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Deepu V, Rai V, Agrawal DK. Quantitative Assessment of Intracellular Effectors and Cellular Response in RAGE Activation. ARCHIVES OF INTERNAL MEDICINE RESEARCH 2024; 7:80-103. [PMID: 38784044 PMCID: PMC11113086 DOI: 10.26502/aimr.0168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The review delves into the methods for the quantitative assessment of intracellular effectors and cellular response of Receptor for Advanced Glycation End products (RAGE), a vital transmembrane receptor involved in a range of physiological and pathological processes. RAGE bind to Advanced Glycation End products (AGEs) and other ligands, which in turn activate diverse downstream signaling pathways that impact cellular responses such as inflammation, oxidative stress, and immune reactions. The review article discusses the intracellular signaling pathways activated by RAGE followed by differential activation of RAGE signaling across various diseases. This will ultimately guide researchers in developing targeted and effective interventions for diseases associated with RAGE activation. Further, we have discussed how PCR, western blotting, and microscopic examination of various molecules involved in downstream signaling can be leveraged to monitor, diagnose, and explore diseases involving proteins with unique post-translational modifications. This review article underscores the pressing need for advancements in molecular approaches for disease detection and management involving RAGE.
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Affiliation(s)
- Vinitha Deepu
- Department of Translational Research, Western University of Health Sciences, Pomona, California 91763, USA
| | - Vikrant Rai
- Department of Translational Research, Western University of Health Sciences, Pomona, California 91763, USA
| | - Devendra K Agrawal
- Department of Translational Research, Western University of Health Sciences, Pomona, California 91763, USA
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Chen A, Yang Q, Ye W, Xu L, Wang Y, Sun D, Han B. Polymorphisms of CYP7A1 and HADHB Genes and Their Effects on Milk Production Traits in Chinese Holstein Cows. Animals (Basel) 2024; 14:1276. [PMID: 38731280 PMCID: PMC11083613 DOI: 10.3390/ani14091276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Our preliminary research proposed the cytochrome P450 family 7 subfamily A member 1 (CYP7A1) and hydroxyacyl-coenzyme A dehydrogenase trifunctional multienzyme complex beta subunit (HADHB) genes as candidates for association with milk-production traits in dairy cattle because of their differential expression across different lactation stages in the liver tissues of Chinese Holstein cows and their potential roles in lipid metabolism. Hence, we identified single-nucleotide polymorphisms (SNPs) of the CYP7A1 and HADHB genes and validated their genetic effects on milk-production traits in a Chinese Holstein population with the goal of providing valuable genetic markers for genomic selection (GS) in dairy cattle, This study identified five SNPs, 14:g.24676921A>G, 14:g.24676224G>A, 14:g.24675708G>T, 14:g.24665961C>T, and 14:g.24664026A>G, in the CYP7A1 gene and three SNPs, 11:g.73256269T>C, 11:g.73256227A>C, and 11:g.73242290C>T, in HADHB. The single-SNP association analysis revealed significant associations (p value ≤ 0.0461) between the eight SNPs of CYP7A1 and HADHB genes and 305-day milk, fat and protein yields. Additionally, using Haploview 4.2, we found that the five SNPs of CYP7A1 formed two haplotype blocks and that the two SNPs of HADHB formed one haplotype block; notably, all three haplotype blocks were also significantly associated with milk, fat and protein yields (p value ≤ 0.0315). Further prediction of transcription factor binding sites (TFBSs) based on Jaspar software (version 2023) showed that the 14:g.24676921A>G, 14:g.24675708G>T, 11:g.73256269T>C, and 11:g.73256227A>C SNPs could alter the 5' terminal TFBS of the CYP7A1 and HADHB genes. The 14:g.24665961C>T SNP caused changes in the structural stability of the mRNA for the CYP7A1 gene. These alterations have the potential to influence gene expression and, consequently, the phenotype associated with milk-production traits. In summary, we have confirmed the genetic effects of CYP7A1 and HADHB genes on milk-production traits in dairy cattle and identified potential functional mutations that we suggest could be used for GS of dairy cattle and in-depth mechanistic studies of animals.
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Affiliation(s)
- Ao Chen
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
| | - Qianyu Yang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
| | - Wen Ye
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
| | - Lingna Xu
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
| | - Yuzhan Wang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
| | - Dongxiao Sun
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
- Beijing Jingwa Agricultural Innovation Center, Beijing 100193, China
| | - Bo Han
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics, National Engineering Laboratory for Animal Breeding, State Key Laboratory of Animal Biotech Breeding, China Agricultural University, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Beijing 100193, China; (A.C.); (Q.Y.); (W.Y.); (L.X.); (Y.W.); (D.S.)
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Spangsberg Petersen US, Dembic M, Martínez-Pizarro A, Richard E, Holm LL, Havelund JF, Doktor TK, Larsen MR, Færgeman NJ, Desviat LR, Andresen BS. Regulating PCCA gene expression by modulation of pseudoexon splicing patterns to rescue enzyme activity in propionic acidemia. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102101. [PMID: 38204914 PMCID: PMC10776996 DOI: 10.1016/j.omtn.2023.102101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024]
Abstract
Pseudoexons are nonfunctional intronic sequences that can be activated by deep-intronic sequence variation. Activation increases pseudoexon inclusion in mRNA and interferes with normal gene expression. The PCCA c.1285-1416A>G variation activates a pseudoexon and causes the severe metabolic disorder propionic acidemia by deficiency of the propionyl-CoA carboxylase enzyme encoded by PCCA and PCCB. We characterized this pathogenic pseudoexon activation event in detail and identified hnRNP A1 to be important for normal repression. The PCCA c.1285-1416A>G variation disrupts an hnRNP A1-binding splicing silencer and simultaneously creates a splicing enhancer. We demonstrate that blocking this region of regulation with splice-switching antisense oligonucleotides restores normal splicing and rescues enzyme activity in patient fibroblasts and in a cellular model created by CRISPR gene editing. Interestingly, the PCCA pseudoexon offers an unexploited potential to upregulate gene expression because healthy tissues show relatively high inclusion levels. By blocking inclusion of the nonactivated wild-type pseudoexon, we can increase both PCCA and PCCB protein levels, which increases the activity of the heterododecameric enzyme. Surprisingly, we can increase enzyme activity from residual levels in not only patient fibroblasts harboring PCCA missense variants but also those harboring PCCB missense variants. This is a potential treatment strategy for propionic acidemia.
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Affiliation(s)
- Ulrika Simone Spangsberg Petersen
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Maja Dembic
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
- Department of Clinical Genetics, Odense University Hospital, 5000 Odense C, Denmark
- Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense M, Denmark
| | - Ainhoa Martínez-Pizarro
- Centro de Biología Molecular Severo Ochoa, UAM-CSIC, CEDEM, CIBERER, IdiPaz, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Eva Richard
- Centro de Biología Molecular Severo Ochoa, UAM-CSIC, CEDEM, CIBERER, IdiPaz, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Lise Lolle Holm
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Jesper Foged Havelund
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Thomas Koed Doktor
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Martin Røssel Larsen
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Nils J. Færgeman
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
| | - Lourdes Ruiz Desviat
- Centro de Biología Molecular Severo Ochoa, UAM-CSIC, CEDEM, CIBERER, IdiPaz, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Brage Storstein Andresen
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, 5230 Odense M, Denmark
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Abraham LN, Croll D. Genome-wide expression QTL mapping reveals the highly dynamic regulatory landscape of a major wheat pathogen. BMC Biol 2023; 21:263. [PMID: 37981685 PMCID: PMC10658818 DOI: 10.1186/s12915-023-01763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/07/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND In agricultural ecosystems, outbreaks of diseases are frequent and pose a significant threat to food security. A successful pathogen undergoes a complex and well-timed sequence of regulatory changes to avoid detection by the host immune system; hence, well-tuned gene regulation is essential for survival. However, the extent to which the regulatory polymorphisms in a pathogen population provide an adaptive advantage is poorly understood. RESULTS We used Zymoseptoria tritici, one of the most important pathogens of wheat, to generate a genome-wide map of regulatory polymorphism governing gene expression. We investigated genome-wide transcription levels of 146 strains grown under nutrient starvation and performed expression quantitative trait loci (eQTL) mapping. We identified cis-eQTLs for 65.3% of all genes and the majority of all eQTL loci are within 2kb upstream and downstream of the transcription start site (TSS). We also show that polymorphism in different gene elements contributes disproportionally to gene expression variation. Investigating regulatory polymorphism in gene categories, we found an enrichment of regulatory variants for genes predicted to be important for fungal pathogenesis but with comparatively low effect size, suggesting a separate layer of gene regulation involving epigenetics. We also show that previously reported trait-associated SNPs in pathogen populations are frequently cis-regulatory variants of neighboring genes with implications for the trait architecture. CONCLUSIONS Overall, our study provides extensive evidence that single populations segregate large-scale regulatory variation and are likely to fuel rapid adaptation to resistant hosts and environmental change.
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Affiliation(s)
- Leen Nanchira Abraham
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, 2000, Neuchâtel, Switzerland
- Present address: Institute of Plant Sciences, University of Cologne, Cologne, Germany
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, 2000, Neuchâtel, Switzerland.
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Fox E. Polygenic scores, and the genome-wide association studies they derive from, will have difficulty identifying genes that predispose one to develop a social behavioral trait. Behav Brain Sci 2023; 46:e214. [PMID: 37694986 DOI: 10.1017/s0140525x22002370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Polygenic scores (PGSs) have several limitations. They are confounded with environmental effects on behavior and cannot be used to study how mutations affect brain function and behavior. For this, mutations with large effects, which often arise in only one geographical population are needed. Genome-wide association studies (GWASs), commonly used for identifying mutations, have difficulty detecting these mutations. A strategy that overcomes this challenge is discussed.
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Affiliation(s)
- Edward Fox
- Department of Psychological Science, Purdue University, West Lafayette, IN, USA https://www.purdue.edu/hhs/psy/directory/faculty/Fox_Edward.html
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Cheung WA, Johnson AF, Rowell WJ, Farrow E, Hall R, Cohen ASA, Means JC, Zion TN, Portik DM, Saunders CT, Koseva B, Bi C, Truong TK, Schwendinger-Schreck C, Yoo B, Johnston JJ, Gibson M, Evrony G, Rizzo WB, Thiffault I, Younger ST, Curran T, Wenger AM, Grundberg E, Pastinen T. Direct haplotype-resolved 5-base HiFi sequencing for genome-wide profiling of hypermethylation outliers in a rare disease cohort. Nat Commun 2023; 14:3090. [PMID: 37248219 DOI: 10.1038/s41467-023-38782-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
Long-read HiFi genome sequencing allows for accurate detection and direct phasing of single nucleotide variants, indels, and structural variants. Recent algorithmic development enables simultaneous detection of CpG methylation for analysis of regulatory element activity directly in HiFi reads. We present a comprehensive haplotype resolved 5-base HiFi genome sequencing dataset from a rare disease cohort of 276 samples in 152 families to identify rare (~0.5%) hypermethylation events. We find that 80% of these events are allele-specific and predicted to cause loss of regulatory element activity. We demonstrate heritability of extreme hypermethylation including rare cis variants associated with short (~200 bp) and large hypermethylation events (>1 kb), respectively. We identify repeat expansions in proximal promoters predicting allelic gene silencing via hypermethylation and demonstrate allelic transcriptional events downstream. On average 30-40 rare hypermethylation tiles overlap rare disease genes per patient, providing indications for variation prioritization including a previously undiagnosed pathogenic allele in DIP2B causing global developmental delay. We propose that use of HiFi genome sequencing in unsolved rare disease cases will allow detection of unconventional diseases alleles due to loss of regulatory element activity.
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Affiliation(s)
- Warren A Cheung
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Adam F Johnson
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | | | - Emily Farrow
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
- Department of Pediatrics, School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | | | - Ana S A Cohen
- Department of Pediatrics, School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO, USA
| | - John C Means
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Tricia N Zion
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | | | | | - Boryana Koseva
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Chengpeng Bi
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Tina K Truong
- Center for Human Genetics and Genomics, Department of Pediatrics, Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Carl Schwendinger-Schreck
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Byunggil Yoo
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Jeffrey J Johnston
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Margaret Gibson
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Gilad Evrony
- Center for Human Genetics and Genomics, Department of Pediatrics, Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
| | - William B Rizzo
- Child Health Research Institute, Department of Pediatrics, Nebraska Medical Center, Omaha, NE, USA
| | - Isabelle Thiffault
- Department of Pediatrics, School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Scott T Younger
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA
- Department of Pediatrics, School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | - Tom Curran
- Children's Mercy Research Institute, Kansas City, MO, USA
| | | | - Elin Grundberg
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA.
- Department of Pediatrics, School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA.
| | - Tomi Pastinen
- Department of Pediatrics, Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, USA.
- Department of Pediatrics, School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA.
