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Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios. [ABSTRACT FROM AUTHOR]

Motivation: Systematic identification of microRNA (miRNA) targets remains a challenge. The miRNA overexpression coupled with genome-wide expression profiling is a promising new approach and calls for a new method that integrates expression and sequence information.Results: We developed a probabilistic scoring method called targetScore. TargetScore infers miRNA targets as the transformed fold-changes weighted by the Bayesian posteriors given observed target features. To this end, we compiled 84 datasets from Gene Expression Omnibus corresponding to 77 human tissue or cells and 113 distinct transfected miRNAs. Comparing with other methods, targetScore achieves significantly higher accuracy in identifying known targets in most tests. Moreover, the confidence targets from targetScore exhibit comparable protein downregulation and are more significantly enriched for Gene Ontology terms. Using targetScore, we explored oncomir–oncogenes network and predicted several potential cancer-related miRNA–messenger RNA interactions.Availability and implementation: TargetScore is available at Bioconductor: http://www.bioconductor.org/packages/devel/bioc/html/TargetScore.html.Contact: yueli@cs.toronto.edu or zhaolei.zhang@utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

The properties (or labels) of nodes in networks can often be predicted based on their proximity and their connections to other labeled nodes. So-called "label propagation algorithms" predict the labels of unlabeled nodes by propagating information about local label density iteratively through the network. These algorithms are fast, simple and scale to large networks but nonetheless regularly perform better than slower and much more complex algorithms on benchmark problems. We show here, however, that these algorithms have an intrinsic limitation that prevents them from adapting to some common patterns of network node labeling; we introduce a new algorithm, 3Prop, that retains all their advantages but is much more adaptive. As we show, 3Prop performs very well on node labeling problems ill-suited to label propagation, including predicting gene function in protein and genetic interaction networks and gender in friendship networks, and also performs slightly better on problems already well-suited to label propagation such as labeling blogs and patents based on their citation networks. 3Prop gains its adaptability by assigning separate weights to label information from different steps of the propagation. Surprisingly, we found that for many networks, the third iteration of label propagation receives a negative weight. [ABSTRACT FROM AUTHOR]

Motivation: Lung cancer is often discovered long after its onset, making identifying genes important in its initiation and progression a challenge. By the time the tumors are discovered, we only observe the final sum of changes of the few genes that initiated cancer and thousands of genes that they have influenced. Gene interactions and heterogeneity of samples make it difficult to identify genes consistent between different cohorts. Using gene and gene–product interaction networks, we propose a principled approach to identify a small subset of genes whose network neighbors exhibit consistently high expression change (in cancerous tissue versus normal) regardless of their own expression. We hypothesize that these genes can shed light on the larger scale perturbations in the overall landscape of expression levels.Results: We benchmark our method on simulated data, and show that we can recover a true gene list in noisy measurement data. We then apply our method to four non-small cell lung cancer and two pancreatic cancer cohorts, finding several genes that are consistent within all cohorts of the same cancer type.Conclusion: Our model is flexible, robust and identifies gene sets that are more consistent across cohorts than several other approaches. Additionally, our method can be applied on a per-patient basis not requiring large cohorts of patients to find genes of influence. Our approach is generally applicable to gene expression studies where the goal is to identify a small set of influential genes that may in turn explain the much larger set of genome-wide expression changes.Availability: The code is available at http://morrislab.med.utoronto.ca/~anna/cannet.zipContact: anna.goldenberg@utoronto.caSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Examines the statistical detection of anthrax outbreaks by tracking over-the-counter medication sales. Description on the monitoring grocery data to detect bioterrorism attack; Use of information technology to improve clinical preparedness for bioterrorism; Evaluation on the detection system for anthrax footprint.

