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Enterohepatic circulation (EC) of bile acids (BAs) and proteins encoded by the four genes associated with GIS in FD patients. Major BA transporters in human hepatocytes and enterocytes are shown. Blue arrows indicate up-regulation, red bars indicate down-regulation, while black arrows indicate transport across the cell. Proteins encoded by the four genes reported in the current study associated with GIS are represented in orange boxes. BAs are synthesized in hepatocytes from cholesterol by CYP7A1 which is thought to be the rate limiting step in BA synthesis. BAs active FXR to inhibit CYP7A1 gene transcription. FXR induces intestinal hormone FGF19 which is released in the portal circulation and in the hepatocytes activates FGFR4/Klotho-? signaling inhibiting CYP7A1 activity. In hepatocytes BSEP excretes monovalent BAs in the bile canaliculus while divalent BAs and anionic conjugates are excreted via MRP2. MDR3 mediates secretion of phopholipids while organic cations are excreted via MDR1. Basolateral bile acid export system (MRP1, MRP3, MRP4, MRP5) excretes accumulated biliary constituents. In the terminal ilieum BAs are reabsorbed by ASBT and effluxed on the basolateral site via OST?/?. BAs are taken up by the hepatocytes via NTCP and OATPs transport systems. BSEP expression is regulated by nuclear receptors (see text for details) and BSEP insertion into canalicular membrane is stimulated by cAMP (blue dashed arrow). AhR = aryl hydrocarbon receptor; ASBT = apical sodium bile salt transporter; BA = bile acid; BSEP = bile salt expert pump; CAR = constitutive androstane receptor; CYP7A1 = cholesterol 7a-hydroxylase; FGF19 = fibroblast growth factor 19; FGFR4 = FGF receptor 4; FXR = farsenoid X receptor; MRP1= multidrug resistance protein 1; MRP2 = multidrug resistance protein 2; MRP3= multidrug resistance protein 3; MRP4 = multidrug resistance protein 4; MRP5 = multidrug resistance protein 5; NA + = sodium ion; NTCP = Na + -dependent taurocholate cotransport peptide; OATPs = Na + -independent organic anion transport proteins; OA-= organic anions; OC-= organic cations; OST?/? = organic solute transporter ?/?; PC = phosphatidylcholine; PXR = pregnance X receptor.
Source publication
Gastrointestinal symptoms (GIS) are often among the earliest presenting events in Fabry disease (FD), an X-linked lysosomal disorder caused by the deficiency of α-galactosidase A. Despite recent advances in clinical and molecular characterization of FD, the pathophysiology of the GIS is still poorly understood. To shed light either on differential...
Context in source publication
Context 1
... approximately 25% to 50% of the patients with IBS BAs induce diarrhea either through decreased ileal reabsorption or increased hepatic synthesis [35,36]. The enterohepatic circulation (EC) of BAs is a physiological process mediated by a complex membrane transport system in the liver and intestine, regulated by nuclear receptors [37], as depicted in Figure 2. BAs are synthesized in the liver from cholesterol and are transported into bile ducts in conjugated form. ...
Citations
... DNA microarrays, including Single Nucleotide Polymorphisms (SNPs) and differential expressed genes (DEGs), are becoming essential tools in omics research such as pharmacogenomics [1] and drug toxicity prediction [2]. Generally, DNA microarrays [3] are used to detect anomalies in case-control studies [4][5][6]. SNPs data [7] provide information about regions in the genome that show differences between individuals at a single base pair site. In comparison, DEGs data give information about the expression of genes responsible for the phenotype abnormalities under investigation. ...
Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.
... Within the specific time frame of December 2016-January 2018, consecutive peripheral blood samples from 32 patients with OC was collected at our institution and genotyped by DMET Plus assay (Thermo Fisher Scientific Inc., Waltham, MA, USA), including 1931 SNPs and five CNVs in ADME genes [29,30] as previously described in common and uncommon diseases [31][32][33][34]. ...
