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We report that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with viral RNA. N-protein condenses with specific RNA genomic elements under physiological buffer conditions and condensation is enhanced at human body temperatures (33°C and 37°C) and reduced at room temperature (22°C). RNA sequence and structure in specific genomic regions regulate N-protein condensation while other genomic regions promote condensate dissolution, potentially preventing aggregation of the large genome. At low concentrations, N-protein preferentially crosslinks to specific regions characterized by single-stranded RNA flanked by structured elements and these features specify the location, number, and strength of N-protein binding sites (valency). Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is RNA sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules, and therefore presents a screenable process for identifying antiviral compounds effective against SARS-CoV-2.
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COVID-19/metabolismo , Proteínas do Nucleocapsídeo de Coronavírus/metabolismo , Genoma Viral , Nucleocapsídeo/metabolismo , RNA Viral/metabolismo , SARS-CoV-2/metabolismo , Animais , Antivirais/farmacologia , COVID-19/genética , Chlorocebus aethiops , Proteínas do Nucleocapsídeo de Coronavírus/genética , Avaliação Pré-Clínica de Medicamentos , Células HEK293 , Humanos , Nucleocapsídeo/genética , Fosfoproteínas/genética , Fosfoproteínas/metabolismo , SARS-CoV-2/genética , Células Vero , Tratamento Farmacológico da COVID-19RESUMO
Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.
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Predisposição Genética para Doença , Variação Genética , Modelos Genéticos , Mutação , Epigenômica , Expressão Gênica , Redes Reguladoras de Genes , Genoma Humano , Humanos , Aprendizado de Máquina , FenótipoRESUMO
To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.
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Aprendizado Profundo , Drosophila melanogaster , Animais , Caenorhabditis elegans/genética , Drosophila melanogaster/genética , Regulação da Expressão Gênica , Camundongos , Peixe-Zebra/genéticaRESUMO
Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.
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Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.
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Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica no Desenvolvimento/genética , Estudo de Associação Genômica Ampla/métodos , Algoritmos , Animais , Biologia Computacional/métodos , Simulação por Computador , Drosophila/genética , Desenvolvimento Embrionário/genética , Previsões/métodos , Regulação da Expressão Gênica no Desenvolvimento/fisiologia , Genes Controladores do Desenvolvimento/genética , Aprendizado de Máquina , Análise Espaço-Temporal , Transcriptoma/genéticaRESUMO
COVID-19 morbidity and mortality are increased via unknown mechanisms in patients with diabetes and kidney disease. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells. Because ACE2 is a susceptibility factor for infection, we investigated how diabetic kidney disease and medications alter ACE2 receptor expression in kidneys. Single cell RNA profiling of kidney biopsies from healthy living donors and patients with diabetic kidney disease revealed ACE2 expression primarily in proximal tubular epithelial cells. This cell-specific localization was confirmed by in situ hybridization. ACE2 expression levels were unaltered by exposures to renin-angiotensin-aldosterone system inhibitors in diabetic kidney disease. Bayesian integrative analysis of a large compendium of public -omics datasets identified molecular network modules induced in ACE2-expressing proximal tubular epithelial cells in diabetic kidney disease (searchable at hb.flatironinstitute.org/covid-kidney) that were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing. The diabetic kidney disease ACE2-positive proximal tubular epithelial cell module overlapped with expression patterns seen in SARS-CoV-2-infected cells. Similar cellular programs were seen in ACE2-positive proximal tubular epithelial cells obtained from urine samples of 13 hospitalized patients with COVID-19, suggesting a consistent ACE2-coregulated proximal tubular epithelial cell expression program that may interact with the SARS-CoV-2 infection processes. Thus SARS-CoV-2 receptor networks can seed further research into risk stratification and therapeutic strategies for COVID-19-related kidney damage.
