Environmental analyses and candidate gene expression of ms9 male-sterility locus and computational identification of candidate genes for male-sterility loci in soybean

Plum pox virus (PPV), the causing agent of the sharka disease, belongs to the genus Potyvirus that contains the largest number of virus species infecting plants. The virus genome has been extensively characterised and sequenced. However, few data are available on its interactions with the host plant, Prunus. In this study, we are focusing on the cloning and characterisation of any candidate genes involved in the expression of the resistance/susceptibility trait and any polymorphic genes putatively involved in the trait variation. In order to clone candidate genes, two main approaches are currently developed: the homology cloning of genes presumed to affect the resistance/susceptibility trait and the differential screening of cDNA pools corresponding to infected and non-infected plant material. The second approach is based on the transcript imaging of the host plant response to PPV infection. Previously, it has been shown that infection by a potyvirus is associated with specific changes in host gene expression, mainly down-regulation, while the expression of some genes remained unchanged. Thereby, in the differential display approach combined to further characterisation of candidate gene expression, we aim to monitor host gene expression in response to the virus and to describe a highly regulated interaction between the Prunus host plant and the infecting Plum pox virus.

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Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Breast Cancer Research and Treatment ◽

10.1007/s10549-008-0038-x ◽

2008 ◽

Vol 113 (2) ◽

pp. 251-252 ◽

Cited By ~ 3

Author(s):

Mads Thomassen ◽

Qihua Tan ◽

Torben A. Kruse

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Candidate Genes ◽

Cancer Metastasis ◽

Meta Analysis ◽

Breast Cancer Metastasis

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Gene expression profiling reveals candidate genes for defining spider silk gland types

Insect Biochemistry and Molecular Biology ◽

10.1016/j.ibmb.2021.103594 ◽

2021 ◽

pp. 103594

Author(s):

R. Crystal Chaw ◽

Thomas H. Clarke ◽

Peter Arensburger ◽

Nadia A. Ayoub ◽

Cheryl Y. Hayashi

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Candidate Genes ◽

Expression Profiling ◽

Spider Silk ◽

Silk Gland

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Increased Expression of VANGL1 is Predictive of Lymph Node Metastasis in Colorectal Cancer: Results from a 20-Gene Expression Signature

Journal of Personalized Medicine ◽

10.3390/jpm11020126 ◽

2021 ◽

Vol 11 (2) ◽

pp. 126

Author(s):

Noshad Peyravian ◽

Stefania Nobili ◽

Zahra Pezeshkian ◽

Meysam Olfatifar ◽

Afshin Moradi ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Candidate Genes ◽

Case Series ◽

Gene Expression Signature ◽

Gene Panel ◽

Expression Levels ◽

Node Metastasis

This study aimed at building a prognostic signature based on a candidate gene panel whose expression may be associated with lymph node metastasis (LNM), thus potentially able to predict colorectal cancer (CRC) progression and patient survival. The mRNA expression levels of 20 candidate genes were evaluated by RT-qPCR in cancer and normal mucosa formalin-fixed paraffin-embedded (FFPE) tissues of CRC patients. Receiver operating characteristic curves were used to evaluate the prognosis performance of our model by calculating the area under the curve (AUC) values corresponding to stage and metastasis. A total of 100 FFPE primary tumor tissues from stage I–IV CRC patients were collected and analyzed. Among the 20 candidate genes we studied, only the expression levels of VANGL1 significantly varied between patients with and without LNMs (p = 0.02). Additionally, the AUC value of the 20-gene panel was found to have the highest predictive performance (i.e., AUC = 79.84%) for LNMs compared with that of two subpanels including 5 and 10 genes. According to our results, VANGL1 gene expression levels are able to estimate LNMs in different stages of CRC. After a proper validation in a wider case series, the evaluation of VANGL1 gene expression and that of the 20-gene panel signature could help in the future in the prediction of CRC progression.

