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COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications.In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile.A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study.Eric and Wendy Schmidt.

Background Large β-globin gene cluster deletions (hereditary persistence of fetal hemoglobin [Hb] or β-, δβ-, γδβ-, and ϵγδβ-thalassemia), are associated with widely disparate phenotypes, including variable degrees of microcytic anemia and Hb F levels. When present, increased Hb A2 is used as a surrogate marker for β-thalassemia. Notably, ϵγδβ-thalassemias lack the essential regulatory locus control region (LCR) and cause severe transient perinatal anemia but normal newborn screen (NBS) results and Hb A2 levels. Herein, we report a novel deletion of the ϵ, Aγ, Gγ, and ψβ loci with intact LCR, δ-, and β-regions in 2 women and newborn twins. Methods Capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), DNA sequencing, multiplex ligation-dependent probe amplification (MLPA), gap-polymerase chain reaction (gap-PCR), and long-read sequencing (LRS) were performed. Results NBS showed an Hb A > Hb F pattern for both twins. At 20 months, Hb A2 was increased similarly to that in the mother and an unrelated woman. Unexplained microcytosis was absent and the twins lacked severe neonatal anemia. MLPA, LRS, and gap-PCR confirmed a 32 599 base pair deletion of ϵ (HBE1) through ψβ (HBBP1) loci. Conclusions This deletion represents a hemoglobinopathy category with a distinct phenotype that has not been previously described, an ϵγ-thalassemia. Both the NBS Hb A > F pattern and the subsequent increased Hb A2 without microcytosis are unusual. A similar deletion should be considered when this pattern is encountered and appropriate test methods selected for detection. Knowledge of the clinical impact of this new category will improve genetic counselling, with distinction from the severe transient anemia associated with ϵγδβ-thalassemia.

In cancer, linking epigenetic alterations to drivers of transformation has been difficult, in part because DNA methylation analyses must capture epigenetic variability, which is central to tumour heterogeneity and tumour plasticity. Here, by conducting a comprehensive analysis, based on information theory, of differences in methylation stochasticity in samples from patients with paediatric acute lymphoblastic leukaemia (ALL), we show that ALL epigenomes are stochastic and marked by increased methylation entropy at specific regulatory regions and genes. By integrating DNA methylation and single-cell gene-expression data, we arrived at a relationship between methylation entropy and gene-expression variability, and found that epigenetic changes in ALL converge on a shared set of genes that overlap with genetic drivers involved in chromosomal translocations across the disease spectrum. Our findings suggest that an epigenetically driven gene-regulation network, with UHRF1 (ubiquitin-like with PHD and RING finger domains 1) as a central node, links genetic drivers and epigenetic mediators in ALL. An information-theoretic analysis of DNA methylation in samples from patients with paediatric acute lymphoblastic leukaemia reveals that a regulatory set of driver genes harbour the greatest differences in methylation stochasticity.

DNA sequencing results in genetic diagnosis of 18-40% of previously unsolved cases, while the incorporation of RNA-Seq analysis has more recently been shown to generate significant numbers of previously unattainable diagnoses. Multiple inborn diseases resulting from disorders of genomic imprinting are well characterized and a growing body of literature suggest the causative or correlative role of aberrant DNA methylation in diverse rare inherited conditions. Therefore, the systematic application of genomic-wide methylation-based sequencing for undiagnosed cases of rare disease is a logical progression from current testing paradigms. Following the rationale previously exploited in RNA-based studies of rare disease, we can assume that disease-associated methylation aberrations in an individual will demonstrate significant differences from individuals with unrelated phenotypes. Thus, aberrantly methylated sites will be outliers from a heterogeneous cohort of individuals.Based on this rationale, we present BOREALIS: Bisulfite-seq OutlieR MEthylation At SingLe-SIte ReSolution. BOREALIS uses a beta binomial model to identify outlier methylation at single CpG site resolution from bisulfite sequencing data. This method addresses a need unmet by standard differential methylation analyses based on case-control groups. Utilizing a heterogeneous cohort of 94 rare disease patients undiagnosed following DNA-based testing we show that BOREALIS can successfully identify outlier methylation linked to phenotypically relevant genes, providing a new avenue of exploration in the quest for increased diagnostic rates in rare disease patients. We highlight the case of a patient with previously undetected hypermethylation patterns that are informing clinical decision-making. BOREALIS is implemented in R and is freely available as a Bioconductor package.

