Inflammasome genetics and complex diseases: a comprehensive review.
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ABSTRACT:
SUBMITTER: Fernandes FP
PROVIDER: S-EPMC7608315 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high-throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history.Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with many human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral.Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enable deleterious rare variants to persist at low frequencies just as long as neutral variants, but low-frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function and that potentially contribute to disease susceptibility.The key question for sequencing-based association studies of complex traits is how to distinguish between deleterious and benign genetic variation. We used population genetic simulations to uncover patterns of genetic variation that distinguish these two categories, especially derived allele age, thereby providing inroads into novel methods for characterizing rare genetic variation driving complex diseases.
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Project description:Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.
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Project description:Canonical WNT signaling through Frizzled and LRP5/6 receptors is transduced to the WNT/β-catenin and WNT/stabilization of proteins (STOP) signaling cascades to regulate cell fate and proliferation, whereas non-canonical WNT signaling through Frizzled or ROR receptors is transduced to the WNT/planar cell polarity (PCP), WNT/G protein-coupled receptor (GPCR) and WNT/receptor tyrosine kinase (RTK) signaling cascades to regulate cytoskeletal dynamics and directional cell movement. WNT/β-catenin signaling cascade crosstalks with RTK/SRK and GPCR-cAMP-PKA signaling cascades to regulate β-catenin phosphorylation and β-catenin-dependent transcription. Germline mutations in WNT signaling molecules cause hereditary colorectal cancer, bone diseases, exudative vitreoretinopathy, intellectual disability syndrome and PCP-related diseases. APC or CTNNB1 mutations in colorectal, endometrial and prostate cancers activate the WNT/β-catenin signaling cascade. RNF43, ZNRF3, RSPO2 or RSPO3 alterations in breast, colorectal, gastric, pancreatic and other cancers activate the WNT/β-catenin, WNT/STOP and other WNT signaling cascades. ROR1 upregulation in B-cell leukemia and solid tumors and ROR2 upregulation in melanoma induce invasion, metastasis and therapeutic resistance through Rho-ROCK, Rac-JNK, PI3K-AKT and YAP signaling activation. WNT signaling in cancer, stromal and immune cells dynamically orchestrate immune evasion and antitumor immunity in a cell context-dependent manner. Porcupine (PORCN), RSPO3, WNT2B, FZD5, FZD10, ROR1, tankyrase and β-catenin are targets of anti-WNT signaling therapy, and ETC-159, LGK974, OMP-18R5 (vantictumab), OMP-54F28 (ipafricept), OMP-131R10 (rosmantuzumab), PRI-724 and UC-961 (cirmtuzumab) are in clinical trials for cancer patients. Different classes of anti-WNT signaling therapeutics are necessary for the treatment of APC/CTNNB1-, RNF43/ZNRF3/RSPO2/RSPO3- and ROR1-types of human cancers. By contrast, Dickkopf-related protein 1 (DKK1), SOST and glycogen synthase kinase 3β (GSK3β) are targets of pro-WNT signaling therapy, and anti-DKK1 (BHQ880 and DKN-01) and anti-SOST (blosozumab, BPS804 and romosozumab) monoclonal antibodies are being tested in clinical trials for cancer patients and osteoporotic post-menopausal women. WNT-targeting therapeutics have also been applied as reagents for in vitro stem-cell processing in the field of regenerative medicine.
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Project description:Both environmental and genetic triggers factor into the etiology of autoimmune thyroid disease (AITD), including Graves' disease (GD) and Hashimoto's thyroiditis (HT). Although the exact pathogenesis and causative interaction between environment and genes are unknown, GD and HT share similar immune-mediated mechanisms of disease. They both are characterized by the production of thyroid autoantibodies and by thyroidal lymphocytic infiltration, despite being clinically distinct entities with thyrotoxicosis in GD and hypothyroidism in HT. Family and population studies confirm the strong genetic influence and inheritability in the development of AITD. AITD susceptibility genes can be categorized as either thyroid specific (Tg, TSHR) or immune-modulating (FOXP3, CD25, CD40, CTLA-4, HLA), with HLA-DR3 carrying the highest risk. Of the AITD susceptibility genes, FOXP3 and CD25 play critical roles in the establishment of peripheral tolerance while CD40, CTLA-4, and the HLA genes are pivotal for T lymphocyte activation and antigen presentation. Polymorphisms in these immune-modulating genes, in particular, significantly contribute to the predisposition for GD, HT and, unsurprisingly, other autoimmune diseases. Emerging evidence suggests that single nucleotide polymorphisms (SNPs) in the immunoregulatory genes may functionally hinder the proper development of central and peripheral tolerance and alter T cell interactions with antigen presenting cells (APCs) in the immunological synapse. Thus, susceptibility genes for AITD contribute directly to the key mechanism underlying the development of organ-specific autoimmunity, namely the breakdown in self-tolerance. Here we review the major immune-modulating genes that are associated with AITD and their potential functional effects on thyroidal immune dysregulation.
| S-EPMC4628844 | biostudies-literature
Project description:To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated genes. Using this strategy, we previously built the following two databases for reproductive disorders: The Database for Preterm Birth (dbPTB) and The Database for Preeclampsia (dbPEC). The completeness and accuracy of these databases is essential for supporting our understanding of these complex conditions. Given the exponential increase in biomedical literature, it is becoming increasingly difficult to manually maintain these databases. Using our curated databases as reference data sets, we implemented a machine learning-based approach to optimize article selection for manual curation. We used logistic regression, random forests and neural networks as our machine learning algorithms to classify articles. We examined features derived from abstract text, annotations and metadata that we hypothesized would best classify articles with genetically relevant content associated to the disorder of interest. Combinations of these features were used build the classifiers and the performance of these feature sets were compared to a standard 'Bag-of-Words'. Several combinations of these genetic based feature sets outperformed 'Bag-of-Words' at a threshold such that 95% of the curated gene set obtained from the original manual curation of all articles were extracted from the articles classified by machine learning as 'considered'. The performance was superior in terms of the reduction of required manual curation and two measures of the harmonic mean of precision and recall. The reduction in workload ranged from 0.814 to 0.846 for the dbPTB and 0.301 to 0.371 for the dbPEC. Additionally, a database of metadata and annotations is generated which allows for rapid query of individual features. Our results demonstrate that machine learning algorithms can identify articles with relevant data for databases of genes associated with complex diseases.
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Project description:Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.
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