Carboplatin is the cornerstone of ovarian cancer (OC) treatment, while platinum-response, dependent on interindividual variability, is the major prognostic factor for long-term outcomes. This retrospective study was focused on explorative search of genetic polymorphisms in the Absorption, Distribution, Metabolism, Excretion (ADME) genes for the identification of biomarkers prognostic/predictive of platinum-response in OC patients. Ninety-two advanced OC patients treated with carboplatin-based therapy were enrolled at our institution. Of these, we showed that 72% of patients were platinum-sensitive, with a significant benefit in terms of OS (p = 0.001). We identified an inflammatory-score with a longer OS in patients with lower scores as compared to patients with the maximum score (p = 0.001). Thirty-two patients were genotyped for 1931 single nucleotide polymorphisms (SNPs) and five copy number variations (CNVs) by the DMET Plus array platform. Among prognostic polymorphisms, we found a potential role of UGT2A1 both as a predictor of platinum-response (p = 0.01) and as prognostic of survival (p = 0.05). Finally, we identified 24 SNPs related to OS. UGT2A1 correlates to an “inflammatory-score” and retains a potential prognostic role in advanced OC. These data provide a proof of concept that warrants further validation in follow-up studies for the definition of novel biomarkers in this aggressive disease.
... The application of this framework supports our initial hypothesis that any strategy of optimization that is unaware of the virus spreading topology without a severe lockdown may fail in virus circulation mitigation. This may favor the insurgence of novel virus variants during the vaccination that are not covered by existing vaccines [29]. ...
The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks.
We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the ”bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available at: (https://github.com/mazzalab/playgrounds)
... Indeed evaluation of the inflammatory and immune profile as well as of the autoimmune HLA haplotypes might significantly improve the identification of patients with high risk of severe irAEs, in association with pharmacogenomics studies on targeted small molecules in advanced and adjuvant setting. [35][36][37][38] Conclusion Management of irAEs in adjuvant immunotherapy for MM patient is complex and controversial due to potential long-term benefits versus risks of a systemic treatment. Our case describes a novel multidisciplinary management of two side effects requiring different combined treatments. ...
Immune checkpoint inhibitors (ICIs) represent an important advance in the adjuvant treatment of patients with high-risk melanoma. Although the safety profile of anti-programmed cell death protein-1 (PD-1) is fairly acceptable, different immune-related adverse events (irAEs) are described. Herein we report for the first time a notably multidisciplinary combined approach on a malignant melanoma (MM) patient treated with anti-PD-1 antibody in adjuvant setting. In this novel approach, corticosteroid-refractory immune-mediated colitis (IMC) was effectively treated with Vedolizumab, a selective blockade of the α4β7 integrin and corticosteroids were successfully administered for autoimmune neutropenia. Notably, our patient also express HLA-B*35, a potential biomarker for predicting a genetic basis of autoimmune susceptibility. Our experience offers a possible future perspective about the use of Vedolizumab together with immunotherapy in a strategic early approach for high-risk patients genotyped for HLA.
... Allele frequency values for the 59 CEU Hapmap samples are available on GPL17860 dataset. The internal dataset including 35 non-cancer samples was previously genotyped with the same platform and previously topic of two publications [12,13]. ...
The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.
... Training and validation sets are used in the case/control study design. For instance, it is possible to compare patients experiencing toxicity vs. matched controls which not experienced toxicity [15][16][17][18][19]. An example of Training and Validation sets creation related to the discovery of potential predictive biomarker is presented in the study of Arbitrio et al. [20] in which, the SNP microarray data is split in a training set (TS) of 79 breast cancer (BC) patients underwent to taxane treatment, and in a Validation set (VS) of independent 54 BC patient underwent taxane-treated, obtained by direct nucleotide sequencing. ...
Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.
... This assay has been widely used in several common and uncommon diseases [12][13][14], treatment regimens, and biological samples (peripheral blood, formalin fixed-paraffin-embedded tissue, buccal swab) to find genetic predictors of clinical phenotypes and/or outcomes [15][16][17][18]. For example, recently, our group identified and validated in breast cancer (BC) patients 5 SNPs in NR1I3 and UGT2B7 genes correlated to grade 2-3 taxane-related peripheral neurotoxicity (G 2-3-TrPN) protection identifying a genetic signature of prognostic relevance for BC outcome [19]. ...