Background: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. Objective: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. Methods: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. Results: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. Conclusion: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children. [ABSTRACT FROM AUTHOR]

Background: The hypothalamus plays a central role in regulating puberty. However, our knowledge of the postnatal gene regulatory networks that control the pubertal transition in males and females is incomplete. Here, we investigate the age-, sex- and cell-type-specific gene regulation in the hypothalamus across the pubertal transition. Methods: We used RNA-seq to profile hypothalamic gene expression in male and female mice at five time points spanning the onset of puberty (postnatal days (PD) 12, 22, 27, 32, and 37). By combining this data with hypothalamic single nuclei RNA-seq data from pre- and postpubertal mice, we assigned gene expression changes to their most likely cell types of origin. In our colony, pubertal onset occurs earlier in male mice, allowing us to focus on genes whose expression is dynamic across ages and offset between sexes, and to explore the bases of sex effects. Results: Our age-by-sex pattern of expression enriched for biological pathways involved hormone production, neuronal activation, and glial maturation. Additionally, we inferred a robust expansion of oligodendrocytes precursor cells into mature oligodendrocytes spanning the prepubertal (PD12) to peri-pubertal (PD27) timepoints. Using spatial transcriptomic data from postpubertal mice, we observed the lateral hypothalamic area and zona incerta were the most oligodendrocyte-rich regions and that these cells expressed genes known to be involved in pubertal regulation. Conclusion: Together, by incorporating multiple biological timepoints and using sex as a variable, we identified gene and cell-type changes that may participate in orchestrating the pubertal transition and provided a resource for future studies of postnatal hypothalamic gene regulation. Summary: The hypothalamus is required to initiate puberty and develop secondary sex characteristics. While several hypothalamic-expressed genes are known to be essential for puberty, the gene regulatory networks that regulate its timing are not well established. Since puberty begins earlier in the male C57BL6/J mice compared to females, our study set out to identify puberty-related genes by focussing on genes whose pattern in expression was conditional on age and sex. We first used RNA sequencing to examine sex-biased developmental trajectories in the mouse hypothalamus, where we tracked gene activity in the hypothalamus at two prepubertal, two peri-pubertal, and one postpubertal timepoint. To address an inherent limitation of using bulk RNA-seq profiling of the hypothalamus, we integrated single-cell RNA sequencing and spatial transcriptomics data to identify which cell types most likely give rise to the observed gene expression patterns. Some of the genes we found are part of specific pathways related to hormone production, nerve cell activity, and the maturation of support cells in the brain (glial cells). By combining bulk RNA-seq data with single cell RNA-seq we inferred an increase in the maturation of cells that form the myelin sheath (oligodendrocytes) coinciding with pubertal onset. In summary, this study highlights gene expression and cellular composition changes that occur in the hypothalamus during postnatal development in a manner conditional on sex. This work should serve as a resource for hypothalamic gene regulation during postnatal development in mice. Highlights: Gene expression in the hypothalamus in male and female mice was obtained at five postnatal ages spanning pubertal development. A set of genes influenced by sex and age were identified and found to be related to pubertal development. Combining these results with single cell RNA-seq and spatial transcriptomic data we profiled puberty-relevant neuronal and glial gene expression signatures. A hypothalamic gene expression resource for male and female mouse was generated to facilitate access to this data. [ABSTRACT FROM AUTHOR]

Background: Autism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs). Methods: Data came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16–35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL's PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted. Results: ImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance. Limitations: Despite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities. Conclusions: The lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs. [ABSTRACT FROM AUTHOR]

Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects. [ABSTRACT FROM AUTHOR]

Objective To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. Methods SLE patients ages 18–65 years underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data were collected on demographic and clinical variables, disease burden/activity, health-related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal–Wallis and χ2 tests. Results Of the 238 patients, 90% were female, with a mean age of 41 years (s. d. 12) and a disease duration of 14 years (s. d. 10) at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (P   <  0.03) compared with subtype B. Subtype A also had greater levels of subjective cognitive function (P  < 0.001), disease burden/damage (P  < 0.04), HRQoL (P  < 0.001) and psychiatric measures (P  < 0.001) compared with subtype B. Conclusion This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multifactorial phenotypes exist. Those with greater disease burden, from SLE-specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately this will aid our understanding of personalized CI trajectories and identification of appropriate treatments. [ABSTRACT FROM AUTHOR]

