Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition.

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

Acyltransferase families that act on thioesters: Sequences, structures, and mechanisms.

Acyltransferases (AT) are enzymes that catalyze the transfer of acyl group to a receptor molecule. This review focuses on ATs that act on thioester-containing substrates. Although many ATs can recognize a wide variety of substrates, sequence similarity analysis allowed us to classify the ATs into fifteen distinct families. Each AT family is originated from enzymes experimentally characterized to have AT activity, classified according to sequence similarity, and confirmed with tertiary structure similarity for families that have crystallized structures available. All the sequences and structures of the AT families described here are present in the thioester-active enzyme (ThYme) database. The AT sequences and structures classified into families and available in the ThYme database could contribute to enlightening the understanding acyl transfer to thioester-containing substrates, most commonly coenzyme A, which occur in multiple metabolic pathways, mostly with fatty acids.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration.

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Canine bufavirus (Carnivore protoparvovirus-3) infection in dogs with respiratory disease.

Canine bufavirus (CBuV) or Carnivore protoparvovirus-3, a nonenveloped DNA virus belonging to the genus Protoparvovirus, family Parvoviridae, has been identified in dogs with respiratory and enteric diseases. Although CBuV detection has been reported in multiple countries, descriptions of pathologic findings associated with infection have not yet been provided. In this study, the authors necropsied 14 dogs (12 puppies and 2 adult dogs) from a breeding colony that died during multiple outbreaks of respiratory diseases. Postmortem investigations revealed extensive bronchointerstitial pneumonia with segmental type II pneumocyte hyperplasia in all necropsied puppies but less severe lesions in adults. With negative results of common pathogen detection by ancillary testing, CBuV DNA was identified in all investigated dogs using a polymerase chain reaction (PCR). Quantitative PCR demonstrated CBuV DNA in several tissues, and in situ hybridization (ISH) indicated CBuV tissue localization in the lung, tracheobronchial lymph node, and spinal cord, suggesting hematogenous spread. Dual CBuV ISH and cellular-specific immunohistochemistry were used to determine the cellular tropism of the virus in the lung and tracheobronchial lymph node, demonstrating viral localization in various cell types, including B-cells, macrophages, and type II pneumocytes, but not T-cells. Three complete CBuV sequences were successfully characterized and revealed that they clustered with the CBuV sequences obtained from dogs with respiratory disease in Hungary. No additional cases were identified in small numbers of healthy dogs. Although association of the bufavirus with enteric disease remains to be determined, a contributory role of CBuV in canine respiratory disease is possible.

A robust and accurate single-cell data trajectory inference method using ensemble pseudotime.

BACKGROUND: The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. RESULTS: We proposed a novel framework for trajectory inference called the single-cell data Trajectory inference method using Ensemble Pseudotime inference (scTEP). scTEP utilizes multiple clustering results to infer robust pseudotime and then uses the pseudotime to fine-tune the learned trajectory. We evaluated the scTEP using 41 real scRNA-seq data sets, all of which had the ground truth development trajectory. We compared the scTEP with state-of-the-art methods using the aforementioned data sets. Experiments on real linear and non-linear data sets demonstrate that our scTEP performed superior on more data sets than any other method. The scTEP also achieved a higher average and lower variance on most metrics than other state-of-the-art methods. In terms of trajectory inference capacity, the scTEP outperforms those methods. In addition, the scTEP is more robust to the unavoidable errors resulting from clustering and dimension reduction. CONCLUSION: The scTEP demonstrates that utilizing multiple clustering results for the pseudotime inference procedure enhances its robustness. Furthermore, robust pseudotime strengthens the accuracy of trajectory inference, which is the most crucial component in the pipeline. scTEP is available at https://cran.r-project.org/package=scTEP .

Photocatalyzed [2σ + 2σ] and [2σ + 2π] Cycloadditions for the Synthesis of Bicyclo[3.1.1]heptanes and 5- or 6-Membered Carbocycles.