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Mirzaei L, Yadollahi A, Kermani MJ, Naderpour M, Zeinanloo AA. Stability investigation of air-dried olive ribo nucleic acids for metavirome studies. PLANT METHODS 2022; 18:19. [PMID: 35184725 PMCID: PMC8858460 DOI: 10.1186/s13007-022-00846-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND The application of ribo nucleic acids for molecular studies requires high integrity and quality of extracted total RNA samples. In addition, the need to transfer RNA samples at room temperature without special treatments such as ice and liquid nitrogen storage according to international transport laws highlights the importance of low cost alternative methods such as RNA air-drying, lyophilisation and transportable agents. In this study, the quality and quantity of air-dried RNA samples from leaf, petiole and bark tissues of different olive genotypes using several RNA extraction methods were compared with lyophilized ground leaves and RNAlater-stored tissue samples before precipitation. The quality of RNA and prepared libraries were checked by several techniques including agarose and polyacrylamide gel electrophoresis, Agilent quality control, RT-PCR amplification of housekeeping and viral genes and high throughput sequencing. RESULTS Although RNA value varied amongst cultivars, RNA extraction with TRIzol™ Reagent in fresh extractions and samples stored in RNAlater before RNA extraction resulted in 455.26 ng/µL and 63.46 ng/µL (mean value of cultivars) as the highest RNA concentration averages, respectively. RNA samples extracted by TRIzol™ Reagents and stored for a short term at - 80 °C before air-drying showed the third highest concentration (44.87 ng/µL). The synthesized cDNAs quality for PCR amplification of housekeeping genes (Rbc 1 and Nad 5) and partial genomes of Arabis mosaic virus and Cucumber mosaic virus showed satisfactory results in RNA samples extracted by TRIzol™ Reagents despite its variation amongst cultivars. CONCLUSIONS Considering the difficulties in the extraction of high quality and quantity RNA in olive for molecular analyses, this study demonstrated that RNA extraction method based on TRIzol™ Reagent can be considered for virobiome studies of both fresh and air-dried samples.
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Affiliation(s)
- Leila Mirzaei
- Department of Horticultural Sciences, Faculty of Agriculture, Tarbiat Modares University, P. O. Box: 14115-111, Tehran, Iran
- Department of Tissue and Cell Culture, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), P. O. Box: 31359-33151, Karaj, Iran
| | - Abbas Yadollahi
- Department of Horticultural Sciences, Faculty of Agriculture, Tarbiat Modares University, P. O. Box: 14115-111, Tehran, Iran.
| | - Maryam Jafarkhani Kermani
- Department of Tissue and Cell Culture, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), P. O. Box: 31359-33151, Karaj, Iran
| | - Masoud Naderpour
- Seed and Plant Certification and Registration Research Institute (SPCRI), Agricultural Research, Education and Extension Organization (AREEO), P. O. Box: 31535-1516, Karaj, Iran.
| | - Ali Asghar Zeinanloo
- Temperate Fruit Research Center, Horticultural Research Institute, Agricultural Research, Education and Extension Organization (AREEO), P. O. Box: 31585-4119, Karaj, Iran
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Petersen USS, Doktor TK, Andresen BS. Pseudoexon activation in disease by non-splice site deep intronic sequence variation - wild type pseudoexons constitute high-risk sites in the human genome. Hum Mutat 2021; 43:103-127. [PMID: 34837434 DOI: 10.1002/humu.24306] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 11/02/2021] [Accepted: 11/06/2021] [Indexed: 12/27/2022]
Abstract
Accuracy of pre-messenger RNA (pre-mRNA) splicing is crucial for normal gene expression. Complex regulation supports the spliceosomal distinction between authentic exons and the many seemingly functional splice sites delimiting pseudoexons. Pseudoexons are nonfunctional intronic sequences that can be activated for aberrant inclusion in mRNA, which may cause disease. Pseudoexon activation is very challenging to predict, in particular when activation occurs by sequence variants that alter the splicing regulatory environment without directly affecting splice sites. As pseudoexon inclusion often evades detection due to activation of nonsense-mediated mRNA decay, and because conventional diagnostic procedures miss deep intronic sequence variation, pseudoexon activation is a heavily underreported disease mechanism. Pseudoexon characteristics have mainly been studied based on in silico predicted sequences. Moreover, because recognition of sequence variants that create or strengthen splice sites is possible by comparison with well-established consensus sequences, this type of pseudoexon activation is by far the most frequently reported. Here we review all known human disease-associated pseudoexons that carry functional splice sites and are activated by deep intronic sequence variants located outside splice site sequences. We delineate common characteristics that make this type of wild type pseudoexons distinct high-risk sites in the human genome.
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Affiliation(s)
- Ulrika S S Petersen
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark
| | - Thomas K Doktor
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark
| | - Brage S Andresen
- Department of Biochemistry and Molecular Biology and the Villum Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark
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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform 2021; 22:6330938. [PMID: 34329375 DOI: 10.1093/bib/bbab259] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/14/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
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Affiliation(s)
- Amarinder Singh Thind
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Isha Monga
- Columbia University, New York City, NY, USA
| | | | - Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | | | - Marie Ranson
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Bruce Ashford
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
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10
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Gebert M, Jaśkiewicz M, Moszyńska A, Collawn JF, Bartoszewski R. The Effects of Single Nucleotide Polymorphisms in Cancer RNAi Therapies. Cancers (Basel) 2020; 12:cancers12113119. [PMID: 33113880 PMCID: PMC7694039 DOI: 10.3390/cancers12113119] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/14/2020] [Accepted: 10/23/2020] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Despite the recent progress in RNAi delivery of siRNA-based therapeutics for cancer therapy, the presence of single nucleotide polymorphisms (SNPs) in the general population could dramatically reduce the effectiveness of RNAi therapy. Their ubiquitous presence can also lead to unpredictable and adverse side effects. Because both SNPs and somatic mosaicisms have also been implicated in a number of human diseases including cancer, however, these specific changes offer the ability to selectively and efficiently target cancer cells. Here, we discuss how SNPs influence the development and success of novel anticancer RNAi therapies. Abstract Tremendous progress in RNAi delivery methods and design has allowed for the effective development of siRNA-based therapeutics that are currently under clinical investigation for various cancer treatments. This approach has the potential to revolutionize cancer therapy by providing the ability to specifically downregulate or upregulate the mRNA of any protein of interest. This exquisite specificity, unfortunately, also has a downside. Genetic variations in the human population are common because of the presence of single nucleotide polymorphisms (SNPs). SNPs lead to synonymous and non-synonymous changes and they occur once in every 300 base pairs in both coding and non-coding regions in the human genome. Much less common are the somatic mosaicism variations associated with genetically distinct populations of cells within an individual that is derived from postzygotic mutations. These heterogeneities in the population can affect the RNAi’s efficacy or more problematically, which can lead to unpredictable and sometimes adverse side effects. From a more positive viewpoint, both SNPs and somatic mosaicisms have also been implicated in human diseases, including cancer, and these specific changes could offer the ability to effectively and, more importantly, selectively target the cancer cells. In this review, we discuss how SNPs in the human population can influence the development and success of novel anticancer RNAi therapies and the importance of why SNPs should be carefully considered.
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Affiliation(s)
- Magdalena Gebert
- Department of Biology and Pharmaceutical Botany, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (M.G.); (M.J.); (A.M.)
| | - Maciej Jaśkiewicz
- Department of Biology and Pharmaceutical Botany, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (M.G.); (M.J.); (A.M.)
| | - Adrianna Moszyńska
- Department of Biology and Pharmaceutical Botany, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (M.G.); (M.J.); (A.M.)
| | - James F. Collawn
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Rafał Bartoszewski
- Department of Biology and Pharmaceutical Botany, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (M.G.); (M.J.); (A.M.)
- Correspondence:
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11
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Liu Y, Liu X, Zheng Z, Ma T, Liu Y, Long H, Cheng H, Fang M, Gong J, Li X, Zhao S, Xu X. Genome-wide analysis of expression QTL (eQTL) and allele-specific expression (ASE) in pig muscle identifies candidate genes for meat quality traits. Genet Sel Evol 2020; 52:59. [PMID: 33036552 PMCID: PMC7547458 DOI: 10.1186/s12711-020-00579-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 09/28/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Genetic analysis of gene expression level is a promising approach for characterizing candidate genes that are involved in complex economic traits such as meat quality. In the present study, we conducted expression quantitative trait loci (eQTL) and allele-specific expression (ASE) analyses based on RNA-sequencing (RNAseq) data from the longissimus muscle of 189 Duroc × Luchuan crossed pigs in order to identify some candidate genes for meat quality traits. RESULTS Using a genome-wide association study based on a mixed linear model, we identified 7192 cis-eQTL corresponding to 2098 cis-genes (p ≤ 1.33e-3, FDR ≤ 0.05) and 6400 trans-eQTL corresponding to 863 trans-genes (p ≤ 1.13e-6, FDR ≤ 0.05). ASE analysis using RNAseq SNPs identified 9815 significant ASE-SNPs in 2253 unique genes. Integrative analysis between the cis-eQTL and ASE target genes identified 540 common genes, including 33 genes with expression levels that were correlated with at least one meat quality trait. Among these 540 common genes, 63 have been reported previously as candidate genes for meat quality traits, such as PHKG1 (q-value = 1.67e-6 for the leading SNP in the cis-eQTL analysis), NUDT7 (q-value = 5.67e-13), FADS2 (q-value = 8.44e-5), and DGAT2 (q-value = 1.24e-3). CONCLUSIONS The present study confirmed several previously published candidate genes and identified some novel candidate genes for meat quality traits via eQTL and ASE analyses, which will be useful to prioritize candidate genes in further studies.
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Affiliation(s)
- Yan Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Zhiwei Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Tingting Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Ying Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Huan Long
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Huijun Cheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Ming Fang
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs, Fisheries College, Jimei University, Xiamen, 361021 China
| | - Jing Gong
- Colleges of Informatics, Huazhong Agricultural University, Wuhan, 430070 China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070 China
- The Cooperative Innovation Center for Sustainable Pig Production, Wuhan, 430070 China
- Key Lab of Swine Genetics and Breeding of Ministry of Agriculture and Rural Affairs, Wuhan, 430070 China
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12
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Finkbeiner S. Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease. Neurobiol Dis 2020; 146:105088. [PMID: 32977020 PMCID: PMC7686089 DOI: 10.1016/j.nbd.2020.105088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 12/03/2022] Open
Abstract
Human genetics provides unbiased insights into the causes of human disease, which can be used to create a foundation for effective ways to more accurately diagnose patients, stratify patients for more successful clinical trials, discover and develop new therapies, and ultimately help patients choose the safest and most promising therapeutic option based on their risk profile. But the process for translating basic observations from human genetics studies into pathogenic disease mechanisms and treatments is laborious and complex, and this challenge has particularly slowed the development of interventions for neurodegenerative disease. In this review, we discuss the many steps in the process, the important considerations at each stage, and some of the latest tools and technologies that are available to help investigators translate insights from human genetics into diagnostic and therapeutic strategies that will lead to the sort of advances in clinical care that make a difference for patients.
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Affiliation(s)
- Steven Finkbeiner
- Center for Systems and Therapeutics, USA; Taube/Koret Center for Neurodegenerative Disease Research, Gladstone Institutes, San Francisco, CA 94158, USA; Departments of Neurology and Physiology, University of Califorina, San Francisco, CA 94158, USA.
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13
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Tian J, Wang Z, Mei S, Yang N, Yang Y, Ke J, Zhu Y, Gong Y, Zou D, Peng X, Wang X, Wan H, Zhong R, Chang J, Gong J, Han L, Miao X. CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer. Nucleic Acids Res 2020; 47:D909-D916. [PMID: 30329095 PMCID: PMC6324030 DOI: 10.1093/nar/gky954] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/04/2018] [Indexed: 12/14/2022] Open
Abstract
Alternative splicing (AS) is a widespread process that increases structural transcript variation and proteome diversity. Aberrant splicing patterns are frequently observed in cancer initiation, progress, prognosis and therapy. Increasing evidence has demonstrated that AS events could undergo modulation by genetic variants. The identification of splicing quantitative trait loci (sQTLs), genetic variants that affect AS events, might represent an important step toward fully understanding the contribution of genetic variants in disease development. However, no database has yet been developed to systematically analyze sQTLs across multiple cancer types. Using genotype data from The Cancer Genome Atlas and corresponding AS values calculated by TCGASpliceSeq, we developed a computational pipeline to identify sQTLs from 9 026 tumor samples in 33 cancer types. We totally identified 4 599 598 sQTLs across all cancer types. We further performed survival analyses and identified 17 072 sQTLs associated with patient overall survival times. Furthermore, using genome-wide association study (GWAS) catalog data, we identified 1 180 132 sQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerSplicingQTL (http://www.cancersplicingqtl-hust.com/) for users to conveniently browse, search and download data of interest. This database provides an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms in human cancer.
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Affiliation(s)
- Jianbo Tian
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Zhihua Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Shufang Mei
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Nan Yang
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Yang Yang
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Juntao Ke
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Ying Zhu
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Yajie Gong
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Danyi Zou
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Xiating Peng
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Xiaoyang Wang
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Hao Wan
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Rong Zhong
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Jiang Chang
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Jing Gong
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.,HubeiKey Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Leng Han
- Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA
| | - Xiaoping Miao
- Key Laboratory of Environmental Health of Ministry of Education, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
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14
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Hood VL, Berger R, Freedman R, Law AJ. Transcription of PIK3CD in human brain and schizophrenia: regulation by proinflammatory cytokines. Hum Mol Genet 2020; 28:3188-3198. [PMID: 31211828 DOI: 10.1093/hmg/ddz144] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 05/23/2019] [Accepted: 06/07/2019] [Indexed: 12/20/2022] Open
Abstract
PIK3CD encodes the phosphoinositide 3-kinase (PI3K) catalytic subunit, p110δ, a lipid kinase linked to neurodevelopmental disorders, including schizophrenia (SZ). PIK3CD is regulated at the transcript level through alternate use of 5' untranslated exons (UTRs), promoters, and proinflammatory cytokines. Increases in global PIK3CD expression and downregulation by neuroleptics are observed in SZ, and preclinical efficacy of a p110δ-selective inhibitor is seen in rodent models of risk. Here, we cloned PIK3CD alternative transcripts in human brain and evaluated temporal- and tissue-specific expression. We quantified PIK3CD transcripts in B-lymphoblastoid cells from patients with SZ and examined 5' UTR transcriptional regulation by tumor necrosis factor α (TNFα) and interleukin-1β (IL1β) in patient-derived fibroblasts. We report that PIK3CD transcripts are differentially expressed in human brain in a developmental-specific manner. Transcripts encoding 5' UTRs -2A and alternative exon -1 (Alt1), P37 and AS1 and AS2 were increased in SZ. Alt1, P37, and AS2 were also preferentially expressed in fetal brain, and all transcripts were regulated by TNFα and IL1β. Our findings provide novel insight into the complexity of PIK3CD regulation in human brain, implicate PIK3CD in human neurodevelopment, and identify isoform-specific disruption in SZ.