The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the promises and challenges of artificial intelligence (AI) in healthcare. Our analysis is directed towards community care practitioners interested in learning more about how AI can change their practice along with the critical considerations required to integrate AI into their practice. We highlight examples of how AI can enable access to new sources of clinical data while augmenting clinical workflows and healthcare delivery. AI can help optimize how and when care is delivered by community practitioners while also improving practice efficiency, accessibility, and the overall quality of care. Unlike virtual care, however, AI is still missing many of the key enablers required to facilitate adoption into the community care landscape and there are challenges we must consider and resolve for AI to successfully improve healthcare delivery. We discuss several critical considerations, including data governance in the clinic setting, healthcare practitioner education, regulation of AI in healthcare, clinician reimbursement, and access to both technology and the internet. [ABSTRACT FROM AUTHOR]

The two-pore domain potassium channel KCNK3 (TASK-1) is expressed in rat and human pulmonary artery smooth muscle cells. There, it is associated with hypoxia-induced signalling, and its dysfunction is linked to pathogenesis of human pulmonary hypertension. We here aimed to determine its role in hypoxic pulmonary vasoconstriction (HPV) in the mouse, and hence the suitability of this model for further mechanistic investigations, using appropriate inhibitors and TASK-1 knockout (KO) mice. RT-PCR revealed expression of TASK-1 mRNA in murine lungs and pre-acinar pulmonary arteries. Protein localization by immunohistochemistry and western blot was unreliable since all antibodies produced labelling also in TASK-1 KO organs/tissues. HPV was investigated by videomorphometric analysis of intra- (inner diameter: 25–40 μm) and pre-acinar pulmonary arteries (inner diameter: 41–60 μm). HPV persisted in TASK-1 KO intra-acinar arteries. Pre-acinar arteries developed initial HPV, but the response faded earlier (after 30 min) in KO vessels. This HPV pattern was grossly mimicked by the TASK-1 inhibitor anandamide in wild-type vessels. Hypoxia-provoked rise in pulmonary arterial pressure (PAP) in isolated ventilated lungs was affected neither by TASK-1 gene deficiency nor by the TASK-1 inhibitor A293. TASK-1 is dispensable for initiating HPV of murine intra-pulmonary arteries, but participates in sustained HPV specifically in pre-acinar arteries. This does not translate into abnormal rise in PAP. While there is compelling evidence that TASK-1 is involved in the pathogenesis of pulmonary arterial hypertension in humans, the mouse does not appear to serve as a suitable model to study the underlying molecular mechanisms. [ABSTRACT FROM AUTHOR]

Background: The pituitary gland regulates essential physiological processes such as growth, pubertal onset, stress response, metabolism, reproduction, and lactation. While sex biases in these functions and hormone production have been described, the underlying identity, temporal deployment, and cell-type specificity of sex-biased pituitary gene regulatory networks are not fully understood. Methods: To capture sex differences in pituitary gene regulation dynamics during postnatal development, we performed 3' untranslated region sequencing and small RNA sequencing to ascertain gene and microRNA expression, respectively, across five postnatal ages (postnatal days 12, 22, 27, 32, 37) that span the pubertal transition in female and male C57BL/6J mouse pituitaries (n = 5–6 biological replicates for each sex at each age). Results: We observed over 900 instances of sex-biased gene expression and 17 sex-biased microRNAs, with the majority of sex differences occurring with puberty. Using miRNA–gene target interaction databases, we identified 18 sex-biased genes that were putative targets of 5 sex-biased microRNAs. In addition, by combining our bulk RNA-seq with publicly available male and female mouse pituitary single-nuclei RNA-seq data, we obtained evidence that cell-type proportion sex differences exist prior to puberty and persist post-puberty for three major hormone-producing cell types: somatotropes, lactotropes, and gonadotropes. Finally, we identified sex-biased genes in these three pituitary cell types after accounting for cell-type proportion differences between sexes. Conclusion: Our study reveals the identity and postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary. This work also highlights the importance of considering sex biases in cell-type composition when understanding sex differences in the processes regulated by the pituitary gland. Highlights: Male and female mouse pituitary gland gene and miRNA expression was profiled across five postnatal ages spanning pubertal development. Sex differences in pituitary gene expression exist prior to puberty and become more prominent upon puberty. Combining expression data from genes and miRNAs revealed 18 putative sex-biased gene targets of 5 sex-biased miRNAs. Sex differences in the proportions of somatotropes, lactotropes, and gonadotropes are predicted to occur prior to puberty. [ABSTRACT FROM AUTHOR]

