Integrating personalized gene expression profiles into predictive disease-associated gene pools

Combinatorial Genomic Biomarkers Associated with High Response in IgE-Dependent Degranulation in Human Mast Cells

Mast cells are the major effector cells that mediate IgE-dependent allergic reactions. We sought to use integrated network analysis to identify genomic biomarkers associated with high response in IgE-mediated activation of primary human mast cells. Primary human mast cell cultures derived from 262 normal donors were categorized into High, Average and Low responder groups according to their activation response profiles. Transcriptome analysis was used to identify genes that were differentially expressed in different responder cultures in their baseline conditions, and the data were analyzed by constructing a personalized perturbed profile (PEEP). For upregulated genes, the construction of PEEP for each individual sample of all three responder groups revealed that High responders exhibited a higher percentage of "perturbed" samples whose PEEP values lay outside the normal range of expression. Moreover, the integration of PEEP of four selected upregulated genes into distinct sets of combinatorial profiles demonstrated that the specific pattern of upregulated expression of these four genes, in a tandem combination, was observed exclusively among the High responders. In conclusion, this combinatorial approach was useful in identifying a set of genomic biomarkers that are associated with high degranulation response in human mast cell cultures derived from the blood of a cohort of normal donors.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders

Autism spectrum disorder (ASD) comprises a large group of neurodevelopmental conditions featuring, over a wide range of severity and combinations, a core set of manifestations (restricted sociality, stereotyped behavior and language impairment) alongside various comorbidities. Common and rare variants in several hundreds of genes and regulatory regions have been implicated in the molecular pathogenesis of ASD along a range of causation evidence strength. Despite significant progress in elucidating the impact of few paradigmatic individual loci, such sheer complexity in the genetic architecture underlying ASD as a whole has hampered the identification of convergent actionable hubs hypothesized to relay between the vastness of risk alleles and the core phenotypes. In turn this has limited the development of strategies that can revert or ameliorate this condition, calling for a systems-level approach to probe the cross-talk of cooperating genes in terms of causal interaction networks in order to make convergences experimentally tractable and reveal their clinical actionability. As a first step in this direction, we have captured from the scientific literature information on the causal links between the genes whose variants have been associated with ASD and the whole human proteome. This information has been annotated in a computer readable format in the SIGNOR database and is made freely available in the resource website. To link this information to cell functions and phenotypes, we have developed graph algorithms that estimate the functional distance of any protein in the SIGNOR causal interactome to phenotypes and pathways. The main novelty of our approach resides in the possibility to explore the mechanistic links connecting the suggested gene-phenotype relations.

Graph-based multi-modality integration for prediction of cancer subtype and severity

Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Capturing the dynamics of microbial interactions through individual-specific networks

Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

Edge and modular significance assessment in individual-specific networks

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes

Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.

Subsequent AS01-adjuvanted vaccinations induce similar transcriptional responses in populations with different disease statuses

Transcriptional responses to adjuvanted vaccines can vary substantially among populations. Interindividual diversity in levels of pathogen exposure, and thus of cell-mediated immunological memory at baseline, may be an important determinant of population differences in vaccine responses. Adjuvant System AS01 is used in licensed or candidate vaccines for several diseases and populations, yet the impact of pre-existing immunity on its adjuvanticity remains to be elucidated. In this exploratory post-hoc analysis of clinical trial samples (clinicalTrials.gov: NCT01424501), we compared gene expression patterns elicited by two immunizations with the candidate tuberculosis (TB) vaccine M72/AS01, between three groups of individuals with different levels of memory responses to TB antigens before vaccination. Analyzed were one group of TB-disease-treated individuals, and two groups of TB-disease-naïve individuals who were (based on purified protein derivative [PPD] skin-test results) stratified into PPD-positive and PPD-negative groups. Although TB-disease-treated individuals displayed slightly stronger transcriptional responses after each vaccine dose, functional gene signatures were overall not distinctly different between groups. Considering the similarities with the signatures found previously for other AS01-adjuvanted vaccines, many features of the response appeared to be adjuvant-driven. Across groups, cell proliferation-related signals at 7 days post-dose 1 were associated with increased anti-M72 antibody response magnitudes. These early signals were stronger in the TB-disease-treated group as compared to both TB-disease-naïve groups. Interindividual homogeneity in gene expression levels was also higher for TB-disease-treated individuals post-dose 1, but increased in all groups post-dose 2 to attain similar levels between the three groups. Altogether, strong cell-mediated memory responses at baseline accelerated and amplified transcriptional responses to a single dose of this AS01-adjuvanted vaccine, resulting in more homogenous gene expression levels among the highly-primed individuals as compared to the disease-naïve individuals. However, after a second vaccination, response heterogeneity decreased and was similar across groups, irrespective of the degree of immune memory acquired at baseline. This information can support the design and analysis of future clinical trials evaluating AS01-adjuvanted vaccines.

Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression

OBJECTIVES

Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions.

METHODS

Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores.

RESULTS

MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes.

CONCLUSIONS

MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Biological Random Walks: multi-omics integration for disease gene prioritization

MOTIVATION

Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration.

RESULTS

In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines.

AVAILABILITY AND IMPLEMENTATION

All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual’s Disease-Related Signature

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases

Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in "prelesional" skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.

Systematic characterization of human response to H1N1 influenza vaccination through the construction and integration of personalized transcriptome response profiles

Gene expression data is commonly used in vaccine studies to characterize differences between treatment groups or sampling time points. Group-wise comparisons of the transcriptional perturbations induced by vaccination have been applied extensively for investigating the mechanisms of action of vaccines. Such approaches, however, may not be sensitive enough for detecting changes occurring within a minority of the population under investigation or in single individuals. In this study, we developed a data analysis framework to characterize individual subject response profiles in the context of repeated measure experiments, which are typical of vaccine mode of action studies. Following the definition of the methodology, this was applied to the analysis of human transcriptome responses induced by vaccination with a subunit influenza vaccine. Results highlighted a substantial heterogeneity in how different subjects respond to vaccination. Moreover, the extent of transcriptional modulation experienced by each individual subject was found to be associated with the magnitude of vaccine-specific functional antibody response, pointing to a mechanistic link between genes involved in protein production and innate antiviral response. Overall, we propose that the improved characterization of the intersubject heterogeneity, enabled by our approach, can help driving the improvement and optimization of current and next-generation vaccines.

The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes

Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.

Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Simple Summary

Here we introduce a new feature selection algorithm DTA, which selects important, non-redundant, and relevant features from diverse omics data. DTA selects non-redundant features by maximizing the similarity between each patient pair by an approximate k-cover algorithm. We successfully applied this algorithm to three different biological problems: (a) disease to healthy sample classification, (b) multiclass classification of different disease samples, and (c) disease subtypes detection. DTA outperformed other feature selection techniques in the binary classification of healthy and disease samples and multiclass classification of various diseases. It also improved the performance of a subtype detection algorithm by selecting the important features for few cancer types.

Abstract

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

Predicting anticancer hyperfoods with graph convolutional networks

Background

Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics.

Results

The proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules’ effects on cancer-related pathways.

Conclusions

We introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics.

Supplementary Information

The online version contains supplementary material available at (10.1186/s40246-021-00333-4).

Systems Approaches to Treatment Response to Imatinib in Severe Asthma: A Pilot Study

There is an acute need for advances in pharmacologic therapies and a better understanding of novel drug targets for severe asthma. Imatinib, a tyrosine kinase inhibitor, has been shown to improve forced expiratory volume in 1 s (FEV1) in a clinical trial of patients with severe asthma. In a pilot study, we applied systems biology approaches to epithelium gene expression from these clinical trial patients treated with imatinib to better understand lung function response with imatinib treatment. Bronchial brushings from ten imatinib-treated patient samples and 14 placebo-treated patient samples were analyzed. We used personalized perturbation profiles (PEEPs) to characterize gene expression patterns at the individual patient level. We found that strong responders-patients with greater than 20% increase in FEV1-uniquely shared multiple downregulated mitochondrial-related pathways. In comparison, weak responders (5-10% FEV1 increase), and non-responders to imatinib shared none of these pathways. The use of PEEP highlights its potential for application as a systems biology tool to develop individual-level approaches to predicting disease phenotypes and response to treatment in populations needing innovative therapies. These results support a role for mitochondrial pathways in airflow limitation in severe asthma and as potential therapeutic targets in larger clinical trials.

