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Inflammasome genetics and complex diseases: a comprehensive review.

ABSTRACT: The inflammasome is a cytoplasmic multiprotein complex responsible for the activation of inflammatory caspases (caspase-1, -4, and -5) in response to pathogen- and/or damage-associated molecular patterns or to homeostasis-altering molecular pathways, and for the consequent release of the pro-inflammatory cytokines interleukin (IL)-1ß and IL-18. Taking in account the complexity of inflammasome activation and that several regulatory steps are involved in maintaining its physiologic role in homeostasis and innate immune response, it does not surprise that several genetic variants in inflammasome components have been associated with common pathologies in the general population, such as autoimmune disorders, cardiovascular diseases, obesity and associated metabolic syndrome, neurodegenerative diseases, and cancer. Moreover, the susceptibility to infectious agents and/or to develop severe complications during infections also has been related to inflammasome genetics. In this work, we revised genetic association studies about polymorphisms of main inflammasome genes in sterile as well as infectious diseases, trying to depict the genetic contribution of inflammasome in disease pathogenesis.

SUBMITTER: Fernandes FP

PROVIDER: S-EPMC7608315 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Inflammasome genetics and complex diseases: a comprehensive review.

Fernandes Fernanda Pereira FP Leal Vinicius N C VNC Souza de Lima Dhemerson D Reis Edione C EC Pontillo Alessandra A

European journal of human genetics : EJHG 20200604 10

The inflammasome is a cytoplasmic multiprotein complex responsible for the activation of inflammatory caspases (caspase-1, -4, and -5) in response to pathogen- and/or damage-associated molecular patterns or to homeostasis-altering molecular pathways, and for the consequent release of the pro-inflammatory cytokines interleukin (IL)-1ß and IL-18. Taking in account the complexity of inflammasome activation and that several regulatory steps are involved in maintaining its physiologic role in homeost ...[more]

PMID: 32499599

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Project description:ObjectivesIdentifying 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.MethodsHere 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.ResultsGenes 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.ConclusionsThe 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.

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.

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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Inflammasome genetics and complex diseases: a comprehensive review.

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Inflammasome genetics and complex diseases: a comprehensive review.

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