Methyl-TWAS: A powerful method for in silico transcriptome-wide association studies (TWAS) using long-range DNA methylation.

In silico transcriptome-wide association studies (TWAS) are commonly used to test whether expression of specific genes is linked to a complex trait. However, genotype-based in silico TWAS such as PrediXcan, exhibit low prediction accuracy for a majority of genes because genotypic data lack tissue- and disease-specificity and are not affected by the environment. Because methylation is tissue-specific and, like gene expression, can be modified by environment or disease status, methylation should predict gene expression with more accuracy than SNPs. Therefore, we propose Methyl-TWAS, the first approach that utilizes long-range methylation markers to impute gene expression for in silico TWAS through penalized regression. Methyl-TWAS 1) predicts epigenetically regulated/associated expression (eGReX), which incorporates tissue-specific expression and both genetically- (GReX) and environmentally-regulated expression to identify differentially expressed genes (DEGs) that could not be identified by genotype-based methods; and 2) incorporates both cis- and trans- CpGs, including various regulatory regions to identify DEGs that would be missed using cis- methylation only. Methyl-TWAS outperforms PrediXcan and two other methods in imputing gene expression in the nasal epithelium, particularly for immunity-related genes and DEGs in atopic asthma. Methyl-TWAS identified 3,681 (85.2%) of the 4,316 DEGs identified in a previous TWAS of atopic asthma using measured expression, while PrediXcan could not identify any gene. Methyl-TWAS also outperforms PrediXcan for expression imputation as well as in silico TWAS in white blood cells. Methyl-TWAS is a valuable tool for in silico TWAS, leveraging a growing body of publicly available genome-wide DNA methylation data for a variety of human tissues.

Mortality Risk Factors in Pediatric Onco-Critical Care Patients and Machine Learning Derived Early Onco-Critical Care Phenotypes in a Retrospective Cohort.

OBJECTIVES: To use supervised and unsupervised statistical methodology to determine risk factors associated with mortality in critically ill pediatric oncology patients to identify patient phenotypes of interest for future prospective study. DESIGN: This retrospective cohort study included nonsurgical pediatric critical care admissions from January 2017 to December 2018. We determined the prevalence of multiple organ failure (MOF), ICU mortality, and associated factors. Consensus k-means clustering analysis was performed using 35 bedside admission variables for early, onco-critical care phenotype development. SETTING: Single critical care unit in a subspeciality pediatric hospital. INTERVENTION: None. PATIENTS: There were 364 critical care admissions in 324 patients with underlying malignancy, hematopoietic cell transplant, or immunodeficiency reviewed. MEASUREMENTS: Prevalence of multiple organ failure, ICU mortality, determination of early onco-critical care phenotypes. MAIN RESULTS: ICU mortality was 5.2% and was increased in those with MOF (18.4% MOF, 1.7% single organ failure [SOF], 0.6% no organ failure; p ≤ 0.0001). Prevalence of MOF was 23.9%. Significantly increased ICU mortality risk was associated with day 1 MOF (hazards ratio [HR] 2.27; 95% CI, 1.10-6.82; p = 0.03), MOF during ICU admission (HR 4.16; 95% CI, 1.09-15.86; p = 0.037), and with invasive mechanical ventilation requirement (IMV; HR 5.12; 95% CI, 1.31-19.94; p = 0.018). Four phenotypes were derived (PedOnc1-4). PedOnc1 and 2 represented patient groups with low mortality and SOF. PedOnc3 was enriched in patients with sepsis and MOF with mortality associated with liver and renal dysfunction. PedOnc4 had the highest frequency of ICU mortality and MOF characterized by acute respiratory failure requiring invasive mechanical ventilation at admission with neurologic dysfunction and/or severe sepsis. Notably, most of the mortality in PedOnc4 was early (i.e., within 72 hr of ICU admission). CONCLUSIONS: Mortality was lower than previously reported in critically ill pediatric oncology patients and was associated with MOF and IMV. These findings were further validated and expanded by the four derived nonsynonymous computable phenotypes. Of particular interest for future prospective validation and correlative biological study was the PedOnc4 phenotype, which was composed of patients with hypoxic respiratory failure requiring IMV with sepsis and/or neurologic dysfunction at ICU admission.