The understanding of the biological differences which underlie the inter-individual variability in drug response improved the efficacy of cancer therapy in the era of precision medicine. In fact molecularly targeted drugs and immunotherapy represent a revolution in cancer treatment. The identification of genetic predictive and/or prognostic biomarkers linked to drug pharmacokinetics (PK) and pharmacodynamics (PD) is allowed by the development of high-throughput omics tools for detecting and understanding biological differences among individuals, in order to improve drug efficacy and minimize risk of toxicity. Personalized medicine in cancer treatment reduces costs of the healthcare system. Unfortunately, pharmacogenomics biomarkers discovery is influenced by complexity, need of high-quality evidence, and a validation process for regulatory purposes. This chapter is focused on the critic analysis of presently available pharmacogenomics tools for discovering or testing genetic polymorphic variants in drug metabolizing enzyme to be introduced in clinical practice for the prospective stratification of cancer patients.
... The DMET platform is used in pharmacogenomics to detect in human samples the presence/absence of SNPs on 225 genes that are related to drug absorption, distribution, metabolism, and excretion (ADME) [4]; nucleotide variations are detected to a reference population. Different recent works demonstrated the roles of genetic variations in ADME genes in association with the heterogeneity in drug treatment effects [5,6,7,8]. ...
Microarrays are experimental methods that can provide information about gene expression and SNP data that hold great potential for new understanding, driving advances in functional genomics and clinical and molecular biology. Cluster analysis is used to analyze data that are not a priori to contain any specific subgroup. The goal is to use the data itself to recognize meaningful and informative subgroups. Also, cluster analysis helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. This chapter outlines a collection of cluster methods suitable for the analysis of microarray data sets.
... Several studies demonstrated the role of polymorphic variants in genes related to drugs metabolism in terms of efficacy or toxicity, especially during cancer treatments [7][8][9][10][11][12]. However, to date, the clinical relevance of the association between somatic mutations and drug response has not been thoroughly elucidated although it is a key step toward the precision medicine [13,14]. ...
Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.
... A disruption of entero-hepatic circulation impairs BAs handling, leading to diarrhea via osmotic mechanisms. Factors triggering the BAs-induced diarrhea are ascribable to a deficiency in fibroblast growth factor 19, that inhibits the BAs synthesis, and to genetic variations [47][48][49]. Abnormalities of the enterohepatic circulation of BAs have been reported to play a role in GI symptom generation in FD patients. Di Martino et al. [49] investigated genetic variants potentially associated with GI symptoms in 49 FD patients. ...
... Abnormalities of the enterohepatic circulation of BAs have been reported to play a role in GI symptom generation in FD patients. Di Martino et al. [49] investigated genetic variants potentially associated with GI symptoms in 49 FD patients. Nine single nucleotide polymorphisms mapped within four genes (ABCB11, SLCO1B1, NR1I3 and ABCC5), involved in BA export, detoxification, and uptake in the liver, with a higher susceptibility to develop GI symptoms compared to patients without polymorphisms. ...
Anderson-Fabry disease (FD) is an X-linked lysosomal storage disorder leading to a wide array of clinical manifestations. Among these, gastrointestinal (GI) symptoms such as abdominal pain, bloating, and diarrhea affect about half of the FD adults and more than half of FD children. GI symptoms could be the first manifestation of FD; however, being non-specific, they overlap with the clinical picture of other conditions, such as irritable bowel syndrome and inflammatory bowel disease. This common overlap is the main reason why FD patients are often unrecognized and diagnosis is delayed for many years. The present narrative review is aimed to promote awareness of the GI manifestations of FD amongst general practitioners and specialists and highlight the latest findings of this rare condition including diagnostic tools and therapies. Finally, we will discuss some preliminary data on a patient presenting with GI symptoms who turned to be affected by a variant of uncertain significance of alpha-galactosidase (GLA) gene.
