Differential expression of MicroRNAs in Alzheimer's disease: a systematic review and meta-analysis.

Alzheimer's disease (AD) results in progressive cognitive decline owing to the accumulation of amyloid plaques and hyperphosphorylated tau. MicroRNAs (miRNAs) have attracted attention as a putative diagnostic and therapeutic target for neurodegenerative diseases. However, existing meta-analyses on AD and its association with miRNAs have produced inconsistent results. The primary objective of this study is to evaluate the magnitude and consistency of differences in miRNA levels between AD patients, mild cognitive impairment (MCI) patients and healthy controls (HC). Articles investigating miRNA levels in blood, brain tissue, or cerebrospinal fluid (CSF) of AD and MCI patients versus HC were systematically searched in PubMed/Medline from inception to February 16th, 2021. Fixed- and random-effects meta-analyses were complemented with the I2 statistic to measure the heterogeneity, assessment of publication bias, sensitivity subgroup analyses (AD severity, brain region, post-mortem versus ante-mortem specimen for CSF and type of analysis used to quantify miRNA) and functional enrichment pathway analysis. Of the 1512 miRNAs included in 61 articles, 425 meta-analyses were performed on 334 miRNAs. Fifty-six miRNAs were significantly upregulated (n = 40) or downregulated (n = 16) in AD versus HC and all five miRNAs were significantly upregulated in MCI versus HC. Functional enrichment analysis confirmed that pathways related to apoptosis, immune response and inflammation were statistically enriched with upregulated pathways in participants with AD relative to HC. This study confirms that miRNAs' expression is altered in AD and MCI compared to HC. These findings open new diagnostic and therapeutic perspectives for this disorder.

RORα is crucial for attenuated inflammatory response to maintain intestinal homeostasis.

Retinoic acid-related orphan receptor α (RORα) functions as a transcription factor for various biological processes, including circadian rhythm, cancer, and metabolism. Here, we generate intestinal epithelial cell (IEC)-specific RORα-deficient (RORαΔIEC) mice and find that RORα is crucial for maintaining intestinal homeostasis by attenuating nuclear factor κB (NF-κB) transcriptional activity. RORαΔIEC mice exhibit excessive intestinal inflammation and highly activated inflammatory responses in the dextran sulfate sodium (DSS) mouse colitis model. Transcriptome analysis reveals that deletion of RORα leads to up-regulation of NF-κB target genes in IECs. Chromatin immunoprecipitation analysis reveals corecruitment of RORα and histone deacetylase 3 (HDAC3) on NF-κB target promoters and subsequent dismissal of CREB binding protein (CBP) and bromodomain-containing protein 4 (BRD4) for transcriptional repression. Together, we demonstrate that RORα/HDAC3-mediated attenuation of NF-κB signaling controls the balance of inflammatory responses, and therapeutic strategies targeting this epigenetic regulation could be beneficial to the treatment of chronic inflammatory diseases, including inflammatory bowel disease (IBD).

Clinical application of a phenotype-based NGS panel for differential diagnosis of inherited kidney disease and beyond.

Understanding the genetic causes of kidney disease is essential for accurate diagnosis and could lead to improved therapeutic strategies and prognosis. To accurately and promptly identify the genetic background of kidney diseases, we applied a targeted next-generation sequencing gene panel including 203 genes associated with kidney disease, as well as diseases originating in other organs with mimicking symptoms of kidney disease, to analyze 51 patients with nonspecific nephrogenic symptoms, followed by validation of its efficacy as a diagnostic tool. We simultaneously screened for copy number variants (CNVs) in each patient to obtain a higher diagnostic yield (molecular diagnostic rate: 39.2%). Notably, one patient suspected of having Bartter syndrome presented with chloride-secreting diarrhea attributable to homozygous SLC26A3 variants. Additionally, in eight patients, NGS confirmed the genetic causes of undefined kidney diseases (8/20, 40%), and initial clinical impression and molecular diagnosis were matched in 11 patients (11/20, 55%). Moreover, we found seven novel pathogenic/likely pathogenic variants in PKD1, PKHD1, COL4A3, and SLC12A1 genes, with a possible pathogenic variant in COL4A3 (c.1229G>A) identified in two unrelated patients. These results suggest that targeted NGS-panel testing performed with CNV analysis might be advantageous for noninvasive and comprehensive diagnosis of suspected genetic kidney diseases.