Background: The Toronto protocol for cancer surveillance in children with Li-Fraumeni syndrome has been adopted worldwide. Objective: To assess the diagnostic accuracy of the imaging used in this protocol. Materials and methods: We conducted a blinded retrospective review of imaging modalities in 31 pediatric patients. We compared imaging findings with the reference standards, which consisted of (1) histopathological diagnosis, (2) corresponding dedicated imaging or subsequent surveillance imaging or (3) clinical outcomes. We individually analyzed each modality's diagnostic performance for cancer detection and assessed it on a per-study basis for chest and abdominal regional whole-body MRI (n=115 each), brain MRI (n=101) and abdominal/pelvic US (n=292), and on a per-lesion basis for skeleton/soft tissues on whole-body MRI (n=140). Results: Of 763 studies/lesions, approximately 80% had reference standards that identified 4 (0.7%) true-positive, 523 (85.3%) true-negative, 5 (0.8%) false-positive, 3 (0.5%) false-negative and 78 (12.7%) indeterminate results. There were 3 true-positives on whole-body MRI and 1 true-positive on brain MRI as well as 3 false-negatives on whole-body MRI. Sensitivities and specificities of tumor diagnosis using a worst-case scenario analysis were, respectively, 40.0% (95% confidence interval [CI]: 7.3%, 83.0%) and 38.2% (95% CI: 29.2%, 48.0%) for skeleton/soft tissues on whole-body MRI; sensitivity non-available and 97.8% (95% CI: 91.4%, 99.6%) for chest regional whole-body MRI; 100.0% (95% CI: 5.5%, 100.0%) and 96.8% (95% CI: 90.2%, 99.2%) for abdominal regional whole-body MRI; sensitivity non-available and 98.3% (95% CI: 95.3, 99.4) for abdominal/pelvic US; and 50.0% (95% CI: 2.7%, 97.3%) and 93.8% (95% CI: 85.6%, 97.7%) for brain MRI. Conclusion: Considerations for optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading may enhance the diagnostic accuracy for tumor surveillance. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications. [ABSTRACT FROM AUTHOR]

Background: The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. Results: Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. Conclusions: We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment. [ABSTRACT FROM AUTHOR]

In the lung, hypoxia induces pulmonary hypertension caused by vasoconstriction and vascular remodeling. Additionally, hypoxia is an inducer of angiogenesis, which is assumed to counteract pulmonary hypertension. We asked whether the anti-angiogenic factor endostatin—a cleavage product of collagen XVIII—participates in the vascular alterations induced by hypoxia. By employing Western blotting of tissue extracts of murine brain, liver and heart an endostatin fragment of 22 kDa was detectable, whereas in lung and aorta additional bands of 24 and 26 kDa were found. The amount of these larger fragments was increased in tissues obtained from mice housed for 4 days or 3 weeks at hypobaric hypoxia. By immunohistochemistry endostatin was detected in association with elastic fibers and in close neighborhood to smooth muscle cells of intrapulmonary vessels and the aorta. In the lung, the activity of matrix metalloproteinases (MMP) known to generate endostatin by cleavage of collagen XVIII was increased (MMP-2) and decreased (proMMP-9), respectively, by hypoxia. Elevated amounts of endostatin within the aortic wall of mice exposed to hypobaric hypoxia may stabilize the vascular wall by inhibition of microvascular sprouting. The surprising finding of increased endostatin in the lung presumably contributes to the development of pulmonary hypertension by reduction of angiogenesis. [ABSTRACT FROM AUTHOR]

