Discovery and validation of candidate smoltification gene expression biomarkers across multiple species and ecotypes of Pacific salmonids

AbstractEarly marine survival of juvenile salmon is intimately associated with their physiological condition during ocean entry and especially smoltification. Smoltification is a developmental parr–smolt transformation allowing salmon to acquire the trait of seawater tolerance in preparation for marine living. Traditionally, this developmental process has been monitored using gill Na+/K+ATPase (NKA) activity or plasma hormones, but gill gene expression can be reliably used. Here, we describe the discovery of candidate genes from gill tissue for staging smoltification using comparisons of microarray studies with particular focus on the commonalities between anadromous Rainbow trout and Sockeye salmon datasets, as well as literature comparison encompassing more species. A subset of 37 candidate genes mainly from the microarray analyses was used for Taq-Man qPCR assay design and their monthly expression patterns were validated using gill samples from four groups, representing three species and two ecotypes: Coho salmon, Sockeye salmon, stream-type Chinook salmon, and ocean-type Chinook salmon. The best smoltification biomarkers, as measured by consistent changes across these four groups, were genes involved in ion regulation, oxygen transport, and immunity. Smoltification gene expression patterns (using the top 10 biomarkers) were confirmed by significant correlations with NKA activity and were associated with changes in body brightness, caudal fin darkness, and caudal peduncle length. We incorporate gene expression patterns of pre-smolt, smolt, and de-smolt trials from acute seawater transfers using a companion study to develop a preliminary seawater tolerance classification model for ocean-type Chinook salmon. This work demonstrates the potential of gene expression biomarkers to stage smoltification and classify juveniles as pre-smolt, smolt, or de-smolt.

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Discovery and validation of candidate smoltification gene expression biomarkers across multiple species and ecotypes of Pacific salmonids

Conservation Physiology ◽

10.1093/conphys/coz051 ◽

2019 ◽

Vol 7 (1) ◽

Cited By ~ 8

Author(s):

Aimee Lee S Houde ◽

Oliver P Günther ◽

Jeffrey Strohm ◽

Tobi J Ming ◽

Shaorong Li ◽

...

Keyword(s):

Gene Expression ◽

Candidate Genes ◽

Chinook Salmon ◽

Sockeye Salmon ◽

Developmental Process ◽

Expression Patterns ◽

Classification Model ◽

Gene Expression Patterns ◽

Ion Regulation ◽

Seawater Tolerance

Abstract Early marine survival of juvenile salmon is intimately associated with their physiological condition during smoltification and ocean entry. Smoltification (parr–smolt transformation) is a developmental process that allows salmon to acquire seawater tolerance in preparation for marine living. Traditionally, this developmental process has been monitored using gill Na+/K+-ATPase (NKA) activity or plasma hormones, but gill gene expression offers the possibility of another method. Here, we describe the discovery of candidate genes from gill tissue for staging smoltification using comparisons of microarray studies with particular focus on the commonalities between anadromous Rainbow trout and Sockeye salmon datasets, as well as a literature comparison encompassing more species. A subset of 37 candidate genes mainly from the microarray analyses was used for TaqMan quantitative PCR assay design and their expression patterns were validated using gill samples from four groups, representing three species and two ecotypes: Coho salmon, Sockeye salmon, stream-type Chinook salmon and ocean-type Chinook salmon. The best smoltification biomarkers, as measured by consistent changes across these four groups, were genes involved in ion regulation, oxygen transport and immunity. Smoltification gene expression patterns (using the top 10 biomarkers) were confirmed by significant correlations with NKA activity and were associated with changes in body brightness, caudal fin darkness and caudal peduncle length. We incorporate gene expression patterns of pre-smolt, smolt and de-smolt trials from acute seawater transfers from a companion study to develop a preliminary seawater tolerance classification model for ocean-type Chinook salmon. This work demonstrates the potential of gene expression biomarkers to stage smoltification and classify juveniles as pre-smolt, smolt or de-smolt.

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Microarray analysis of gene expression patterns in the leaf during potato tuberization in the potato somatic hybrid Solanum tuberosum and Solanum etuberosum

Genome ◽

10.1139/gen-2014-0191 ◽

2015 ◽

Vol 58 (6) ◽

pp. 305-313 ◽

Cited By ~ 6

Author(s):

Jagesh Kumar Tiwari ◽

Sapna Devi ◽

S. Sundaresha ◽

Poonam Chandel ◽

Nilofer Ali ◽

...

