Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data.

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Multicellular communities are perturbed in the aging human brain and Alzheimer's disease.

The role of different cell types and their interactions in Alzheimer's disease (AD) is a complex and open question. Here, we pursued this question by assembling a high-resolution cellular map of the aging frontal cortex using single-nucleus RNA sequencing of 24 individuals with a range of clinicopathologic characteristics. We used this map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA sequencing, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including a somatostatin inhibitory neuronal subtype and oligodendroglial states. We further identified a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and we found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

The Organ-Disease Annotations (ODiseA) Database of Hereditary Diseases and Inflicted Tissues.

Hereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable. Well-established resources, such as the Online Mendelian Inheritance in Man (OMIM) database and Human Phenotype Ontology (HPO), report on a spectrum of disease manifestations, yet do not highlight the main inflicted tissues. The Organ-Disease Annotations (ODiseA) database contains 4,357 thoroughly-curated annotations for 2,181 hereditary diseases and 45 inflicted tissues. Additionally, ODiseA reports 692 annotations of 635 diseases and the pathogenic tissues where they emerge. ODiseA can be queried by disease, disease gene, or inflicted tissue. Owing to its expansive, high-quality annotations, ODiseA serves as a valuable and unique tool for biomedical and computational researchers studying genotype-phenotype relationships of hereditary diseases. ODiseA is available at https://netbio.bgu.ac.il/odisea.

Fractional exhaled Nitric Oxide (FeNO) level as a predictor of COVID-19 disease severity.

OBJECTIVE: To assess the feasibility of Fractional exhaled Nitric Oxide (FeNO) as a simple, non-invasive, cost-effective and portable biomarker and decision support tool for risk stratification of COVID-19 patients. METHODS: We conducted a single-center prospective cohort study of COVID-19 patients whose FeNO levels were measured upon ward admission by the Vivatmo-me handheld device. Demographics, COVID-19 symptoms, and relevant hospitalization details were retrieved from the hospital databases. The patients were divided into those discharged to recover at home and those who died during hospitalization or required admission to an intensive care unit, internal medicine ward, or dedicated facility (severe outcomes group). RESULTS: Fifty-six patients were enrolled. The only significant demographic difference between the severe outcomes patients (n = 14) and the home discharge patients (n = 42) was age (64.21 ± 13.97 vs. 53.98 ± 15.57 years, respectively, P = .04). The admission FeNO measurement was significantly lower in the former group compared with the latter group (15.86 ± 14.74 vs. 25.77 ± 13.79, parts per billion [PPB], respectively, P = .008). Time to severe outcome among patients with FeNO measurements ≤11.8 PPB was significantly shorter compared with patients whose FeNO measured >11.8 PPB (19.25 ± 2.96 vs. 24.41 ± 1.09 days, respectively, 95% confidence interval [CI] 1.06 to 4.25). An admission FeNO ≤11.8 PPB was a significant risk factor for severe outcomes (odds ratio = 12.8, 95% CI: 2.78 to 58.88, P = .001), with a receiver operating characteristics curve of 0.752. CONCLUSIONS: FeNO measurements by the Vivatmo-me handheld device can serve as a biomarker and COVID-19 support tool for medical teams. These easy-to-use, portable, and noninvasive devices may serve as valuable ED bedside tools during a pandemic.

The differential activity of biological processes in tissues and cell subsets can illuminate disease-related processes and cell-type identities.