In the pulmonary vasculature, the mechanisms responsible for oxygen sensing and the initiation of hypoxia-induced vasoconstriction and vascular remodeling are still unclear. Nitric oxide (NO) and reactive oxygen species (ROS) are discussed as early mediators of the hypoxic response. Here, we describe a quantitative analysis of NO- and ROS-producing cells within the vascular walls of murine lung sections cultured at normoxia or hypoxia. Whereas the number of NO-producing ceils was not changed by hypoxia, the number of ROS-generating cells was significantly increased. Addition of specific inhibitors revealed that mitochondria were the source of ROS. The participation of the individual mitochondrial complexes differed in normoxic and hypoxic ROS generation. Whereas normoxic ROS production required complexes I and III, hypoxic ROS generation additionally demanded complex II. Histochemically demonstrable succinate dehydrogenase activity of complex II in the arterial wall decreased during hypoxia. Inhibition of the reversed enzymatic reaction, i.e., fumarate reductase, by application of succinate, specifically abolished hypoxic, but not normoxic, ROS generation. Thus complex II plays an essential role in hypoxic ROS production. Presumably, its catalytic activity switches from succinate dehydrogenase to fumarate reductase at reduced oxygen tension, thereby modulating the directionality of the electron flow. [ABSTRACT FROM AUTHOR]

PURPOSE: Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma and accounts for 3% of all pediatric cancer. In this study, we investigated germline sequence and structural variation in a broad set of genes in two large, independent RMS cohorts. MATERIALS AND METHODS: Genome sequencing of the discovery cohort (n = 273) and exome sequencing of the secondary cohort (n = 121) were conducted on germline DNA. Analyses were performed on 130 cancer susceptibility genes (CSG). Pathogenic or likely pathogenic (P/LP) variants were predicted using the American College of Medical Genetics and Genomics (ACMG) criteria. Structural variation and survival analyses were performed on the discovery cohort. RESULTS: We found that 6.6%-7.7% of patients with RMS harbored P/LP variants in dominant-acting CSG. An additional approximately 1% have structural variants (ATM , CDKN1C) in CSGs. CSG variants did not influence survival, although there was a significant correlation with an earlier age of tumor onset. There was a nonsignificant excess of P/LP variants in dominant inheritance genes in the patients with FOXO1 fusion–negative RMS patients versus the patients with FOXO1 fusion–positive RMS. We identified pathogenic germline variants in CSGs previously (TP53 , NF1 , DICER1 , mismatch repair genes), rarely (BRCA2 , CBL , CHEK2 , SMARCA4), or never (FGFR4) reported in RMS. Numerous genes (TP53 , BRCA2 , mismatch repair) were on the ACMG Secondary Findings 2.0 list. CONCLUSION: In two cohorts of patients with RMS, we identified pathogenic germline variants for which gene-specific therapies and surveillance guidelines may be beneficial. In families with a proband with an RMS-risk P/LP variant, genetic counseling and cascade testing should be considered, especially for ACMG Secondary Findings genes and/or with gene-specific surveillance guidelines. [ABSTRACT FROM AUTHOR]

PURPOSE: Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma and accounts for 3% of all pediatric cancer. In this study, we investigated germline sequence and structural variation in a broad set of genes in two large, independent RMS cohorts. MATERIALS AND METHODS: Genome sequencing of the discovery cohort (n = 273) and exome sequencing of the secondary cohort (n = 121) were conducted on germline DNA. Analyses were performed on 130 cancer susceptibility genes (CSG). Pathogenic or likely pathogenic (P/LP) variants were predicted using the American College of Medical Genetics and Genomics (ACMG) criteria. Structural variation and survival analyses were performed on the discovery cohort. RESULTS: We found that 6.6%-7.7% of patients with RMS harbored P/LP variants in dominant-acting CSG. An additional approximately 1% have structural variants (ATM , CDKN1C) in CSGs. CSG variants did not influence survival, although there was a significant correlation with an earlier age of tumor onset. There was a nonsignificant excess of P/LP variants in dominant inheritance genes in the patients with FOXO1 fusion–negative RMS patients versus the patients with FOXO1 fusion–positive RMS. We identified pathogenic germline variants in CSGs previously (TP53 , NF1 , DICER1 , mismatch repair genes), rarely (BRCA2 , CBL , CHEK2 , SMARCA4), or never (FGFR4) reported in RMS. Numerous genes (TP53 , BRCA2 , mismatch repair) were on the ACMG Secondary Findings 2.0 list. CONCLUSION: In two cohorts of patients with RMS, we identified pathogenic germline variants for which gene-specific therapies and surveillance guidelines may be beneficial. In families with a proband with an RMS-risk P/LP variant, genetic counseling and cascade testing should be considered, especially for ACMG Secondary Findings genes and/or with gene-specific surveillance guidelines. [ABSTRACT FROM AUTHOR]