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Affiliation(s)
| | | | | | - Amanda J Law
- Department of Psychiatry.,Department of Medicine.,Cell and Developmental Biology, School of Medicine, University of Colorado, Aurora, CO, USA
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15
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A Comprehensive Genome-Wide and Phenome-Wide Examination of BMI and Obesity in a Northern Nevadan Cohort. G3-GENES GENOMES GENETICS 2020; 10:645-664. [PMID: 31888951 PMCID: PMC7003082 DOI: 10.1534/g3.119.400910] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The aggregation of Electronic Health Records (EHR) and personalized genetics leads to powerful discoveries relevant to population health. Here we perform genome-wide association studies (GWAS) and accompanying phenome-wide association studies (PheWAS) to validate phenotype-genotype associations of BMI, and to a greater extent, severe Class 2 obesity, using comprehensive diagnostic and clinical data from the EHR database of our cohort. Three GWASs of 500,000 variants on the Illumina platform of 6,645 Healthy Nevada participants identified several published and novel variants that affect BMI and obesity. Each GWAS was followed with two independent PheWASs to examine associations between extensive phenotypes (incidence of diagnoses, condition, or disease), significant SNPs, BMI, and incidence of extreme obesity. The first GWAS examines associations with BMI in a cohort with no type 2 diabetics, focusing exclusively on BMI. The second GWAS examines associations with BMI in a cohort that includes type 2 diabetics. In the second GWAS, type 2 diabetes is a comorbidity, and thus becomes a covariate in the statistical model. The intersection of significant variants of these two studies is surprising. The third GWAS is a case vs. control study, with cases defined as extremely obese (Class 2 or 3 obesity), and controls defined as participants with BMI between 18.5 and 25. This last GWAS identifies strong associations with extreme obesity, including established variants in the FTO and NEGR1 genes, as well as loci not yet linked to obesity. The PheWASs validate published associations between BMI and extreme obesity and incidence of specific diagnoses and conditions, yet also highlight novel links. This study emphasizes the importance of our extensive longitudinal EHR database to validate known associations and identify putative novel links with BMI and obesity.
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16
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Movassat M, Forouzmand E, Reese F, Hertel KJ. Exon size and sequence conservation improves identification of splice-altering nucleotides. RNA (NEW YORK, N.Y.) 2019; 25:1793-1805. [PMID: 31554659 PMCID: PMC6859846 DOI: 10.1261/rna.070987.119] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 09/23/2019] [Indexed: 06/10/2023]
Abstract
Pre-mRNA splicing is regulated through multiple trans-acting splicing factors. These regulators interact with the pre-mRNA at intronic and exonic positions. Given that most exons are protein coding, the evolution of exons must be modulated by a combination of selective coding and splicing pressures. It has previously been demonstrated that selective splicing pressures are more easily deconvoluted when phylogenetic comparisons are made for exons of identical size, suggesting that exon size-filtered sequence alignments may improve identification of nucleotides evolved to mediate efficient exon ligation. To test this hypothesis, an exon size database was created, filtering 76 vertebrate sequence alignments based on exon size conservation. In addition to other genomic parameters, such as splice-site strength, gene position, or flanking intron length, this database permits the identification of exons that are size- and/or sequence-conserved. Highly size-conserved exons are always sequence-conserved. However, sequence conservation does not necessitate exon size conservation. Our analysis identified evolutionarily young exons and demonstrated that length conservation is a strong predictor of alternative splicing. A published data set of approximately 5000 exonic SNPs associated with disease was analyzed to test the hypothesis that exon size-filtered sequence comparisons increase detection of splice-altering nucleotides. Improved splice predictions could be achieved when mutations occur at the third codon position, especially when a mutation decreases exon inclusion efficiency. The results demonstrate that coding pressures dominate nucleotide composition at invariable codon positions and that exon size-filtered sequence alignments permit identification of splice-altering nucleotides at wobble positions.
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Affiliation(s)
- Maliheh Movassat
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, California 92697, USA
| | - Elmira Forouzmand
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, California 92697, USA
| | - Fairlie Reese
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, California 92697, USA
| | - Klemens J Hertel
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, California 92697, USA
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17
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Raghupathy N, Choi K, Vincent MJ, Beane GL, Sheppard KS, Munger SC, Korstanje R, Pardo-Manual de Villena F, Churchill GA. Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression. Bioinformatics 2019; 34:2177-2184. [PMID: 29444201 DOI: 10.1093/bioinformatics/bty078] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 02/09/2018] [Indexed: 02/06/2023] Open
Abstract
Motivation Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. RNA sequencing (RNA-seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. When short-read sequences are aligned to a diploid transcriptome, read-mapping ambiguities confound our ability to directly count reads. Multi-mapping reads aligning equally well to multiple genomic locations, isoforms or alleles can comprise the majority (>85%) of reads. Discarding them can result in biases and substantial loss of information. Methods have been developed that use weighted allocation of read counts but these methods treat the different types of multi-reads equivalently. We propose a hierarchical approach to allocation of read counts that first resolves ambiguities among genes, then among isoforms, and lastly between alleles. We have implemented our model in EMASE software (Expectation-Maximization for Allele Specific Expression) to estimate total gene expression, isoform usage and ASE based on this hierarchical allocation. Results Methods that align RNA-seq reads to a diploid transcriptome incorporating known genetic variants improve estimates of ASE and total gene expression compared to methods that use reference genome alignments. Weighted allocation methods outperform methods that discard multi-reads. Hierarchical allocation of reads improves estimation of ASE even when data are simulated from a non-hierarchical model. Analysis of RNA-seq data from F1 hybrid mice using EMASE reveals widespread ASE associated with cis-acting polymorphisms and a small number of parent-of-origin effects. Availability and implementation EMASE software is available at https://github.com/churchill-lab/emase. Supplementary information Supplementary data are available at Bioinformatics online.
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18
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Rotival M, Quach H, Quintana-Murci L. Defining the genetic and evolutionary architecture of alternative splicing in response to infection. Nat Commun 2019; 10:1671. [PMID: 30975994 PMCID: PMC6459842 DOI: 10.1038/s41467-019-09689-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 03/21/2019] [Indexed: 12/17/2022] Open
Abstract
Host and environmental factors contribute to variation in human immune responses, yet the genetic and evolutionary drivers of alternative splicing in response to infection remain largely uncharacterised. Leveraging 970 RNA-sequencing profiles of resting and stimulated monocytes from 200 individuals of African- and European-descent, we show that immune activation elicits a marked remodelling of the isoform repertoire, while increasing the levels of erroneous splicing. We identify 1,464 loci associated with variation in isoform usage (sQTLs), 9% of them being stimulation-specific, which are enriched in disease-related loci. Furthermore, we detect a longstanding increased plasticity of immune gene splicing, and show that positive selection and Neanderthal introgression have both contributed to diversify the splicing landscape of human populations. Together, these findings suggest that differential isoform usage has been an important substrate of innovation in the long-term evolution of immune responses and a more recent vehicle of population local adaptation. Genetic ancestry might influence immunological response to infection at different regulatory levels. Here, the authors use RNA-Seq to investigate the variability of alternative splicing patterns in resting and stimulated monocytes of African- and European-descent.
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Affiliation(s)
- Maxime Rotival
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, 25-28 rue Dr Roux, Paris, 75015, France.
| | - Hélène Quach
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, 25-28 rue Dr Roux, Paris, 75015, France
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Institut Pasteur, CNRS UMR2000, 25-28 rue Dr Roux, Paris, 75015, France.
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19
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Smith SB, Parisien M, Bair E, Belfer I, Chabot-Doré AJ, Gris P, Khoury S, Tansley S, Torosyan Y, Zaykin DV, Bernhardt O, de Oliveira Serrano P, Gracely RH, Jain D, Järvelin MR, Kaste LM, Kerr KF, Kocher T, Lähdesmäki R, Laniado N, Laurie CC, Laurie CA, Männikkö M, Meloto CB, Nackley AG, Nelson SC, Pesonen P, Ribeiro-Dasilva MC, Rizzatti-Barbosa CM, Sanders AE, Schwahn C, Sipilä K, Sofer T, Teumer A, Mogil JS, Fillingim RB, Greenspan JD, Ohrbach R, Slade GD, Maixner W, Diatchenko L. Genome-wide association reveals contribution of MRAS to painful temporomandibular disorder in males. Pain 2019; 160:579-591. [PMID: 30431558 PMCID: PMC6377338 DOI: 10.1097/j.pain.0000000000001438] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 10/25/2018] [Indexed: 02/06/2023]
Abstract
Painful temporomandibular disorders (TMDs) are the leading cause of chronic orofacial pain, but its underlying molecular mechanisms remain obscure. Although many environmental factors have been associated with higher risk of developing painful TMD, family and twin studies support a heritable genetic component as well. We performed a genome-wide association study assuming an additive genetic model of TMD in a discovery cohort of 999 cases and 2031 TMD-free controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study. Using logistic models adjusted for sex, age, enrollment site, and race, we identified 3 distinct loci that were significant in combined or sex-segregated analyses. A single-nucleotide polymorphism on chromosome 3 (rs13078961) was significantly associated with TMD in males only (odds ratio = 2.9, 95% confidence interval: 2.02-4.27, P = 2.2 × 10). This association was nominally replicated in a meta-analysis of 7 independent orofacial pain cohorts including 160,194 participants (odds ratio = 1.16, 95% confidence interval: 1.0-1.35, P = 2.3 × 10). Functional analysis in human dorsal root ganglia and blood indicated this variant is an expression quantitative trait locus, with the minor allele associated with decreased expression of the nearby muscle RAS oncogene homolog (MRAS) gene (beta = -0.51, P = 2.43 × 10). Male mice, but not female mice, with a null mutation of Mras displayed persistent mechanical allodynia in a model of inflammatory pain. Genetic and behavioral evidence support a novel mechanism by which genetically determined MRAS expression moderates the resiliency to chronic pain. This effect is male-specific and may contribute to the lower rates of painful TMD in men.
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Affiliation(s)
- Shad B. Smith
- Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States
| | - Marc Parisien
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
| | - Eric Bair
- Center for Pain Research and Innovation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Inna Belfer
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, United States
| | | | - Pavel Gris
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
| | - Samar Khoury
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
| | - Shannon Tansley
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Yelizaveta Torosyan
- Division of Epidemiology, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States
| | - Dmitri V. Zaykin
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, United States
| | - Olaf Bernhardt
- Department of Restorative Dentistry, Periodontology, Endodontology, Preventive Dentistry and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Priscila de Oliveira Serrano
- Department of Prosthesis and Periodontology, Piracicaba Dental School, State University of Campinas, Piracicaba, São Paulo, Brazil
| | - Richard H. Gracely
- Center for Pain Research and Innovation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, and Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, United Kingdom
| | - Linda M. Kaste
- Department of Pediatric Dentistry, College of Dentistry, University of Illinois at Chicago, Chicago, IL, United States
| | - Kathleen F. Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, Preventive Dentistry and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Raija Lähdesmäki
- Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Nadia Laniado
- Department of Dentistry, Jacobi Medical Center/Albert Einstein College of Medicine, Bronx, NY, United States
| | - Cathy C. Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Cecelia A. Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Minna Männikkö
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Carolina B. Meloto
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
| | - Andrea G. Nackley
- Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States
| | - Sarah C. Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Paula Pesonen
- Northern Finland Birth Cohorts, Faculty of Medicine, University of Oulu, Finland
| | - Margarete C. Ribeiro-Dasilva
- Division of Prosthodontics, Restorative Dental Science Department, College of Dentistry, University of Florida, Gainesville, FL, United States
| | - Celia M. Rizzatti-Barbosa
- Department of Prosthesis and Periodontology, Piracicaba Dental School, State University of Campinas, Piracicaba, São Paulo, Brazil
| | - Anne E. Sanders
- Center for Pain Research and Innovation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Dental Ecology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Christian Schwahn
- Department of Prosthetic Dentistry, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Kirsi Sipilä
- Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
- Institute of Dentistry, University of Eastern Finland, Kuopio, Finland
- Oral and Maxillofacial Department, Kuopio University Hospital, Kuopio, Finland
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, United States
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Jeffrey S. Mogil
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Roger B. Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, FL, United States
| | - Joel D. Greenspan
- Department of Neural and Pain Sciences, Brotman Facial Pain Clinic, University of Maryland, School of Dentistry, Baltimore, MD, United States
| | - Richard Ohrbach
- Department of Oral Diagnostic Services, University at Buffalo, Buffalo, NY, United States
| | - Gary D. Slade
- Center for Pain Research and Innovation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Dental Ecology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - William Maixner
- Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States
| | - Luda Diatchenko
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
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20
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Fine mapping of the major QTL for seed coat color in Brassica rapa var. Yellow Sarson by use of NIL populations and transcriptome sequencing for identification of the candidate genes. PLoS One 2019; 14:e0209982. [PMID: 30716096 PMCID: PMC6361427 DOI: 10.1371/journal.pone.0209982] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 12/15/2018] [Indexed: 11/19/2022] Open
Abstract
Yellow seed is a desirable trait in Brassica oilseed crops. The B. rapa var. Yellow Sarson carry unique yellow seed color genes which are not only important for the development of yellow-seeded oilseed B. rapa cultivars but this variant can also be used to develop yellow-seeded B. napus. In this study, we developed near-isogenic lines (NILs) of Yellow Sarson for the major seed coat color QTL SCA9-2 of the chromosome A9 and used the NILs to fine map this QTL region and to identify the candidate genes through linkage mapping and transcriptome sequencing of the developing seeds. From the 18.4 to 22.79 Mb region of SCA9-2, six SSR markers showing 0.63 to 5.65% recombination were developed through linkage analysis and physical mapping. A total of 55 differentially expressed genes (DEGs) were identified in the SCA9-2 region through transcriptome analysis; these included three transcription factors, Bra028039 (NAC), Bra023223 (C2H2 type zinc finger), Bra032362 (TIFY), and several other genes which encode unknown or nucleic acid binding protein; these genes might be the candidates and involved in the regulation of seed coat color in the materials used in this study. Several biosynthetic pathways, including the flavonoid, phenylpropanoid and suberin biosynthetic pathways were significantly enriched through GO and KEGG enrichment analysis of the DEGs. This is the first comprehensive study to understand the yellow seed trait of Yellow Sarson through employing linkage mapping and global transcriptome analysis approaches.