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Enzima de Conversão de Angiotensina 2/metabolismo , COVID-19/metabolismo , Nefropatias Diabéticas/metabolismo , Túbulos Renais Proximais/metabolismo , SARS-CoV-2/metabolismo , Adulto , Idoso , Antagonistas de Receptores de Angiotensina/farmacologia , Antagonistas de Receptores de Angiotensina/uso terapêutico , Inibidores da Enzima Conversora de Angiotensina/farmacologia , Inibidores da Enzima Conversora de Angiotensina/uso terapêutico , COVID-19/complicações , COVID-19/virologia , Estudos de Casos e Controles , Nefropatias Diabéticas/tratamento farmacológico , Feminino , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Interações Hospedeiro-Patógeno , Humanos , Túbulos Renais Proximais/efeitos dos fármacos , Masculino , Pessoa de Meia-IdadeRESUMO
We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.
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Teste de Complementação Genética/métodos , Doenças Genéticas Inatas , Saccharomyces cerevisiae , Transcrição Gênica , Doenças Genéticas Inatas/genética , Doenças Genéticas Inatas/metabolismo , Humanos , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismoRESUMO
The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical-protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
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Biologia Computacional , Bases de Dados Genéticas , Proteínas , Animais , Biologia Computacional/métodos , Curadoria de Dados , Mineração de Dados , Humanos , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas , Processamento de Proteína Pós-Traducional , Proteínas/química , Proteínas/genética , Proteínas/metabolismo , SoftwareRESUMO
The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein interactions curated from the primary biomedical literature for all major model organism species and humans. As of September 2014, the BioGRID contains 749,912 interactions as drawn from 43,149 publications that represent 30 model organisms. This interaction count represents a 50% increase compared to our previous 2013 BioGRID update. BioGRID data are freely distributed through partner model organism databases and meta-databases and are directly downloadable in a variety of formats. In addition to general curation of the published literature for the major model species, BioGRID undertakes themed curation projects in areas of particular relevance for biomedical sciences, such as the ubiquitin-proteasome system and various human disease-associated interaction networks. BioGRID curation is coordinated through an Interaction Management System (IMS) that facilitates the compilation interaction records through structured evidence codes, phenotype ontologies, and gene annotation. The BioGRID architecture has been improved in order to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.
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Bases de Dados Genéticas , Redes Reguladoras de Genes , Mapeamento de Interação de Proteínas , Ácido Araquidônico/metabolismo , Doença/genética , Humanos , InternetRESUMO
Insulin-induced gene proteins (INSIGs) function in control of cellular cholesterol. Mammalian INSIGs exert control by directly interacting with proteins containing sterol-sensing domains (SSDs) when sterol levels are elevated. Mammalian 3-hydroxy-3-methylglutaryl (HMG)-CoA reductase (HMGR) undergoes sterol-dependent, endoplasmic-reticulum (ER)-associated degradation (ERAD) that is mediated by INSIG interaction with the HMGR SSD. The yeast HMGR isozyme Hmg2 also undergoes feedback-regulated ERAD in response to the early pathway-derived isoprene gernanylgeranyl pyrophosphate (GGPP). Hmg2 has an SSD, and its degradation is controlled by the INSIG homologue Nsg1. However, yeast Nsg1 promotes Hmg2 stabilization by inhibiting GGPP-stimulated ERAD. We have proposed that the seemingly disparate INSIG functions can be unified by viewing INSIGs as sterol-dependent chaperones of SSD clients. Accordingly, we tested the role of sterols in the Nsg1 regulation of Hmg2. We found that both Nsg1-mediated stabilization of Hmg2 and the Nsg1-Hmg2 interaction required the early sterol lanosterol. Lowering lanosterol in the cell allowed GGPP-stimulated Hmg2 ERAD. Thus, Hmg2-regulated degradation is controlled by a two-signal logic; GGPP promotes degradation, and lanosterol inhibits degradation. These data reveal that the sterol dependence of INSIG-client interaction has been preserved for over 1 billion years. We propose that the INSIGs are a class of sterol-dependent chaperones that bind to SSD clients, thus harnessing ER quality control in the homeostasis of sterols.