Network Analysis Reveals Proteins Associated with Aortic Dilatation in Mucopolysaccharidoses

Mucopolysaccharidoses are caused by a deficiency of enzymes involved in the degradation of glycosaminoglycans. Heart diseases are a significant cause of morbidity and mortality in MPS patients, even in conditions in which enzyme replacement therapy is available. In this sense, cardiovascular manifestations, such as heart hypertrophy, cardiac function reduction, increased left ventricular chamber, and aortic dilatation, are among the most frequent. However, the downstream events which influence the heart dilatation process are unclear. Here, we employed systems biology tools together with transcriptomic data to investigate new elements that may be involved in aortic dilatation in Mucopolysaccharidoses syndrome. We identified candidate genes involved in biological processes related to inflammatory responses, deposition of collagen, and lipid accumulation in the cardiovascular system that may be involved in aortic dilatation in the Mucopolysaccharidoses I and VII. Furthermore, we investigated the molecular mechanisms of losartan treatment in Mucopolysaccharidoses I mice to underscore how this drug acts to prevent aortic dilation. Our data indicate that the association between the TGF-b signaling pathway, Fos, and Col1a1 proteins can play an essential role in aortic dilation's pathophysiology and its subsequent improvement by losartan treatment.

Multiple therapeutic targets in rare cholestatic liver diseases: Time to redefine treatment strategies

Primary biliary cholangitis and primary sclerosing cholangitis are rare diseases affecting the bile ducts and the liver. The limited knowledge of their pathogenesis leads to limited therapeutic options. Nevertheless, the landscape of novel therapies for these cholangiopathies is now rapidly changing, providing new treatment opportunities for patients and clinicians involved in their care. The aim of this review is to summarize the evidence of novel molecules under investigation for primary biliary cholangitis and primary sclerosing cholangitis and to discuss how they can potentially change current treatment paradigms.

A Systems Bioinformatics Approach to Interconnect Biological Pathways

Signal transduction tasks as well as other complex biological processes involve many different changes in groups of genes, proteins, and metabolites linked together in chains or networks called pathways or networks of pathways. In a classical functional analysis, the biomolecules found to play a role in the biological status under investigation are members of a group of pathways that are not necessarily interconnected. However, interconnectivity is a critical factor for functionality. Thus, it is necessary to be able to construct "connected functional stories" to understand better the complex biological processes. PathwayConnector is a recently introduced web-tool that facilitates the construction of complementary pathway-to-pathway networks, bringing to our attention missing pathways that are crucial links towards the understanding of the molecular mechanisms related to complex diseases. Current version of the web-tool draws from an expanded pathway reference network and provides information deriving from 19 different organisms and 2 different pathway repositories: the KEGG and the REACTOME. Novel genes, proteins, and pathways derived from any experimental/computational method either in large-scale (omics) or even in smaller scale (specific laboratory experiments) can potentially be projected and analyzed through PathwayConnector. This chapter describes in details the pipeline and methodologies used for the latest updated version of PathwayConnector, providing an easy way for rapidly relating human or other organism's pathways together. Recent studies have shown that pathway networks and subnetworks, generated by PathwayConnector, are an integral part towards the individualization of disease, leading to a more precise and personalized management of the treatment.

Transcriptional profiles of adjuvanted hepatitis B vaccines display variable interindividual homogeneity but a shared core signature

The current routine use of adjuvants in human vaccines provides a strong incentive to increase our understanding of how adjuvants differ in their ability to stimulate innate immunity and consequently enhance vaccine immunogenicity. Here, we evaluated gene expression profiles in cells from whole blood elicited in naive subjects receiving the hepatitis B surface antigen formulated with different adjuvants. We identified a core innate gene signature emerging 1 day after the second vaccination and that was shared by the recipients of vaccines formulated with adjuvant systems AS01B, AS01E, or AS03. This core signature associated with the magnitude of the hepatitis B surface-specific antibody response and was characterized by positive regulation of genes associated with interferon-related responses or the innate cell compartment and by negative regulation of natural killer cell–associated genes. Analysis at the individual subject level revealed that the higher immunogenicity of AS01B-adjuvanted vaccine was linked to its ability to induce this signature in most vaccinees even after the first vaccination. Therefore, our data suggest that adjuvanticity is not strictly defined by the nature of the receptors or signaling pathways it activates but by the ability of the adjuvant to consistently induce a core inflammatory signature across individuals.