MOTIVATION: The distinct functionalities of human tissues and cell types underlie complex phenotype-genotype relationships, yet often remain elusive. Harnessing the multitude of bulk and single-cell human transcriptomes while focusing on processes can help reveal these distinct functionalities. RESULTS: The Tissue-Process Activity (TiPA) method aims to identify processes that are preferentially active or under-expressed in specific contexts, by comparing the expression levels of process genes between contexts. We tested TiPA on 1579 tissue-specific processes and bulk tissue transcriptomes, finding that it performed better than another method. Next, we used TiPA to ask whether the activity of certain processes could underlie the tissue-specific manifestation of 1233 hereditary diseases. We found that 21% of the disease-causing genes indeed participated in such processes, thereby illuminating their genotype-phenotype relationships. Lastly, we applied TiPA to single-cell transcriptomes of 108 human cell types, revealing that process activities often match cell-type identities and can thus aid annotation efforts. Hence, differential activity of processes can highlight the distinct functionality of tissues and cells in a robust and meaningful manner. AVAILABILITY AND IMPLEMENTATION: TiPA code is available in GitHub (https://github.com/moranshar/TiPA). In addition, all data are available as part of the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones.

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Through computational analyses of large-scale tissue transcriptomes, we unveil that the chaperone system is composed of core elements that are uniformly expressed across tissues, and variable elements that are differentially expressed to fit with tissue-specific requirements. We demonstrate via a proteomic analysis that the muscle-specific signature is functional and conserved. Core chaperones are significantly more abundant across tissues and more important for cell survival than variable chaperones. Together with variable chaperones, they form tissue-specific functional networks. Analysis of human organ development and aging brain transcriptomes reveals that these functional networks are established in development and decline with age. In this work, we expand the known functional organization of de novo versus stress-inducible eukaryotic chaperones into a layered core-variable architecture in multi-cellular organisms.

Cost containment by peer prior authorization program for second line treatment in patients with retinal disease.

BACKGROUND: High and increasing drug prices have prompted the establishment of a broad range of cost-containment treatment policies in health systems globally. In 2012, the supplemental insurance program of a large Israeli health maintenance organization (Clalit Health Services) introduced a prior authorization process for second-line use of ranibizumab in patients with retinal disease for whom treatment with bevacizumab proved to be ineffective. A Clalit steering committee established authorization criteria based on cost and periodically updated clinical considerations, while a team of ophthalmic specialists evaluated their colleagues' individual patient subsidization requests, based on the funding criteria. The objectives of this study were to detail this unique authorization process and study its effectiveness in limiting unwarranted spending, while allowing for a smooth transition to a second-line more expensive drug when needed. METHODS: A retrospective cohort study including all applications for a first or ongoing treatment with ranibizumab, for one or both eyes, received during March 1, 2012 - December 31, 2015. The key parameters examined were percentages of requests from patients treated by first line treatment bevacizumab, requests approved, reapplications, and results. Requests studied include reapplications and requests for treatment continuation. RESULTS: During the study period, Clalit affiliated ophthalmologists' submitted 16,778 funding applications for intravitreal ranibizumab treatment on behalf of 5642 patients who applied for approximately three applications. An efficient sentinel effect was achieved, resulting in only 31% of patients treated with bevacizumab applying for treatment, while maintaining extremely high accessibility to second line treatment with almost 95% of requests being approved. CONCLUSIONS: The data presented shows a low request rate for funding with a high approval rate, proving this peer reviewed report-based authorization process successfully achieved a sentinel effect while controlling cost. We suggest this innovative model be considered in similar decisions processes.

Dosage-sensitive molecular mechanisms are associated with the tissue-specificity of traits and diseases.

Hereditary diseases and complex traits often manifest in specific tissues, whereas their causal genes are expressed in many tissues that remain unaffected. Among the mechanisms that have been suggested for this enigmatic phenomenon is dosage-sensitive compensation by paralogs of causal genes. Accordingly, tissue-selectivity stems from dosage imbalance between causal genes and paralogs that occurs particularly in disease-susceptible tissues. Here, we used a large-scale dataset of thousands of tissue transcriptomes and applied a linear mixed model (LMM) framework to assess this and other dosage-sensitive mechanisms. LMM analysis of 382 hereditary diseases consistently showed evidence for dosage-sensitive compensation by paralogs across diseases subsets and susceptible tissues. LMM analysis of 135 candidate genes that are strongly associated with 16 tissue-selective complex traits revealed a similar tendency among half of the trait-associated genes. This suggests that dosage-sensitive compensation by paralogs affects the tissue-selectivity of complex traits, and can be used to illuminate candidate genes' modes of action. Next, we applied LMM to analyze dosage imbalance between causal genes and three classes of genetic modifiers, including regulatory micro-RNAs, pseudogenes, and genetic interactors. Our results propose modifiers as a fundamental axis in tissue-selectivity of diseases and traits, and demonstrates the power of LMM as a statistical framework for discovering treatment avenues.

