Cluster Headache Genomewide Association Study and Meta-Analysis Identifies Eight Loci and Implicates Smoking as Causal Risk Factor.

OBJECTIVE: The objective of this study was to aggregate data for the first genomewide association study meta-analysis of cluster headache, to identify genetic risk variants, and gain biological insights. METHODS: A total of 4,777 cases (3,348 men and 1,429 women) with clinically diagnosed cluster headache were recruited from 10 European and 1 East Asian cohorts. We first performed an inverse-variance genomewide association meta-analysis of 4,043 cases and 21,729 controls of European ancestry. In a secondary trans-ancestry meta-analysis, we included 734 cases and 9,846 controls of East Asian ancestry. Candidate causal genes were prioritized by 5 complementary methods: expression quantitative trait loci, transcriptome-wide association, fine-mapping of causal gene sets, genetically driven DNA methylation, and effects on protein structure. Gene set and tissue enrichment analyses, genetic correlation, genetic risk score analysis, and Mendelian randomization were part of the downstream analyses. RESULTS: The estimated single nucleotide polymorphism (SNP)-based heritability of cluster headache was 14.5%. We identified 9 independent signals in 7 genomewide significant loci in the primary meta-analysis, and one additional locus in the trans-ethnic meta-analysis. Five of the loci were previously known. The 20 genes prioritized as potentially causal for cluster headache showed enrichment to artery and brain tissue. Cluster headache was genetically correlated with cigarette smoking, risk-taking behavior, attention deficit hyperactivity disorder (ADHD), depression, and musculoskeletal pain. Mendelian randomization analysis indicated a causal effect of cigarette smoking intensity on cluster headache. Three of the identified loci were shared with migraine. INTERPRETATION: This first genomewide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor. ANN NEUROL 2023;94:713-726.

Distinct developmental pathways from blood monocytes generate human lung macrophage diversity.

The study of human macrophages and their ontogeny is an important unresolved issue. Here, we use a humanized mouse model expressing human cytokines to dissect the development of lung macrophages from human hematopoiesis in vivo. Human CD34+ hematopoietic stem and progenitor cells (HSPCs) generated three macrophage populations, occupying separate anatomical niches in the lung. Intravascular cell labeling, cell transplantation, and fate-mapping studies established that classical CD14+ blood monocytes derived from HSPCs migrated into lung tissue and gave rise to human interstitial and alveolar macrophages. In contrast, non-classical CD16+ blood monocytes preferentially generated macrophages resident in the lung vasculature (pulmonary intravascular macrophages). Finally, single-cell RNA sequencing defined intermediate differentiation stages in human lung macrophage development from blood monocytes. This study identifies distinct developmental pathways from circulating monocytes to lung macrophages and reveals how cellular origin contributes to human macrophage identity, diversity, and localization in vivo.

RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics.

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the "RankerGUI pipeline", a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms' data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status.

Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (≤40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants' predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted "feature selection" as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions.

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification.

BACKGROUND: System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models. RESULTS: The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure. CONCLUSIONS: The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.

An integrated approach to infer cross-talks between intracellular protein transport and signaling pathways.

BACKGROUND: The endomembrane system, known as secretory pathway, is responsible for the synthesis and transport of protein molecules in cells. Therefore, genes involved in the secretory pathway are essential for the cellular development and function. Recent scientific investigations show that ER and Golgi apparatus may provide a convenient drug target for cancer therapy. On the other hand, it is known that abundantly expressed genes in different cellular organelles share interconnected pathways and co-regulate each other activities. The cross-talks among these genes play an important role in signaling pathways, associated to the regulation of intracellular protein transport. RESULTS: In the present study, we device an integrated approach to understand these complex interactions. We analyze gene perturbation expression profiles, reconstruct a directed gene interaction network and decipher the regulatory interactions among genes involved in protein transport signaling. In particular, we focus on expression signatures of genes involved in the secretory pathway of MCF7 breast cancer cell line. Furthermore, network biology analysis delineates these gene-centric cross-talks at the level of specific modules/sub-networks, corresponding to different signaling pathways. CONCLUSIONS: We elucidate the regulatory connections between genes constituting signaling pathways such as PI3K-Akt, Ras, Rap1, calcium, JAK-STAT, EFGR and FGFR signaling. Interestingly, we determine some key regulatory cross-talks between signaling pathways (PI3K-Akt signaling and Ras signaling pathway) and intracellular protein transport.

A computational integrative approach based on alternative splicing analysis to compare immortalized and primary cancer cells.

Immortalized cell lines are widely used to study the effectiveness and toxicity of anti cancer drugs as well as to assess the phenotypic characteristics of cancer cells, such as proliferation and migration ability. Unfortunately, cell lines often show extremely different properties than tumor tissues. Also the primary cells, that are deprived of the in vivo environment, might adapt to artificial conditions, and differ from the tissue they should represent. Despite these considerations, cell lines are still one of the most used cancer models due to their availability and capability to expand without limitation, but the clinical relevance of their use is still a big issue in cancer research. Many studies tried to overcome this task, comparing cell lines and tumor samples through the definition of the genomic and transcriptomic differences. To this aim, most of them used nucleotide variation or gene expression data. Here we introduce a different strategy based on alternative splicing detection and integration of DNA and RNA sequencing data, to explore the differences between immortalized and tissue-derived cells at isoforms level. Furthermore, in order to better investigate the heterogeneity of both cell populations, we took advantage of a public available dataset obtained with a new simultaneous omics single cell sequencing methodology. The proposed pipeline allowed us to identify, through a computational and prediction approach, putative mutated and alternative spliced transcripts responsible for the dissimilarity between immortalized and primary hepato carcinoma cells.

An Atlas of annotations of Hydra vulgaris transcriptome.

BACKGROUND: RNA sequencing takes advantage of the Next Generation Sequencing (NGS) technologies for analyzing RNA transcript counts with an excellent accuracy. Trying to interpret this huge amount of data in biological information is still a key issue, reason for which the creation of web-resources useful for their analysis is highly desiderable. RESULTS: Starting from a previous work, Transcriptator, we present the Atlas of Hydra's vulgaris, an extensible web tool in which its complete transcriptome is annotated. In order to provide to the users an advantageous resource that include the whole functional annotated transcriptome of Hydra vulgaris water polyp, we implemented the Atlas web-tool contains 31.988 accesible and downloadable transcripts of this non-reference model organism. CONCLUSION: Atlas, as a freely available resource, can be considered a valuable tool to rapidly retrieve functional annotation for transcripts differentially expressed in Hydra vulgaris exposed to the distinct experimental treatments. WEB RESOURCE URL: http://www-labgtp.na.icar.cnr.it/Atlas .