Motivation Next-generation sequencing is rapidly improving diagnostic rates in rare Mendelian diseases, but even with whole genome or whole exome sequencing, the majority of cases remain unsolved. Increasingly, RNA sequencing is being used to solve many cases that evade diagnosis through sequencing alone. Specifically, the detection of aberrant splicing in many rare disease patients suggests that identifying RNA splicing outliers is particularly useful for determining causal Mendelian disease genes. However, there is as yet a paucity of statistical methodologies to detect splicing outliers. Results We developed LeafCutterMD, a new statistical framework that significantly improves the previously published LeafCutter in the context of detecting outlier splicing events. Through simulations and analysis of real patient data, we demonstrate that LeafCutterMD has better power than the state-of-the-art methodology while controlling false-positive rates. When applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, LeafCutterMD recovered all aberrantly spliced genes that had previously been identified by manual curation efforts. Availability and implementation The source code for this method is available under the opensource Apache 2.0 license in the latest release of the LeafCutter software package available online at http://davidaknowles.github.io/leafcutter. Supplementary information Supplementary data are available at Bioinformatics online.

Translocations of the KMT2A (MLL) gene define a biologically distinct and clinically aggressive subtype of acute myeloid leukaemia (AML), marked by a characteristic gene expression profile and few cooperating mutations. Although dysregulation of the epigenetic landscape in this leukaemia is particularly interesting given the low mutation frequency, its comprehensive analysis using whole genome bisulphite sequencing (WGBS) has not been previously performed. Here we investigated epigenetic dysregulation in nine MLL-rearranged (MLL-r) AML samples by comparing them to six normal myeloid controls, using a computational method that encapsulates mean DNA methylation measurements along with analyses of methylation stochasticity. We discovered a dramatically altered epigenetic profile in MLL-r AML, associated with genome-wide hypomethylation and a markedly increased DNA methylation entropy reflecting an increasingly disordered epigenome. Methylation discordance mapped to key genes and regulatory elements that included bivalent promoters and active enhancers. Genes associated with significant changes in methylation stochasticity recapitulated known MLL-r AML expression signatures, suggesting a role for the altered epigenetic landscape in the transcriptional programme initiated by MLL translocations. Accordingly, we established statistically significant associations between discordances in methylation stochasticity and gene expression in MLL-r AML, thus providing a link between the altered epigenetic landscape and the phenotype.

Purpose Exome sequencing often identifies pathogenic genetic variants in patients with undiagnosed diseases. Nevertheless, frequent findings of variants of uncertain significance necessitate additional efforts to establish causality before reaching a conclusive diagnosis. To provide comprehensive genomic testing to patients with undiagnosed disease, we established an Individualized Medicine Clinic, which offered clinical exome testing and included a Translational Omics Program (TOP) that provided variant curation, research activities, or research exome sequencing. Methods From 2012 to 2018, 1101 unselected patients with undiagnosed diseases received exome testing. Outcomes were reviewed to assess impact of the TOP and patient characteristics on diagnostic rates through descriptive and multivariate analyses. Results The overall diagnostic yield was 24.9% (274 of 1101 patients), with 174 (15.8% of 1101) diagnosed on the basis of clinical exome sequencing alone. Four hundred twenty-three patients with nondiagnostic or without access to clinical exome sequencing were evaluated by the TOP, with 100 (9% of 1101) patients receiving a diagnosis, accounting for 36.5% of the diagnostic yield. The identification of a genetic diagnosis was influenced by the age at time of testing and the disease phenotype of the patient. Conclusion Integration of translational research activities into clinical practice of a tertiary medical center can significantly increase the diagnostic yield of patients with undiagnosed disease.

