212B) was able to differentiate asthma from HCs with 100% accuracy. CONCLUSION: Tape-strips detected epithelial barrier and asthma-associated signatures in normal-appearing skin from children with allergic asthma and may serve as an alternative to invasive approaches for evaluating asthma endotypes.

Transcriptomic and clinical heterogeneity of metastatic disease timing within metastatic castration-sensitive prostate cancer.

BACKGROUND: Metastatic castration-sensitive prostate cancer (mCSPC) is commonly classified into high- and low-volume subgroups which have demonstrated differential biology, prognosis, and response to therapy. Timing of metastasis has similarly demonstrated differences in clinical outcomes; however, less is known about any underlying biologic differences between these disease states. Herein, we aim to compare transcriptomic differences between synchronous and metachronous mCSPC and identify any differential responses to therapy. PATIENTS AND METHODS: We performed an international multi-institutional retrospective review of men with mCSPC who completed RNA expression profiling evaluation of their primary tumor. Patients were stratified according to disease timing (synchronous versus metachronous). The primary endpoint was to identify differences in transcriptomic profiles between disease timing. The median transcriptomic scores between groups were compared with the Mann-Whitney U test. Secondary analyses included determining clinical and transcriptomic variables associated with overall survival (OS) from the time of metastasis. Survival analysis was carried out with the Kaplan-Meier method and multivariable Cox regression. RESULTS: A total of 252 patients were included with a median follow-up of 39.6 months. Patients with synchronous disease experienced worse 5-year OS (39% versus 79%; P < 0.01) and demonstrated lower median androgen receptor (AR) activity (11.78 versus 12.64; P < 0.01) and hallmark androgen response (HAR; 3.15 versus 3.32; P < 0.01). Multivariable Cox regression identified only high-volume disease [hazard ratio (HR) = 4.97, 95% confidence interval (CI) 2.71-9.10; P < 0.01] and HAR score (HR = 0.51, 95% CI 0.28-0.88; P = 0.02) significantly associated with OS. Finally, patients with synchronous (HR = 0.47, 95% CI 0.30-0.72; P < 0.01) but not metachronous (HR = 1.37, 95% CI 0.50-3.92; P = 0.56) disease were found to have better OS with AR and non-AR combination therapy as compared with monotherapy (P value for interaction = 0.05). CONCLUSIONS: We have demonstrated a potential biologic difference between metastatic timing of mCSPC. Specifically, for patients with low-volume disease, those with metachronous low-volume disease have a more hormone-dependent transcriptional profile and exhibit a better prognosis than synchronous low-volume disease.

Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis.

The fight against the spread of antibiotic resistance is one of the most important challenges facing health systems worldwide. Given the limitations of current diagnostic methods, the development of fast and accurate tests for the diagnosis of viral and bacterial infections would improve patient management and treatment, as well as contribute to reducing antibiotic misuse in clinical settings. In this scenario, analysis of host transcriptomics constitutes a promising target to develop new diagnostic tests based on the host-specific response to infections. We carried out a multi-cohort meta-analysis of blood transcriptomic data available in public databases, including 11 different studies and 1209 samples from virus- (n = 695) and bacteria- (n = 514) infected patients. We applied a Parallel Regularized Regression Model Search (PReMS) on a set of previously reported genes that distinguished viral from bacterial infection to find a minimum gene expression bio-signature. This strategy allowed us to detect three genes, namely BAFT, ISG15 and DNMT1, that clearly differentiate groups of infection with high accuracy (training set: area under the curve (AUC) 0.86 (sensitivity: 0.81; specificity: 0.87); testing set: AUC 0.87 (sensitivity: 0.82; specificity: 0.86)). BAFT and ISG15 are involved in processes related to immune response, while DNMT1 is related to the preservation of methylation patterns, and its expression is modulated by pathogen infections. We successfully tested this three-transcript signature in the 11 independent studies, demonstrating its high performance under different scenarios. The main advantage of this three-gene signature is the low number of genes needed to differentiate both groups of patient categories.

Baseline Expression of Immune Gene Modules in Blood is Associated With Primary Response to Anti-TNF Therapy in Crohn's Disease Patients.