Status and future directions of clinical trials in Parkinson's disease

Novel therapies are needed to treat Parkinson's disease (PD) in which the clinical unmet need is pressing. Currently, no clinically available therapeutic strategy can either retard or reverse PD or repair its pathological consequences. l-DOPA (levodopa) is still the gold standard therapy for motor symptoms yet symptomatic therapies for both motor and non-motor symptoms are improving. Many on-going, intervention trials cover a broad range of targets, including cell replacement and gene therapy approaches, quality of life improving technologies, and disease-modifying strategies (e.g., controlling aberrant α-synuclein accumulation and regulating cellular/neuronal bioenergetics). Notably, the repurposing of glucagon-like peptide-1 analogues with potential disease-modifying effects based on metabolic pathology associated with PD has been promising. Nevertheless, there is a clear need for improved therapeutic and diagnostic options, disease progression tracking and patient stratification capabilities to deliver personalized treatment and optimize trial design. This review discusses some of the risk factors and consequent pathology associated with PD and particularly the metabolic aspects of PD, novel therapies targeting these pathologies (e.g., mitochondrial and lysosomal dysfunction, oxidative stress, and inflammation/neuroinflammation), including the repurposing of metabolic therapies, and unmet needs as potential drivers for future clinical trials and research in PD.

A personalised approach for identifying disease-relevant pathways in heterogeneous diseases

Numerous time-course gene expression datasets have been generated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data while accounting for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive treatments. We present a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to datasets from heterogenous diseases. Our method can be applied to study the time-course dynamics, as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on three longitudinal type 1 diabetes (T1D) datasets, where the first two are used to determine perturbations taking place during early prognosis of the disease, as well as in time-windows before autoantibody positivity and T1D diagnosis; and the third is used to assess the generalisability of our method. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying disease-relevant pathways, our approach could be useful for predicting events in the progression of heterogeneous diseases and even for biomarker identification.

Disease characterization using a partial correlation-based sample-specific network

A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.

Restoring synovial homeostasis in rheumatoid arthritis by targeting fibroblast-like synoviocytes

Rheumatoid arthritis (RA) is a chronic immune-mediated disease that primarily affects the synovium of diarthrodial joints. During the course of RA, the synovium transforms into a hyperplastic invasive tissue that causes destruction of cartilage and bone. Fibroblast-like synoviocytes (FLS), which form the lining of the joint, are epigenetically imprinted with an aggressive phenotype in RA and have an important role in these pathological processes. In addition to producing the extracellular matrix and joint lubricants, FLS in RA produce pathogenic mediators such as cytokines and proteases that contribute to disease pathogenesis and perpetuation. The development of multi-omics integrative analyses have enabled new ways to dissect the mechanisms that imprint FLS, have helped to identify potential FLS subsets with distinct functions and have identified differences in FLS phenotypes between joints in individual patients. This Review provides an overview of advances in understanding of FLS biology and highlights omics approaches and studies that hold promise for identifying future therapeutic targets.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

New Therapeutic Targets in Autoimmune Cholangiopathies

Primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) are autoimmune cholangiopathies characterized by limited treatment options. A more accurate understanding of the several pathways involved in these diseases has fostered the development of novel and promising targeted drugs. For PBC, the characterization of the role of farnesoid X receptor (FXR) and perixosome-proliferator activated receptor (PPAR) has paved the way to several clinical trials including different molecules with choleretic and antinflammatory action. Conversely, different pathogenetic models have been proposed in PSC such as the “leaky gut” hypothesis, a dysbiotic microbiota or a defect in mechanisms protecting against bile acid toxicity. Along these theories, new treatment approaches have been developed, ranging from drugs interfering with trafficking of lymphocytes from the gut to the liver, fecal microbiota transplantation or new biliary acids with possible immunomodulatory potential. Finally, for both diseases, antifibrotic agents are under investigation. In this review, we will illustrate current understanding of molecular mechanisms in PBC and PSC, focusing on actionable biological pathways for which novel treatments are being developed.

RNAseq expression patterns of canine invasive urothelial carcinoma reveal two distinct tumor clusters and shared regions of dysregulation with human bladder tumors

BACKGROUND

Invasive urothelial carcinoma (iUC) is highly similar between dogs and humans in terms of pathologic presentation, molecular subtypes, response to treatment and age at onset. Thus, the dog is an established and relevant model for testing and development of targeted drugs benefiting both canine and human patients. We sought to identify gene expression patterns associated with two primary types of canine iUC tumors: those that express a common somatic mutation in the BRAF gene, and those that do not.

