| 1. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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Ingår i: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Forskningsöversikt (refereegranskat)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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| 2. |
- Broderick, Peter, et al.
(författare)
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Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk
- 2012
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 44:1, s. 58-83
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Tidskriftsartikel (refereegranskat)abstract
- To identify risk variants for multiple myeloma, we conducted a genome-wide association study of 1,675 individuals with multiple myeloma and 5,903 control subjects. We identified risk loci for multiple myeloma at 3p22.1 (rs1052501 in ULK4; odds ratio (OR) = 1.32; P = 7.47 x 10(-9)) and 7p15.3 (rs4487645, OR = 1.38; P = 3.33 x 10(-15)). In addition, we observed a promising association at 2p23.3 (rs6746082, OR = 1.29; P = 1.22 x 10(-7)). Our study identifies new genomic regions associated with multiple myeloma risk that may lead to new etiological insights.
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| 3. |
- Chubb, Daniel, et al.
(författare)
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Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 45:10, s. 366-1221
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Tidskriftsartikel (refereegranskat)abstract
- To identify variants for multiple myeloma risk, we conducted a genome-wide association study with validation in additional series totaling 4,692 individuals with multiple myeloma (cases) and 10,990 controls. We identified four risk loci at 3q26.2 (rs10936599, P = 8.70 x 10(-14)), 6p21.33 (rs2285803, PSORS1C2, P = 9.67 x 10(-11)), 17p11.2 (rs4273077, TNFRSF13B, P = 7.67 x 10(-9)) and 22q13.1 (rs877529, CBX7, P = 7.63 x 10(-16)). These data provide further evidence for genetic susceptibility to this B-cell hematological malignancy, as well as insight into the biological basis of predisposition.
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| 4. |
- Weinhold, Niels, et al.
(författare)
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The CCND1 c.870G > A polymorphism is a risk factor for t(11;14)(q13;q32) multiple myeloma
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 45:5, s. 522-525
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Tidskriftsartikel (refereegranskat)abstract
- A number of specific chromosomal abnormalities define the subgroups of multiple myeloma. In a meta-analysis of two genome-wide association studies of multiple myeloma including a total of 1,661 affected individuals, we investigated risk for developing a specific tumor karyotype. The t(11;14)(q13;q32) translocation in which CCND1 is placed under the control of the immunoglobulin heavy chain enhancer was strongly associated with the CCND1 c.870G>A polymorphism (P = 7.96 x 10(-11)). These results provide a model in which a constitutive genetic factor is associated with risk of a specific chromosomal translocation.
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| 5. |
- Went, Molly, et al.
(författare)
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Genetic correlation between multiple myeloma and chronic lymphocytic leukaemia provides evidence for shared aetiology
- 2018
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Ingår i: Blood Cancer Journal. - : Springer Science and Business Media LLC. - 2044-5385. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- The clustering of different types of B-cell malignancies in families raises the possibility of shared aetiology. To examine this, we performed cross-trait linkage disequilibrium (LD)-score regression of multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) genome-wide association study (GWAS) data sets, totalling 11,734 cases and 29,468 controls. A significant genetic correlation between these two B-cell malignancies was shown (Rg = 0.4, P = 0.0046). Furthermore, four of the 45 known CLL risk loci were shown to associate with MM risk and five of the 23 known MM risk loci associate with CLL risk. By integrating eQTL, Hi-C and ChIP-seq data, we show that these pleiotropic risk loci are enriched for B-cell regulatory elements and implicate B-cell developmental genes. These data identify shared biological pathways influencing the development of CLL and, MM and further our understanding of the aetiological basis of these B-cell malignancies.
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| 6. |
- Mitchell, Jonathan S., et al.
(författare)
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Genome-wide association study identifies multiple susceptibility loci for multiple myeloma
- 2016
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls. We confirm all nine known risk loci and discover eight new loci at 6p22.3 (rs34229995, P = 1.31 x 10(-8)), 6q21 (rs9372120, P = 9.09 x 10(-15)), 7q36.1 (rs7781265, P = 9.71 x 10(-9)), 8q24.21 (rs1948915, P = 4.20 x 10(-11)), 9p21.3 (rs2811710, P = 1.72 x 10(-13)), 10p12.1 (rs2790457, P = 1.77 x 10(-8)), 16q23.1 (rs7193541, P = 5.00 x 10(-12)) and 20q13.13 (rs6066835, P = 1.36 x 10(-13)), which localize in or near to JARID2, ATG5, SMARCD3, CCAT1, CDKN2A, WAC, RFWD3 and PREX1. These findings provide additional support for a polygenic model of MM and insight into the biological basis of tumour development.
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| 7. |
- Weinhold, Niels, et al.
(författare)
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Inherited genetic susceptibility to monoclonal gammopathy of unknown significance
- 2014
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Ingår i: Blood. - : American Society of Hematology. - 1528-0020 .- 0006-4971. ; 123:16, s. 2513-2517
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Tidskriftsartikel (refereegranskat)abstract
- Monoclonal gammopathy of undetermined significance (MGUS) is present in similar to 2% of individuals age >50 years. The increased risk of multiple myeloma (MM) in relatives of individuals with MGUS is consistent with MGUS being a marker of inherited genetic susceptibility to MM. Common single-nucleotide polymorphisms (SNPs) at 2p23.3 (rs6746082), 3p22.1 (rs1052501), 3q26.2 (rs10936599), 6p21.33 (rs2285803), 7p15.3 (rs4487645), 17p11.2 (rs4273077), and 22q13.1 (rs877529) have recently been shown to influence MM risk. To examine the impact of these 7 SNPs on MGUS, we analyzed two case-control series totaling 492 cases and 7306 controls. Each SNP independently influenced MGUS risk with statistically significant associations (P < .02) for rs1052501, rs2285803, rs4487645, and rs4273077. SNP associations were independent, with risk increasing with a larger number of risk alleles carried (per allele odds ratio, 1.18; P < 10(-7)). Collectively these data are consistent with a polygenic model of disease susceptibility to MGUS.
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| 8. |
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| 9. |
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| 10. |
- Went, Molly, et al.
(författare)
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Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes
- 2019
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Ingår i: Human Genomics. - : Springer Science and Business Media LLC. - 1479-7364. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundWhile genome-wide association studies (GWAS) of multiple myeloma (MM) have identified variants at 23 regions influencing risk, the genes underlying these associations are largely unknown. To identify candidate causal genes at these regions and search for novel risk regions, we performed a multi-tissue transcriptome-wide association study (TWAS).ResultsGWAS data on 7319 MM cases and 234,385 controls was integrated with Genotype-Tissue Expression Project (GTEx) data assayed in 48 tissues (sample sizes, N = 80–491), including lymphocyte cell lines and whole blood, to predict gene expression. We identified 108 genes at 13 independent regions associated with MM risk, all of which were in 1 Mb of known MM GWAS risk variants. Of these, 94 genes, located in eight regions, had not previously been considered as a candidate gene for that locus.ConclusionsOur findings highlight the value of leveraging expression data from multiple tissues to identify candidate genes responsible for GWAS associations which provide insight into MM tumorigenesis. Among the genes identified, a number have plausible roles in MM biology, notably APOBEC3C, APOBEC3H, APOBEC3D, APOBEC3F, APOBEC3G, or have been previously implicated in other malignancies. The genes identified in this TWAS can be explored for follow-up and validation to further understand their role in MM biology.
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