Hyperferritinemic sepsis, macrophage activation syndrome, and mortality in a pediatric research network: a causal inference analysis.

BACKGROUND: One of five global deaths are attributable to sepsis. Hyperferritinemic sepsis (> 500 ng/mL) is associated with increased mortality in single-center studies. Our pediatric research network's objective was to obtain rationale for designing anti-inflammatory clinical trials targeting hyperferritinemic sepsis. METHODS: We assessed differences in 32 cytokines, immune depression (low whole blood ex vivo TNF response to endotoxin) and thrombotic microangiopathy (low ADAMTS13 activity) biomarkers, seven viral DNAemias, and macrophage activation syndrome (MAS) defined by combined hepatobiliary dysfunction and disseminated intravascular coagulation, and mortality in 117 children with hyperferritinemic sepsis (ferritin level > 500 ng/mL) compared to 280 children with sepsis without hyperferritinemia. Causal inference analysis of these 41 variables, MAS, and mortality was performed. RESULTS: Mortality was increased in children with hyperferritinemic sepsis (27/117, 23% vs 16/280, 5.7%; Odds Ratio = 4.85, 95% CI [2.55-9.60]; z = 4.728; P-value < 0.0001). Hyperferritinemic sepsis had higher C-reactive protein, sCD163, IL-22, IL-18, IL-18 binding protein, MIG/CXCL9, IL-1ß, IL-6, IL-8, IL-10, IL-17a, IFN-γ, IP10/CXCL10, MCP-1/CCL2, MIP-1α, MIP-1ß, TNF, MCP-3, IL-2RA (sCD25), IL-16, M-CSF, and SCF levels; lower ADAMTS13 activity, sFasL, whole blood ex vivo TNF response to endotoxin, and TRAIL levels; more Adenovirus, BK virus, and multiple virus DNAemias; and more MAS (P-value < 0.05). Among these variables, only MCP-1/CCL2 (the monocyte chemoattractant protein), MAS, and ferritin levels were directly causally associated with mortality. MCP-1/CCL2 and hyperferritinemia showed direct causal association with depressed ex vivo whole blood TNF response to endotoxin. MCP-1/CCL2 was a mediator of MAS. MCP-1/CCL2 and MAS were mediators of hyperferritinemia. CONCLUSIONS: These findings establish hyperferritinemic sepsis as a high-risk condition characterized by increased cytokinemia, viral DNAemia, thrombotic microangiopathy, immune depression, macrophage activation syndrome, and death. The causal analysis provides rationale for designing anti-inflammatory trials that reduce macrophage activation to improve survival and enhance infection clearance in pediatric hyperferritinemic sepsis.

Cis- and trans-eQTM analysis reveals novel epigenetic and transcriptomic immune markers of atopic asthma in airway epithelium.

BACKGROUND: Expression quantitative trait methylation (eQTM) analyses uncover associations between DNA methylation markers and gene expression. Most eQTM analyses of complex diseases have focused on cis-eQTM pairs (within 1 megabase). OBJECTIVES: This study sought to identify cis- and trans-methylation markers associated with gene expression in airway epithelium from youth with and without atopic asthma. METHODS: In this study, the investigators conducted both cis- and trans-eQTM analyses in nasal (airway) epithelial samples from 158 Puerto Rican youth with atopic asthma and 100 control subjects without atopy or asthma. The investigators then attempted to replicate their findings in nasal epithelial samples from 2 studies of children, while also examining whether their results in nasal epithelium overlap with those from an eQTM analysis in white blood cells from the Puerto Rican subjects. RESULTS: This study identified 9,108 cis-eQTM pairs and 2,131,500 trans-eQTM pairs. Trans-associations were significantly enriched for transcription factor and microRNA target genes. Furthermore, significant cytosine-phosphate-guanine sites (CpGs) were differentially methylated in atopic asthma and significant genes were enriched for genes differentially expressed in atopic asthma. In this study, 50.7% to 62.6% of cis- and trans-eQTM pairs identified in Puerto Rican youth were replicated in 2 smaller cohorts at false discovery rate-adjusted P < .1. Replicated genes in the trans-eQTM analysis included biologically plausible asthma-susceptibility genes (eg, HDC, NLRP3, ITGAE, CDH26, and CST1) and are enriched in immune pathways. CONCLUSIONS: Studying both cis- and trans-epigenetic regulation of airway epithelial gene expression can identify potential causal and regulatory pathways or networks for childhood asthma. Trans-eQTM CpGs may regulate gene expression in airway epithelium through effects on transcription factor and microRNA target genes.

Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data.

Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid "one-size-fits-all" approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.

The microRNA target site profile is a novel biomarker in the immunotherapy response.

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

Mortality Risk in Pediatric Sepsis Based on C-reactive Protein and Ferritin Levels.

OBJECTIVES: Interest in using bedside C-reactive protein (CRP) and ferritin levels to identify patients with hyperinflammatory sepsis who might benefit from anti-inflammatory therapies has piqued with the COVID-19 pandemic experience. Our first objective was to identify patterns in CRP and ferritin trajectory among critically ill pediatric sepsis patients. We then examined the association between these different groups of patients in their inflammatory cytokine responses, systemic inflammation, and mortality risks. DATA SOURCES: A prospective, observational cohort study. STUDY SELECTION: Children with sepsis and organ failure in nine pediatric intensive care units in the United States. DATA EXTRACTION: Two hundred and fifty-five children were enrolled. Five distinct clinical multi-trajectory groups were identified. Plasma CRP (mg/dL), ferritin (ng/mL), and 31 cytokine levels were measured at two timepoints during sepsis (median Day 2 and Day 5). Group-based multi-trajectory models (GBMTM) identified groups of children with distinct patterns of CRP and ferritin. DATA SYNTHESIS: Group 1 had normal CRP and ferritin levels ( n = 8; 0% mortality); Group 2 had high CRP levels that became normal, with normal ferritin levels throughout ( n = 80; 5% mortality); Group 3 had high ferritin levels alone ( n = 16; 6% mortality); Group 4 had very high CRP levels, and high ferritin levels ( n = 121; 11% mortality); and Group 5 had very high CRP and very high ferritin levels ( n = 30; 40% mortality). Cytokine responses differed across the five groups, with ferritin levels correlated with macrophage inflammatory protein 1α levels and CRP levels reflective of many cytokines. CONCLUSIONS: Bedside CRP and ferritin levels can be used together to distinguish groups of children with sepsis who have different systemic inflammation cytokine responses and mortality risks. These data suggest future potential value in personalized clinical trials with specific targets for anti-inflammatory therapies.

Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials.

BACKGROUND: Thrombotic microangiopathy-induced thrombocytopenia-associated multiple organ failure and hyperinflammatory macrophage activation syndrome are important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. The National Institutes of General Medical Science sepsis research working group recommendations call for application of new research approaches in extant clinical data sets to improve efficiency of early trials of new sepsis therapies. Our objective is to apply machine learning approaches to derive computable 24-h sepsis phenotypes to facilitate personalized enrollment in early anti-inflammatory trials targeting these conditions. METHODS: We applied consensus, k-means clustering analysis to our extant PHENOtyping sepsis-induced Multiple organ failure Study (PHENOMS) dataset of 404 children. 24-hour computable phenotypes are derived using 25 available bedside variables including C-reactive protein and ferritin. RESULTS: Four computable phenotypes (PedSep-A, B, C, and D) are derived. Compared to all other phenotypes, PedSep-A patients (n = 135; 2% mortality) were younger and previously healthy, with the lowest C-reactive protein and ferritin levels, the highest lymphocyte and platelet counts, highest heart rate, and lowest creatinine (p < 0.05); PedSep-B patients (n = 102; 12% mortality) were most likely to be intubated and had the lowest Glasgow Coma Scale Score (p < 0.05); PedSep-C patients (n = 110; mortality 10%) had the highest temperature and Glasgow Coma Scale Score, least pulmonary failure, and lowest lymphocyte counts (p < 0.05); and PedSep-D patients (n = 56, 34% mortality) had the highest creatinine and number of organ failures, including renal, hepatic, and hematologic organ failure, with the lowest platelet counts (p < 0.05). PedSep-D had the highest likelihood of developing thrombocytopenia-associated multiple organ failure (Adj OR 47.51 95% CI [18.83-136.83], p < 0.0001) and macrophage activation syndrome (Adj OR 38.63 95% CI [13.26-137.75], p < 0.0001). CONCLUSIONS: Four computable phenotypes are derived, with PedSep-D being optimal for enrollment in early personalized anti-inflammatory trials targeting thrombocytopenia-associated multiple organ failure and macrophage activation syndrome in pediatric sepsis. A computer tool for identification of individual patient membership ( www.pedsepsis.pitt.edu ) is provided. Reproducibility will be assessed at completion of two ongoing pediatric sepsis studies.