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21
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Abstract
Allele-specific expression arises when transcriptional activity at the different alleles of a gene differs considerably. Although extensive research has been carried out to detect and characterize this phenomenon, the landscape of allele-specific expression in cancer is still poorly understood. In this chapter, we describe a fast and reliable analysis pipeline to study allele-specific expression in cancer using next-generation sequencing data. The pipeline provides a gene-level analysis approach that exploits paired germline DNA and tumor RNA sequencing data and benefits from parallel computation resources when available.
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Affiliation(s)
- Alessandro Romanel
- Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy.
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22
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Ye CJ, Chen J, Villani AC, Gate RE, Subramaniam M, Bhangale T, Lee MN, Raj T, Raychowdhury R, Li W, Rogel N, Simmons S, Imboywa SH, Chipendo PI, McCabe C, Lee MH, Frohlich IY, Stranger BE, De Jager PL, Regev A, Behrens T, Hacohen N. Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of ERAP2 transcripts under balancing selection. Genome Res 2018; 28:1812-1825. [PMID: 30446528 PMCID: PMC6280757 DOI: 10.1101/gr.240390.118] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 10/09/2018] [Indexed: 02/02/2023]
Abstract
While genetic variants are known to be associated with overall gene abundance in stimulated immune cells, less is known about their effects on alternative isoform usage. By analyzing RNA-seq profiles of monocyte-derived dendritic cells from 243 individuals, we uncovered thousands of unannotated isoforms synthesized in response to influenza infection and type 1 interferon stimulation. We identified more than a thousand quantitative trait loci (QTLs) associated with alternate isoform usage (isoQTLs), many of which are independent of expression QTLs (eQTLs) for the same gene. Compared with eQTLs, isoQTLs are enriched for splice sites and untranslated regions, but depleted of sequences upstream of annotated transcription start sites. Both eQTLs and isoQTLs explain a significant proportion of the disease heritability attributed to common genetic variants. At the ERAP2 locus, we shed light on the function of the gene and how two frequent, highly differentiated haplotypes with intermediate frequencies could be maintained by balancing selection. At baseline and following type 1 interferon stimulation, the major haplotype is associated with low ERAP2 expression caused by nonsense-mediated decay, while the minor haplotype, known to increase Crohn's disease risk, is associated with high ERAP2 expression. In response to influenza infection, we found two uncharacterized isoforms expressed from the major haplotype, likely the result of multiple perfectly linked variants affecting the transcription and splicing at the locus. Thus, genetic variants at a single locus could modulate independent gene regulatory processes in innate immune responses and, in the case of ERAP2, may confer a historical fitness advantage in response to virus.
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Affiliation(s)
- Chun Jimmie Ye
- Institute for Human Genetics, Institute for Health and Computational Sciences, Department of Biostatistics and Epidemiology, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143, USA
| | - Jenny Chen
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, USA
| | - Rachel E Gate
- Institute for Human Genetics, Institute for Health and Computational Sciences, Department of Biostatistics and Epidemiology, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143, USA.,Biomedical Informatics Program, University of California, San Francisco, California 94143, USA
| | - Meena Subramaniam
- Institute for Human Genetics, Institute for Health and Computational Sciences, Department of Biostatistics and Epidemiology, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143, USA.,Biomedical Informatics Program, University of California, San Francisco, California 94143, USA
| | - Tushar Bhangale
- Genentech Incorporated, South San Francisco, California 94080, USA
| | - Mark N Lee
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, USA.,Harvard Medical School, Boston, Massachusetts 02116, USA
| | - Towfique Raj
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Harvard Medical School, Boston, Massachusetts 02116, USA.,Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | | | - Weibo Li
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Noga Rogel
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Sean Simmons
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | | | | | - Cristin McCabe
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - Michelle H Lee
- Harvard Medical School, Boston, Massachusetts 02116, USA
| | | | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, Institute for Genomics and Systems Biology, Center for Data Intensive Science, The University of Chicago, Chicago, Illinois 60637, USA
| | - Philip L De Jager
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Harvard Medical School, Boston, Massachusetts 02116, USA.,Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA
| | - Tim Behrens
- Genentech Incorporated, South San Francisco, California 94080, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Department of Medicine, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, USA
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23
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Ebrahimi D, Richards CM, Carpenter MA, Wang J, Ikeda T, Becker JT, Cheng AZ, McCann JL, Shaban NM, Salamango DJ, Starrett GJ, Lingappa JR, Yong J, Brown WL, Harris RS. Genetic and mechanistic basis for APOBEC3H alternative splicing, retrovirus restriction, and counteraction by HIV-1 protease. Nat Commun 2018; 9:4137. [PMID: 30297863 PMCID: PMC6175962 DOI: 10.1038/s41467-018-06594-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 09/13/2018] [Indexed: 12/11/2022] Open
Abstract
Human APOBEC3H (A3H) is a single-stranded DNA cytosine deaminase that inhibits HIV-1. Seven haplotypes (I–VII) and four splice variants (SV154/182/183/200) with differing antiviral activities and geographic distributions have been described, but the genetic and mechanistic basis for variant expression and function remains unclear. Using a combined bioinformatic/experimental analysis, we find that SV200 expression is specific to haplotype II, which is primarily found in sub-Saharan Africa. The underlying genetic mechanism for differential mRNA splicing is an ancient intronic deletion [del(ctc)] within A3H haplotype II sequence. We show that SV200 is at least fourfold more HIV-1 restrictive than other A3H splice variants. To counteract this elevated antiviral activity, HIV-1 protease cleaves SV200 into a shorter, less restrictive isoform. Our analyses indicate that, in addition to Vif-mediated degradation, HIV-1 may use protease as a counter-defense mechanism against A3H in >80% of sub-Saharan African populations. Human APOBEC3H has several haplotypes and splice variants with distinct anti-HIV-1 activities, but the genetics underlying the expression of these variants are unclear. Here, the authors identify an intronic deletion in A3H haplotype II resulting in production of the most active splice variant, which is counteracted by HIV-1 protease.
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Affiliation(s)
- Diako Ebrahimi
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Christopher M Richards
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Michael A Carpenter
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.,Howard Hughes Medical Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jiayi Wang
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Terumasa Ikeda
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.,Howard Hughes Medical Institute, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jordan T Becker
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Adam Z Cheng
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jennifer L McCann
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Nadine M Shaban
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Daniel J Salamango
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Gabriel J Starrett
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.,Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jairam R Lingappa
- Departments of Global Health, Medicine and Pediatrics, University of Washington, Seattle, WA, 98104, USA
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - William L Brown
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Reuben S Harris
- Department of Biochemistry, Molecular Biology and Biophysics, Masonic Cancer Center, Institute for Molecular Virology, Center for Genome Engineering, University of Minnesota, Minneapolis, MN, 55455, USA. .,Howard Hughes Medical Institute, University of Minnesota, Minneapolis, MN, 55455, USA.
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24
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Rivandi M, Martens JWM, Hollestelle A. Elucidating the Underlying Functional Mechanisms of Breast Cancer Susceptibility Through Post-GWAS Analyses. Front Genet 2018; 9:280. [PMID: 30116257 PMCID: PMC6082943 DOI: 10.3389/fgene.2018.00280] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 07/09/2018] [Indexed: 12/12/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified more than 170 single nucleotide polymorphisms (SNPs) associated with the susceptibility to breast cancer. Together, these SNPs explain 18% of the familial relative risk, which is estimated to be nearly half of the total familial breast cancer risk that is collectively explained by low-risk susceptibility alleles. An important aspect of this success has been the access to large sample sizes through collaborative efforts within the Breast Cancer Association Consortium (BCAC), but also collaborations between cancer association consortia. Despite these achievements, however, understanding of each variant's underlying mechanism and how these SNPs predispose women to breast cancer remains limited and represents a major challenge in the field, particularly since the vast majority of the GWAS-identified SNPs are located in non-coding regions of the genome and are merely tags for the causal variants. In recent years, fine-scale mapping studies followed by functional evaluation of putative causal variants have begun to elucidate the biological function of several GWAS-identified variants. In this review, we discuss the findings and lessons learned from these post-GWAS analyses of 22 risk loci. Identifying the true causal variants underlying breast cancer susceptibility and their function not only provides better estimates of the explained familial relative risk thereby improving polygenetic risk scores (PRSs), it also increases our understanding of the biological mechanisms responsible for causing susceptibility to breast cancer. This will facilitate the identification of further breast cancer risk alleles and the development of preventive medicine for those women at increased risk for developing the disease.
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Affiliation(s)
- Mahdi Rivandi
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands.,Department of Modern Sciences and Technologies, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - John W M Martens
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands.,Cancer Genomics Centre, Utrecht, Netherlands
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25
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Ozgyin L, Horvath A, Balint BL. Lyophilized human cells stored at room temperature preserve multiple RNA species at excellent quality for RNA sequencing. Oncotarget 2018; 9:31312-31329. [PMID: 30140372 PMCID: PMC6101130 DOI: 10.18632/oncotarget.25764] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 06/22/2018] [Indexed: 11/25/2022] Open
Abstract
Biobanks operating at ambient temperatures would dramatically reduce the costs associated with standard cryogenic storage. In the present study, we used lyophilization to stabilize unfractionated human cells in a dried state at room temperature and tested the yield and integrity of the isolated RNA by microfluidic electrophoresis, RT-qPCR and RNA sequencing. RNA yields and integrity measures were not reduced for lyophilized cells (unstored, stored for two weeks or stored for two months) compared to their paired controls. The abundance of the selected mRNAs with various expression levels, as well as enhancer-associated RNAs and cancer biomarker long non-coding RNAs (MALAT1, GAS5 and TUG1), were not significantly different between the two groups as assessed by RT-qPCR. RNA sequencing data of three lyophilized samples stored for two weeks at room temperature revealed a high degree of similarity with their paired controls in terms of the RNA biotype distribution, cumulative gene diversity, gene body read coverage and per base mismatch rate. Among the 28 differentially expressed genes transcriptional regulators, as well as certain transcript properties suggestive of a residual active decay mechanism were enriched. Our study suggests that freeze-drying of human cells is a suitable alternative for the long-term stabilization of total RNA in whole human cells for routine diagnostics and high-throughput biomedical research.
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Affiliation(s)
- Lilla Ozgyin
- Department of Biochemistry and Molecular Biology, Genomic Medicine and Bioinformatic Core Facility, University of Debrecen, Debrecen H-4012, Hungary
| | - Attila Horvath
- Department of Biochemistry and Molecular Biology, Genomic Medicine and Bioinformatic Core Facility, University of Debrecen, Debrecen H-4012, Hungary.,Department of Biochemistry and Molecular Biology, Nuclear Hormone Receptor Research Laboratory, University of Debrecen, Debrecen H-4012, Hungary
| | - Balint Laszlo Balint
- Department of Biochemistry and Molecular Biology, Genomic Medicine and Bioinformatic Core Facility, University of Debrecen, Debrecen H-4012, Hungary
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26
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Vishweswaraiah S, George L, Purushothaman N, Ganguly K. A candidate gene identification strategy utilizing mouse to human big-data mining: "3R-tenet" in COPD genetic research. Respir Res 2018; 19:92. [PMID: 29871630 PMCID: PMC5989378 DOI: 10.1186/s12931-018-0795-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 04/27/2018] [Indexed: 12/13/2022] Open
Abstract
Background Early life impairments leading to lower lung function by adulthood are considered as risk factors for chronic obstructive pulmonary disease (COPD). Recently, we compared the lung transcriptomic profile between two mouse strains with extreme total lung capacities to identify plausible pulmonary function determining genes using microarray analysis (GSE80078). Advancement of high-throughput techniques like deep sequencing (eg. RNA-seq) and microarray have resulted in an explosion of genomic data in the online public repositories which however remains under-exploited. Strategic curation of publicly available genomic data with a mouse-human translational approach can effectively implement “3R- Tenet” by reducing screening experiments with animals and performing mechanistic studies using physiologically relevant in vitro model systems. Therefore, we sought to analyze the association of functional variations within human orthologs of mouse lung function candidate genes in a publicly available COPD lung RNA-seq data-set. Methods Association of missense single nucleotide polymorphisms, insertions, deletions, and splice junction variants were analyzed for susceptibility to COPD using RNA-seq data of a Korean population (GSE57148). Expression of the associated genes were studied using the Gene Paint (mouse embryo) and Human Protein Atlas (normal adult human lung) databases. The genes were also assessed for replication of the associations and expression in COPD−/mouse cigarette smoke exposed lung tissues using other datasets. Results Significant association (p < 0.05) of variations in 20 genes to higher COPD susceptibility have been detected within the investigated cohort. Association of HJURP, MCRS1 and TLR8 are novel in relation to COPD. The associated ADAM19 and KIT loci have been reported earlier. The remaining 15 genes have also been previously associated to COPD. Differential transcript expression levels of the associated genes in COPD- and/ or mouse emphysematous lung tissues have been detected. Conclusion Our findings suggest strategic mouse-human datamining approaches can identify novel COPD candidate genes using existing datasets in the online repositories. The candidates can be further evaluated for mechanistic role through in vitro studies using appropriate primary cells/cell lines. Functional studies can be limited to transgenic animal models of only well supported candidate genes. This approach will lead to a significant reduction of animal experimentation in respiratory research. Electronic supplementary material The online version of this article (10.1186/s12931-018-0795-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Leema George
- SRM Research Institute, SRM University, Chennai, 603203, India
| | - Natarajan Purushothaman
- Department of Genetic Engineering, School of Bioengineering, Faculty of Engineering and Technology, SRM University, Chennai, 603203, India
| | - Koustav Ganguly
- SRM Research Institute, SRM University, Chennai, 603203, India. .,Work Environment Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Box 287, SE-171 77, Stockholm, Sweden.