Mechanisms of tissue and cell-type specificity in heritable traits and diseases.

Hundreds of heritable traits and diseases that are caused by germline aberrations in ubiquitously expressed genes manifest in a remarkably limited number of cell types and tissues across the body. Unravelling mechanisms that govern their tissue-specific manifestations is critical for our understanding of disease aetiologies and may direct efforts to develop treatments. Owing to recent advances in high-throughput technologies and open resources, data and tools are now available to approach this enigmatic phenomenon at large scales, both computationally and experimentally. Here, we discuss the large prevalence of tissue-selective traits and diseases, describe common molecular mechanisms underlying their tissue-selective manifestation and present computational strategies and publicly available resources for elucidating the molecular basis of their genotype-phenotype relationships.

Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes.

MOTIVATION: Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. RESULTS: Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82-0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. SUMMARY: Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. AVAILABILITY AND IMPLEMENTATION: Datasets are available as part of the Supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes.

Age-associated changes in CD4 T-cell functionality have been linked to chronic inflammation and decreased immunity. However, a detailed characterization of CD4 T cell phenotypes that could explain these dysregulated functional properties is lacking. We used single-cell RNA sequencing and multidimensional protein analyses to profile thousands of CD4 T cells obtained from young and old mice. We found that the landscape of CD4 T cell subsets differs markedly between young and old mice, such that three cell subsets-exhausted, cytotoxic, and activated regulatory T cells (aTregs)-appear rarely in young mice but gradually accumulate with age. Most unexpected were the extreme pro- and anti-inflammatory phenotypes of cytotoxic CD4 T cells and aTregs, respectively. These findings provide a comprehensive view of the dynamic reorganization of the CD4 T cell milieu with age and illuminate dominant subsets associated with chronic inflammation and immunity decline, suggesting new therapeutic avenues for age-related diseases.

Role of duplicate genes in determining the tissue-selectivity of hereditary diseases.

A longstanding puzzle in human genetics is what limits the clinical manifestation of hundreds of hereditary diseases to certain tissues, while their causal genes are expressed throughout the human body. A general conception is that tissue-selective disease phenotypes emerge when masking factors operate in unaffected tissues, but are specifically absent or insufficient in disease-manifesting tissues. Although this conception has critical impact on the understanding of disease manifestation, it was never challenged in a systematic manner across a variety of hereditary diseases and affected tissues. Here, we address this gap in our understanding via rigorous analysis of the susceptibility of over 30 tissues to 112 tissue-selective hereditary diseases. We focused on the roles of paralogs of causal genes, which are presumably capable of compensating for their aberration. We show for the first time at large-scale via quantitative analysis of omics datasets that, preferentially in the disease-manifesting tissues, paralogs are under-expressed relative to causal genes in more than half of the diseases. This was observed for several susceptible tissues and for causal genes with varying number of paralogs, suggesting that imbalanced expression of paralogs increases tissue susceptibility. While for many diseases this imbalance stemmed from up-regulation of the causal gene in the disease-manifesting tissue relative to other tissues, it was often combined with down-regulation of its paralog. Notably in roughly 20% of the cases, this imbalance stemmed only from significant down-regulation of the paralog. Thus, dosage relationships between paralogs appear as important, yet currently under-appreciated, modifiers of disease manifestation.

The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues.

Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein-protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues. TissueNet v.2 is available at http://netbio.bgu.ac.il/tissuenet.