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.

Liver X receptor regulates Th17 and RORγt+ Treg cells by distinct mechanisms.

The gastrointestinal microenvironment, dominated by dietary compounds and the commensal bacteria, is a major driver of intestinal CD4+ T helper (Th) cell differentiation. Dietary compounds can be sensed by nuclear receptors (NRs) that consequently exert pleiotropic effects including immune modulation. Here, we found that under homeostatic conditions the NR Liver X receptor (LXR), a sensor of cholesterol metabolites, regulates RORγt+ CD4 T cells in the intestine draining mesenteric lymph node (MLN). While LXR activation led to a decrease, LXR-deficiency resulted in an increase in MLN Th17 and RORγt+ Tregs. Mechanistically, LXR signaling in CD11c+ myeloid cells was required to control RORγt+ Treg. By contrast, modulation of MLN Th17 was independent of LXR signaling in either immune or epithelial cells. Of note, horizontal transfer of microbiota between LXRα-/- and WT mice was sufficient to only partially increase MLN Th17 in WT mice. Despite LXRα deficiency resulted in an increased abundance of Ruminococcaceae and Lachnospiraceae bacterial families compared to littermate controls, microbiota ablation (including SFB) was not sufficient to dampen LXRα-mediated expansion of MLN Th17. Altogether, our results suggest that LXR modulates RORγt+ Treg and Th17 cells in the MLN through distinct mechanisms.

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.

Effects of Mecp2 loss of function in embryonic cortical neurons: a bioinformatics strategy to sort out non-neuronal cells variability from transcriptome profiling.

BACKGROUND: Mecp2 null mice model Rett syndrome (RTT) a human neurological disorder affecting females after apparent normal pre- and peri-natal developmental periods. Neuroanatomical studies in cerebral cortex of RTT mouse models revealed delayed maturation of neuronal morphology and autonomous as well as non-cell autonomous reduction in dendritic complexity of postnatal cortical neurons. However, both morphometric parameters and high-resolution expression profile of cortical neurons at embryonic developmental stage have not yet been studied. Here we address these topics by using embryonic neuronal primary cultures from Mecp2 loss of function mouse model. RESULTS: We show that embryonic primary cortical neurons of Mecp2 null mice display reduced neurite complexity possibly reflecting transcriptional changes. We used RNA-sequencing coupled with a bioinformatics comparative approach to identify and remove the contribution of variable and hard to quantify non-neuronal brain cells present in our in vitro cell cultures. CONCLUSIONS: Our results support the need to investigate both Mecp2 morphological as well as molecular effect in neurons since prenatal developmental stage, long time before onset of Rett symptoms.

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 .

Transcriptator: An Automated Computational Pipeline to Annotate Assembled Reads and Identify Non Coding RNA.

RNA-seq is a new tool to measure RNA transcript counts, using high-throughput sequencing at an extraordinary accuracy. It provides quantitative means to explore the transcriptome of an organism of interest. However, interpreting this extremely large data into biological knowledge is a problem, and biologist-friendly tools are lacking. In our lab, we developed Transcriptator, a web application based on a computational Python pipeline with a user-friendly Java interface. This pipeline uses the web services available for BLAST (Basis Local Search Alignment Tool), QuickGO and DAVID (Database for Annotation, Visualization and Integrated Discovery) tools. It offers a report on statistical analysis of functional and Gene Ontology (GO) annotation's enrichment. It helps users to identify enriched biological themes, particularly GO terms, pathways, domains, gene/proteins features and protein-protein interactions related informations. It clusters the transcripts based on functional annotations and generates a tabular report for functional and gene ontology annotations for each submitted transcript to the web server. The implementation of QuickGo web-services in our pipeline enable the users to carry out GO-Slim analysis, whereas the integration of PORTRAIT (Prediction of transcriptomic non coding RNA (ncRNA) by ab initio methods) helps to identify the non coding RNAs and their regulatory role in transcriptome. In summary, Transcriptator is a useful software for both NGS and array data. It helps the users to characterize the de-novo assembled reads, obtained from NGS experiments for non-referenced organisms, while it also performs the functional enrichment analysis of differentially expressed transcripts/genes for both RNA-seq and micro-array experiments. It generates easy to read tables and interactive charts for better understanding of the data. The pipeline is modular in nature, and provides an opportunity to add new plugins in the future. Web application is freely available at: http://www-labgtp.na.icar.cnr.it/Transcriptator.

