The thermal regime modifies the response of aquatic keystone species Daphnia to microplastics: Evidence from population fitness, accumulation, histopathological analysis and candidate gene expression

2021 ◽  
Vol 783 ◽  
pp. 147154
Author(s):  
Kai Lyu ◽  
Cheng Cao ◽  
Da Li ◽  
Siddiq Akbar ◽  
Zhou Yang
2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.


2010 ◽  
Vol 26 (18) ◽  
pp. i618-i624 ◽  
Author(s):  
Rosario M. Piro ◽  
Ivan Molineris ◽  
Ugo Ala ◽  
Paolo Provero ◽  
Ferdinando Di Cunto

2020 ◽  
Vol 57 (7) ◽  
pp. 2944-2958 ◽  
Author(s):  
Andrea Bieder ◽  
Masahito Yoshihara ◽  
Shintaro Katayama ◽  
Kaarel Krjutškov ◽  
Anna Falk ◽  
...  

Planta ◽  
2014 ◽  
Vol 239 (5) ◽  
pp. 1041-1053 ◽  
Author(s):  
Yongfang Wan ◽  
Cristina Gritsch ◽  
Theodora Tryfona ◽  
Mike J. Ray ◽  
Ambrose Andongabo ◽  
...  

BMC Ecology ◽  
2010 ◽  
Vol 10 (1) ◽  
pp. 3 ◽  
Author(s):  
Maaria Kankare ◽  
Tiina Salminen ◽  
Asta Laiho ◽  
Laura Vesala ◽  
Anneli Hoikkala

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2312-2312
Author(s):  
Glenda J. McGonigle ◽  
Damian P.J. Finnegan ◽  
Mary Frances McMullin ◽  
Terence R.J. Lappin ◽  
Alexander Thompson

Abstract Molecular classification of acute myeloid leukemia (AML) has identified several candidate genes that could potentially define prognosis and response to therapy. One such candidate, identified from microarray studies, is the Class I homeobox gene HOXA9. The HOX gene network encodes master regulators of developmental processes including hemopoiesis. To quantify the contribution of this network of genes in AML, we carried out specific RQ-PCR analysis on twenty-four de novo patient samples using a subset of genes (12 HOX and MEIS1) selected on the basis of their recently reported expression in AML. HOXA6 was ranked, as the most highly expressed gene (range 1 x 103 – 2 x 107 copies per 50 ng RNA), substantially higher than HOXA9 (see Table). Further analysis identified high expression of HOXA6 in both human myeloid cell lines and CD34+ enriched primary progenitors. Parallel studies with murine progenitors (c-Kit+, Lin−) and cell lines also showed a preponderance of Hoxa6 expression over other family members including Hoxa9 and Hoxb4. Several hemopoietic cell lines, namely Ba/F3, EML, FDCP-Mix A4 and 32Dcl3 were subsequently used to investigate Hoxa6 regulation following differentiation or growth factor stimuli. Hoxa6 expression decreased with cell differentiation and growth factor depletion/replenishment studies indicated a cell-cycle component for Hoxa6 regulation. Direct evaluation of cell-cycle status, using Hoechst 33342 staining and cell sorting, identified peak expression of Hoxa6 during S-phase. Gene deletion studies involving Hox tend to result in either a moderate or no phenotype, presumably due to intrinsic compensatory mechanisms. We therefore overexpressed HOXA6 in the Ba/F3 cell line to gain functional insights. Ba/F3-A6 cells were compared to mock-transfected and vector controls on the basis of proliferation, maturation, cell-cycle status, growth factor-dependence and apoptosis. The Ba/F3-A6 cells displayed a growth advantage over normal cells in the presence of IL-3 and maturation was not impaired. Cell-cycle analysis showed a reduction in the number of cells in both G2M and S-phase, associated with accumulation in the pre G1-phase, indicative of increased apoptosis. IL-3 depletion studies of Ba/F3-A6 cells indicated substantial factor-independent growth compared to controls, implying oncogenic potential for HOXA6. In support of this, a recent report (Mamo et al, Blood. 2006 Jul 15;108(2):622–9) indicated Hoxa6 as a potential collaborator in a Meis1-induced model of AML. Taken together these findings identify Hoxa6 as a novel candidate gene in AML with the capacity to alter growth and survival of hemopoietic cells. Gene Expression Ranking of HOX and MEIS1 in AML. GENE EXPRESSION RANGE MEAN RANK S.D. OVERALL RANK Expression values (copies per 50 ng RNA) compiled from primary AML patient samples (n=24) or * (n=12). S.D = standard deviation. HOXA6 1.2 x 103 – 1.7 x 107 2.2 1.6 1 HOXB3 9.3 x 101 – 8.4 x 106 3.2 2.5 2 HOXB2* 7.9 x 102 – 5.4 x 106 3.4 2.0 3 HOXA9 4.0 x 101 – 5.3 x 106 5.3 2.4 4 MEIS1 0.6 x 101 – 8.4 x 106 5.4 2.7 5 HOXA10* 2.4 x 102 – 1.7 x 105 5.5 3.2 6 HOXB4 1.5 x 102 – 7.8 x 105 5.5 3.2 7 HOXA7* 5.3 x 103 – 1.8 x 106 5.7 1.7 8 HOXB6 2.3 x 101 – 8.8 x 105 6.6 2.8 9 HOXA4 4.1 x 101 – 1.1 x 105 7.9 3.4 10 HOXA5* 3.4 x 101 – 4.3 x 104 9.3 2.8 11 HOXC6 1.0 x 101 – 3.2 x 103 9.7 2.3 12 HOXA11* 4.0 x 101 – 6.1 x 103 10.6 2.2 13


2010 ◽  
Vol 128 (1) ◽  
pp. 28-34 ◽  
Author(s):  
N.V.L. Serão ◽  
R. Veroneze ◽  
A.M.F. Ribeiro ◽  
L.L. Verardo ◽  
J. Braccini Neto ◽  
...  

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