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Abstract Background DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal methylation levels. The standardized cutoff values used in these methods do not take into account methylation patterns which are known to differ between the sexes and with age. Results Here we profile genome-wide DNA methylation from blood samples drawn from within a cohort composed of healthy controls of different age and sex alongside patients with Prader–Willi syndrome (PWS), Beckwith–Wiedemann syndrome, Fragile-X syndrome, Angelman syndrome, and Silver–Russell syndrome. We propose a Generalized Additive Model to perform age and sex adjusted outlier analysis of around 700,000 CpG sites throughout the human genome. Utilizing z-scores among the cohort for each site, we deployed an ensemble based machine learning pipeline and achieved a combined prediction accuracy of 0.96 (Binomial 95% Confidence Interval 0.868 $$-$$ - 0.995). Conclusion We demonstrate a method for age and sex adjusted outlier detection of differentially methylated loci based on a large cohort of healthy individuals. We present a custom machine learning pipeline utilizing this outlier analysis to classify samples for potential methylation associated congenital disorders. These methods are able to achieve high accuracy when used with machine learning methods to classify abnormal methylation patterns.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease and its underlying epigenomic heterogeneity is not fully understood. Here, the authors utilize patient-derived PDAC xenografts to define the epigenomic landscape of PDAC, highlighting chromatin states linked to differing disease aggressiveness and survival.

ABSTRACT IMPACT: This work sheds diagnostic insight on patients with idiopathic rare disease and has the potential to further their care and treatment as a result. OBJECTIVES/GOALS: Correct diagnosis is imperative to treating patients with idiopathic, suspected genetic conditions, yet sequencing approaches leave up to 70% of these patients undiagnosed. We sought to improve diagnosis rates for a cohort patients referred for sequencing by characterizing deleterious variants within the 5’UTR. METHODS/STUDY POPULATION: We retrospectively analyzed whole exome sequencing (WES) data from 472 unsolved rare disease patients within the Mayo Clinic Center for Individualized Medicine to identify variants within the 5’UTR that affect the presence of upstream open reading frames (uORFs). uORFs are short regions (typically 30bp - 600bp) that typically influence downstream gene translation by sequestering ribosomes. We specifically searched for variants with the potential to disrupt existing uORFs or introduce new uORFs within the 5’UTR, and developed a pipeline to annotate these variants with information including GnomAD allele frequency and gene loss of function intolerance (pLI) score. To aid in variant interpretation, we applied two deep learning tools to predict variant impacts on transcript ribosome load (TITER and FramePool). RESULTS/ANTICIPATED RESULTS: Our pipeline identified a median of 21 variants per patient that were predicted to have a deleterious impact on the translational efficiency of protein coding transcripts, primarily by introducing new start codons within the 5’UTR or by altering the Kozak consensus of existing start codons. A median of 10 of these variants occur upstream of haploinsufficient genes with an existing disease association. We also identified a subset of variants that are predicted to introduce translationally active N-terminal extensions to protein coding transcripts, with the potential to disrupt protein localization and processing. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work demonstrates that analysis of 5’UTR variants can be incorporated into existing WES pipelines, and identifies a group of variants with potential significance to patient disease. Further experimental evidence is necessary to ascertain the pathogenicity of these variants.