Assuntos
Degradação Associada com o Retículo Endoplasmático , Hidroximetilglutaril-CoA Redutases/metabolismo , Lanosterol/fisiologia , Chaperonas Moleculares/fisiologia , Proteínas de Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/fisiologia , Saccharomyces cerevisiae/metabolismo , Vias Biossintéticas , Inibidores Enzimáticos/farmacologia , Lanosterol/metabolismo , Ácido Mevalônico/metabolismo , Naftalenos/farmacologia , Fosfatos de Poli-Isoprenil/biossíntese , Ligação Proteica , Estabilidade Proteica , Proteólise , Esteróis/biossíntese , Terbinafina , Terpenos/metabolismoRESUMO
Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. Genetic variation in the enormous noncoding space is linked to the majority of disease risk. To address the problem of linking these variants to expression changes in primary human cells, we introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. We provide models for 105 primary human cell types covering 7 organ systems, demonstrate their accuracy, and then apply them to prioritize relevant cell types for complex human diseases. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We demonstrate the accuracy of our approach through systematic evaluations and apply the models to prioritize ClinVar clinical variants of uncertain significance, verifying our top predictions experimentally.
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Ascomicetos , Humanos , Expressão Gênica/genéticaRESUMO
Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
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The sterol-sensing domain (SSD) is a conserved motif in membrane proteins responsible for sterol regulation. Mammalian proteins SREBP cleavage-activating protein (SCAP) and HMG-CoA reductase (HMGR) both possess SSDs required for feedback regulation of sterol-related genes and sterol synthetic rate. Although these two SSD proteins clearly sense sterols, the range of signals detected by this eukaryotic motif is not clear. The yeast HMG-CoA reductase isozyme Hmg2, like its mammalian counterpart, undergoes endoplasmic reticulum (ER)-associated degradation that is subject to feedback control by the sterol pathway. The primary degradation signal for yeast Hmg2 degradation is the 20-carbon isoprene geranylgeranyl pyrophosphate, rather than a sterol. Nevertheless, the Hmg2 protein possesses an SSD, leading us to test its role in feedback control of Hmg2 stability. We mutated highly conserved SSD residues of Hmg2 and evaluated regulated degradation. Our results indicated that the SSD was required for sterol pathway signals to stimulate Hmg2 ER-associated degradation and was employed for detection of both geranylgeranyl pyrophosphate and a secondary oxysterol signal. Our data further indicate that the SSD allows a signal-dependent structural change in Hmg2 that promotes entry into the ER degradation pathway. Thus, the eukaryotic SSD is capable of significant plasticity in signal recognition or response. We propose that the harnessing of cellular quality control pathways to bring about feedback regulation of normal proteins is a unifying theme for the action of all SSDs.
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Retículo Endoplasmático/metabolismo , Hidroximetilglutaril-CoA Redutases/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/enzimologia , Retículo Endoplasmático/genética , Hidroximetilglutaril-CoA Redutases/genética , Isoenzimas/genética , Isoenzimas/metabolismo , Fosfatos de Poli-Isoprenil/metabolismo , Estrutura Terciária de Proteína , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genéticaRESUMO
Systematic study of tissue-specific function of enhancers and their disease associations is a major challenge. We present an integrative machine-learning framework, FENRIR, that integrates thousands of disparate epigenetic and functional genomics datasets to infer tissue-specific functional relationships between enhancers for 140 diverse human tissues and cell types, providing a regulatory-region-centric approach to systematically identify disease-associated enhancers. We demonstrated its power to accurately prioritize enhancers associated with 25 complex diseases. In a case study on autism, FENRIR-prioritized enhancers showed a significant proband-specific de novo mutation enrichment in a large, sibling-controlled cohort, indicating pathogenic signal. We experimentally validated transcriptional regulatory activities of eight enhancers, including enhancers not previously reported with autism, and demonstrated their differential regulatory potential between proband and sibling alleles. Thus, FENRIR is an accurate and effective framework for the study of tissue-specific enhancers and their role in disease. FENRIR can be accessed at fenrir.flatironinstitute.org/.