BACKGROUND COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. OBJECTIVE Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test result. RESULTS The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P CONCLUSIONS Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19–positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Context.—Small case series have evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection in formalin-fixed, paraffin-embedded tissue using reverse transcription–polymerase chain reaction, immunohistochemistry (IHC), and/or RNA in situ hybridization (RNAish).Objective.—To compare droplet digital polymerase chain reaction, IHC, and RNAish to detect SARS-CoV-2 in formalin-fixed, paraffin-embedded tissue in a large series of lung specimens from coronavirus disease 2019 (COVID-19) patients.Design.—Droplet digital polymerase chain reaction and RNAish used commercially available probes; IHC used clone 1A9. Twenty-six autopsies of COVID-19 patients with formalin-fixed, paraffin-embedded tissue blocks of 62 lung specimens, 22 heart specimens, 2 brain specimens, and 1 liver, and 1 umbilical cord were included. Control cases included 9 autopsy lungs from patients with other infections/inflammation and virus-infected tissue or cell lines.Results.—Droplet digital polymerase chain reaction had the highest sensitivity for SARS-CoV-2 (96%) when compared with IHC (31%) and RNAish (36%). All 3 tests had a specificity of 100%. Agreement between droplet digital polymerase chain reaction and IHC or RNAish was fair (κ = 0.23 and κ = 0.35, respectively). Agreement between IHC and in situ hybridization was substantial (κ = 0.75). Interobserver reliability was almost perfect for IHC (κ = 0.91) and fair to moderate for RNAish (κ = 0.38–0.59). Lung tissues from patients who died earlier after onset of symptoms revealed higher copy numbers by droplet digital polymerase chain reaction (P = .03, Pearson correlation = −0.65) and were more likely to be positive by RNAish (P = .02) than lungs from patients who died later. We identified SARS-CoV-2 in hyaline membranes, in pneumocytes, and rarely in respiratory epithelium. Droplet digital polymerase chain reaction showed low copy numbers in 7 autopsy hearts from ProteoGenex Inc. All other extrapulmonary tissues were negative.Conclusions.—Droplet digital polymerase chain reaction was the most sensitive and highly specific test to identify SARS-CoV-2 in lung specimens from COVID-19 patients.

Background DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads. Results We present a powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics. This approach takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available in WGBS methylation reads and produces accurate results even when applied on single WGBS samples with low coverage. Using the Shannon entropy, our approach provides a rigorous quantification of methylation stochasticity in individual WGBS samples genome-wide. Furthermore, it utilizes the Jensen-Shannon distance to evaluate differences in methylation distributions between a test and a reference sample. Differential performance assessment using simulated and real human lung normal/cancer data demonstrate a clear superiority of our approach over DSS, a recently proposed method for WGBS data analysis. Critically, these results demonstrate that marginal methods become statistically invalid when correlations are present in the data. Conclusions This contribution demonstrates clear benefits and the necessity of modeling joint probability distributions of methylation using the 1D Ising model of statistical physics and of quantifying methylation stochasticity using concepts from information theory. By employing this methodology, substantial improvement of DNA methylation analysis can be achieved by effectively taking into account the massive amount of statistical information available in WGBS data, which is largely ignored by existing methods. Electronic supplementary material The online version of this article (10.1186/s12859-018-2086-5) contains supplementary material, which is available to authorized users.