ID1-Mediated BMP Signaling Pathway Potentiates Glucagon-Like Peptide-1 Secretion in Response to Nutrient Replenishment.

Glucagon-like peptide-1 (GLP-1) is a well-known incretin hormone secreted from enteroendocrinal L cells in response to nutrients, such as glucose and dietary fat, and controls glycemic homeostasis. However, the detailed intracellular mechanisms of how L cells control GLP-1 secretion in response to nutrients still remain unclear. Here, we report that bone morphogenetic protein (BMP) signaling pathway plays a pivotal role to control GLP-1 secretion in response to nutrient replenishment in well-established mouse enteroendocrinal L cells (GLUTag cells). Nutrient starvation dramatically reduced cellular respiration and GLP-1 secretion in GLUTag cells. Transcriptome analysis revealed that nutrient starvation remarkably reduced gene expressions involved in BMP signaling pathway, whereas nutrient replenishment rescued BMP signaling to potentiate GLP-1 secretion. Transient knockdown of inhibitor of DNA binding (ID)1, a well-known target gene of BMP signaling, remarkably reduced GLP-1 secretion. Consistently, LDN193189, an inhibitor of BMP signaling, markedly reduced GLP-1 secretion in L cells. In contrast, BMP4 treatment activated BMP signaling pathway and potentiated GLP-1 secretion in response to nutrient replenishment. Altogether, we demonstrated that BMP signaling pathway is a novel molecular mechanism to control GLP-1 secretion in response to cellular nutrient status. Selective activation of BMP signaling would be a potent therapeutic strategy to stimulate GLP-1 secretion in order to restore glycemic homeostasis.

mySyntenyPortal: an application package to construct websites for synteny block analysis.

BACKGROUND: Advances in sequencing technologies have facilitated large-scale comparative genomics based on whole genome sequencing. Constructing and investigating conserved genomic regions among multiple species (called synteny blocks) are essential in the comparative genomics. However, they require significant amounts of computational resources and time in addition to bioinformatics skills. Many web interfaces have been developed to make such tasks easier. However, these web interfaces cannot be customized for users who want to use their own set of genome sequences or definition of synteny blocks. RESULTS: To resolve this limitation, we present mySyntenyPortal, a stand-alone application package to construct websites for synteny block analyses by using users' own genome data. mySyntenyPortal provides both command line and web-based interfaces to build and manage websites for large-scale comparative genomic analyses. The websites can be also easily published and accessed by other users. To demonstrate the usability of mySyntenyPortal, we present an example study for building websites to compare genomes of three mammalian species (human, mouse, and cow) and show how they can be easily utilized to identify potential genes affected by genome rearrangements. CONCLUSIONS: mySyntenyPortal will contribute for extended comparative genomic analyses based on large-scale whole genome sequences by providing unique functionality to support the easy creation of interactive websites for synteny block analyses from user's own genome data.

Synteny Portal: a web-based application portal for synteny block analysis.