Keyword(s):

Gene Expression ◽

Candidate Genes ◽

Microarray Analysis ◽

Somatic Hybrid ◽

Developmental Stages ◽

Expression Patterns ◽

Pcr Analysis ◽

Gene Expression Patterns ◽

Potato Tuberization ◽

Solanum Etuberosum

Genes involved in photoassimilate partitioning and changes in hormonal balance are important for potato tuberization. In the present study, we investigated gene expression patterns in the tuber-bearing potato somatic hybrid (E1-3) and control non-tuberous wild species Solanum etuberosum (Etb) by microarray. Plants were grown under controlled conditions and leaves were collected at eight tuber developmental stages for microarray analysis. A t-test analysis identified a total of 468 genes (94 up-regulated and 374 down-regulated) that were statistically significant (p ≤ 0.05) and differentially expressed in E1-3 and Etb. Gene Ontology (GO) characterization of the 468 genes revealed that 145 were annotated and 323 were of unknown function. Further, these 145 genes were grouped based on GO biological processes followed by molecular function and (or) PGSC description into 15 gene sets, namely (1) transport, (2) metabolic process, (3) biological process, (4) photosynthesis, (5) oxidation-reduction, (6) transcription, (7) translation, (8) binding, (9) protein phosphorylation, (10) protein folding, (11) ubiquitin-dependent protein catabolic process, (12) RNA processing, (13) negative regulation of protein, (14) methylation, and (15) mitosis. RT-PCR analysis of 10 selected highly significant genes (p ≤ 0.01) confirmed the microarray results. Overall, we show that candidate genes induced in leaves of E1-3 were implicated in tuberization processes such as transport, carbohydrate metabolism, phytohormones, and transcription/translation/binding functions. Hence, our results provide an insight into the candidate genes induced in leaf tissues during tuberization in E1-3.

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Pan-cancer classification by regularized multi-task learning

Scientific Reports ◽

10.1038/s41598-021-03554-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sk Md Mosaddek Hossain ◽

Lutfunnesa Khatun ◽

Sumanta Ray ◽

Anirban Mukhopadhyay

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Cancer Classification ◽

Functional Enrichment ◽

Classification Model ◽

Gene Expression Patterns ◽

Task Learning ◽

Cancer Types ◽

Pan Cancer ◽

Kernel Svm

AbstractClassifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across diversified diseases. In our present work, we have developed PC-RMTL, a pan-cancer classification model using regularized multi-task learning (RMTL) for classifying 21 cancer types and adjacent normal samples using RNASeq data obtained from TCGA. PC-RMTL is observed to outperform when compared with five state-of-the-art classification algorithms, viz. SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF), random forest (RF), k-nearest neighbours (kNN), and decision trees (DT). The PC-RMTL achieves 96.07% accuracy and 95.80% MCC score for a completely unknown independent test set. The only method that appears as the real competitor is SVM-Lin, which nearly equalizes the accuracy in prediction of PC-RMTL but only when complete feature sets are provided for training; otherwise, PC-RMTL outperformed all other classification models. To the best of our knowledge, this is a significant improvement over all the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably. We have also compared gene expression patterns of the top discriminating genes across the cancers and performed their functional enrichment analysis that uncovers several interesting facts in distinguishing pan-cancer samples.

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Transcriptional responses in developing lesions of European common ash (Fraxinus excelsior) reveal genes responding to infection by Hymenoscyphus fraxineus

BMC Plant Biology ◽

10.1186/s12870-020-02656-1 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Shadi Eshghi Sahraei ◽

Michelle Cleary ◽

Jan Stenlid ◽

Mikael Brandström Durling ◽

Malin Elfstrand

Keyword(s):

Gene Expression ◽

Candidate Genes ◽

Expression Patterns ◽

Fraxinus Excelsior ◽

Differentially Expressed ◽

Gene Expression Patterns ◽

Transcriptional Responses ◽

Ash Dieback ◽

Hymenoscyphus Fraxineus ◽

Resistant Genotypes

Abstract Background With the expanding ash dieback epidemic that has spread across the European continent, an improved functional understanding of the disease development in afflicted hosts is needed. The study investigated whether differences in necrosis extension between common ash (Fraxinus excelsior) trees with different levels of susceptibility to the fungus Hymenoscyphus fraxineus are associated with, and can be explained by, the differences in gene expression patterns. We inoculated seemingly healthy branches of each of two resistant and susceptible ash genotypes with H. fraxineus grown in a common garden. Results Ten months after the inoculation, the length of necrosis on the resistant genotypes were shorter than on the susceptible genotypes. RNA sequencing of bark samples collected at the border of necrotic lesions and from healthy tissues distal to the lesion revealed relatively limited differences in gene expression patterns between susceptible and resistant genotypes. At the necrosis front, only 138 transcripts were differentially expressed between the genotype categories while 1082 were differentially expressed in distal, non-symptomatic tissues. Among these differentially expressed genes, several genes in the mevalonate (MVA) and iridoid pathways were found to be co-regulated, possibly indicating increased fluxes through these pathways in response to H. fraxineus. Comparison of transcriptional responses of symptomatic and non-symptomatic ash in a controlled greenhouse experiment revealed a relatively small set of genes that were differentially and concordantly expressed in both studies. This gene-set included the rate-limiting enzyme in the MVA pathway and a number of transcription factors. Furthermore, several of the concordantly expressed candidate genes show significant similarity to genes encoding players in the abscisic acid- or Jasmonate-signalling pathways. Conclusions A set of candidate genes, concordantly expressed between field and greenhouse experiments, was identified. The candidates are associated with hormone signalling and specialized metabolite biosynthesis pathways indicating the involvement of these pathways in the response of the host to infection by H. fraxineus.