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27
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Park E, Pan Z, Zhang Z, Lin L, Xing Y. The Expanding Landscape of Alternative Splicing Variation in Human Populations. Am J Hum Genet 2018; 102:11-26. [PMID: 29304370 PMCID: PMC5777382 DOI: 10.1016/j.ajhg.2017.11.002] [Citation(s) in RCA: 213] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 11/03/2017] [Indexed: 12/16/2022] Open
Abstract
Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine.
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Affiliation(s)
- Eddie Park
- Department of Microbiology, Immunology, & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zhicheng Pan
- Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zijun Zhang
- Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lan Lin
- Department of Microbiology, Immunology, & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Xing
- Department of Microbiology, Immunology, & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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28
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Arora K, Panda KK, Mittal S, Mallikarjuna MG, Rao AR, Dash PK, Thirunavukkarasu N. RNAseq revealed the important gene pathways controlling adaptive mechanisms under waterlogged stress in maize. Sci Rep 2017; 7:10950. [PMID: 28887464 PMCID: PMC5591269 DOI: 10.1038/s41598-017-10561-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 08/10/2017] [Indexed: 02/04/2023] Open
Abstract
Waterlogging causes yield penalty in maize-growing countries of subtropical regions. Transcriptome analysis of the roots of a tolerant inbred HKI1105 using RNA sequencing revealed 21,364 differentially expressed genes (DEGs) under waterlogged stress condition. These 21,364 DEGs are known to regulate important pathways including energy-production, programmed cell death (PCD), aerenchyma formation, and ethylene responsiveness. High up-regulation of invertase (49-fold) and hexokinase (36-fold) in roots explained the ATP requirement in waterlogging condition. Also, high up-regulation of expansins (42-fold), plant aspartic protease A3 (19-fold), polygalacturonases (16-fold), respiratory burst oxidase homolog (12-fold), and hydrolases (11-fold) explained the PCD of root cortical cells followed by the formation of aerenchyma tissue during waterlogging stress. We hypothesized that the oxygen transfer in waterlogged roots is promoted by a cross-talk of fermentative, metabolic, and glycolytic pathways that generate ATPs for PCD and aerenchyma formation in root cortical cells. SNPs were mapped to the DEGs regulating aerenchyma formation (12), ethylene-responsive factors (11), and glycolysis (4) under stress. RNAseq derived SNPs can be used in selection approaches to breed tolerant hybrids. Overall, this investigation provided significant evidence of genes operating in the adaptive traits such as ethylene production and aerenchyma formation to cope-up the waterlogging stress.
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Affiliation(s)
- Kanika Arora
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Noida, 201 313, India
| | - Kusuma Kumari Panda
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Noida, 201 313, India
| | - Shikha Mittal
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, 110012, India
| | | | - Atmakuri Ramakrishna Rao
- Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa, Library Avenue, New Delhi, 110 012, India
| | - Prasanta Kumar Dash
- National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, 110012, India
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29
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Persson ME, Trottier AJ, Bélteky J, Roth LSV, Jensen P. Intranasal oxytocin and a polymorphism in the oxytocin receptor gene are associated with human-directed social behavior in golden retriever dogs. Horm Behav 2017; 95:85-93. [PMID: 28765081 DOI: 10.1016/j.yhbeh.2017.07.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 12/25/2022]
Abstract
The oxytocin system may play an important role in dog domestication from the wolf. Dogs have evolved unique human analogue social skills enabling them to communicate and cooperate efficiently with people. Genomic differences in the region surrounding the oxytocin receptor (OXTR) gene have previously been associated with variation in dogs' communicative skills. Here we have utilized the unsolvable problem paradigm to investigate the effects of oxytocin and OXTR polymorphisms on human-directed contact seeking behavior in 60 golden retriever dogs. Human-oriented behavior was quantified employing a previously defined unsolvable problem paradigm. Behaviors were tested twice in a repeated, counterbalanced design, where dogs received a nasal dose of either oxytocin or saline 45min before each test occasion. Buccal DNA was analysed for genotype on three previously identified SNP-markers associated with OXTR. The same polymorphisms were also genotyped in 21 wolf blood samples to explore potential genomic differences between the species. Results showed that oxytocin treatment decreased physical contact seeking with the experimenter and one of the three polymorphisms was associated with degree of physical contact seeking with the owner. Dogs with the AA-genotype at this locus increased owner physical contact seeking in response to oxytocin while the opposite effect was found in GG-genotype individuals. Hence, intranasal oxytocin treatment, an OXTR polymorphism and their interaction are associated with dogs' human-directed social skills, which can explain previously described breed differences in oxytocin response. Genotypic variation at the studied locus was also found in wolves indicating that it was present even at the start of dog domestication.
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Affiliation(s)
- Mia E Persson
- IFM Biology, AVIAN Behaviour Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Agaia J Trottier
- IFM Biology, AVIAN Behaviour Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Johan Bélteky
- IFM Biology, AVIAN Behaviour Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Lina S V Roth
- IFM Biology, AVIAN Behaviour Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Per Jensen
- IFM Biology, AVIAN Behaviour Genomics and Physiology Group, Linköping University, Linköping, Sweden.
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30
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Chen Q, Mao Y, Meng F, Wang L, Zhang H, Wang W, Hua D. Rs7911488 modified the efficacy of capecitabine-based therapy in colon cancer through altering miR-1307-3p and TYMS expression. Oncotarget 2017; 8:74312-74319. [PMID: 29088787 PMCID: PMC5650342 DOI: 10.18632/oncotarget.19670] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/09/2017] [Indexed: 01/04/2023] Open
Abstract
Capecitabine is an orally administered prodrug of 5-fluouracil (5-FU) and is used in first-line treatment of metastatic colorectal cancer. Studies have demonstrated that polymorphisms in 5-FU related ADME genes are associated with the efficacy of capecitabine. However, the relationship between the polymorphisms within the microRNA precursors and the efficacy of capecitabine is still largely unknown. We detected six polymorphisms in 274 colon cancer patients and statistically analyzed the association of the genotypes with the efficacy of capecitabine-based chemotherapy. The mechanisms underlying the effect of genotypes on the efficacy of capecitabine were also studied. We identified a polymorphism rs7911488 T>C in pre-miR-1307 to be significantly associated with the efficacy of capecitabine chemotherapy in colon cancer patients. The response rates of capecitabine chemotherapy for the patients with TT, TC, and CC genotypes were 44.35% (55/124), 51.33% (58/113), and 24.32% (9/37), respectively. In the C-allelic patients, miR-1307-3p is down-regulated and TYMS, a direct target of miR-1307-3p, is over-expressed, which leads to insensitivity of cancer cells to capecitabine chemotherapy. The cancer cells with rs7911488 C allele were further observed to be resistant to 5-FU treatment in vitro and in vivo. Our findings show that rs7911488 C-allelic pre-miR-1307 leads to attenuated miR-1307-3p and elevated TYMS, thus insensitive to capecitabine chemotherapy in colon cancer.
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Affiliation(s)
- Qi Chen
- Department of Medical Oncology, Institute of Cancer, Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi, Wuxi 214062, China.,Center for Drug Metabolism and Pharmacokinetics, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
| | - Yong Mao
- Department of Medical Oncology, Institute of Cancer, Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi, Wuxi 214062, China
| | - Fanyi Meng
- Center for Drug Metabolism and Pharmacokinetics, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
| | - Lei Wang
- Department of Pharmacy, Jiangsu Cancer Hospital, Nanjing 210000, China
| | - Hongjian Zhang
- Center for Drug Metabolism and Pharmacokinetics, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
| | - Weipeng Wang
- Center for Drug Metabolism and Pharmacokinetics, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
| | - Dong Hua
- Department of Medical Oncology, Institute of Cancer, Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi, Wuxi 214062, China
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31
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Evidence of selection on splicing-associated loci in human populations and relevance to disease loci mapping. Sci Rep 2017; 7:5980. [PMID: 28729732 PMCID: PMC5519721 DOI: 10.1038/s41598-017-05744-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 06/14/2017] [Indexed: 12/27/2022] Open
Abstract
We performed a whole-genome scan of genetic variants in splicing regulatory elements (SREs) and evaluated the extent to which natural selection has shaped extant patterns of variation in SREs. We investigated the degree of differentiation of single nucleotide polymorphisms (SNPs) in SREs among human populations and applied long-range haplotype- and multilocus allelic differentiation-based methods to detect selection signatures. We describe an approach, sampling a large number of loci across the genome from functional classes and using the consensus from multiple tests, for identifying candidates for selection signals. SRE SNPs in various SNP functional classes show different patterns of population differentiation compared with their non-SRE counterparts. Intronic regions display a greater enrichment for extreme population differentiation among the potentially tissue-dependent transcript ratio quantitative trait loci (trQTLs) than SRE SNPs in general and includ outlier trQTLs for cross-population composite likelihood ratio, suggesting that incorporation of context annotation for regulatory variation may lead to improved detection of signature of selection on these loci. The proportion of extremely rare SNPs disrupting SREs is significantly higher in European than in African samples. The approach developed here will be broadly useful for studies of function and disease-associated variation in the human genome.
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32
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Li H, Reksten TR, Ice JA, Kelly JA, Adrianto I, Rasmussen A, Wang S, He B, Grundahl KM, Glenn SB, Miceli-Richard C, Bowman S, Lester S, Eriksson P, Eloranta ML, Brun JG, Gøransson LG, Harboe E, Guthridge JM, Kaufman KM, Kvarnström M, Cunninghame Graham DS, Patel K, Adler AJ, Farris AD, Brennan MT, Chodosh J, Gopalakrishnan R, Weisman MH, Venuturupalli S, Wallace DJ, Hefner KS, Houston GD, Huang AJW, Hughes PJ, Lewis DM, Radfar L, Vista ES, Edgar CE, Rohrer MD, Stone DU, Vyse TJ, Harley JB, Gaffney PM, James JA, Turner S, Alevizos I, Anaya JM, Rhodus NL, Segal BM, Montgomery CG, Scofield RH, Kovats S, Mariette X, Rönnblom L, Witte T, Rischmueller M, Wahren-Herlenius M, Omdal R, Jonsson R, Ng WF, Nordmark G, Lessard CJ, Sivils KL. Identification of a Sjögren's syndrome susceptibility locus at OAS1 that influences isoform switching, protein expression, and responsiveness to type I interferons. PLoS Genet 2017. [PMID: 28640813 PMCID: PMC5501660 DOI: 10.1371/journal.pgen.1006820] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Sjögren's syndrome (SS) is a common, autoimmune exocrinopathy distinguished by keratoconjunctivitis sicca and xerostomia. Patients frequently develop serious complications including lymphoma, pulmonary dysfunction, neuropathy, vasculitis, and debilitating fatigue. Dysregulation of type I interferon (IFN) pathway is a prominent feature of SS and is correlated with increased autoantibody titers and disease severity. To identify genetic determinants of IFN pathway dysregulation in SS, we performed cis-expression quantitative trait locus (eQTL) analyses focusing on differentially expressed type I IFN-inducible transcripts identified through a transcriptome profiling study. Multiple cis-eQTLs were associated with transcript levels of 2'-5'-oligoadenylate synthetase 1 (OAS1) peaking at rs10774671 (PeQTL = 6.05 × 10-14). Association of rs10774671 with SS susceptibility was identified and confirmed through meta-analysis of two independent cohorts (Pmeta = 2.59 × 10-9; odds ratio = 0.75; 95% confidence interval = 0.66-0.86). The risk allele of rs10774671 shifts splicing of OAS1 from production of the p46 isoform to multiple alternative transcripts, including p42, p48, and p44. We found that the isoforms were differentially expressed within each genotype in controls and patients with and without autoantibodies. Furthermore, our results showed that the three alternatively spliced isoforms lacked translational response to type I IFN stimulation. The p48 and p44 isoforms also had impaired protein expression governed by the 3' end of the transcripts. The SS risk allele of rs10774671 has been shown by others to be associated with reduced OAS1 enzymatic activity and ability to clear viral infections, as well as reduced responsiveness to IFN treatment. Our results establish OAS1 as a risk locus for SS and support a potential role for defective viral clearance due to altered IFN response as a genetic pathophysiological basis of this complex autoimmune disease.