Recent advances in next-generation sequencing technologies and genome assembly algorithms have enabled the accumulation of a huge volume of genome sequences from various species. This has provided new opportunities for large-scale comparative genomics studies. Identifying and utilizing synteny blocks, which are genomic regions conserved among multiple species, is key to understanding genomic architecture and the evolutionary history of genomes. However, the construction and visualization of such synteny blocks from multiple species are very challenging, especially for biologists with a lack of computational skills. Here, we present Synteny Portal, a versatile web-based application portal for constructing, visualizing and browsing synteny blocks. With Synteny Portal, users can easily (i) construct synteny blocks among multiple species by using prebuilt alignments in the UCSC genome browser database, (ii) visualize and download syntenic relationships as high-quality images, (iii) browse synteny blocks with genetic information and (iv) download the details of synteny blocks to be used as input for downstream synteny-based analyses, all in an intuitive and easy-to-use web-based interface. We believe that Synteny Portal will serve as a highly valuable tool that will enable biologists to easily perform comparative genomics studies by compensating limitations of existing tools. Synteny Portal is freely available at http://bioinfo.konkuk.ac.kr/synteny_portal.

AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species.

Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.

Unravelling the mechanism of action of enzyme replacement therapy in Fabry disease.

Fabry disease (FD) is a rare X-linked recessive glycosphingolipid-storage disorder caused by deficient activity of the lysosomal enzyme alpha-galactosidase A. Intravenous enzyme replacement therapy (ERT) has been used to supplement deficient enzyme activity in patients with FD. Despite its clinical effect and manifestations, clear criteria for the clinical effectiveness and cost-effectiveness of ERT have not been well established. In this study, we investigated the pharmacodynamic actions and short-term effects of ERT in patients with FD through direct molecular profiling from blood samples of patients before and after ERT. Based on this comparison, we observed that immune/inflammation-related pathways and growth factor-related pathways such as innate/adaptive immune pathway, lymphocyte proliferation and leukocyte proliferation were actively regulated under ERT. We also found that TINAGL1, DAAM2, CDK5R1 and MYO5B known to be related with clinical symptoms of FD showed increased levels after ERT, leading to the amelioration of clinical manifestations. Especially the catabolic process-related genes, including USP15 and ERUN1, showed direct increasing after ERT in vivo in male patients. These results suggest that male patients with FD respond more actively to ERT than do female patients with FD. Pathway analysis revealed that oxidative phosphorylation pathway-related genes are downregulated under ERT. ERT has a role to protect the proteins from oxidative damage and such deactivation of oxidative phosphorylation is one of direct pharmacodynamic actions of ERT. These results extended our understanding of the pathophysiology of ERT. To our knowledge, this is the first study to observe the molecular basis for the mechanism of ERT in vivo through the comprehensive comparison of transcriptome study with next-generation sequencing data.

Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain.

To characterize gene expression patterns in the regional subdivisions of the mammalian brain, we integrated spatial gene expression patterns from the Allen Brain Atlas for the adult mouse with panels of cell type-specific genes for neurons, astrocytes, and oligodendrocytes from previously published transcriptome profiling experiments. We found that the combined spatial expression patterns of 170 neuron-specific transcripts revealed strikingly clear and symmetrical signatures for most of the brain's major subdivisions. Moreover, the brain expression spatial signatures correspond to anatomical structures and may even reflect developmental ontogeny. Spatial expression profiles of astrocyte- and oligodendrocyte-specific genes also revealed regional differences; these defined fewer regions and were less distinct but still symmetrical in the coronal plane. Follow-up analysis suggested that region-based clustering of neuron-specific genes was related to (i) a combination of individual genes with restricted expression patterns, (ii) region-specific differences in the relative expression of functional groups of genes, and (iii) regional differences in neuronal density. Products from some of these neuron-specific genes are present in peripheral blood, raising the possibility that they could reflect the activities of disease- or injury-perturbed networks and collectively function as biomarkers for clinical disease diagnostics.

AUREA: an open-source software system for accurate and user-friendly identification of relative expression molecular signatures.

BACKGROUND: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery. RESULTS: Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets. CONCLUSIONS: We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis 'point-and-click' simple. The unified interface and the adaptive parameter tuning of AUREA provide an effective framework in which to investigate the massive amounts of publically available data by both 'in silico' and 'bench' scientists. AUREA can be found at http://price.systemsbiology.net/AUREA/.

PRID: Prediction Model Using RWR for Interactions between Drugs.

