| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Fu, SS, et al.
(author)
-
Genetic diversity of the enterohaemolysin gene (ehxA) in non-O157 Shiga toxin-producing Escherichia coli strains in China
- 2018
-
In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8:1, s. 4233-
-
Journal article (peer-reviewed)abstract
- Non-O157 Shiga toxin-producing Escherichia coli (STEC) is increasingly recognized as an important enteric foodborne pathogen. The hallmark of the disease is the production of Shiga toxins; however, there are other virulence factors that contribute to the pathogenesis of STEC. This study aimed to investigate the prevalence and genetic diversity of the enterohaemolysin gene, ehxA, among non-O157 STEC strains from human, animal, and food sources. The ehxA gene was amplified from 138 (31.8%) of 434 non-O157 STEC strains, among which 36 unique ehxA sequences were identified. Based on ehxA sequence analysis, three phylogenetic ehxA groups (I II, and III) were determined. Correlations between ehxA groups and sources, serotypes, and virulent gene profiles were observed. The ehxA group II strains were mostly diarrhoeal patient-derived and may demonstrate higher pathogenic potential compared with the ehxA group I and group III strains. Five types of replicons (I1-Ig, FIB, K, F, and B/O) were identified in the 138 ehxA-positive strains, and 3.6%, 5.8%, and 52.2% of the strains harboured toxB, katP and espP genes, respectively, implying marked genetic diversity of ehxA containing plasmids in non-O157 STEC strains. Sequence-based ehxA genotyping might be important in modern strain typing and in epidemiological surveillance of non-O157 STEC infections.
|
|
| 8. |
- Guo, JP, et al.
(author)
-
Sequencing of the MHC region defines HLA-DQA1 as the major genetic risk for seropositive rheumatoid arthritis in Han Chinese population
- 2019
-
In: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 78:6, s. 773-780
-
Journal article (peer-reviewed)abstract
- The strong genetic contribution of the major histocompatibility complex (MHC) region to rheumatoid arthritis (RA) has been generally attributed to human leukocyte antigen (HLA)-DRB1. However, due to the high polymorphisms and linkage disequilibrium within MHC, it is difficult to define novel and/or independent genetic risks using conventional HLA genotyping or chip-based microarray technology. This study aimed to identify novel RA risk variants by performing deep sequencing for MHC.MethodsWe first conducted target sequencing for the entire MHC region in 357 anticitrullinated protein antibodies (ACPA)-positive patients with RA and 1001 healthy controls, and then performed HLA typing in an independent case–control cohort consisting of 1415 samples for validation. All study subjects were Han Chinese. Genetic associations for RA susceptibility and severity were analysed. Comparative modelling was constructed to predict potential functions for the newly discovered RA association variants.ResultsHLA-DQα1:160D conferred the strongest and independent susceptibility to ACPA-positive RA (p=6.16×10−36, OR=2.29). DRβ1:37N had an independent protective effect (p=5.81×10−16, OR=0.49). As predicted by comparative modelling, the negatively charged DQα1:160D stabilises the dimer of dimers, thus may lead to an increased T cell activation. The negatively charged DRβ1:37N encoding alleles preferentially bind with epitope P9 arginine, thus may result in a decreased RA susceptibility.ConclusionsWe provide the first evidence that HLA-DQα1:160D, instead of HLA-DRB1*0405, is the strongest and independent genetic risk for ACPA-positive RA in Han Chinese. Our study also illustrates the value of deep sequencing for fine-mapping disease risk variants in the MHC region.
|
|
| 9. |
|
|
| 10. |
|
|
| 11. |
|
|
| 12. |
|
|
| 13. |
- Menden, MP, et al.
(author)
-
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- 2019
-
In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
-
Journal article (peer-reviewed)abstract
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
|
|
| 14. |
|
|
| 15. |
|
|