Background COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. Objective Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. Methods We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test result. Results The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P Conclusions Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19–positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Background Establishment and maintenance of DNA methylation throughout the genome is an important epigenetic mechanism that regulates gene expression whose disruption has been implicated in human diseases like cancer. It is therefore crucial to know which genes, or other genomic features of interest, exhibit significant discordance in DNA methylation between two phenotypes. We have previously proposed an approach for ranking genes based on methylation discordance within their promoter regions, determined by centering a window of fixed size at their transcription start sites. However, we cannot use this method to identify statistically significant genomic features and handle features of variable length and with missing data. Results We present a new approach for computing the statistical significance of methylation discordance within genomic features of interest in single and multiple test/reference studies. We base the proposed method on a well-articulated hypothesis testing problem that produces p- and q-values for each genomic feature, which we then use to identify and rank features based on the statistical significance of their epigenetic dysregulation. We employ the information-theoretic concept of mutual information to derive a novel test statistic, which we can evaluate by computing Jensen-Shannon distances between the probability distributions of methylation in a test and a reference sample. We design the proposed methodology to simultaneously handle biological, statistical, and technical variability in the data, as well as variable feature lengths and missing data, thus enabling its wide-spread use on any list of genomic features. This is accomplished by estimating, from reference data, the null distribution of the test statistic as a function of feature length using generalized additive regression models. Differential assessment, using normal/cancer data from healthy fetal tissue and pediatric high-grade glioma patients, illustrates the potential of our approach to greatly facilitate the exploratory phases of clinically and biologically relevant methylation studies. Conclusions The proposed approach provides the first computational tool for statistically testing and ranking genomic features of interest based on observed DNA methylation discordance in comparative studies that accounts, in a rigorous manner, for biological, statistical, and technical variability in methylation data, as well as for variability in feature length and for missing data. Electronic supplementary material The online version of this article (10.1186/s12859-019-2777-6) contains supplementary material, which is available to authorized users.

INTRODUCTION To date, 559 humans have been flown into space, but long-duration (>300 days) missions are rare (n = 8 total). Long-duration missions that will take humans to Mars and beyond are planned by public and private entities for the 2020s and 2030s; therefore, comprehensive studies are needed now to assess the impact of long-duration spaceflight on the human body, brain, and overall physiology. The space environment is made harsh and challenging by multiple factors, including confinement, isolation, and exposure to environmental stressors such as microgravity, radiation, and noise. The selection of one of a pair of monozygotic (identical) twin astronauts for NASA’s first 1-year mission enabled us to compare the impact of the spaceflight environment on one twin to the simultaneous impact of the Earth environment on a genetically matched subject. RATIONALE The known impacts of the spaceflight environment on human health and performance, physiology, and cellular and molecular processes are numerous and include bone density loss, effects on cognitive performance, microbial shifts, and alterations in gene regulation. However, previous studies collected very limited data, did not integrate simultaneous effects on multiple systems and data types in the same subject, or were restricted to 6-month missions. Measurement of the same variables in an astronaut on a year-long mission and in his Earth-bound twin indicated the biological measures that might be used to determine the effects of spaceflight. Presented here is an integrated longitudinal, multidimensional description of the effects of a 340-day mission onboard the International Space Station. RESULTS Physiological, telomeric, transcriptomic, epigenetic, proteomic, metabolomic, immune, microbiomic, cardiovascular, vision-related, and cognitive data were collected over 25 months. Some biological functions were not significantly affected by spaceflight, including the immune response (T cell receptor repertoire) to the first test of a vaccination in flight. However, significant changes in multiple data types were observed in association with the spaceflight period; the majority of these eventually returned to a preflight state within the time period of the study. These included changes in telomere length, gene regulation measured in both epigenetic and transcriptional data, gut microbiome composition, body weight, carotid artery dimensions, subfoveal choroidal thickness and peripapillary total retinal thickness, and serum metabolites. In addition, some factors were significantly affected by the stress of returning to Earth, including inflammation cytokines and immune response gene networks, as well as cognitive performance. For a few measures, persistent changes were observed even after 6 months on Earth, including some genes’ expression levels, increased DNA damage from chromosomal inversions, increased numbers of short telomeres, and attenuated cognitive function. CONCLUSION Given that the majority of the biological and human health variables remained stable, or returned to baseline, after a 340-day space mission, these data suggest that human health can be mostly sustained over this duration of spaceflight. The persistence of the molecular changes (e.g., gene expression) and the extrapolation of the identified risk factors for longer missions (>1 year) remain estimates and should be demonstrated with these measures in future astronauts. Finally, changes described in this study highlight pathways and mechanisms that may be vulnerable to spaceflight and may require safeguards for longer space missions; thus, they serve as a guide for targeted countermeasures or monitoring during future missions.