BACKGROUND AND AIMS: Anti-tumour necrosis factor [anti-TNF] therapy is widely used for the treatment of inflammatory bowel disease, yet many patients are primary non-responders, failing to respond to induction therapy. We aimed to identify blood gene expression differences between primary responders and primary non-responders to anti-TNF monoclonal antibodies [infliximab and adalimumab], and to predict response status from blood gene expression and clinical data. METHODS: The Personalised Anti-TNF Therapy in Crohn's Disease [PANTS] study is a UK-wide prospective observational cohort study of anti-TNF therapy outcome in anti-TNF-naive Crohn's disease patients [ClinicalTrials.gov identifier: NCT03088449]. Blood gene expression in 324 unique patients was measured by RNA-sequencing at baseline [week 0], and at weeks 14, 30, and 54 after treatment initiation [total sample size = 814]. RESULTS: After adjusting for clinical covariates and estimated blood cell composition, baseline expression of major histocompatibility complex, antigen presentation, myeloid cell enriched receptor, and other innate immune gene modules was significantly higher in anti-TNF responders vs non-responders. Expression changes from baseline to week 14 were generally of consistent direction but greater magnitude [i.e. amplified] in responders, but interferon-related genes were upregulated uniquely in non-responders. Expression differences between responders and non-responders observed at week 14 were maintained at weeks 30 and 54. Prediction of response status from baseline clinical data, cell composition, and module expression was poor. CONCLUSIONS: Baseline gene module expression was associated with primary response to anti-TNF therapy in PANTS patients. However, these baseline expression differences did not predict response with sufficient sensitivity for clinical use.

A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction.

BACKGROUND: Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD. METHODS: In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI). RESULTS: Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05). CONCLUSIONS: Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.

A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades.

Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.

Increased mRNA Levels of ADAM17, IFITM3, and IFNE in Peripheral Blood Cells Are Present in Patients with Obesity and May Predict Severe COVID-19 Evolution.

Gene expression patterns in blood cells from SARS-CoV-2 infected individuals with different clinical phenotypes and body mass index (BMI) could help to identify possible early prognosis factors for COVID-19. We recruited patients with COVID-19 admitted in Hospital Universitari Son Espases (HUSE) between March 2020 and November 2021, and control subjects. Peripheral blood cells (PBCs) and plasma samples were obtained on hospital admission. Gene expression of candidate transcriptomic biomarkers in PBCs were compared based on the patients' clinical status (mild, severe and critical) and BMI range (normal weight, overweight, and obesity). mRNA levels of ADAM17, IFITM3, IL6, CXCL10, CXCL11, IFNG and TYK2 were increased in PBCs of COVID-19 patients (n = 73) compared with controls (n = 47), independently of sex. Increased expression of IFNE was observed in the male patients only. PBC mRNA levels of ADAM17, IFITM3, CXCL11, and CCR2 were higher in those patients that experienced a more serious evolution during hospitalization. ADAM17, IFITM3, IL6 and IFNE were more highly expressed in PBCs of patients with obesity. Interestingly, the expression pattern of ADAM17, IFITM3 and IFNE in PBCs was related to both the severity of COVID-19 evolution and obesity status, especially in the male patients. In conclusion, gene expression in PBCs can be useful for the prognosis of COVID-19 evolution.

Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy.

BACKGROUND: Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient. METHODS: We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers. RESULTS: Overall, "Immune-checkpoint molecule" biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the "Effector molecule" biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy. CONCLUSIONS: Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.

Transcriptomic biomarker pathways associated with death in HIV-infected patients with cryptococcal meningitis.

BACKGROUND: Cryptococcal meningitis (CM) is a major cause of death in HIV-infected patients in sub-Saharan Africa. Many CM patients experience cryptococcosis-associated immune reconstitution inflammatory syndrome (C-IRIS), which is often fatal. We sought to identify transcriptomic biomarker pathways in peripheral blood that are associated with or predict the development of death or fatal C-IRIS among patients with CM who were enrolled in the Cryptococcal Optimal ART Timing Trial. METHODS: We assessed peripheral blood gene expression using next-generation RNA sequencing in 4 groups of patients with CM: (1) no C-IRIS or Death; (2) C-IRIS survivors; (3) fatal C-IRIS; (4) Death without C-IRIS. Gene expression was assessed at the time of ART initiation, at 1, 4, and 8 weeks on ART, and at the time of C-IRIS events. RESULTS: We identified 12 inflammatory and stress response pathways, including interferon type 1 signaling, that were upregulated at the time of ART initiation in patients with future fatal C-IRIS, as compared with survivors. The upregulation of transcripts involved in innate immunity (inflammasome, Toll-like receptor signaling), was observed at the time of fatal or nonfatal C-IRIS events. At the time of fatal C-IRIS events, numerous transcripts within fMLP, Rho family GTPases, HMGB1, and other acute phase response signaling pathways were upregulated, which reflects the severity of inflammation and systemic oxidative stress. Patients who died without recognized C-IRIS also had increased expression of pathways associated with oxidative stress and tissue damage. CONCLUSIONS: Our results showed that overactivated innate immunity, involving Toll-like receptor/inflammasome pathways, and inflammation-induced oxidative stress, are associated with fatal outcomes. The results of this study provide insight into the molecular drivers of death and fatal C-IRIS to inform future diagnostic test development or guide targeted treatments.