METHODS

We performed RNAseq on tumor and normal tissues from pet dogs. Analysis of differential expression and clustering, and positional and individual expression was used to develop gene set enrichment profiles distinguishing iUC tumors with and without BRAFV595E mutations, as well as genomic regions harboring excessive numbers of dysregulated genes.

RESULTS

We identified two expression clusters that are defined by the presence/absence of a BRAFV595E (BRAFV600E in humans) somatic mutation. BRAFV595E tumors shared significantly more dysregulated genes than BRAF wild-type tumors, and vice versa, with 398 genes differentiating the two clusters. Key genes fall into clades of limited function: tissue development, cell cycle regulation, immune response, and membrane transport. The genomic site with highest number of dysregulated genes overall lies in a locus corresponding to human chromosome 8q24, a region frequently amplified in human urothelial cancers.

CONCLUSIONS

These data identify critical sets of genes that are differently regulated in association with an activating mutation in the MAPK/ERK pathway in canine iUC tumors. The experiments also highlight the value of the canine system in identifying expression patterns associated with a common, shared cancer.

Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome

The molecular and clinical features of a complex disease can be influenced by other diseases affecting the same individual. Understanding disease-disease interactions is therefore crucial for revealing shared molecular mechanisms among diseases and designing effective treatments. Here we introduce Flow Centrality (FC), a network-based approach to identify the genes mediating the interaction between two diseases in a protein-protein interaction network. We focus on asthma and COPD, two chronic respiratory diseases that have been long hypothesized to share common genetic determinants and mechanisms. We show that FC highlights potential mediator genes between the two diseases, and observe similar outcomes when applying FC to 66 additional pairs of related diseases. Further, we perform in vitro perturbation experiments on a widely replicated asthma gene, GSDMB, showing that FC identifies candidate mediators of the interactions between GSDMB and COPD-associated genes. Our results indicate that FC predicts promising gene candidates for further study of disease-disease interactions.

Whole Genome Analysis of a Single Scottish Deerhound Dog Family Provides Independent Corroboration That a SGK3 Coding Variant Leads to Hairlessness

The breeds of domestic dog, Canis lupus familiaris, display a range of coat types with variation in color, texture, length, curl, and growth pattern. One trait of interest is that of partial or full hairlessness, which is found in a small number of breeds. While the standard for some breeds, such as the Xoloitzcuintli, requires sparse hair on their extremities, others are entirely bald, including the American Hairless Terrier. We identified a small, rare family of Scottish Deerhounds in which coated parents produced a mixed litter of coated and hairless offspring. To identify the underlying variant, we performed whole genome sequencing of the dam and five offspring, comparing single nucleotide polymorphisms and small insertions/deletions against an established catalog of 91 million canine variants. Of 325 homozygous alternative alleles found in both hairless dogs, 56 displayed the expected pattern of segregation and only a single, high impact variant within a coding region was observed: a single base pair insertion in exon two of SGK3 leading to a potential frameshift, thus verifying recently published findings. In addition, we observed that gene expression levels between coated and hairless dogs are similar, suggesting a mechanism other than non-sense mediated decay is responsible for the phenotype.

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.

Dysregulated expression of repetitive DNA in ER+/HER2- breast cancer

Limited studies on breast cancer indicated pathogenic changes in the expressions of some repeat elements. A global analysis was much needed within this context to distinguish the most significant repeats from more than a thousand repeat motifs. Utilising a previously presented RNA-seq dataset, we studied expression changes of all repeats in ER+/HER2- human breast tumour samples obtained from 22 patients in comparison to matched normal tissues. Fifty six (56) repeat subtypes including satellites and transposons were found to be differentially expressed and most of them were novel for breast cancer. HERVKC4-int and HERV1_LTRc, whose expressions correlated well with that of the estrogen receptor gene ESR1, were upregulated at the highest level. REP522 and D20S16 satellites were also significantly upregulated along with insignificant increases in the expressions of other satellites including HSATI and BSR/beta. Interestingly, expressions of REP522 and D20S16 correlated with many key breast cancer pathway (e.g. BRCA1, BRCA2, AKT1, MTOR, KRAS) and survival genes; possibly highlighting their importance in the carcinogenesis of breast. Additional differentially expressed elements such as L1P and various MER transposons also exhibited a similar pattern. Finally, our repeat enrichment analysis on the promoters of differentially expressed genes revealed further links between additional repeats and nearby genes.