We report the use of photocatalysis for the homolytic ring-opening of carbonyl cyclopropanes. In contrast to previous studies, our approach does not require a metal cocatalyst or a strong reductant. The carbonyl cyclopropanes can be employed for both [2σ + 2σ] and [2σ + 2π] annulation with either alkenes/alkynes or bicyclo[1.1.0]butanes, yielding cyclopent-anes/-enes and bicyclo[3.1.1]heptanes (BCHs), respectively. BCHs are promising bioisosteres for 1,2,4,5 tetra-substituted aromatic rings. Mechanistic studies, including density functional theory computation and a trapping experiment with DMPO, support a 1,3-biradical generated from cyclopropane as a key intermediate for these transformations.

A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.

Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.

The metabolic contribution of SKN-1/Nrf2 to the lifespan of Caenorhabditis elegans.

BACKGROUND AND AIMS: SKN-1, a C. elegans transcription factor analogous to the mammalian NF-E2-related factor (Nrf2), has been known to promote oxidative stress resistance aiding nematodes' longevity. Although SKN-1's functions suggest its implication in lifespan modulation through cellular metabolism, the actual mechanism of how metabolic rearrangements contribute to SKN-1's lifespan modulation has yet to be well characterized. Therefore, we performed the metabolomic profiling of the short-lived skn-1-knockdown C. elegans. METHODS: We analyzed the metabolic profile of the skn-1-knockdown worms with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-tandem mass spectrometry (LC-MS/MS) and obtained distinctive metabolomic profiles compared to WT worms. We further extended our study with gene expression analysis to examine the expression level of genes encoding all metabolic enzymes. RESULTS: A significant increase in the phosphocholine and AMP/ATP ratio, potential biomarkers of aging, was observed, accompanied by a decrease in the transsulfuration metabolites, NADPH/NADP+ ratio, and total glutathione (GSHt), which are known to be involved in oxidative stress defense. skn-1-RNAi worms also exhibited an impairment in the phase II detoxification system, confirmed by the lower conversion rate of paracetamol to paracetamol-glutathione. By further examining the transcriptomic profile, we found a decrease in the expression of cbl-1, gpx, T25B9.9, ugt, and gst, which are involved in GSHt and NADPH synthesis as well as in the phase II detoxification system. CONCLUSION: Our multi-omics results consistently revealed that the cytoprotective mechanisms, including cellular redox reactions and xenobiotic detoxification system, contribute to the roles of SKN-1/Nrf2 in the lifespan of worms.

CPA: a web-based platform for consensus pathway analysis and interactive visualization.

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

Activation of aminocyclopropanes via radical intermediates.

Aminocyclopropanes are versatile building blocks for accessing high value-added nitrogen-containing products. To control ring-opening promoted by ring strain, the Lewis acid activation of donor-acceptor substituted systems is now well established. Over the last decade, alternative approaches have emerged proceeding via the formation of radical intermediates, alleviating the need for double activation of the cyclopropanes. This tutorial review summarizes key concepts and recent progress in ring-opening transformations of aminocyclopropanes via radical intermediates, divided into formal cycloadditions and 1,3-difunctionalizations.

DrGA: cancer driver gene analysis in a simpler manner.

BACKGROUND: To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient. RESULTS: This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism. CONCLUSIONS: DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA .

Real-Time Monitoring of Host-Gut Microbial Interspecies Interaction in Anticancer Drug Metabolism.

Gut microbiome can affect drug metabolism considerably, leading to modified drug response. However, quantitative estimation of host vs. microbial contributions in a living host-gut microbiome system has been challenging. Using the interspecies system of Caenorhabditis elegans and gut bacteria, we developed a real-time approach for monitoring their metabolic interaction in vivo during anticancer drug 5-fluorouracil (5-FU) metabolism. The fluorine NMR-based approach yielded the quantitative contributions to the host 5-FU metabolism made by human gut-microbial species of variable genetic backgrounds. It also experimentally confirmed a bacterial gene-metabolism relationship. Differential 5-FU catabolism among bacterial substrains and the contributions to the host metabolism, unobservable by conventional 16S rRNA metagenomic sequencing, were also found. The metabolic contributions could be correlated with phenotypic developmental toxicity of 5-FU to the host fed with different substrains. Our convenient platform should help to reveal heterogeneity in host-gut microbiome interactions for many drugs in a living symbiotic system.

