Interspecies translation of disease networks increases robustness and predictive accuracy

Deubiquitinases in muscle physiology and disorders

In vivo, muscle and neuronal cells are post-mitotic, and their function is predominantly regulated by proteostasis, a multilayer molecular process that maintains a delicate balance of protein homeostasis. The ubiquitin-proteasome system (UPS) is a key regulator of proteostasis. A dysfunctional UPS is a hallmark of muscle ageing and is often impacted in neuromuscular disorders (NMDs). Malfunction of the UPS often results in aberrant protein accumulation which can lead to protein aggregation and/or mis-localization affecting its function. Deubiquitinating enzymes (DUBs) are key players in the UPS, controlling protein turnover and maintaining the free ubiquitin pool. Several mutations in DUB encoding genes are linked to human NMDs, such as ATXN3, OTUD7A, UCHL1 and USP14, whilst other NMDs are associated with dysregulation of DUB expression. USP5, USP9X and USP14 are implicated in synaptic transmission and remodeling at the neuromuscular junction. Mice lacking USP19 show increased maintenance of lean muscle mass. In this review, we highlight the involvement of DUBs in muscle physiology and NMDs, particularly in processes affecting muscle regeneration, degeneration and inflammation following muscle injury. DUBs have recently garnered much respect as promising drug targets, and their roles in muscle maturation, regeneration and degeneration may provide the framework for novel therapeutics to treat muscular disorders including NMDs, sarcopenia and cachexia.

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.

METHODS

Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.

RESULTS

Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.

DISCUSSION

AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.

HIGHLIGHTS

There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Research perspectives on animal health in the era of artificial intelligence

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009-2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

Age-Associated Salivary MicroRNA Biomarkers for Oculopharyngeal Muscular Dystrophy

Small non-coding microRNAs (miRNAs) are involved in the regulation of mRNA stability. Their features, including high stability and secretion to biofluids, make them attractive as potential biomarkers for diverse pathologies. This is the first study reporting miRNA as potential biomarkers for oculopharyngeal muscular dystrophy (OPMD), an adult-onset myopathy. We hypothesized that miRNA that is differentially expressed in affected muscles from OPMD patients is secreted to biofluids and those miRNAs could be used as biomarkers for OPMD. We first identified candidate miRNAs from OPMD-affected muscles and from muscles from an OPMD mouse model using RNA sequencing. We then compared the OPMD-deregulated miRNAs to the literature and, subsequently, we selected a few candidates for expression studies in serum and saliva biofluids using qRT-PCR. We identified 126 miRNAs OPMD-deregulated in human muscles, but 36 deregulated miRNAs in mice only (pFDR < 0.05). Only 15 OPMD-deregulated miRNAs overlapped between the in humans and mouse studies. The majority of the OPMD-deregulated miRNAs showed opposite deregulation direction compared with known muscular dystrophies miRNAs (myoMirs), which are associated. In contrast, similar dysregulation direction was found for 13 miRNAs that are common between OPMD and aging muscles. A significant age-association (p < 0.05) was found for 17 OPMD-deregulated miRNAs (13.4%), whereas in controls, only six miRNAs (1.4%) showed a significant age-association, suggesting that miRNA expression in OPMD is highly age-associated. miRNA expression in biofluids revealed that OPMD-associated deregulation in saliva was similar to that in muscles, but not in serum. The same as in muscle, miRNA expression levels in saliva were also found to be associated with age (p < 0.05). Moreover, the majority of OPMD-miRNAs were found to be associated with dysphagia as an initial symptom. We suggest that levels of specific miRNAs in saliva can mark muscle degeneration in general and dysphagia in OPMD.

Deacetylation Inhibition Reverses PABPN1-Dependent Muscle Wasting

Reduced poly(A)-binding protein nuclear 1 (PABPN1) levels cause aging-associated muscle wasting. PABPN1 is a multifunctional regulator of mRNA processing. To elucidate the molecular mechanisms causing PABPN1-mediated muscle wasting, we compared the transcriptome with the proteome in mouse muscles expressing short hairpin RNA to PABPN1 (shPab). We found greater variations in the proteome than in mRNA expression profiles. Protein accumulation in the shPab proteome was concomitant with reduced proteasomal activity. Notably, protein acetylation appeared to be decreased in shPab versus control proteomes (63%). Acetylome profiling in shPab muscles revealed prominent peptide deacetylation associated with elevated sirtuin-1 (SIRT1) deacetylase. We show that SIRT1 mRNA levels are controlled by PABPN1 via alternative polyadenylation site utilization. Most importantly, SIRT1 deacetylase inhibition by sirtinol increased PABPN1 levels and reversed muscle wasting. We suggest that perturbation of a multifactorial regulatory loop involving PABPN1 and SIRT1 plays an imperative role in aging-associated muscle wasting.

Video Abstract

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The PABPN1 transcriptome has smaller changes than its corresponding proteome

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The PABPN1 proteome is marked by protein deacetylation and elevated SIRT1 deacetylase

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SIRT1 levels are controlled by PABPN1 via alternative polyadenylation utilization

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Deacetylation inhibition reversed hallmark of muscle wasting in shPab muscles

Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.