Diffuse intrinsic pontine glioma (DIPG) is a childhood brainstem tumor with a dismal prognosis and no effective treatment. Recent studies point to a critical role for epigenetic dysregulation in this disease. Nearly 80% of DIPGs harbor mutations in histone H3 encoding replacement of lysine 27 with methionine (K27M), leading to global loss of the repressive histone H3K27 trimethylation mark, global DNA hypomethylation, and a unique gene expression profile. Recent studies across diverse cancers have highlighted the role of epigenetic variability as a driving force in tumor evolution. Epigenetic variability may underlie the heterogeneity and phenotypic plasticity of cancer cells and allow for the selection of cellular traits that promote survival and resistance to therapy. Our group has recently described a novel mathematical framework for analyzing variability of DNA methylation directly from whole-genome bisulfite sequencing data by modeling stochastic Markov processes, allowing rigorous computation of DNA methylation entropy at precise genomic locations. We have carried out genome-wide characterization of DNA methylation at single-base resolution in DIPG in order to generate comprehensive genome-wide maps of DNA methylation entropy. We find a global increase in DNA methylation entropy in DIPG. Dissection of the targets of this perturbation highlights specific genes and regulatory regions under epigenetic control in DIPG cells. We assess the effect of pharmacologic DNA methyltransferase inhibition on the DNA methylation landscape and on gene expression in DIPG patient-derived neurosphere cell lines. We find that DNA methyltransferase inhibition alters expression of genes involved in cell death, differentiation, cellular defense, and immune signaling.

Trichorhinophalangeal syndrome type I (TRPSI) is a rare disorder that causes distinctive ectodermal, facial, and skeletal features affecting the hair (tricho-), nose (rhino-), and fingers and toes (phalangeal) and is inherited in an autosomal dominant pattern. TRPSI is caused by loss of function variants in TRPS1, involved in the regulation of chondrocyte and perichondrium development. Pathogenic variants in TRPS1 include missense mutations and deletions with variable breakpoints, with only a single instance of an intragenic duplication reported to date. Here we report an affected individual presenting with a classic TRPSI phenotype who is heterozygous for a de novo intragenic ∼36.3-kbp duplication affecting exons 2–4 of TRPS1. Molecular analysis revealed the duplication to be in direct tandem orientation affecting the splicing of TRPS1. The aberrant transcripts are predicted to produce a truncated TRPS1 missing the nuclear localization signal and the GATA and IKAROS-like zinc-finger domains resulting in functional TRPS1 haploinsufficiency. Our study identifies a novel intragenic tandem duplication of TRPS1 and highlights the importance of molecular characterization of intragenic duplications.

BackgroundRNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5-35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data.MethodsWe describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization.ResultsWe demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance.ConclusionsThe approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.

Epigenetics is the study of biochemical modifications carrying information independent of DNA sequence, which are heritable through cell division. In 1940, Waddington coined the term "epigenetic landscape" as a metaphor for pluripotency and differentiation, but methylation landscapes have not yet been rigorously computed. Using principles from statistical physics and information theory, we derive epigenetic energy landscapes from whole-genome bisulfite sequencing (WGBS) data that enable us to quantify methylation stochasticity genome-wide using Shannon's entropy, associating it with chromatin structure. Moreover, we consider the Jensen-Shannon distance between sample-specific energy landscapes as a measure of epigenetic dissimilarity and demonstrate its effectiveness for discerning epigenetic differences. By viewing methylation maintenance as a communications system, we introduce methylation channels and show that higher-order chromatin organization can be predicted from their informational properties. Our results provide a fundamental understanding of the information-theoretic nature of the epigenome that leads to a powerful approach for studying its role in disease and aging.

