During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical ...strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.
IMPORTANCE: Deviation from normal adolescent brain development precedes manifestations of many major psychiatric symptoms. Such altered developmental trajectories in adolescents may be linked to ...genetic risk for psychopathology. OBJECTIVE: To identify genetic variants associated with adolescent brain structure and explore psychopathologic relevance of such associations. DESIGN, SETTING, AND PARTICIPANTS: Voxelwise genome-wide association study in a cohort of healthy adolescents aged 14 years and validation of the findings using 4 independent samples across the life span with allele-specific expression analysis of top hits. Group comparison of the identified gene-brain association among patients with schizophrenia, unaffected siblings, and healthy control individuals. This was a population-based, multicenter study combined with a clinical sample that included participants from the IMAGEN cohort, Saguenay Youth Study, Three-City Study, and Lieber Institute for Brain Development sample cohorts and UK biobank who were assessed for both brain imaging and genetic sequencing. Clinical samples included patients with schizophrenia and unaffected siblings of patients from the Lieber Institute for Brain Development study. Data were analyzed between October 2015 and April 2018. MAIN OUTCOMES AND MEASURES: Gray matter volume was assessed by neuroimaging and genetic variants were genotyped by Illumina BeadChip. RESULTS: The discovery sample included 1721 adolescents (873 girls 50.7%), with a mean (SD) age of 14.44 (0.41) years. The replication samples consisted of 8690 healthy adults (4497 women 51.8%) from 4 independent studies across the life span. A nonsynonymous genetic variant (minor T allele of rs13107325 in SLC39A8, a gene implicated in schizophrenia) was associated with greater gray matter volume of the putamen (variance explained of 4.21% in the left hemisphere; 8.66; 95% CI, 6.59-10.81; P = 5.35 × 10−18; and 4.44% in the right hemisphere; t = 8.90; 95% CI, 6.75-11.19; P = 6.80 × 10−19) and also with a lower gene expression of SLC39A8 specifically in the putamen (t127 = −3.87; P = 1.70 × 10−4). The identified association was validated in samples across the life span but was significantly weakened in both patients with schizophrenia (z = −3.05; P = .002; n = 157) and unaffected siblings (z = −2.08; P = .04; n = 149). CONCLUSIONS AND RELEVANCE: Our results show that a missense mutation in gene SLC39A8 is associated with larger gray matter volume in the putamen and that this association is significantly weakened in schizophrenia. These results may suggest a role for aberrant ion transport in the etiology of psychosis and provide a target for preemptive developmental interventions aimed at restoring the functional effect of this mutation.
•AI allows to integrate massive multi-modal data to build up predictive models.•Modelling of complex heterogeneous diseases allows to identify therapeutic targets.•AI facilitates the design, ...selection and repurposing of drugs interacting with targets.•AI drives the emergence of a computational precision medicine.
Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.
Combination therapies exploit the chances for better efficacy, decreased toxicity, and reduced development of drug resistance and owing to these advantages, have become a standard for the treatment ...of several diseases and continue to represent a promising approach in indications of unmet medical need. In this context, studying the effects of a combination of drugs in order to provide evidence of a significant superiority compared to the single agents is of particular interest. Research in this field has resulted in a large number of papers and revealed several issues. Here, we propose an overview of the current methodological landscape concerning the study of combination effects. First, we aim to provide the minimal set of mathematical and pharmacological concepts necessary to understand the most commonly used approaches, divided into effect‐based approaches and dose–effect‐based approaches, and introduced in light of their respective practical advantages and limitations. Then, we discuss six main common methodological issues that scientists have to face at each step of the development of new combination therapies. In particular, in the absence of a reference methodology suitable for all biomedical situations, the analysis of drug combinations should benefit from a collective, appropriate, and rigorous application of the concepts and methods reviewed here.
Selection of patients with KL radiographic grade 2 and 3 is widely used in clinical trials, but this approach could have some limitations. The purpose of this study performed on OsteoArthritis ...Initiative (OAI) data is to assess whether adding OARSI-JSN to KL grading could select a population with increased rate of cartilage loss. Indeed, KL is not compartment-specific and not uniformly graded amongst expert readers. OARSI-JSN is another established, compartment-specific grading scale that specifically captures the joint space narrowing from radiographs.