Empirical comparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. We address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and enriched pathways. Semi-supervised training of a feed forward neural network was the most efficacious model for translating experimentally derived mouse biological associations to the human in vivo disease context. We find that computational generalization of signaling insights substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

Dysfunctional transcripts are formed by alternative polyadenylation in OPMD

Post-transcription mRNA processing in the 3’-untranslated region (UTR) of transcripts alters mRNA landscape. Alternative polyadenylation (APA) utilization in the 3’-UTR often leads to shorter 3’-UTR affecting mRNA stability, a process that is regulated by PABPN1. In skeletal muscles PABPN1 levels reduce with age and a greater decrease in found in Oculopharyngeal muscular dystrophy (OPMD). OPMD is a late onset autosomal dominant myopathy caused by expansion mutation in PABPN1. In OPMD models a shift from distal to proximal polyadenylation site utilization in the 3’-UTR, and PABPN1 was shown to play a prominent role in APA. Whether PABPN1-mediated APA transcripts are functional is not fully understood. We investigate nuclear export and translation efficiency of transcripts in OPMD models. We focused on autophagy-regulated genes (ATGs) with APA utilization in cell models with reduced functional PABPN1. We provide evidence that ATGs transcripts from distal PAS retain in the nucleus and thus have reduced translation efficiency in cells with reduced PABPN1. In contrast, transcripts from proximal PAS showed a higher cytoplasmic abundance but a reduced occupancy in the ribosome. We therefore suggest that in reduced PABPN1 levels ATG transcripts from APA may not effectively translate to proteins. In those conditions we found constitutive autophagosome fusion and reduced autophagy flux. Augmentation of PABPN1 restored autophagosome fusion, suggesting that PABPN1-mediated APA plays a role in autophagy in OPMD and in aging muscles.

PABPN1-Dependent mRNA Processing Induces Muscle Wasting

Poly(A) Binding Protein Nuclear 1 (PABPN1) is a multifunctional regulator of mRNA processing, and its expression levels specifically decline in aging muscles. An expansion mutation in PABPN1 is the genetic cause of oculopharyngeal muscle dystrophy (OPMD), a late onset and rare myopathy. Moreover, reduced PABPN1 expression correlates with symptom manifestation in OPMD. PABPN1 regulates alternative polyadenylation site (PAS) utilization. However, the impact of PAS utilization on cell and tissue function is poorly understood. We hypothesized that altered PABPN1 expression levels is an underlying cause of muscle wasting. To test this, we stably down-regulated PABPN1 in mouse tibialis anterior (TA) muscles by localized injection of adeno-associated viruses expressing shRNA to PABPN1 (shPab). We found that a mild reduction in PABPN1 levels causes muscle pathology including myofiber atrophy, thickening of extracellular matrix and myofiber-type transition. Moreover, reduced PABPN1 levels caused a consistent decline in distal PAS utilization in the 3’-UTR of a subset of OPMD-dysregulated genes. This alternative PAS utilization led to up-regulation of Atrogin-1, a key muscle atrophy regulator, but down regulation of proteasomal genes. Additionally reduced PABPN1 levels caused a reduction in proteasomal activity, and transition in MyHC isotope expression pattern in myofibers. We suggest that PABPN1-mediated alternative PAS utilization plays a central role in aging-associated muscle wasting.

PABPN1 is a multifunctional regulator of mRNA processing and its levels are reduced in skeletal muscles from midlife onwards. Reduced PABPN1 levels in a mouse model causes muscle atrophy and muscle fiber switches. We show that PABPN1-regulated muscle atrophy is regulated, in part, by up regulation of Atrogin1 and reduced expression of proteasome genes via an alternative polyadenylation site utilization. This study reveals a functional role for alternative polyadenylation site utilization in muscle atrophy and suggests a role for RNA processing in muscle aging.

Taking Bioinformatics to Systems Medicine

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

Evidence-based translation for the genomic responses of murine models for the study of human immunity

Murine models are an essential tool to study human immune responses and related diseases. However, the use of traditional murine models has been challenged by recent systemic surveys that show discordance between human and model immune responses in their gene expression. This is a significant problem in translational biomedical research for human immunity. Here, we describe evidence-based translation (EBT) to improve the analysis of genomic responses of murine models in the translation to human immune responses. Based on evidences from prior experiments, EBT introduces pseudo variances, penalizes gene expression changes in a model experiment, and finally detects false positive translations of model genomic responses that poorly correlate with human responses. Demonstrated over multiple data sets, EBT significantly improves the agreement of overall responses (up to 56%), experiment-specific responses (up to 143%), and enriched biological contexts (up to 100%) between human and model systems. In addition, we provide the category of genes specifically benefiting from EBT and the factors affecting the performance of EBT. The overall result indicates the usefulness of the proposed computational translation in biomedical research for human immunity using murine models.

Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating’ those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species.

Contact:pmeyerr@us.ibm.com or Julia.Hoeng@pmi.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions

MOTIVATION

MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power. However, such simple aggregation analysis results in identifying miRNA-gene interactions that are mostly common across datasets, whereas specific interactions may be missed by these methods. Recent releases of The Cancer Genome Atlas data provide paired expression profiling of miRNAs and genes in multiple tumors with sufficiently large sample size. To study both common and cancer-specific interactions, it is desirable to develop a method that can jointly analyze multiple cancers to study miRNA-gene interactions without combining all the data into one single dataset.

RESULTS

We developed a novel statistical method to jointly analyze expression profiles from multiple cancers to identify miRNA-gene interactions that are both common across cancers and specific to certain cancers. The benefit of this joint analysis approach is demonstrated by both simulation studies and real data analysis of The Cancer Genome Atlas datasets. Compared with simple aggregate analysis or single sample analysis, our method can effectively use the shared information among different but related cancers to improve the identification of miRNA-gene interactions. Another useful property of our method is that it can estimate similarity among cancers through their shared miRNA-gene interactions.

AVAILABILITY AND IMPLEMENTATION

The program, MCMG, implemented in R is available at http://bioinformatics.med.yale.edu/group/.

Bioinformatics tools in predictive ecology: applications to fisheries

There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their 'crossover potential' with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.