Epigenetics is defined as genomic modifications carrying information independent of DNA sequence heritable through cell division. In 1940, Waddington coined the term “epigenetic landscape” as a metaphor for pluripotency and differentiation, but epigenetic potential energy landscapes have not yet been rigorously defined. Using well-grounded biological assumptions and principles of statistical physics and information theory, we derive potential energy landscapes from whole genome bisulfite sequencing data that allow us to quantify methylation stochasticity genome-wide and discern epigenetic differences using Shannon’s entropy and the Jensen-Shannon distance. We discover a “developmental wheel” of germ cell lineages and an association between entropy and chromatin structure. Viewing methylation maintenance as a communications system, we introduce methylation channels and show that higher-order chromatin organization can be predicted from their informational properties. Our results provide a fundamental understanding of the information-theoretic nature of the epigenome and a powerful methodology for studying its role in disease and aging.

A number of analytical and Monte Carlo sampling algorithms have been proposed to provide approximate solutions to the master equation. Unfortunately, to maintain accuracy and computational efficiency, most algorithms require specification of well-chosen parameter values. We have recently developed a rigorous statistical hypothesis testing framework that is capable of determining the validity of a given approximation scheme with a specific choice for the parameter values. In this paper, we extend this technique to address the “multiple-testing” problem, in which a set of parameter values is tested simultaneously. This allows for effective tuning of approximation algorithms and for empirically studying the range of validity of a given approximation method.

The role intrinsic statistical fluctuations play in creating avalanches – patterns of complex bursting activity with scale-free properties – is examined in leaky Markovian networks. Using this broad class of models, we develop a probabilistic approach that employs a potential energy landscape perspective coupled with a macroscopic description based on statistical thermodynamics. We identify six important thermodynamic quantities essential for characterizing system behavior as a function of network size: the internal potential energy, entropy, free potential energy, internal pressure, pressure, and bulk modulus. In agreement with classical phase transitions, these quantities evolve smoothly as a function of the network size until a critical value is reached. At that value, a discontinuity in pressure is observed that leads to a spike in the bulk modulus demarcating loss of thermodynamic robustness. We attribute this novel result to a reallocation of the ground states (global minima) of the system's stationary potential energy landscape caused by a noise-induced deformation of its topographic surface. Further analysis demonstrates that appreciable levels of intrinsic noise can cause avalanching, a complex mode of operation that dominates system dynamics at near-critical or subcritical network sizes. Illustrative examples are provided using an epidemiological model of bacterial infection, where avalanching has not been characterized before, and a previously studied model of computational neuroscience, where avalanching was erroneously attributed to specific neural architectures. The general methods developed here can be used to study the emergence of avalanching (and other complex phenomena) in many biological, physical and man-made interaction networks., Author Summary Networks of noisy interacting components arise in diverse scientific disciplines. Here, we develop a mathematical framework to study the underlying causes of a bursting phenomenon in network activity known as avalanching. As prototypical examples, we study a model of disease spreading in a population of individuals and a model of brain activity in a neural network. Although avalanching is well-documented in neural networks, thought to be crucial for learning, information processing, and memory, it has not been studied before in disease spreading. We employ tools originally used to analyze thermodynamic systems to argue that randomness in the actions of individual network components plays a fundamental role in avalanche formation. We show that avalanching is a spontaneous behavior, brought about by a phenomenon reminiscent to a phase transition in statistical mechanics, caused by increasing randomness as the network size decreases. Our work demonstrates that a previously suggested balanced feed-forward network structure is not necessary for neuronal avalanching. Instead, we attribute avalanching to a reallocation of the global minima of the network's stationary potential energy landscape, caused by a noise-induced deformation of its topographic surface.