Biological embedding occurs when life experience alters biological processes to affect later life health and well-being. Although extensive correlative data exist supporting the notion that epigenetic mechanisms such as DNA methylation underlie biological embedding, causal data are lacking. We describe specific epigenetic mechanisms and their potential roles in the biological embedding of experience. We also consider the nuanced relationships between the genome, the epigenome, and gene expression. Our ability to connect biological embedding to the epigenetic landscape in its complexity is challenging and complicated by the influence of multiple factors. These include cell type, age, the timing of experience, sex, and DNA sequence. Recent advances in molecular profiling and epigenome editing, combined with the use of comparative animal and human longitudinal studies, should enable this field to transition from correlative to causal analyses. [ABSTRACT FROM AUTHOR]

Motivation Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet. Results We introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity. Availability and implementation Processed data and software implementation using PyTorch (Paszke et al. , 2017) are available at: https://github.com/rampasek/DrVAE. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Highlights • New biomedical technologies generate measurements at scale and in multiple dimensions. • Large and diverse biomedical data present fundamentally new challenges for machine learning. • Integrative approaches combine different types of data to provide a comprehensive systems view. • Data integration creates a holistic picture of the cell, human body, and disease. • Advances in machine learning bring exciting future for biomedical data integration. Abstract New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field. [ABSTRACT FROM AUTHOR]

Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks. [ABSTRACT FROM AUTHOR]

Objectives Conditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the 'conditional random slope' (CRS) model, based on a linear-mixed effects model that incorporates a baseline-time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM. METHODS In two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years. RESULTS RTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation ( r = 0.65-0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38-0.78). Estimates of the velocity-stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics. CONCLUSION The CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses. [ABSTRACT FROM AUTHOR]

Energy substrates and metabolic intermediates are proven ligands of a growing number of G-protein coupled receptors. In 2004, GPR91 and GPR99 were identified as receptors for the citric acid cycle intermediates, succinate and α-ketoglutarate, respectively. GPR91 seems to act as a first responder to local stress and GPR99 participates in the regulation of the acid-base balance through an intrarenal paracrine mechanism. However, a systematic analysis of the distribution of both receptors in mouse organs is still missing. The aim of this study was to examine the expression of GPR91 and GPR99 in a large number of different murine organs both at mRNA and protein level. Whereas GPR91 mRNA was detectable in almost all organs, GPR99 mRNA was mainly expressed in neuronal tissues. Widespread expression of GPR91 was also detected at the protein level by western blotting and immunohistochemistry. In addition to neuronal cells, GPR99 protein was found in renal intercalated cells and epididymal narrow cells. Double-labeling immunohistochemistry demonstrated the colocalization of GPR99 with the B1 subunit isoform of vacuolar H-ATPases which is expressed only by a very limited number of cell types. In summary, our detailed expression analysis of GPR91 and GPR99 in murine tissues will allow a more directed search for additional functions of both receptors. [ABSTRACT FROM AUTHOR]

Growth and pubertal timing differ in boys and girls. Variants in/near LIN28B associate with age at menarche (AAM) in genome-wide association studies and some AAM-related variants associate with growth in a sex-specific manner. Sex-specific growth patterns in response to Lin28b perturbation have been detected in mice, and overexpression of Lin28a has been shown to alter pubertal timing in female mice. To investigate further how Lin28a and Lin28b affect growth and puberty in both males and females, we evaluated Lin28b loss-of-function (LOF) mice and Lin28a gain-of-function (GOF) mice. Because both Lin28a and Lin28b can act via the conserved microRNA let-7, we also examined let-7 GOF mice. As reported previously, Lin28b LOF led to lighter body weights only in male mice while Lin28a GOF yielded heavier mice of both sexes. Let-7 GOF mice weighed less than controls, and males were more affected than females. Timing of puberty was assessed by vaginal opening (VO) and preputial separation (PS). Male Lin28b LOF and male let-7 GOF, but not female, mice displayed alteration of pubertal timing, with later PS than controls. In contrast, both male and female Lin28a GOF mice displayed late onset of puberty. Together, these data point toward a complex system of regulation by Lin28a, Lin28b, and let-7, in which Lin28b and let-7 can impact both puberty and growth in a sex-specific manner, raising the possibility that this pathway may contribute to differential regulation of male and female growth and puberty in humans. [ABSTRACT FROM AUTHOR]