Drug-drug interactions (DDI) occur because of the unexpected pharmacological effects of drug pairs. Although drug efficacy can be improved by taking two or more drugs in the short term, this may cause inevitable side effects. Currently, multiple drugs are prescribed based on the experience or knowledge of the clinician, and there is no standard database that can be referred to as safe co-prescriptions. Thus, accurately identifying DDI is critical for patient safety and treatment modalities. Many computational methods have been developed to predict DDIs based on chemical structures or biological features, such as target genes or functional mechanisms. However, some features are only available for certain drugs, and their pathological mechanisms cannot be fully employed to predict DDIs by considering the direct overlap of target genes. In this study, we propose a novel deep learning model to predict DDIs by utilizing chemical structure similarity and protein-protein interaction (PPI) information among drug-binding proteins, such as carriers, transporters, enzymes, and targets (CTET) proteins. We applied the random walk with restart (RWR) algorithm to propagate drug CTET proteins across a PPI network derived from the STRING database, which will lead to the successful incorporation of the hidden biological mechanisms between CTET proteins and disease-associated genes. We confirmed that the RWR propagation of CTET proteins helps predict DDIs by utilizing indirectly co-regulated biological mechanisms. Our method identified the known DDIs between clinically proven epilepsy drugs. Our results demonstrated the effectiveness of PRID in predicting DDIs in known drug combinations as well as unknown drug pairs. PRID could be helpful in identifying novel DDIs and associated pharmacological mechanisms to cause the DDIs.

Reference-based read clustering improves the de novo genome assembly of microbial strains.

Constructing accurate microbial genome assemblies is necessary to understand genetic diversity in microbial genomes and its functional consequences. However, it still remains as a challenging task especially when only short-read sequencing technologies are used. Here, we present a new read-clustering algorithm, called RBRC, for improving de novo microbial genome assembly, by accurately estimating read proximity using multiple reference genomes. The performance of RBRC was confirmed by simulation-based evaluation in terms of assembly contiguity and the number of misassemblies, and was successfully applied to existing fungal and bacterial genomes by improving the quality of the assemblies without using additional sequencing data. RBRC is a very useful read-clustering algorithm that can be used (i) for generating high-quality genome assemblies of microbial strains when genome assemblies of related strains are available, and (ii) for upgrading existing microbial genome assemblies when the generation of additional sequencing data, such as long reads, is difficult.

Multi-omics segregate different transcriptomic impacts of anti-IL-17A blockade on type 17 T-cells and regulatory immune cells in psoriasis skin.

Durable psoriasis improvement has been reported in a subset of psoriasis patients after treatment withdrawal of biologics blocking IL-23/Type 17 T-cell (T17) autoimmune axis. However, it is not well understood if systemic blockade of the IL-23/T17 axis promotes immune tolerance in psoriasis skin. The purpose of the study was to find translational evidence that systemic IL-17A blockade promotes regulatory transcriptome modification in human psoriasis skin immune cell subsets. We analyzed human psoriasis lesional skin 6 mm punch biopsy tissues before and after systemic IL-17A blockade using the muti-genomics approach integrating immune cell-enriched scRNA-seq (n = 18), microarray (n = 61), and immunohistochemistry (n = 61) with repository normal control skin immune cell-enriched scRNA-seq (n = 10) and microarray (n = 8) data. For the T17 axis transcriptome, systemic IL-17A blockade depleted 100% of IL17A + T-cells and 95% of IL17F + T-cells in psoriasis skin. The expression of IL23A in DC subsets was also downregulated by IL-17A blockade. The expression of IL-17-driven inflammatory mediators (IL36G, S100A8, DEFB4A, and DEFB4B) in suprabasal keratinocytes was correlated with psoriasis severity and was downregulated by IL-17A blockade. For the regulatory DC transcriptome, the proportion of regulatory semimature DCs expressing regulatory DC markers of BDCA-3 (THBD) and DCIR (CLEC4A) was increased in posttreatment psoriasis lesional skin compared to pretreatment psoriasis lesional skin. In addition, IL-17A blockade induced higher expression of CD1C and CD14, which are markers of CD1c+ CD14+ dendritic cell (DC) subset that suppresses antigen-specific T-cell responses, in posttreatment regulatory semimature DCs compared to pretreatment regulatory semimature DCs. In conclusion, systemic IL-17A inhibition not only blocks the entire IL-23/T17 cell axis but also promotes regulatory gene expression in regulatory DCs in human psoriasis skin.

Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup.

SUMMARY: The top-scoring pair (TSP) and top-scoring triplet (TST) algorithms are powerful methods for classification from expression data, but analysis of all combinations across thousands of human transcriptome samples is computationally intensive, and has not yet been achieved for TST. Implementation of these algorithms for the graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing the searchable combinations and accelerating the pace of discovery. AVAILABILITY: http://www.igb.illinois.edu/labs/price/downloads/.

Transcriptional analysis of sweet corn hybrids in response to crowding stress.

Crop tolerance to crowding stress, specifically plant population density, is an important target to improve productivity in processing sweet corn. Due to limited knowledge of biological mechanisms involved in crowding stress in sweet corn, a study was conducted to 1) investigate phenotypic and transcriptional response of sweet corn hybrids under different plant densties, 2) compare the crowding stress response mechanisms between hybrids and 3) identify candidate biological mechanisms involved in crowding stress response. Yield per hectare of a tolerant hybrid (DMC21-84) increased with plant density. Yield per hectare of a sensitive hybrid (GSS2259P) declined with plant density. Transcriptional analysis found 694, 537, 359 and 483 crowding stress differentially expressed genes (DEGs) for GSS2259P at the Fruit Farm and Vegetable Farm and for DMC21-84 at the Fruit Farm and Vegetable Farm, respectively. Strong transcriptional change due to hybrid was observed. Functional analyses of DEGs involved in crowding stress also revealed that protein folding and photosynthetic processes were common response mechanisms for both hybrids. However, DEGs related to starch biosynthetic, carbohydrate metabolism, and ABA related processes were significant only for DMC21-84, suggesting the genes have closer relationship to plant productivity under stress than other processes. These results collectively provide initial insight into potential crowding stress response mechanisms in sweet corn.

Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis.

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

The Effect of Interleukin-4 and Dexamethasone on RNA-Seq-Based Transcriptomic Profiling of Human Podocytes: A Potential Role in Minimal Change Nephrotic Syndrome.

Interleukin-4 (IL-4) expression is implicated in the pathogenesis of nephrotic syndrome (NS). This study aimed to investigate the changes in the transcriptomes of human podocytes induced by IL-4 treatment and to analyze whether these changes could be affected by simultaneous steroid treatment. Three groups of human podocytes were treated with control, IL-4, and IL-4 plus dexamethasone (DEX), respectively. We performed whole-transcriptome sequencing to identify differentially expressed genes (DEGs) between the groups. We investigated relevant biological pathways using Gene Ontology (GO) enrichment analyses. We also attempted to compare and validate the DEGs with the genes listed in PodNet, a literature-based database on mouse podocyte genes. A total of 176 genes were differentially expressed among the three groups. GO analyses showed that pathways related to cytoskeleton organization and cell signaling were significantly enriched. Among them, 24 genes were listed in PodNet, and 12 of them were previously reported to be associated with IL-4-induced changes in human podocytes. Of the 12 genes, the expression levels of BMP4, RARB, and PLCE1 were reversed when podocytes were simultaneously treated with DEX. In conclusion, this study explored changes in the transcriptome profiles of human podocytes treated with IL-4. Few genes were reported in previous studies and were previously validated in experiments with human podocytes. We speculate that IL-4 may exert pathogenic effects on the transcriptome of human podocytes, and a few genes may be involved in the pathogenesis.