DISNOR: a disease network open resource

DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage. DISNOR can be freely accessed at http://DISNOR.uniroma2.it/ where >3700 disease-networks, linking ∼2600 disease genes, can be explored. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred ‘patho-pathways’ at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes—either annotated in DisGeNET or user-defined—DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function offers additional information on disease-associated cellular pathways and disease similarity.

Evaluation of the Common Molecular Basis in Alzheimer's and Parkinson's Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificant similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.

Disease classification: from phenotypic similarity to integrative genomics and beyond

A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

Δ9-tetrahydrocannabinol modulates the proteasome system in the brain

Cannabis is the most consumed illicit drug worldwide. Its principal psychoactive component, Δ⁹-tetrahydrocannabinol (THC), affects multiple brain functions, including cognitive performance, by modulating cannabinoid type-1 (CB1) receptors. These receptors are strongly enriched in presynaptic terminals, where they modulate neurotransmitter release. We analyzed, through a proteomic screening of hippocampal synaptosomal fractions, those proteins and pathways modulated 3 h after a single administration of an amnesic dose of THC (10 mg/kg, i.p.). Using an isobaric labeling approach, we identified 2040 proteins, 1911 of them previously reported in synaptic proteomes, confirming the synaptic content enrichment of the samples. Initial analysis revealed a significant alteration of 122 proteins, where 42 increased and 80 decreased their expression. Gene set enrichment analysis indicated an over-representation of mitochondrial associated functions and cellular metabolic processes. A second analysis focusing on extreme changes revealed 28 proteins with altered expression after THC treatment, 15 of them up-regulated and 13 down-regulated. Using a network topology-based scoring algorithm we identified those proteins in the mouse proteome with the greatest association to the 28 modulated proteins. This analysis pinpointed a significant alteration of the proteasome function, since top scoring proteins were related to the proteasome system (PS), a protein complex involved in ATP-dependent protein degradation. In this regard, we observed that THC decreases 20S proteasome chymotrypsin-like protease activity in the hippocampus. Our data describe for the first time the modulation of the PS in the hippocampus following THC administration under amnesic conditions that may contribute to an aberrant plasticity at synapses.

A Census of Disease Ontologies

For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.

An integrative approach to develop computational pipeline for drug-target interaction network analysis

Understanding the general principles governing the functioning of biological networks is a major challenge of the current era. Functionality of biological networks can be observed from drug and target interaction perspective. All possible modes of operations of biological networks are confined by the interaction analysis. Several of the existing approaches in this direction, however, are data-driven and thus lack potential to be generalized and extrapolated to different species. In this paper, we demonstrate a systems pharmacology pipeline and discuss how the network theory, along with gene ontology (GO) analysis, co-expression analysis, module re-construction, pathway mapping and structure level analysis can be used to decipher important properties of biological networks with the aim to propose lead molecule for the therapeutic interventions of various diseases.

Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective

Introduction

Comorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on healthcare. Identification of cost-effective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways explaining co-occurrence of diseases.

Methods

The study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset.

Results

The results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it.

Conclusion

The results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients.

Inborn errors of metabolism and the human interactome: a systems medicine approach

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

A roadmap towards personalized immunology

Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.

BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology

Biotechnology revolution generates a plethora of omics data with an exponential growth pace. Therefore, biological data mining demands automatic, 'high quality' curation efforts to organize biomedical knowledge into online databases. BioDataome is a database of uniformly preprocessed and disease-annotated omics data with the aim to promote and accelerate the reuse of public data. We followed the same preprocessing pipeline for each biological mart (microarray gene expression, RNA-Seq gene expression and DNA methylation) to produce ready for downstream analysis datasets and automatically annotated them with disease-ontology terms. We also designate datasets that share common samples and automatically discover control samples in case-control studies. Currently, BioDataome includes ∼5600 datasets, ∼260 000 samples spanning ∼500 diseases and can be easily used in large-scale massive experiments and meta-analysis. All datasets are publicly available for querying and downloading via BioDataome web application. We demonstrate BioDataome's utility by presenting exploratory data analysis examples. We have also developed BioDataome R package found in: https://github.com/mensxmachina/BioDataome/.Database URL: http://dataome.mensxmachina.org/.