Abstract Background Human microbiome studies in clinical settings generally focus on distinguishing the microbiota in health from that in disease at a specific point in time. However, microbiome samples may be associated with disease severity or continuous clinical health indicators that are often assessed at multiple time points. While the temporal data from clinical and microbiome samples may be informative, analysis of this type of data can be problematic for standard statistical methods. Results To identify associations between microbiota and continuous clinical variables measured repeatedly in two studies of the respiratory tract, we adapted a statistical method, the lasso-penalized generalized linear mixed model (LassoGLMM). LassoGLMM can screen for associated clinical variables, incorporate repeated measures of individuals, and address the large number of species found in the microbiome. As is common in microbiome studies, when the number of variables is an order of magnitude larger than the number of samples LassoGLMM can be imperfect in its variable selection. We overcome this limitation by adding a pre-screening step to reduce the number of variables evaluated in the model. We assessed the use of this adapted two-stage LassoGLMM for its ability to determine which microbes are associated with continuous repeated clinical measures. We found associations (retaining a non-zero coefficient in the LassoGLMM) between 10 laboratory measurements and 43 bacterial genera in the oral microbiota, and between 2 cytokines and 3 bacterial genera in the lung. We compared our associations with those identified by the Wilcoxon test after dichotomizing our outcomes and identified a non-significant trend towards differential abundance between high and low outcomes. Our two-step LassoGLMM explained more of the variance seen in the outcome of interest than other variants of the LassoGLMM method. Conclusions We demonstrated a method that can account for the large number of genera detected in microbiome studies and repeated measures of clinical or longitudinal studies, allowing for the detection of strong associations between microbes and clinical measures. By incorporating the design strengths of repeated measurements and a prescreening step to aid variable selection, our two-step LassoGLMM will be a useful analytic method for investigating relationships between microbes and repeatedly measured continuous outcomes.

Abstract Background No microbe exists in isolation, and few live in environments with only members of their own kingdom or domain. As microbiome studies become increasingly more interested in the interactions between microbes than in cataloging which microbes are present, the variety of microbes in the community should be considered. However, the majority of ecological interaction networks for microbiomes built to date have included only bacteria. Joint association inference across multiple domains of life, e.g., fungal communities (the mycobiome) and bacterial communities, has remained largely elusive. Results Here, we present a novel extension of the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework that allows statistical inference of cross-domain associations from targeted amplicon sequencing data. For human lung and skin micro- and mycobiomes, we show that cross-domain networks exhibit higher connectivity, increased network stability, and similar topological re-organization patterns compared to single-domain networks. We also validate in vitro a small number of cross-domain interactions predicted by the skin association network. Conclusions For the human lung and skin micro- and mycobiomes, our findings suggest that fungi play a stabilizing role in ecological network organization. Our study suggests that computational efforts to infer association networks that include all forms of microbial life, paired with large-scale culture-based association validation experiments, will help formulate concrete hypotheses about the underlying biological mechanisms of species interactions and, ultimately, help understand microbial communities as a whole.

Studies demonstrate that Tropheryma whipplei (T. whipplei) is present in the lungs of healthy individuals without acute respiratory symptoms or acute respiratory infection and is more common in the lungs of HIV-infected individuals and in smokers. The impact of T. whipplei colonization in the lung on local inflammation and pulmonary dysfunction in HIV-infected individuals is currently unknown. In this study, we performed specific polymerase chain reaction (PCR) and sequencing for T. whipplei in bronchoalveolar lavage (BAL) and induced sputum (IS) samples in 76 HIV-infected participants from three clinical sites. Pulmonary function and proinflammatory cytokine and chemokine levels in BAL were measured. Frequency of T. whipplei in either BAL or IS was 43.4%. The sensitivity and specificity of IS compared to BAL for detection of T. whipplei was 92.3% and 84.2%, respectively, and isolates of T. whipplei in the BAL and IS in the same subject shared genetic identity. Pulmonary function measures were not associated with T. whipplei colonization, and proinflammatory cytokine and chemokine levels in BAL and plasma as well as percentages of inflammatory cells in BAL and IS were not higher in colonized individuals. Overall, these results indicate that T. whipplei colonization in the lung is common, but may not be associated with decreased pulmonary function or inflammation in HIV-infected individuals.

Individuals with HIV are at increased risk for coronary artery disease (CAD). Early detection of subclinical CAD by assessment of coronary artery calcium (CAC) may help risk stratify and prevent CAD events in these individuals. However, the current standard to quantify CAC i.e. Agatston scoring requires EKG-gated cardiac CT imaging.To determine if the assessment of CAC using non-EKG-gated chest CT and the Weston scoring system is a useful surrogate for Agatston scores in HIV-infected and HIV-uninfected individuals.CAC was assessed by both the Weston and Agatston score in 108 men enrolled in the Multicenter AIDS Cohort Study.Participants were 55.2 (IQR 50.4; 59.9) years old and 62 (57.4%) were seropositive for HIV. Inter-observer agreement (rs = 0.94, κ = 90.0%, p