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Genome-Wide Association Study (GWAS) to Identify Salt-Tolerance QTLs Carrying Novel Candidate Genes in Rice During Early Vegetative Stage

Rice ◽

10.1186/s12284-020-00433-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Leila Nayyeripasand ◽

Ghasem Ali Garoosi ◽

Asadollah Ahmadikhah

Keyword(s):

Association Study ◽

Candidate Gene ◽

Candidate Genes ◽

Salinity Tolerance ◽

Genome Wide Association Study ◽

Dry Weight ◽

Genome Wide Association ◽

Vegetative Stage ◽

Genome Wide ◽

Genomic Regions

Abstract Background Rice is considered as a salt-sensitive plant, particularly at early vegetative stage, and its production is suffered from salinity due to expansion of salt affected land in areas under cultivation. Hence, significant increase of rice productivity on salinized lands is really necessary. Today genome-wide association study (GWAS) is a method of choice for fine mapping of QTLs involved in plant responses to abiotic stresses including salinity stress at early vegetative stage. In this study using > 33,000 SNP markers we identified rice genomic regions associated to early stage salinity tolerance. Eight salinity-related traits including shoot length (SL), root length (RL), root dry weight (RDW), root fresh weight (RFW), shoot fresh weight (SFW), shoot dry weight (SDW), relative water content (RWC) and TW, and 4 derived traits including SL-R, RL-R, RDW-R and RFW-R in a diverse panel of rice were evaluated under salinity (100 mM NaCl) and normal conditions in growth chamber. Genome-wide association study (GWAS) was applied based on MLM(+Q + K) model. Results Under stress conditions 151 trait-marker associations were identified that were scattered on 10 chromosomes of rice that arranged in 29 genomic regions. A genomic region on chromosome 1 (11.26 Mbp) was identified which co-located with a known QTL region SalTol1 for salinity tolerance at vegetative stage. A candidate gene (Os01g0304100) was identified in this region which encodes a cation chloride cotransporter. Furthermore, on this chromosome two other candidate genes, Os01g0624700 (24.95 Mbp) and Os01g0812000 (34.51 Mbp), were identified that encode a WRKY transcription factor (WRKY 12) and a transcriptional activator of gibberellin-dependent alpha-amylase expression (GAMyb), respectively. Also, a narrow interval on the same chromosome (40.79–42.98 Mbp) carries 12 candidate genes, some of them were not so far reported for salinity tolerance at seedling stage. Two of more interesting genes are Os01g0966000 and Os01g0963000, encoding a plasma membrane (PM) H+-ATPase and a peroxidase BP1 protein. A candidate gene was identified on chromosome 2 (Os02g0730300 at 30.4 Mbp) encoding a high affinity K+ transporter (HAK). On chromosome 6 a DnaJ-encoding gene and pseudouridine synthase gene were identified. Two novel genes on chromosome 8 including the ABI/VP1 transcription factor and retinoblastoma-related protein (RBR), and 3 novel genes on chromosome 11 including a Lox, F-box and Na+/H+ antiporter, were also identified. Conclusion Known or novel candidate genes in this research were identified that can be used for improvement of salinity tolerance in molecular breeding programmes of rice. Further study and identification of effective genes on salinity tolerance by the use of candidate gene-association analysis can help to precisely uncover the mechanisms of salinity tolerance at molecular level. A time dependent relationship between salt tolerance and expression level of candidate genes could be recognized.

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Abstract PO006: Computational identification of female-biased breast cancer candidate genes correlated with tumor-infiltrating immune cell types

10.1158/2326-6074.tumimm20-po006 ◽

2021 ◽

Author(s):

Eric Li ◽

Yongsheng Bai

Keyword(s):

Breast Cancer ◽

Candidate Genes ◽

Immune Cell ◽

Cell Types ◽

Computational Identification

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FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab131.001 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Floranne Boulogne ◽

Laura Claus ◽

Henry Wiersma ◽

Roy Oelen ◽

Floor Schukking ◽

...

Keyword(s):

Gene Expression ◽

Kidney Disease ◽

Candidate Gene ◽

Exome Sequencing ◽

Rna Sequencing ◽

Expression Patterns ◽

Genetic Diagnosis ◽

Specific Gene ◽

Sequencing Data ◽

Exome Sequencing Data

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.

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