Analysis of unigene derived microsatellite markers in family solanaceae.

The family Solanaceae is the source of several economically important plants. The aim of this study was to trace and characterize simple sequence repeat (SSR) markers from unigene sequences of Solanum lycopersicum, an important member of family Solanaceae. 18,228 unigene sequences of Solanum lycopersicum was taken in order to develop SSR markers and analyzed for the in-silico design of PCR primers. A total of 12,090 (66.32 %) unigenes containing 17,524 SSRs (microsatellites) were identified. The average frequency of microsatellites in unigenes was one in every 1.3 kb of sequence. The analysis revealed that trinucleotide motifs, coding for Glutamic acid (GAA) and AT/TA were the most frequent repeat of dinucleotide SSRs. Flanking sequences of the SSRs generated 877 primers with forward and reverse strands. Functional categorization of SSRs containing unigenes was done through gene ontology terms like Biological process, Cellular component and Molecular function.

Predicted metal binding sites for phytoremediation.

Metal ion binding domains are found in proteins that mediate transport, buffering or detoxification of metal ions. The objective of the study is to design and analyze metal binding motifs against the genes involved in phytoremediation. This is being done on the basis of certain pre-requisite amino-acid residues known to bind metal ions/metal complexes in medicinal and aromatic plants (MAP's). Earlier work on MAP's have shown that heavy metals accumulated by aromatic and medicinal plants do not appear in the essential oil and that some of these species are able to grow in metal contaminated sites. A pattern search against the UniProtKB/Swiss-Prot and UniProtKB/TrEMBL databases yielded true positives in each case showing the high specificity of the motifs designed for the ions of nickel, lead, molybdenum, manganese, cadmium, zinc, iron, cobalt and xenobiotic compounds. Motifs were also studied against PDB structures. Results of the study suggested the presence of binding sites on the surface of protein molecules involved. PDB structures of proteins were finally predicted for the binding sites functionality in their respective phytoremediation usage. This was further validated through CASTp server to study its physico-chemical properties. Bioinformatics implications would help in designing strategy for developing transgenic plants with increased metal binding capacity. These metal binding factors can be used to restrict metal update by plants. This helps in reducing the possibility of metal movement into the food chain.

Pharmacovigilance: effects of herbal components on human drugs interactions involving cytochrome P450.

Cytochrome P450 (CYP P450) enzymes are a superfamily of mono-oxygenases that are found in all kingdoms of life. The CYP P450 enzymes constitute a large superfamily of haem-thiolate proteins involved in the metabolism of a wide variety of both exogenous and endogenous compounds. The CYP activities have been shown to be involved in numerous interactions especially between drugs and herbal constituents. The majority of serious cases of drug interactions are as a result of the interference of the metabolic clearance of one drug by yet another co-administered drug, food or natural product. Gaining mechanistic knowledge towards such interactions has been accepted as an approach to avoid adverse reactions. The inductions and inhibition of CYP enzymes by natural products in the presence of a prescribed drug has led to adverse effects. Herbal medicines such as St. John's wort (Hypericum perforatum), garlic (Allium sativa), piperine (from Piper sp.), ginseng (Ginseng sp.), gingko (Gingko biloba), soya beans (Glycine max), alfalfa (Medicago sativa) and grape fruit juice show clinical interactions when co-administered with medicines. This review documents the involvement of CYP enzymes in the metabolism of known available drugs and herbal products. We also document the interactions between herbal constituents & CYP enzymes showing potential drug-herb interactions. Data on CYP450 enzymes in activation (i.e. induction or inhibition) with natural constituents is also reviewed.