Motivation: Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping. Results: Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform. [ABSTRACT FROM AUTHOR]

Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival. [ABSTRACT FROM AUTHOR]

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA's differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html. [ABSTRACT FROM AUTHOR]

High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r² = 0.94) with the predicted abundances. [ABSTRACT FROM AUTHOR]

Aims Alveolar hypoxia acutely elicits contraction of pulmonary arteries, leading to a rise in pulmonary arterial pressure (PAP) and shifting blood to better ventilated areas of the lung. The molecular mechanisms underlying this hypoxic pulmonary vasoconstriction (HPV) are still incompletely understood. Here, we investigated the role of succinate dehydrogenase (SDH; synonymous to mitochondrial complex II) in HPV, with particular emphasis on regional differences along the vascular bed and consequences for PAP and perfusion-to-ventilation matching, using mutant mice heterozygous for the SDHD subunit of complex II (SDHD+/−). Methods and results Western blots revealed reduced protein content of complex II subunits SDHA, SDHB, and SDHC in lungs of SDHD+/− mice, despite unaffected mRNA content as determined by real-time PCR. Hypoxic pulmonary vasoconstriction of small (20–50 µm) intra-acinar and larger (51–100 µm) pre-acinar arteries was evaluated by videomorphometric analysis of precision-cut lung slices. The hypoxic response was detectable in pre-acinar arteries but absent from intra-acinar arteries of SDHD+/− mice. In isolated perfused lungs, basal PAP and its hypoxia-induced increase were indistinguishable between both mouse strains. Arterial oxygenation was measured after provocation of regional ventilatory failure by tracheal fluid instillation in anaesthetized mice, and it declined more in SDHD+/− than in wild-type mice. Conclusion SDHD is required for the formation of a stable mitochondrial complex II and it is selectively important for HPV of intra-acinar vessels. This specialized vascular segment participates in perfusion-to-ventilation matching but does not significantly contribute to the acute hypoxic rise in PAP that results from more proximal vasoconstriction. [ABSTRACT FROM AUTHOR]

Summary: Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data. Availability and implementation: PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub. [ABSTRACT FROM AUTHOR]

Accurnulating evidence suggests a pivotal role of the calcitonin receptor-like receptor (CRLR) signaling pathway in preventing damage of the lung by stabilizing pulmonary barrier function. Intermedin (IMD), also termed adrenomedullin-2, is the most recently identified peptide targeting this receptor. Here we investigated the effect of hypoxia on the expression of IMD in the murine lung and cultured murine pulmonary microvascular endothelial cells (PMEC) as well as the role of IMD in regulating vascular permeability. Monoclonal IMD antibodies were generated, and transcript levels were assayed by quantitative RT-PCR. The promoter region of IMD gene was analyzed, and the effect of hypoxia-inducible factor (HIF)-1α on IMD expression was investigated in HEK293T cells. Isolated murine lungs and a human lung microvascular endothelial cell monolayer model were used to study the effect of IMD on vascular permeability. IMD was identified as a pulmonary endothelial peptide by immunohistochemistry and RTPCR. Hypoxia caused an upregulation of IMD mRNA in the murine lung and PMEC. As shown by these results, HIF-1α enhances IMD promoter activity. Our functional studies showed that IMD abolished the increase in pressure-induced endothelial permeability. Moreover, IMD decreased basal and thrombin-induced hyperpermeability of an endothelial cell monolayer in a receptor-dependent manner and activated PKA in these cells. In conclusion, IMD is a novel hypoxiainduced gene and a potential interventional agent for the improvement of endothelial barrier function in systemic inflammatory responses and hypoxia-induced vascular leakage. [ABSTRACT FROM AUTHOR]