Level of advanced oxidation protein products is associated with subclinical atherosclerosis.

BACKGROUND: Oxidative stress is an important factor in the pathomechanism of atherosclerosis. Advanced oxidation protein products (AOPPs) are considered markers of oxidative stress. Thickening of the carotid intima-media layers indicates subclinical atherosclerosis and can be detected by carotid ultrasound. OBJECTIVE: Our aim was to examine the association between carotid intima-media thickness (CIMT) and the level of AOPPs. METHODS: Carotid duplex scans and measurements of AOPPs were performed on 476 participants of a cardiovascular population study. The presence of conventional cardiovascular risk factors was investigated with a questionnaire, physical examination, and laboratory tests. RESULTS: There was a positive correlation between maximum CIMT and the level of AOPPs only in the male population (r = 0.219, p = 0.033). Multivariate analysis has revealed that the association between AOPPs and mean or maximum CIMT was independent of cardiovascular risk factors (OR = 1.458, p = 0.004, and OR = 2.038, p < 0.001). CONCLUSIONS: Among males, the elevated level of AOPPs as a marker of oxidative stress may signal the existence of early atherosclerotic alterations.

Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing.

It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.

Diamine Synthesis via the Nitrogen-Directed Azidation of σ- and π-C-C Bonds.

Diamines are essential building blocks for the synthesis of agrochemicals, drugs, and organic materials, yet their synthesis remains challenging, as both nitrogens need to be differentiated and diverse substitution patterns (1,2, 1,3, or 1,4) are required. We report herein a new strategy giving access to 1,2, 1,3, and 1,4 amido azides as orthogonally protected diamines based on the nitrogen-directed diazidation of alkenes, cyclopropanes, and cyclobutanes. Commercially available copper thiophene-2-carboxylate (CuTc, 2 mol %) as catalyst promoted the diazidation of both π and σ C-C bonds within 10 min in the presence of readily available oxidants and trimethylsilyl azide. Selective substitution of the formed α-amino azide by carbon nucleophiles (electron-rich aromatic, malonate, organosilicon, organoboron, organozinc, and organomagnesium compounds) was then achieved in a one-pot fashion, leading to the formation of 1,2-, 1,3-, and 1,4-diamines with the amino groups protected orthogonally as an amide/carbamate and an azide.

GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis.

MOTIVATION: Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher's method, Stouffer's method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power. RESULTS: Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer's disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Early and long-term results of the endovascular treatment of patients with isolated infrarenal aortic stenosis.

OBJECTIVE: The purpose of this study was to report our results of patients' characteristics, procedural complications, and long-term patency in treatment of isolated infrarenal aortic stenosis (IIAS). METHODS: Forty symptomatic patients (28 female, 12 male; median age, 60 years [54.8-68 years]) with IIAS who underwent endovascular intervention between 2001 and 2017 were retrospectively analyzed. Patient, lesion, procedure, and balloon/stent characteristics were assessed. Follow-up included clinical status evaluation and color Doppler ultrasound examination. RESULTS: The cause of IIAS was atherosclerosis in all patients. Twenty percent of the patients were younger than 50 years; 85% had hypertension, 80% were smokers, 38% had hyperlipidemia, 23% had diabetes mellitus, 15% were obese (body mass index ≥30 kg/m2), and 8% had chronic kidney disease. The median stenosis grade was 80% (70%-80%), and the median lesion length was 19.9 mm (13-29.4 mm). Severe calcification was present in 8% of the patients. Percutaneous transluminal angioplasty was performed in four cases (10%), whereas stenting was performed in 36 (90%). One complication, an aortic rupture requiring surgical repair, occurred. The median follow-up was 61 months (17-101 months). The primary patency rate was 100% at 6 months, 97% at 12 and 24 months, and 88% at 60 and 96 months. Restenosis developed in three patients (8%); reintervention was carried out in two cases (5%). CONCLUSIONS: Endovascular therapy for IIAS provides a safe and effective long-term treatment strategy.