Blood transcriptome profiling as potential biomarkers of suboptimal health status: potential utility of novel biomarkers for predictive, preventive, and personalized medicine strategy.

The early identification of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) in chronic diseases. Previous studies have observed the alterations in several mRNA levels in SHS individuals. As a promising "omics" technology offering comprehension of genome structure and function at RNA level, transcriptome profiling can provide innovative molecular biomarkers for the predictive identification and targeted prevention of SHS. To explore the potential biomarkers, biological functions, and signalling pathways involved in SHS, an RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Transcriptome analysis identified a total of 46 differentially expressed genes (DEGs), in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts were selected as candidate predictive biomarkers for SHS. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette (ABC) transporter and neurodegeneration. Protein-protein interaction (PPI) network analysis identified 10 hub genes related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM, and TMEM98. A transcriptome predictive model can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3% (95% confidence interval (CI): 73.9-92.7%), a specificity of 90.0% (95% CI: 82.4-97.6%), and an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.882-0.994). In the present study, we demonstrated that blood (buffy coat) samples appear to be a very promising and easily accessible biological material for the transcriptomic analyses focused on the objective identification of SHS by using our transcriptome predictive model. The pattern of particularly determined DEGs can be used as predictive transcriptomic biomarkers for the identification of SHS in an individual who may, subjectively, feel healthy, but at the level of subcellular mechanisms, the changes can provide early information about potential health problems in this person. Our findings also indicate the potential therapeutic targets in dealing with chronic diseases related to SHS, such as T2DM and CVD, and an early onset of neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, as well as the findings suggest the targets for personalized interventions as promoted in PPPM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00238-1.

Automation of RNA-based biomarker extraction from dried blood spots for the detection of blood doping.

Aim: Transcriptomic biomarkers originating from reticulocytes measured in dried blood spots (DBSs) may be reliable indicators of blood doping. Methods/results: Here, we examined changes in the expression levels of the erythropoiesis-related ALAS2, CA1 and SLC4A1 genes in DBS samples from elite athletes and volunteers of clinical study with recombinant erythropoietin dose. Conclusion: By comparing the mean intraday coefficients of variation for ALAS2L, ALASLC, CA1 and SLC4A1 between manual and automated RNA extractions, an average improvement was observed, whereas the assessment of interday variability provided comparable results for both manual and automated approaches. Our results confirmed that RNA biomarkers on DBS support are efficient to detect blood doping.

Transcriptomic Predictors of Paradoxical Cryptococcosis-Associated Immune Reconstitution Inflammatory Syndrome.

BACKGROUND: Paradoxical cryptococcosis-associated immune reconstitution inflammatory syndrome (C-IRIS) affects ~25% of human immunodeficiency virus (HIV)-infected patients with cryptococcal meningitis (CM) after they commence antiretroviral therapy (ART) resulting in significant morbidity and mortality. Genomic studies in cryptococcal meningitis and C-IRIS are rarely performed. METHODS: We assessed whole blood transcriptomic profiles in 54 HIV-infected subjects with CM who developed C-IRIS (27) and compared the results with control subjects (27) who did not experience neurological deterioration over 24 weeks after ART initiation. Samples were analyzed by whole genome microarrays. RESULTS: The predictor screening algorithms identified the low expression of the components of interferon-driven antiviral defense pathways, such as interferon-inducible genes, and higher expression of transcripts that encode granulocyte-dependent proinflammatory response molecules as predictive biomarkers of subsequent C-IRIS. Subjects who developed early C-IRIS (occurred within 12 weeks of ART initiation) were characterized by upregulation of biomarker transcripts involved in innate immunity such as the inflammasome pathway, whereas those with late C-IRIS events (after 12 weeks of ART) were characterized by abnormal upregulation of transcripts expressed in T, B, and natural killer cells, such as IFNG, IL27, KLRB1, and others. The AIM2, BEX1, and C1QB were identified as novel biomarkers for both early and late C-IRIS events. CONCLUSIONS: An inability to mount effective interferon-driven antiviral immune response, accompanied by a systemic granulocyte proinflammatory signature, prior to ART initiation, predisposes patients to the development of C-IRIS. Although early and late C-IRIS have seemingly similar clinical manifestations, they have different molecular phenotypes (as categorized by bioinformatics analysis) and are driven by contrasting inflammatory signaling cascades.