Background: Chronic hypoxia induces pulmonary arterial hypertension (PAH). Smooth muscle cell (SMC) proliferation and hypertrophy are important contributors to the remodeling that occurs in chronic hypoxic pulmonary vasculature. We hypothesized that rapamycin (RAPA), a potent cell cycle inhibitor, prevents pulmonary hypertension in chronic hypoxic mice. Methods: Mice were held either at normoxia (N; 21% O2) or at hypobaric hypoxia (H; 0.5 atm; ∼10% O2). RAPA-treated animals (3 mg/kg*d, i.p.) were compared to animals injected with vehicle alone. Proliferative activity within the pulmonary arteries was quantified by staining for Ki67 (positive nuclei/vessel) and media area was quantified by computer-aided planimetry after immunelabeling for a-smooth muscle actin (pixel/vessel). The ratio of right ventricle to left ventricle plus septum (RV/[LV+S]) was used to determine right ventricular hypertrophy. Results: Proliferative activity increased by 34% at day 4 in mice held under H (median: 0.38) compared to N (median: 0.28, p = 0.028) which was completely blocked by RAPA (median HO+RAPA: 0.23, p = 0.003). H-induced proliferation had leveled off within 3 weeks. At this time point media area had, however, increased by 53% from 91 (N) to 139 (H, p < 0.001) which was prevented by RAPA (H+RAPA: 102; p < 0.001). RV/[LV+S] ratio which had risen from 0.17 (N) to 0.26 (H, p < 0.001) was attenuated in the H+RAPA group (0.22, p = 0.041). For a therapeutic approach animals were exposed to H for 21 days followed by 21 days in H ± RAPA. Forty two days of H resulted in a media area of 129 (N: 83) which was significantly attenuated in RAPA-treated mice (H+RAPA: 92). RV/[LV+S] ratios supported prevention of PH (N 0.13; H 0.27; H+RAPA 0.17). RAPA treatment of N mice did not influence any parameter examined. Conclusion: Therapy with rapamycin may represent a new strategy for the treatment of pulmonary hypertension. [ABSTRACT FROM AUTHOR]

Background: Acute alveolar hypoxia causes pulmonary vasoconstriction (HPV) which serves to match lung perfusion to ventilation. The underlying mechanisms are not fully resolved yet. The major vascular segment contributing to HPV, the intra-acinar artery, is mostly located in that part of the lung that cannot be selectively reached by the presently available techniques, e.g. hemodynamic studies of isolated perfused lungs, recordings from dissected proximal arterial segments or analysis of subpleural vessels. The aim of the present study was to establish a model which allows the investigation of HPV and its underlying mechanisms in small intra-acinar arteries. Methods: Intra-acinar arteries of the mouse lung were studied in 200 μm thick precision-cut lung slices (PCLS). The organisation of the muscle coat of these vessels was characterized by a-smooth muscle actin immunohistochemistry. Basic features of intra-acinar HPV were characterized, and then the impact of reactive oxygen species (ROS) scavengers, inhibitors of the respiratory chain and Krebs cycle metabolites was analysed. Results: Intra-acinar arteries are equipped with a discontinuous spiral of α-smooth muscle actinimmunoreactive cells. They exhibit a monophasic HPV (medium gassed with 1% O2) that started to fade after 40 min and was lost after 80 min. This HPV, but not vasoconstriction induced by the thromboxane analogue U46619, was effectively blocked by nitro blue tetrazolium and diphenyleniodonium, indicating the involvement of ROS and flavoproteins. Inhibition of mitochondrial complexes II (3-nitropropionic acid, thenoyltrifluoroacetone) and III (antimycin A) specifically interfered with HPV, whereas blockade of complex IV (sodium azide) unspecifically inhibited both HPV and U46619-induced constriction. Succinate blocked HPV whereas fumarate had minor effects on vasoconstriction. Conclusion: This study establishes the first model for investigation of basic characteristics of HPV directly in intra-acinar murine pulmonary vessels. The data are consistent with a critical involvement of ROS, flavoproteins, and of mitochondrial complexes II and III in intra-acinar HPV. In view of the lack of specificity of any of the classical inhibitors used in such types of experiments, validation awaits the use of appropriate knockout strains and siRNA interference, for which the present model represents a well-suited approach. [ABSTRACT FROM AUTHOR]