scMAPA: Identification of cell-type-specific alternative polyadenylation in complex tissues.

BACKGROUND: Alternative polyadenylation (APA) causes shortening or lengthening of the 3'-untranslated region (3'-UTR) of genes (APA genes) in diverse cellular processes such as cell proliferation and differentiation. To identify cell-type-specific APA genes in scRNA-Seq data, current bioinformatic methods have several limitations. First, they assume certain read coverage shapes in the scRNA-Seq data, which can be violated in multiple APA genes. Second, their identification is limited between 2 cell types and not directly applicable to the data of multiple cell types. Third, they do not control undesired source of variance, which potentially introduces noise to the cell-type-specific identification of APA genes. FINDINGS: We developed a combination of a computational change-point algorithm and a statistical model, single-cell Multi-group identification of APA (scMAPA). To avoid the assumptions on the read coverage shape, scMAPA formulates a change-point problem after transforming the 3' biased scRNA-Seq data to represent the full-length 3'-UTR signal. To identify cell-type-specific APA genes while adjusting for undesired source of variation, scMAPA models APA isoforms in consideration of the cell types and the undesired source. In our novel simulation data and data from human peripheral blood mononuclear cells, scMAPA outperforms existing methods in sensitivity, robustness, and stability. In mouse brain data consisting of multiple cell types sampled from multiple regions, scMAPA identifies cell-type-specific APA genes, elucidating novel roles of APA for dividing immune cells and differentiated neuron cells and in multiple brain disorders. CONCLUSIONS: scMAPA elucidates the cell-type-specific function of APA events and sheds novel insights into the functional roles of APA events in complex tissues.

Selection of Optimal Ancestry Informative Markers for Classification and Ancestry Proportion Estimation in Pigs.

Using small sets of ancestry informative markers (AIMs) constitutes a cost-effective method to accurately estimate the ancestry proportions of individuals. This study aimed to generate a small and effective number of AIMs from ∼60 K single nucleotide polymorphism (SNP) data of porcine and estimate three ancestry proportions [East China pig (ECHP), South China pig (SCHP), and European commercial pig (EUCP)] from Asian breeds and European domestic breeds. A total of 186 samples of 10 pure breeds were divided into three groups: ECHP, SCHP, and EUCP. Using these samples and a one-vs.-rest SVM classifier, we found that using only seven AIMs could completely separate the three groups. Subsequently, we utilized supervised ADMIXTURE to calculate ancestry proportions and found that the 129 AIMs performed well on ancestry estimates when pseudo admixed individuals were used. Furthermore, another 969 samples of 61 populations were applied to evaluate the performance of the 129 AIMs. We also observed that the 129 AIMs were highly correlated with estimates using ∼60 K SNP data for three ancestry components: ECHP (Pearson correlation coefficient (r) = 0.94), SCHP (r = 0.94), and EUCP (r = 0.99). Our results provided an example of using a small number of pig AIMs for classifications and estimating ancestry proportions with high accuracy and in a cost-effective manner.