TNF-α Pretreatment Improves the Survival and Function of Transplanted Human Neural Progenitor Cells Following Hypoxic-Ischemic Brain Injury.

Neural progenitor cells (NPCs) therapy offers great promise in hypoxic-ischemic (HI) brain injury. However, the poor survival of implanted NPCs in the HI host environment limits their therapeutic effects. Tumor necrosis factor-alpha (TNF-α) is a pleiotropic cytokine that is induced in response to a variety of pathological processes including inflammation and immunity. On the other hand, TNF-α has protective effects on cell apoptosis and death and affects the differentiation, proliferation, and survival of neural stem/progenitor cells in the brain. The present study investigated whether TNF-α pretreatment on human NPCs (hNPCs) enhances the effectiveness of cell transplantation therapy under ischemic brain. Fetal brain tissue-derived hNPCs were pretreated with TNF-α before being used in vitro experiments or transplantation. TNF-α significantly increased expression of cIAP2, and the use of short hairpin RNA-mediated knockdown of cIAP2 demonstrated that cIAP2 protected hNPCs against HI-induced cytotoxicity. In addition, pretreatment of hNPCs with TNF-α mediated neuroprotection by altering microglia polarization via increased expression of CX3CL1 and by enhancing expression of neurotrophic factors. Furthermore, transplantation of TNF-α-treated hNPCs reduced infarct volume and improved neurological functions in comparison with non-pretreated hNPCs or vehicle. These findings show that TNF-α pretreatment, which protects hNPCs from HI-injured brain-induced apoptosis and increases neuroprotection, is a simple and safe approach to improve the survival of transplanted hNPCs and the therapeutic efficacy of hNPCs in HI brain injury.

Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome: A systematic review.

BACKGROUND: Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a monogenic disorder characterized by early onset fatal multi-system autoimmunity due to loss-of-function mutations in the gene encoding the forkhead box P3 (FOXP3) transcription factor which is crucial for the development, maturation, and maintenance of CD4+ regulatory T (T-reg) cells. Various autoimmune phenomena such as enteropathy, endocrinopathies, cytopenias, renal disease, and skin manifestations are characteristic findings in patients affected by IPEX syndrome. OBJECTIVES: In this systematic review, we focus on both clinical and demographic characteristics of IPEX patients, highlighting possible genotype-phenotype correlations and address prognostic factors for disease outcome. METHODS: We performed a literature search to systematically investigate the case reports of IPEX which were published before August 7th, 2017. RESULTS: A total of 75 articles (195 patients) were identified. All IPEX patients included had FOXP3 mutations which were most frequently located in the forkhead domain (n = 68, 34.9%) followed by the leucine-zipper domain (n = 30, 15.4%) and repressor domain (n = 36, 18.4%). Clinical manifestations were as follows: enteropathy (n = 191, 97.9%), skin manifestations (n = 121, 62.1%), endocrinopathy (n = 104, 53.3%), hematologic abnormalities (n = 75, 38.5%), infections (n = 78, 40.0%), other immune-related complications (n = 43, 22.1%), and renal involvement (n = 32, 16.4%). Enteropathic presentations (P = 0.017), eczema (P = 0.030), autoimmune hemolytic anemia (P = 0.022) and food allergy (P = 0.009) were associated with better survival, while thrombocytopenia (P = 0.034), septic shock (P = 0.045) and mutations affecting the repressor domain (P = 0.021), intron 7 (P = 0.033) or poly A sequence (P = 0.025) were associated with increased risk of death. Immunosuppressive therapy alone was significantly associated with increased cumulative survival compared to patients who received no treatment (P = 0.041). CONCLUSIONS: We report the most comprehensive summary of demographic and clinical profiles derived from a total of 195 IPEX patients with deleterious mutations in FOXP3. Analysis of our findings provides new insights into genotype/phenotype correlations, and clinical and genetic factors associated with increased risk of death and response to treatment strategies.