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Affiliation(s)
- He Li
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Tove Ragna Reksten
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - John A. Ice
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Jennifer A. Kelly
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Indra Adrianto
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Astrid Rasmussen
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Shaofeng Wang
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Bo He
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Kiely M. Grundahl
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Stuart B. Glenn
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Corinne Miceli-Richard
- Université Paris-Sud, AP-HP, Hôpitaux Universitaires Paris-Sud, INSERM U1012, Le Kremlin Bicêtre, France
| | - Simon Bowman
- Rheumatology Department, University Hospital Birmingham, Birmingham, United Kingdom
| | - Sue Lester
- The Queen Elizabeth Hospital, Adelaide, South Australia, Australia
| | - Per Eriksson
- Department of Rheumatology, Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Maija-Leena Eloranta
- Department of Medical Sciences, Rheumatology, SciLIfeLab, Uppsala University, Uppsala, Sweden
| | - Johan G. Brun
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Rheumatology, Haukeland University Hospital, Bergen, Norway
| | - Lasse G. Gøransson
- Clinical Immunology Unit, Department of Internal Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Erna Harboe
- Clinical Immunology Unit, Department of Internal Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Joel M. Guthridge
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Kenneth M. Kaufman
- Division of Rheumatology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, United States of America
| | | | | | - Ketan Patel
- Division of Oral and Maxillofacial Surgery, Department of Developmental and Surgical Science, University of Minnesota School of Dentistry, Minneapolis, Minnesota, United States of America
- Department of Oral and Maxillofacial Surgery, North Memorial Medical Center, Robbinsdale, Minnesota, United States of America
| | - Adam J. Adler
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - A. Darise Farris
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Michael T. Brennan
- Department of Oral Medicine, Carolinas Medical Center, Charlotte, North Carolina, United States of America
| | - James Chodosh
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rajaram Gopalakrishnan
- Division of Oral Pathology, Department of Diagnostic and Biological Sciences, University of Minnesota School of Dentistry, Minneapolis, Minnesota, United States of America
| | - Michael H. Weisman
- Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Swamy Venuturupalli
- Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Daniel J. Wallace
- Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Kimberly S. Hefner
- Hefner Eye Care and Optical Center, Oklahoma City, Oklahoma, United States of America
| | - Glen D. Houston
- Department of Oral and Maxillofacial Pathology, University of Oklahoma College of Dentistry, Oklahoma City, Oklahoma, United States of America
- Heartland Pathology Consultants, Edmond, Oklahoma, United States of America
| | - Andrew J. W. Huang
- Department of Ophthalmology and Visual Sciences, Washington University, St. Louis, Missouri, United States of America
| | - Pamela J. Hughes
- Division of Oral and Maxillofacial Surgery, Department of Developmental and Surgical Science, University of Minnesota School of Dentistry, Minneapolis, Minnesota, United States of America
| | - David M. Lewis
- Department of Oral and Maxillofacial Pathology, University of Oklahoma College of Dentistry, Oklahoma City, Oklahoma, United States of America
| | - Lida Radfar
- Oral Diagnosis and Radiology Department, University of Oklahoma College of Dentistry, Oklahoma City, Oklahoma, United States of America
| | - Evan S. Vista
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- University of Santo Tomas Hospital, Manila, The Philippines
| | - Contessa E. Edgar
- The Biology Department, Oklahoma Baptist University, Oklahoma City, Oklahoma, United States of America
| | - Michael D. Rohrer
- Hard Tissue Research Laboratory, University of Minnesota School of Dentistry, Minneapolis, Minnesota, United States of America
| | - Donald U. Stone
- Department of Ophthalmology, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Timothy J. Vyse
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - John B. Harley
- Division of Rheumatology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- US Department of Veterans Affairs Medical Center, Cincinnati, Ohio, United States of America
| | - Patrick M. Gaffney
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Judith A. James
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Sean Turner
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Ilias Alevizos
- National Institute of Dental and Craniofacial Research, NIH, Bethesda, Maryland, United States of America
| | - Juan-Manuel Anaya
- Center for Autoimmune Diseases Research, Universidad del Rosario, Bogotá, Colombia
| | - Nelson L. Rhodus
- Department of Oral Surgery, University of Minnesota School of Dentistry, Minneapolis, Minnesota, United States of America
| | - Barbara M. Segal
- Division of Rheumatology, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Courtney G. Montgomery
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - R. Hal Scofield
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
- US Department of Veterans Affairs Medical Center, Oklahoma City, Oklahoma, United States of America
| | - Susan Kovats
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Xavier Mariette
- Université Paris-Sud, AP-HP, Hôpitaux Universitaires Paris-Sud, INSERM U1012, Le Kremlin Bicêtre, France
| | - Lars Rönnblom
- Department of Medical Sciences, Rheumatology, SciLIfeLab, Uppsala University, Uppsala, Sweden
| | - Torsten Witte
- Clinic for Immunology and Rheumatology, Hannover Medical School, Hannover, Germany
| | - Maureen Rischmueller
- The Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- The University of Adelaide, Adelaide, South Australia, Australia
| | | | - Roald Omdal
- Clinical Immunology Unit, Department of Internal Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Roland Jonsson
- Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Rheumatology, Haukeland University Hospital, Bergen, Norway
| | - Wan-Fai Ng
- Institute of Cellular Medicine & NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Gunnel Nordmark
- Department of Medical Sciences, Rheumatology, SciLIfeLab, Uppsala University, Uppsala, Sweden
| | - Christopher J. Lessard
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Kathy L. Sivils
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
- * E-mail:
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33
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Pasutto F, Zenkel M, Hoja U, Berner D, Uebe S, Ferrazzi F, Schödel J, Liravi P, Ozaki M, Paoli D, Frezzotti P, Mizoguchi T, Nakano S, Kubota T, Manabe S, Salvi E, Manunta P, Cusi D, Gieger C, Wichmann HE, Aung T, Khor CC, Kruse FE, Reis A, Schlötzer-Schrehardt U. Pseudoexfoliation syndrome-associated genetic variants affect transcription factor binding and alternative splicing of LOXL1. Nat Commun 2017; 8:15466. [PMID: 28534485 PMCID: PMC5457519 DOI: 10.1038/ncomms15466] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 03/27/2017] [Indexed: 02/08/2023] Open
Abstract
Although lysyl oxidase-like 1 (LOXL1) is known as the principal genetic risk factor for pseudoexfoliation (PEX) syndrome, a major cause of glaucoma and cardiovascular complications, no functional variants have been identified to date. Here, we conduct a genome-wide association scan on 771 German PEX patients and 1,350 controls, followed by independent testing of associated variants in Italian and Japanese data sets. We focus on a 3.5-kb four-component polymorphic locus positioned spanning introns 1 and 2 of LOXL1 with enhancer-like chromatin features. We find that the rs11638944:C>G transversion exerts a cis-acting effect on the expression levels of LOXL1, mediated by differential binding of the transcription factor RXRα (retinoid X receptor alpha) and by modulating alternative splicing of LOXL1, eventually leading to reduced levels of LOXL1 mRNA in cells and tissues of risk allele carriers. These findings uncover a functional mechanism by which common noncoding variants influence LOXL1 expression. LOXL1 is a genetic risk factor for pseudoexfoliation syndrome of the eye but a causal variant has not been identified. Here, Pasutto et al., find intronic LOXL1 risk variants influence transcription factor binding and alternative splicing of LOXL1 in affected tissues reducing levels of LOXL1 mRNA.
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Affiliation(s)
- Francesca Pasutto
- Institute of Human Genetics, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054 Erlangen, Germany
| | - Matthias Zenkel
- Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Ursula Hoja
- Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Daniel Berner
- Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Steffen Uebe
- Institute of Human Genetics, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054 Erlangen, Germany
| | - Fulvia Ferrazzi
- Institute of Human Genetics, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054 Erlangen, Germany
| | - Johannes Schödel
- Department of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Ulmenweg 18, 91054 Erlangen, Germany
| | - Panah Liravi
- Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Mineo Ozaki
- Ozaki Eye Hospital, 1-15 Kamezaki, Hyuga, Miyazaki 883-0066, Japan
| | - Daniela Paoli
- Ospedale Monfalcone, Centro Glaucomi, Via Galvani 1, 34074 Monfalcone, Italy
| | - Paolo Frezzotti
- Ophthalmology Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Viale Bracci SNC, 53100 Siena, Italy
| | - Takanori Mizoguchi
- Mizoguchi Eye Clinic, 6-13 Tawara-machi, Sasebo, Nagasaki 857-0016, Japan
| | - Satoko Nakano
- Department of Ophthalmology, Oita University, Faculty of Medicine, 1-1 Idaigaoka, Hasana-machi, Oita 879-5593, Japan
| | - Toshiaki Kubota
- Department of Ophthalmology, Oita University, Faculty of Medicine, 1-1 Idaigaoka, Hasana-machi, Oita 879-5593, Japan
| | - Shinichi Manabe
- Hayashi Eye Hospital, 4-23-35 Hakataekimae, Hakata-ku, Fukuoka 812-0011, Japan
| | - Erika Salvi
- Department of Health Sciences, University of Milano, Via Ortles 22/4, 20139 Milano, Italy
| | - Paolo Manunta
- Department of Nephrology, University Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milano, Italy
| | - Daniele Cusi
- Institute of Biomedical Technologies, National Research Centre (ITB-CNR), Via Fratelli Cervi 93, 20090 Segrate-Milano, Italy
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Munich, Germany
| | - Heinz-Erich Wichmann
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Munich, Germany
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Center, 11 Third Hospital Avenue, Singapore 168751, Singapore
| | | | - Friedrich E Kruse
- Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - André Reis
- Institute of Human Genetics, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054 Erlangen, Germany
| | - Ursula Schlötzer-Schrehardt
- Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054 Erlangen, Germany
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34
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Odhams CA, Cortini A, Chen L, Roberts AL, Viñuela A, Buil A, Small KS, Dermitzakis ET, Morris DL, Vyse TJ, Cunninghame Graham DS. Mapping eQTLs with RNA-seq reveals novel susceptibility genes, non-coding RNAs and alternative-splicing events in systemic lupus erythematosus. Hum Mol Genet 2017; 26:1003-1017. [PMID: 28062664 PMCID: PMC5409091 DOI: 10.1093/hmg/ddw417] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 12/05/2016] [Indexed: 12/19/2022] Open
Abstract
Studies attempting to functionally interpret complex-disease susceptibility loci by GWAS and eQTL integration have predominantly employed microarrays to quantify gene-expression. RNA-Seq has the potential to discover a more comprehensive set of eQTLs and illuminate the underlying molecular consequence. We examine the functional outcome of 39 variants associated with Systemic Lupus Erythematosus (SLE) through the integration of GWAS and eQTL data from the TwinsUK microarray and RNA-Seq cohort in lymphoblastoid cell lines. We use conditional analysis and a Bayesian colocalisation method to provide evidence of a shared causal-variant, then compare the ability of each quantification type to detect disease relevant eQTLs and eGenes. We discovered the greatest frequency of candidate-causal eQTLs using exon-level RNA-Seq, and identified novel SLE susceptibility genes (e.g. NADSYN1 and TCF7) that were concealed using microarrays, including four non-coding RNAs. Many of these eQTLs were found to influence the expression of several genes, supporting the notion that risk haplotypes may harbour multiple functional effects. Novel SLE associated splicing events were identified in the T-reg restricted transcription factor, IKZF2, and other candidate genes (e.g. WDFY4) through asQTL mapping using the Geuvadis cohort. We have significantly increased our understanding of the genetic control of gene-expression in SLE by maximising the leverage of RNA-Seq and performing integrative GWAS-eQTL analysis against gene, exon, and splice-junction quantifications. We conclude that to better understand the true functional consequence of regulatory variants, quantification by RNA-Seq should be performed at the exon-level as a minimum, and run in parallel with gene and splice-junction level quantification.
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Affiliation(s)
| | - Andrea Cortini
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Lingyan Chen
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Amy L Roberts
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Ana Viñuela
- Department of Twin Research, King's College London, London, UK
| | | | - Kerrin S Small
- Department of Twin Research, King's College London, London, UK
| | | | - David L Morris
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Timothy J Vyse
- Department of Medical & Molecular Genetics, King's College London, London, UK.,Division of Immunology, Infection and Inflammatory Disease, King's College London, London, UK
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35
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Abstract
BACKGROUND Alternative splicing plays important roles in many regulatory processes and diseases in human. Many genetic variants contribute to phenotypic differences in gene expression and splicing that determine variations in human traits. Detecting genetic variants that affect splicing phenotypes is essential for understanding the functional impact of genetic variations on alternative splicing. For many situations, the key phenotype is the relative splicing ratios of alternative isoforms rather than the expression values of individual isoforms. Splicing quantitative trait loci (sQTL) analysis methods have been proposed for detecting associations of genetic variants with the vectors of isoform splicing ratios of genes. We call this task as composite sQTL analysis. Existing methods are computationally intensive and cannot scale up for whole genome analysis. RESULTS We developed an ultra-fast method named ulfasQTL for this task based on a previous method sQTLseekeR. It transforms tests of splicing ratios of multiple genes to a matrix form for efficient computation, and therefore can be applied for sQTL analysis at whole-genome scales at the speed thousands times faster than the existing method. We tested ulfasQTL on the data from the GEUVADIS project and compared it with an existing method. CONCLUSIONS ulfasQTL is a very efficient tool for composite splicing QTL analysis and can be applied on whole-genome analysis with acceptable time.
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Affiliation(s)
- Qian Yang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yue Hu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China. .,School of Life Sciences, Tsinghua University, Beijing, 100084, China. .,Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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36
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Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, Casey G, Hunter DJ, Sellers TA, Gruber SB, Dunning AM, Michailidou K, Fachal L, Doheny K, Spurdle AB, Li Y, Xiao X, Romm J, Pugh E, Coetzee GA, Hazelett DJ, Bojesen SE, Caga-Anan C, Haiman CA, Kamal A, Luccarini C, Tessier D, Vincent D, Bacot F, Van Den Berg DJ, Nelson S, Demetriades S, Goldgar DE, Couch FJ, Forman JL, Giles GG, Conti DV, Bickeböller H, Risch A, Waldenberger M, Brüske-Hohlfeld I, Hicks BD, Ling H, McGuffog L, Lee A, Kuchenbaecker K, Soucy P, Manz J, Cunningham JM, Butterbach K, Kote-Jarai Z, Kraft P, FitzGerald L, Lindström S, Adams M, McKay JD, Phelan CM, Benlloch S, Kelemen LE, Brennan P, Riggan M, O'Mara TA, Shen H, Shi Y, Thompson DJ, Goodman MT, Nielsen SF, Berchuck A, Laboissiere S, Schmit SL, Shelford T, Edlund CK, Taylor JA, Field JK, Park SK, Offit K, Thomassen M, Schmutzler R, Ottini L, Hung RJ, Marchini J, Amin Al Olama A, Peters U, Eeles RA, Seldin MF, Gillanders E, Seminara D, Antoniou AC, Pharoah PDP, Chenevix-Trench G, Chanock SJ, Simard J, Easton DF. The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomarkers Prev 2017; 26:126-135. [PMID: 27697780 PMCID: PMC5224974 DOI: 10.1158/1055-9965.epi-16-0106] [Citation(s) in RCA: 240] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 06/30/2016] [Accepted: 07/29/2016] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. METHODS The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. RESULTS The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. CONCLUSIONS Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. IMPACT Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126-35. ©2016 AACR.