The master equation is used extensively to model chemical reaction systems with stochastic dynamics. However, and despite its phenomenological simplicity, it is not in general possible to compute the solution of this equation. Drawing exact samples from the master equation is possible, but can be computationally demanding, especially when estimating high-order statistical summaries or joint probability distributions. As a consequence, one often relies on analytical approximations to the solution of the master equation or on computational techniques that draw approximative samples from this equation. Unfortunately, it is not in general possible to check whether a particular approximation scheme is valid. The main objective of this paper is to develop an effective methodology to address this problem based on statistical hypothesis testing. By drawing a moderate number of samples from the master equation, the proposed techniques use the well-known Kolmogorov-Smirnov statistic to reject the validity of a given approximation method or accept it with a certain level of confidence. Our approach is general enough to deal with any master equation and can be used to test the validity of any analytical approximation method or any approximative sampling technique of interest. A number of examples, based on the Schlogl model of chemistry and the SIR model of epidemiology, clearly illustrate the effectiveness and potential of the proposed statistical framework.

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples., Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html

The study of complex networks has uncovered many unifying topological principles from a plethora of apparently unrelated applications. However, progress has been slower in understanding dynamical processes taking place on networks. This difficulty largely arises from the diversity of networks and their inherent nonlinearities. Here, we demonstrate that chemical reactions can serve as a unifying framework for studying deterministic and stochastic processes on networks from the fields of cell biology, epidemiology, ecology, sociology, and game theory. This allows advanced techniques for the modeling, reverse engineering, and analysis of chemical reaction networks to be applied to a wide variety of applications with relative ease.

Background The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function. Results We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database. Conclusions TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.

Background Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable. Results We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html. Conclusions Our approach provides an attractive statistical methodology for estimating thermodynamically feasible values for the rate constants of a biochemical reaction system from noisy time series observations of molecular concentrations obtained through perturbations. The proposed technique is theoretically sound and computationally feasible, but restricted to quantitative data obtained from closed biochemical reaction systems. This necessitates development of similar techniques for estimating the rate constants of open biochemical reaction systems, which are more realistic models of cellular function.

Estimating the rate constants of a biochemical reaction model of cellular function is an important, albeit computationally intensive, problem in systems biology. In this paper, a variance-based sensitivity analysis approach is proposed, which can be used, as a pre-screening step, to identify parameters in a biochemical reaction system that do not appreciably influence the cost of estimation and, therefore, whose values cannot be precisely determined by parameter estimation. By only estimating the remaining parameters, appreciable qualitative and quantitative improvements can be achieved. A subset of a well-known biochemical reaction model of the EGF/ERK signaling pathway is used to illustrate the benefits achieved by the proposed method.

Experimental construction of biochemical reaction systems that model cellular behavior often leads to kinetic parameter values that do not satisfy important thermodynamic constraints, thus resulting in models that are not physically realizable. In this paper, we propose a method that takes thermodynamically infeasible published kinetic parameter values and recomputes a new set of thermodynamically consistent values. The method is based on formulating and implementing an appropriate constrained optimization problem by assuming that the molecular dynamics produced by the published values are “noisy” versions of the corresponding dynamics produced by the thermodynamically consistent “true” values.

The processes by which disease spreads in a population of individuals are inherently stochastic. The master equation has proven to be a useful tool for modeling such processes. Unfortunately, solving the master equation analytically is possible only in limited cases (e.g., when the model is linear), and thus numerical procedures or approximation methods must be employed. Available approximation methods, such as the system size expansion method of van Kampen, may fail to provide reliable solutions, whereas current numerical approaches can induce appreciable computational cost. In this paper, we propose a new numerical technique for solving the master equation. Our method is based on a more informative stochastic process than the population process commonly used in the literature. By exploiting the structure of the master equation governing this process, we develop a novel technique for calculating the exact solution of the master equation – up to a desired precision – in certain models of stochastic epidemiology. We demonstrate the potential of our method by solving the master equation associated with the stochastic SIR epidemic model. MATLAB software that implements the methods discussed in this paper is freely available as Supporting Information S1.