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Elementos Facilitadores Genéticos/genética , Redes Reguladoras de Genes/genética , HumanosRESUMO
Despite the strong genetic basis of psychiatric disorders, the underlying molecular mechanisms are largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translation to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. However, quantifying the pathogenic contribution of noncoding variants impacting RBP target sites is challenging. Here, we leverage a deep learning approach that can accurately predict the RBP target site dysregulation effects of mutations and discover that RBP dysregulation is a principal contributor to psychiatric disorder risk. RBP dysregulation explains a substantial amount of heritability not captured by large-scale molecular quantitative trait loci studies and has a stronger impact than common coding region variants. We share the genome-wide profiles of RBP dysregulation, which we use to identify DDHD2 as a candidate schizophrenia risk gene. This resource provides a new analytical framework to connect the full range of RNA regulation to complex disease.
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Transtornos Mentais/genética , Fosfolipases/genética , Proteínas de Ligação a RNA/genética , Regiões 3' não Traduzidas , Aprendizado Profundo , Regulação da Expressão Gênica , Frequência do Gene , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Mutação , Proteínas do Fator Nuclear 90/genética , Fatores de Alongamento de Peptídeos/genética , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , RNA Helicases/genética , Processamento Pós-Transcricional do RNA , Ribonucleoproteína Nuclear Pequena U5/genética , Esquizofrenia/genética , Transativadores/genéticaRESUMO
A mechanistic understanding of the SARS-CoV-2 viral replication cycle is essential to develop new therapies for the COVID-19 global health crisis. In this study, we show that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with the viral genome, and propose a model of viral packaging through LLPS. N-protein condenses with specific RNA sequences in the first 1000 nts (5'-End) under physiological conditions and is enhanced at human upper airway temperatures. N-protein condensates exclude non-packaged RNA sequences. We comprehensively map sites bound by N-protein in the 5'-End and find preferences for single-stranded RNA flanked by stable structured elements. Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules thus presenting screenable processes for identifying antiviral compounds effective against SARS-CoV-2.
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COVID-19 morbidity and mortality is increased in patients with diabetes and kidney disease via unknown mechanisms. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells. Since ACE2 is a susceptibility factor for infection, we investigated how diabetic kidney disease (DKD) and medications alter ACE2 receptor expression in kidneys. Single cell RNA profiling of healthy living donor (LD) and DKD kidney biopsies revealed ACE2 expression primarily in proximal tubular epithelial cells (PTEC). This cell specific localization was confirmed by in situ hybridization. ACE2 expression levels were unaltered by exposures to renin angiotensin aldosterone system inhibitors in DKD. Bayesian integrative analysis of a large compendium of public -omics datasets identified molecular network modules induced in ACE2-expressing PTEC in DKD (searchable at hb.flatironinstitute.org/covid-kidney) that were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing. The DKD ACE2-positive PTEC module overlapped with expression patterns seen in SARS-CoV-2 infected cells. Similar cellular programs were seen in ACE2-positive PTEC obtained from urine samples of 13 COVID-19 patients who were hospitalized, suggesting a consistent ACE2-coregulated PTEC expression program that may interact with the SARS-CoV-2 infection processes. Thus SARS-CoV-2 receptor networks can seed further research into risk stratification and therapeutic strategies for COVID-19 related kidney damage.
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The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82,000 manually-curated interactions.
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Bases de Dados de Proteínas , Proteômica , Proteínas de Saccharomyces cerevisiae/química , Gráficos por Computador , Genoma Fúngico , Internet , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genética , Análise de Sequência de Proteína , Interface Usuário-ComputadorRESUMO
A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSAHD (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine learning and the hierarchy of anatomical relationships present among diseases to integrate thousands of clinical gene expression profiles and identify molecular characteristics specific to each of the hundreds of complex diseases. URSAHD can distinguish between closely related diseases more accurately than literature-validated genes or traditional differential-expression-based computational approaches and is applicable to any disease, including rare and understudied ones. We demonstrate the utility of URSAHD in classifying related nervous system cancers and experimentally verifying novel neuroblastoma-associated genes identified by URSAHD. We highlight the applications for potential targeted drug-repurposing and for quantitatively assessing the molecular response to clinical therapies. URSAHD is freely available for public use, including the use of underlying models, at ursahd.princeton.edu.