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Affiliation(s)
- Christopher I Amos
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Zhaoming Wang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Jinyoung Byun
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Fredrick R Schumacher
- Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Simon A Gayther
- The Center for Bioinformatics and Functional Genomics at Cedars Sinai Medical Center, Greater Los Angeles Area, Los Angeles, California
| | - Graham Casey
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - David J Hunter
- Department of Epidemiology, Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Thomas A Sellers
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Stephen B Gruber
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Kimberly Doheny
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Amanda B Spurdle
- Molecular Cancer Epidemiology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Yafang Li
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Xiangjun Xiao
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Jane Romm
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Elizabeth Pugh
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | | | | | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Charlisse Caga-Anan
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland
| | - Christopher A Haiman
- The Center for Bioinformatics and Functional Genomics at Cedars Sinai Medical Center, Greater Los Angeles Area, Los Angeles, California
| | - Ahsan Kamal
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Daniel Tessier
- Génome Québec Innovation Centre, Montreal, Canada and McGill University, Montreal, Canada
| | - Daniel Vincent
- Génome Québec Innovation Centre, Montreal, Canada and McGill University, Montreal, Canada
| | - François Bacot
- Génome Québec Innovation Centre, Montreal, Canada and McGill University, Montreal, Canada
| | - David J Van Den Berg
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Stefanie Nelson
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland
| | - Stephen Demetriades
- University Health Network- The Princess Margaret Cancer Centre, Toronto, California
| | | | | | - Judith L Forman
- Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Cancer, Genetics and Immunology, Menzies Institute for Medical Research, Hobart, Australia
| | - David V Conti
- Division of Biostatistics, Department of Preventive Medicine, Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - Angela Risch
- University of Salzburg and Cancer Cluster Salzburg, Salzburg, Austria
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, Member of the German Center for Lung Research, Heidelberg, Germany
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Irene Brüske-Hohlfeld
- Helmholtz Zentrum München, Institut für Epidemiologie I, Neuherberg, Oberschleissheim, Germany
| | - Belynda D Hicks
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Hua Ling
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Lesley McGuffog
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Cancer, Genetics and Immunology, Menzies Institute for Medical Research, Hobart, Australia
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Karoline Kuchenbaecker
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Penny Soucy
- Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Québec and Laval University, Québec City, Canada
| | - Judith Manz
- Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Katja Butterbach
- Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Peter Kraft
- Department of Epidemiology, Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Liesel FitzGerald
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Cancer, Genetics and Immunology, Menzies Institute for Medical Research, Hobart, Australia
| | - Sara Lindström
- Department of Epidemiology, Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
- Department of Epidemiology, University of Washington, Seattle, Washington
| | - Marcia Adams
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - James D McKay
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Catherine M Phelan
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Sara Benlloch
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Linda E Kelemen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Paul Brennan
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Marjorie Riggan
- Department of Gynecology, Duke University Medical Center, Durham, North Carolina
| | - Tracy A O'Mara
- Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Yongyong Shi
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | | | - Sune F Nielsen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Andrew Berchuck
- Department of Gynecology, Duke University Medical Center, Durham, North Carolina
| | - Sylvie Laboissiere
- Génome Québec Innovation Centre, Montreal, Canada and McGill University, Montreal, Canada
| | - Stephanie L Schmit
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida
| | - Tameka Shelford
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Christopher K Edlund
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Jack A Taylor
- Molecular and Genetic Epidemiology Group, National Institute for Environmental Health Sciences, Research Triangle Park, North Carolina
| | - John K Field
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Sue K Park
- College of Medicine, Seoul National University, Gwanak-gu, Seoul, Korea
| | - Kenneth Offit
- Clinical Genetics Service, Memorial Hospital, New York, New York
- Cancer Biology and Genetics Program, Sloan Kettering Institute, New York, New York
- Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Rita Schmutzler
- Zentrum Familiärer Brust- und Eierstockkrebs, Universitätsklinikum Köln, Köln, Germany
| | - Laura Ottini
- Department of Molecular Medicine, Sapienza, University of Rome, Rome, Italy
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | | | - Michael F Seldin
- Department of Biochemistry and Molecular Medicine, University of California at Davis, Davis, California
- Department of Internal Medicine, University of California at Davis, Davis, California
| | - Elizabeth Gillanders
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland
| | - Daniela Seminara
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland
| | - Jacques Simard
- Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Québec and Laval University, Québec City, Canada
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
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Teruel M, Alarcón-Riquelme ME. The genetic basis of systemic lupus erythematosus: What are the risk factors and what have we learned. J Autoimmun 2016; 74:161-175. [PMID: 27522116 DOI: 10.1016/j.jaut.2016.08.001] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 08/02/2016] [Indexed: 12/19/2022]
Abstract
The genome-wide association study is a free-hypothesis approach based on screening of thousands or even millions of genetic variants distributed throughout the whole human genome in relation to a phenotype. The relevant role of the genome-wide association studies in the last decade is undisputed because it has permitted to elucidate multiple risk genetic factors associated with the susceptibility to several human complex diseases. Regarding systemic lupus erythematosus (SLE) this approach has allowed to identify more than 60 risk loci for SLE susceptibility across populations to date, increasing our understanding on the pathogenesis of this disease. We present the latest findings in the genetic of SLE across populations using genome-wide approaches. These studies revealed that most of the genetic risk is shared across borders and ethnicities. Finally, we focus on describing the most important risk loci for SLE attempting to cover the genetic findings in relation to functional polymorphisms, such as missense single nucleotide polymorphisms (SNPs) or regulatory variants involved in the development of the disease. The functional studies try to identify the causality of some GWAS-associated variants, many of which fall in non-coding regions of the genome, suggesting a regulatory role. Many loci show an environmental interaction, another aspect revealed by the studies of epigenetic modifications and those associated with genetic variants. Finally, new-generation sequencing technologies can open other paths in the research on SLE genetics, the role of rare variants and the detailed identification of causal regulatory variation. The clinical relevance of the genetic factors will be shown when we are able to use them or in combination with other molecular measurements to re-classify a heterogeneous disease such as SLE.
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Affiliation(s)
- Maria Teruel
- Center for Genomics and Oncological Research, GENYO, Pfizer/University of Granada/Andalusian Government, PTS, Granada, 18016, Spain.
| | - Marta E Alarcón-Riquelme
- Center for Genomics and Oncological Research, GENYO, Pfizer/University of Granada/Andalusian Government, PTS, Granada, 18016, Spain; Institute of Environmental Medicine, Karolinska Institute, Stockholm, 171 67, Sweden.
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38
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Nowicka M, Robinson MD. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. F1000Res 2016; 5:1356. [PMID: 28105305 PMCID: PMC5200948 DOI: 10.12688/f1000research.8900.2] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/01/2016] [Indexed: 02/03/2023] Open
Abstract
There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.
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Affiliation(s)
- Malgorzata Nowicka
- Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland; SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland
| | - Mark D Robinson
- Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland; SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland
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Nowicka M, Robinson MD. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. F1000Res 2016; 5:1356. [PMID: 28105305 PMCID: PMC5200948 DOI: 10.12688/f1000research.8900.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/01/2016] [Indexed: 08/31/2023] Open
Abstract
There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.
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Affiliation(s)
- Malgorzata Nowicka
- Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland
| | - Mark D. Robinson
- Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland
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40
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A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals. Nat Commun 2016; 7:11101. [PMID: 27089393 PMCID: PMC4837449 DOI: 10.1038/ncomms11101] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 02/19/2016] [Indexed: 02/07/2023] Open
Abstract
Large-scale sequencing in the 1000 Genomes Project has revealed multitudes of single nucleotide variants (SNVs). Here, we provide insights into the functional effect of these variants using allele-specific behaviour. This can be assessed for an individual by mapping ChIP-seq and RNA-seq reads to a personal genome, and then measuring 'allelic imbalances' between the numbers of reads mapped to the paternal and maternal chromosomes. We annotate variants associated with allele-specific binding and expression in 382 individuals by uniformly processing 1,263 functional genomics data sets, developing approaches to reduce the heterogeneity between data sets due to overdispersion and mapping bias. Since many allelic variants are rare, aggregation across multiple individuals is necessary to identify broadly applicable 'allelic elements'. We also found SNVs for which we can anticipate allelic imbalance from the disruption of a binding motif. Our results serve as an allele-specific annotation for the 1000 Genomes variant catalogue and are distributed as an online resource (alleledb.gersteinlab.org).
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Pacheco-Gonzalez RM, Avila C, Dávila I, García-Sánchez A, Hernández-Hernández L, Benito-Pescador D, Torres R, Prieto-Matos P, Isidoro-Garcia M, Lorente F, Sanz C. Analysis of FOXP3 gene in children with allergy and autoimmune diseases. Allergol Immunopathol (Madr) 2016; 44:32-40. [PMID: 25982578 DOI: 10.1016/j.aller.2015.01.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 01/26/2015] [Indexed: 01/16/2023]
Abstract
BACKGROUND Allergy and autoimmunity are important immunological entities underlying chronic diseases in children. In some cases both entities develop simultaneously in the same patient. FOXP3 gene codes for a transcription factor involved in regulation of the immune system. Considering that regulatory T cells are involved in controlling immunological disease development, and the relevant role of FOXP3 in this kind of T cells, the objective of this study was to analyse the FOXP3 gene in the most prevalent autoimmune diseases and/or allergies in childhood in a European population. METHODS A total of 255 Caucasian individuals, 95 controls and 160 patients diagnosed with allergic, autoimmune or both diseases were included in this study. The molecular analysis of FOXP3 was performed by DNA sequencing following the recommendations for quality of the European Molecular Genetics Quality Network. Genomic DNA was extracted from peripheral blood of all participants and was amplified using the polymerase chain reaction. After the visualisation of the amplified fragments by agarose gel-electrophoresis, they were sequenced. RESULTS Thirteen different polymorphisms in FOXP3 gene were found, seven of which had not been previously described. The mutated allele of SNP 7340C>T was observed more frequently in the group of male children suffering from both allergic and autoimmune diseases simultaneously (p=0.004, OR=16.2 [1.34-195.15]). CONCLUSIONS In this study we identified for first time genetic variants of FOXP3 that are significantly more frequent in children who share allergic and autoimmune diseases. These variants mainly affect regulatory sequences that could alter the expression levels of FOXP3 modifying its function including its role in Treg cells.
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Affiliation(s)
| | - C Avila
- Department of Statistics, University of Salamanca, Salamanca, Spain; Biosanitary Institute of Salamanca IBSAL, Salamanca, Spain
| | - I Dávila
- Biosanitary Institute of Salamanca IBSAL, Salamanca, Spain; Department of Allergy, University Hospital of Salamanca, Salamanca, Spain.
| | | | | | | | - R Torres
- Department of Paediatrics, University Hospital of Salamanca, Salamanca, Spain
| | - P Prieto-Matos
- Biosanitary Institute of Salamanca IBSAL, Salamanca, Spain; Department of Paediatrics, University Hospital of Salamanca, Salamanca, Spain
| | - M Isidoro-Garcia
- Biosanitary Institute of Salamanca IBSAL, Salamanca, Spain; Department of Clinical Biochemistry, University Hospital of Salamanca, Salamanca, Spain; Department of Medicine, University of Salamanca, Salamanca, Spain.
| | - F Lorente
- Biosanitary Institute of Salamanca IBSAL, Salamanca, Spain; Department of Paediatrics, University Hospital of Salamanca, Salamanca, Spain
| | - C Sanz
- Biosanitary Institute of Salamanca IBSAL, Salamanca, Spain; Department of Microbiology and Genetics, University of Salamanca, Salamanca, Spain
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De Novo Transcriptome Sequencing Analysis of cDNA Library and Large-Scale Unigene Assembly in Japanese Red Pine (Pinus densiflora). Int J Mol Sci 2015; 16:29047-59. [PMID: 26690126 PMCID: PMC4691086 DOI: 10.3390/ijms161226139] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Revised: 11/13/2015] [Accepted: 11/26/2015] [Indexed: 01/28/2023] Open
Abstract
Japanese red pine (Pinus densiflora) is extensively cultivated in Japan, Korea, China, and Russia and is harvested for timber, pulpwood, garden, and paper markets. However, genetic information and molecular markers were very scarce for this species. In this study, over 51 million sequencing clean reads from P. densiflora mRNA were produced using Illumina paired-end sequencing technology. It yielded 83,913 unigenes with a mean length of 751 bp, of which 54,530 (64.98%) unigenes showed similarity to sequences in the NCBI database. Among which the best matches in the NCBI Nr database were Picea sitchensis (41.60%), Amborella trichopoda (9.83%), and Pinus taeda (4.15%). A total of 1953 putative microsatellites were identified in 1784 unigenes using MISA (MicroSAtellite) software, of which the tri-nucleotide repeats were most abundant (50.18%) and 629 EST-SSR (expressed sequence tag- simple sequence repeats) primer pairs were successfully designed. Among 20 EST-SSR primer pairs randomly chosen, 17 markers yielded amplification products of the expected size in P. densiflora. Our results will provide a valuable resource for gene-function analysis, germplasm identification, molecular marker-assisted breeding and resistance-related gene(s) mapping for pine for P. densiflora.