1019 knee radiographs data from the progression cohort of the OAI public database were used. Cartilage loss measured with magnetic resonance imaging was evaluated using change over 1 year from baseline in cartilage thickness in the central Medial Tibio-Femoral Compartment (cMTFC) in the KL2-3 and KL2-3+JSN1-2 populations.
The mean cMTFC cartilage loss over one year was −0.135 ± 0.29 mm (median = −0.095 mm) in the KL2-3 population and −0.176 ± 0.29 mm (median = −0.140 mm) in the KL2-3 +JSN1-2 population.
OARSI-JSN appears to be an effective inclusion criterion to be considered in combination with the KL grade in future clinical trials testing the structural efficacy of DMOADs in a time window of 1-year as it contributes to identify knees in whom the disease progresses rapidly.
While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing ...existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1β, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.
Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chemical compounds. The molecular entities in question are either natural ...products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/
Background Increased expression of type I IFN genes, also referred to as an IFN signature, has been detected in various autoimmune diseases including rheumatoid arthritis (RA). Interferon regulatory ...factors, such as IRF5, coordinate type I IFN expression. Multiple IRF5 variants were suggested as autoimmunity susceptibility factors. Objective As the linkage proof remains important to establish fully any genetic RA susceptibility factor, the authors took advantage of the largest reported European trio family resource dedicated to RA to test for linkage IRF5 and performed a genotype–phenotype analysis. Methods 1140 European Caucasian individuals from 380 RA trio families were genotyped for IRF5 rs3757385, rs2004640 and rs10954213 single nucleotide polymorphisms (SNP). Results Single marker analysis provided linkage evidence for each IRF5 SNP investigated. IRF5 linked to RA with two haplotypes: the CTA risk haplotype ‘R’ (transmission (T)=60.6%, p=23.1×10−5) and the AGG protective haplotype ‘P’ (T=39.6%, p=0.0015). Linkage was significantly stronger in non-erosive disease for both IRF5 R and P haplotypes (T=73.9%, p=4.20×10−5 and T=19.6%, p=3.66×10−5, respectively). Multivariate logistic regression analysis found IRF5 linked to RA independently of the rheumatoid factor status. IRF5 RR and PP haplotypic genotypes were associated with RA, restricted to the non-erosive phenotype: p=1.68×10−4, OR 4.80, 95% CI 2.06 to 11.19; p=0.003, OR 0.17, 95% CI 0.05 to 0.57, respectively. Conclusion This study provides the ‘association and linkage proof’ establishing IRF5 as a RA susceptibility gene and the identification of a genetic factor that seems to contribute to the modulation of the erosive phenotype. Further studies are warranted to clarify the role of IRF5 in RA and its subphenotypes.
Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population ...stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies.
Introduction Systemic sclerosis (SSc)-related pulmonary arterial hypertension (PAH) has emerged as a major mortality prognostic factor. Mutations of transforming growth factor beta (TGFβ) receptor ...genes strongly contribute to idiopathic and familial PAH. Objective To explore the genetic bases of SSc–PAH, we combined direct sequencing and genotyping of candidate genes encoding TGFβ receptor family members. Materials and methods TGFβ receptor genes, BMPR2, ALK1, TGFR2 and ENG, were sequenced in 10 SSc–PAH patients, nine SSc and seven controls. In addition, 22 single-nucleotide polymorphisms (SNP) of these four candidate genes were tested for association in a first set of 824 French Caucasian SSc patients (including 54 SSc–PAH) and 939 controls. The replication set consisted of 1516 European SSc (including 219 SSc–PAH) and 3129 controls from the European League Against Rheumatism Scleroderma Trials and Research group network. Results No mutation was identified by direct sequencing. However, two repertoried SNP, ENG rs35400405 and ALK1 rs2277382, were found in SSc–PAH patients only. The genotyping of 22 SNP including the latter showed that only rs2277382 was associated with SSc–PAH (p=0.0066, OR 2.13, 95% CI 1.24 to 3.65). Nevertheless, this was not replicated with the following result in combined analysis: p=0.123, OR 0.79, 95% CI 0.59 to 1.07. Conclusions This study demonstrates the lack of association between these TGFβ receptor gene polymorphisms and SSc–PAH using both sequencing and genotyping methods.
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