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Stein S, Lu ZX, Bahrami-Samani E, Park JW, Xing Y. Discover hidden splicing variations by mapping personal transcriptomes to personal genomes. Nucleic Acids Res 2015; 43:10612-22. [PMID: 26578562 PMCID: PMC4678817 DOI: 10.1093/nar/gkv1099] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 10/09/2015] [Indexed: 01/27/2023] Open
Abstract
RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify ‘hidden’ splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as OAS1), as well as novel associations between alternative splicing and diseases (such as ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations.
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Affiliation(s)
- Shayna Stein
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zhi-Xiang Lu
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Emad Bahrami-Samani
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Juw Won Park
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Xing
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Leigh-Brown S, Goncalves A, Thybert D, Stefflova K, Watt S, Flicek P, Brazma A, Marioni JC, Odom DT. Regulatory Divergence of Transcript Isoforms in a Mammalian Model System. PLoS One 2015; 10:e0137367. [PMID: 26339903 PMCID: PMC4560434 DOI: 10.1371/journal.pone.0137367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 08/15/2015] [Indexed: 11/24/2022] Open
Abstract
Phenotypic differences between species are driven by changes in gene expression and, by extension, by modifications in the regulation of the transcriptome. Investigation of mammalian transcriptome divergence has been restricted to analysis of bulk gene expression levels and gene-internal splicing. Using allele-specific expression analysis in inter-strain hybrids of Mus musculus, we determined the contribution of multiple cellular regulatory systems to transcriptome divergence, including: alternative promoter usage, transcription start site selection, cassette exon usage, alternative last exon usage, and alternative polyadenylation site choice. Between mouse strains, a fifth of genes have variations in isoform usage that contribute to transcriptomic changes, half of which alter encoded amino acid sequence. Virtually all divergence in isoform usage altered the post-transcriptional regulatory instructions in gene UTRs. Furthermore, most genes with isoform differences between strains contain changes originating from multiple regulatory systems. This result indicates widespread cross-talk and coordination exists among different regulatory systems. Overall, isoform usage diverges in parallel with and independently to gene expression evolution, and the cis and trans regulatory contribution to each differs significantly.
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Affiliation(s)
- Sarah Leigh-Brown
- University of Cambridge, Cancer Research UK - Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
| | - Angela Goncalves
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - David Thybert
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Klara Stefflova
- California Institute of Technology, Division of Biology, Pasadena, California, United States of America
| | - Stephen Watt
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - John C. Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Duncan T. Odom
- University of Cambridge, Cancer Research UK - Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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45
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Buchkovich ML, Eklund K, Duan Q, Li Y, Mohlke KL, Furey TS. Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci. BMC Med Genomics 2015. [PMID: 26210163 PMCID: PMC4515314 DOI: 10.1186/s12920-015-0117-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Genetic variation can alter transcriptional regulatory activity contributing to variation in complex traits and risk of disease, but identifying individual variants that affect regulatory activity has been challenging. Quantitative sequence-based experiments such as ChIP-seq and DNase-seq can detect sites of allelic imbalance where alleles contribute disproportionately to the overall signal suggesting allelic differences in regulatory activity. Methods We created an allelic imbalance detection pipeline, AA-ALIGNER, to remove reference mapping biases influencing allelic imbalance detection and evaluate accuracy of allelic imbalance predictions in the absence of complete genotype data. Using the sequence aligner, GSNAP, and varying amounts of genotype information to remove mapping biases we investigated the accuracy of allelic imbalance detection (binomial test) in CREB1 ChIP-seq reads from the GM12878 cell line. Additionally we thoroughly evaluated the influence of experimental and analytical parameters on imbalance detection. Results Compared to imbalances identified using complete genotypes, using imputed partial sample genotypes, AA-ALIGNER detected >95 % of imbalances with >90 % accuracy. AA-ALIGNER performed nearly as well using common variants when genotypes were unknown. In contrast, predicting additional heterozygous sites and imbalances using the sequence data led to >50 % false positive rates. We evaluated effects of experimental data characteristics and key analytical parameter settings on imbalance detection. Overall, total base coverage and signal dispersion across the genome most affected our ability to detect imbalances, while parameters such as imbalance significance, imputation quality thresholds, and alignment mismatches had little effect. To assess the biological relevance of imbalance predictions, we used electrophoretic mobility shift assays to functionally test for predicted allelic differences in CREB1 binding in the GM12878 lymphoblast cell line. Six of nine tested variants exhibited allelic differences in binding. Two of these variants, rs2382818 and rs713875, are located within inflammatory bowel disease-associated loci. Conclusions AA-ALIGNER accurately detects allelic imbalance in quantitative sequence data using partial genotypes or common variants filling a critical methodological gap in these analyses, as full genotypes are rarely available. Importantly, we demonstrate how experimental and analytical features impact imbalance detection providing guidance for similar future studies. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0117-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Martin L Buchkovich
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Karl Eklund
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA. .,Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA. .,Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Terrence S Furey
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA. .,Department of Biology, University of North Carolina, Chapel Hill, NC, 27599, USA.
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46
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Wood DLA, Nones K, Steptoe A, Christ A, Harliwong I, Newell F, Bruxner TJC, Miller D, Cloonan N, Grimmond SM. Recommendations for Accurate Resolution of Gene and Isoform Allele-Specific Expression in RNA-Seq Data. PLoS One 2015; 10:e0126911. [PMID: 25965996 PMCID: PMC4428808 DOI: 10.1371/journal.pone.0126911] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2014] [Accepted: 04/09/2015] [Indexed: 11/19/2022] Open
Abstract
Genetic variation modulates gene expression transcriptionally or post-transcriptionally, and can profoundly alter an individual’s phenotype. Measuring allelic differential expression at heterozygous loci within an individual, a phenomenon called allele-specific expression (ASE), can assist in identifying such factors. Massively parallel DNA and RNA sequencing and advances in bioinformatic methodologies provide an outstanding opportunity to measure ASE genome-wide. In this study, matched DNA and RNA sequencing, genotyping arrays and computationally phased haplotypes were integrated to comprehensively and conservatively quantify ASE in a single human brain and liver tissue sample. We describe a methodological evaluation and assessment of common bioinformatic steps for ASE quantification, and recommend a robust approach to accurately measure SNP, gene and isoform ASE through the use of personalized haplotype genome alignment, strict alignment quality control and intragenic SNP aggregation. Our results indicate that accurate ASE quantification requires careful bioinformatic analyses and is adversely affected by sample specific alignment confounders and random sampling even at moderate sequence depths. We identified multiple known and several novel ASE genes in liver, including WDR72, DSP and UBD, as well as genes that contained ASE SNPs with imbalance direction discordant with haplotype phase, explainable by annotated transcript structure, suggesting isoform derived ASE. The methods evaluated in this study will be of use to researchers performing highly conservative quantification of ASE, and the genes and isoforms identified as ASE of interest to researchers studying those loci.
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Affiliation(s)
- David L. A. Wood
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
- * E-mail:
| | - Katia Nones
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - Anita Steptoe
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - Angelika Christ
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - Ivon Harliwong
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - Felicity Newell
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - Timothy J. C. Bruxner
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - David Miller
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
| | - Nicole Cloonan
- QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD, 4006, Australia
| | - Sean M. Grimmond
- Queensland Centre for Medical Genomics, University of Queensland, Brisbane, Australia
- Translational Research Centre, University of Glasgow, Glasgow, Scotland
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47
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Abstract
RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.
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Affiliation(s)
- Kimberly R Kukurba
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305; Department of Computer Science, Stanford University School of Medicine, Stanford, California 94305
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48
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Locke JM, Hysenaj G, Wood AR, Weedon MN, Harries LW. Targeted allelic expression profiling in human islets identifies cis-regulatory effects for multiple variants identified by type 2 diabetes genome-wide association studies. Diabetes 2015; 64:1484-91. [PMID: 25392243 DOI: 10.2337/db14-0957] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Genome-wide association studies (GWAS) have identified variation at >65 genomic loci associated with susceptibility to type 2 diabetes, but little progress has been made in elucidating the molecular mechanisms behind most of these associations. Using samples heterozygous for transcribed single nucleotide polymorphisms (SNPs), allelic expression profiling is a powerful technique for identifying cis-regulatory variants controlling gene expression. In this study, exonic SNPs, suitable for measuring mature mRNA levels and in high linkage disequilibrium with 65 lead type 2 diabetes GWAS SNPs, were identified and allelic expression determined by real-time PCR using RNA and DNA isolated from islets of 36 white nondiabetic donors. A significant allelic expression imbalance (AEI) was identified for 7/14 (50%) genes tested (ANPEP, CAMK2B, HMG20A, KCNJ11, NOTCH2, SLC30A8, and WFS1), with significant AEI confirmed for five of these genes using other linked exonic SNPs. Lastly, results of a targeted islet expression quantitative trait loci experiment support the AEI findings for ANPEP, further implicating ANPEP as the causative gene at its locus. The results of this study support the hypothesis that changes to cis-regulation of gene expression are involved in a large proportion of SNP associations with type 2 diabetes susceptibility.
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Affiliation(s)
- Jonathan M Locke
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, U.K
| | - Gerald Hysenaj
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, U.K
| | - Andrew R Wood
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, U.K
| | - Michael N Weedon
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, U.K
| | - Lorna W Harries
- Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, U.K.
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49
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Merkin JJ, Chen P, Alexis MS, Hautaniemi SK, Burge CB. Origins and impacts of new mammalian exons. Cell Rep 2015; 10:1992-2005. [PMID: 25801031 DOI: 10.1016/j.celrep.2015.02.058] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 01/09/2015] [Accepted: 02/23/2015] [Indexed: 02/08/2023] Open
Abstract
Mammalian genes are composed of exons, but the evolutionary origins and functions of new internal exons are poorly understood. Here, we analyzed patterns of exon gain using deep cDNA sequencing data from five mammals and one bird, identifying thousands of species- and lineage-specific exons. Most new exons derived from unique rather than repetitive intronic sequence. Unlike exons conserved across mammals, species-specific internal exons were mostly located in 5' UTRs and alternatively spliced. They were associated with upstream intronic deletions, increased nucleosome occupancy, and RNA polymerase II pausing. Genes containing new internal exons had increased gene expression, but only in tissues in which the exon was included. Increased expression correlated with the level of exon inclusion, promoter proximity, and signatures of cotranscriptional splicing. Altogether, these findings suggest that increased splicing at the 5' ends of genes enhances expression and that changes in 5' end splicing alter gene expression between tissues and between species.
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Affiliation(s)
- Jason J Merkin
- Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Ping Chen
- Research Programs Unit, Genome-Scale Biology and Institute of Biomedicine, University of Helsinki, 00014 Helsinki, Finland
| | - Maria S Alexis
- Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Sampsa K Hautaniemi
- Research Programs Unit, Genome-Scale Biology and Institute of Biomedicine, University of Helsinki, 00014 Helsinki, Finland
| | - Christopher B Burge
- Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
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50
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Kurmangaliyev YZ, Favorov AV, Osman NM, Lehmann KV, Campo D, Salomon MP, Tower J, Gelfand MS, Nuzhdin SV. Natural variation of gene models in Drosophila melanogaster. BMC Genomics 2015; 16:198. [PMID: 25888292 PMCID: PMC4373058 DOI: 10.1186/s12864-015-1415-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Accepted: 02/28/2015] [Indexed: 09/03/2023] Open
Abstract
Background Variation within splicing regulatory sequences often leads to differences in gene models among individuals within a species. Two alleles of the same gene may express transcripts with different exon/intron structures and consequently produce functionally different proteins. Matching genomic and transcriptomic data allows us to identify putative regulatory variants associated with changes in splicing patterns. Results Here we analyzed natural variation of splicing patterns in the transcriptomes of 81 natural strains of Drosophila melanogaster with known genotypes. We identified dozens of genotype-specific splicing patterns associated with putative cis-splicing quantitative trait loci (sQTL). The majority of changes can be explained by mutations in splice sites. Allelic-imbalance in splicing patterns confirmed that the majority are regulated mainly by cis-genetic effects. Remarkably, allele-specific splicing changes often lead to qualitative changes in gene models, yielding many isoforms not previously annotated. The observed alterations are typically outside protein-coding regions or affect only very short protein segments. Conclusions Overall, the sets of gene models appear to be flexible within D. melanogaster populations. The observed variation in splicing patterns are predicted to have limited effects on the encoded protein sequences. To our knowledge, this is the first sQTL mapping study in Drosophila. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1415-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yerbol Z Kurmangaliyev
- University of Southern California, Los Angeles, CA, USA. .,Institute for Information Transmission Problems (Kharkevich Institute), Moscow, Russia.
| | - Alexander V Favorov
- Johns Hopkins University School of Medicine, Baltimore, MD, USA. .,Vavilov Institute of General Genetics, Moscow, Russia. .,Research Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russia.
| | - Noha M Osman
- University of Southern California, Los Angeles, CA, USA. .,National Research Center, Dokki, Giza, Egypt.
| | - Kjong-Van Lehmann
- Memorial Sloan Kettering Cancer Center, Zuckerman Research Center, New York, NY, USA.
| | - Daniel Campo
- University of Southern California, Los Angeles, CA, USA.
| | | | - John Tower
- University of Southern California, Los Angeles, CA, USA.
| | - Mikhail S Gelfand
- Institute for Information Transmission Problems (Kharkevich Institute), Moscow, Russia. .,Lomonosov Moscow State University, Moscow, Russia.
| | - Sergey V Nuzhdin
- University of Southern California, Los Angeles, CA, USA. .,Saint Petersburg Polytechnical University, St Petersburg, Russia.
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