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Differential SPL gene expression patterns reveal candidate genes underlying flowering time and architectural differences in Mimulus and Arabidopsis

Molecular Phylogenetics and Evolution ◽

10.1016/j.ympev.2014.01.029 ◽

2014 ◽

Vol 73 ◽

pp. 129-139 ◽

Cited By ~ 11

Author(s):

Stacy A. Jorgensen ◽

Jill C. Preston

Keyword(s):

Gene Expression ◽

Flowering Time ◽

Candidate Genes ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Spl Gene

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A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates

10.1101/463547 ◽

2018 ◽

Cited By ~ 4

Author(s):

Ying Lin ◽

Anjali M. Rajadhyaksha ◽

James B. Potash ◽

Shizhong Han

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Human Brain ◽

Candidate Genes ◽

De Novo ◽

Expression Patterns ◽

Autism Spectrum ◽

Gene Expression Patterns ◽

Risk Genes ◽

Gene Level

AbstractAutism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role ofde novomutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be differentially expressed in ASD brains, especially in the frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example,TCF20andFBOX11), but also indicated potentially novel candidates, such asDOCK3,MYCBP2andCAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-the-art scoring systems for ranking ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

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Altered Gene Expression in CD34+ Cells from PBSC Grafts from Patients Developing t-MDS Following Autologous Transplantation for Lymphoma.

Blood ◽

10.1182/blood.v108.11.2326.2326 ◽

2006 ◽

Vol 108 (11) ◽

pp. 2326-2326

Author(s):

Liang Li ◽

Xiwei Wu ◽

Jerald P. Radich ◽

Lue Ping Zhao ◽

Liton Francisco ◽

...

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Hodgkin Lymphoma ◽

Error Rate ◽

Expression Patterns ◽

Differentially Expressed ◽

Classification Model ◽

Gene Expression Patterns ◽

Cell Transplant ◽

Cd34 Cells

Abstract Therapy-related myelodysplasia (t-MDS) is a lethal complication of autologous hematopoietic cell transplant (HCT) for Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). The sequence of molecular abnormalities leading to t-MDS is unknown. To gain insights in pathogenesis of t-MDS, we have initiated a prospective study of a cohort of patients undergoing autologous HCT for HL and NHL. Patients are followed longitudinally from before HCT to five years post-HCT, with serial collection of samples. Here we investigated the association of gene expression patterns in hematopoietic stem cells (HSC) from peripheral blood stem cell (PBSC) autografts with development of t-MDS after HCT. We analyzed samples from 11 patients who developed t-MDS and 33 controls (matched for primary diagnosis, age at HCT, race/ethnicity, and length of follow-up) from within the cohort who did not develop t-MDS. RNA from 1000 CD34+ cells selected by flow cytometry sorting was processed using the Affymetrix 2-Cycle Target labeling kit and hybridized on Affymetrix U133 Plus 2.0 microarrays. Following quality control assessment, 7 informative t-MDS samples and their 18 matched controls were selected for further assessment. Raw data were normalized using a GCRMA algorithm. Limma package with paired test was used to identify genes differentially expressed between t-MDS and control samples (P Value < = 0.01; and > 2-fold up or down-regulation). 148 differentially expressed transcripts representing 136 unique genes were identified. The log2 intensity of these transcripts was mean-centered within each group and two-way clustering applied. Samples clustered into 2 major groups, with one sub-cluster containing all t-MDS samples. Supervised classification analysis using DLDA (diagonal linear discriminant analysis) provided the best classification model when using all 148 transcripts. To select the best genes for classification, redundant transcripts were removed. Staring with the top 2 genes and incrementing one gene at a time, the top 8 genes best classified case vs. control using leave-one-out validation and DLDA, with an 8% error rate. To select the best 2 or 3 gene combinations, we tested the error rate using all such possible combinations within the top 20 genes. The three best 3-gene combinations (of 1140 possible combinations) accurately classified cases and controls with a 0% error rate. Functional assessment of differentially expressed genes identified overexpression of immediate-early "stress" response genes (e.g. NR4A1-3, EGR-1-4, WT-1) and reduced expression of xenobiotic processing (e.g. GSTM2, GSTT1, and CYP1B1) and DNA repair genes (e.g. XPA, ERCC4, FANCF) in samples from patients developing t-MDS. We also observed altered expression of genes playing important roles in regulating HSC growth and associated with leukemogenesis (e.g. HOXA5, A6, B6, MEIS1, MLL5, TCF4, HES1, MCL1, SMAD5, and SMAD7). In conclusion our results suggest that additional studies in a test population are warranted to examine whether gene expression patterns in PBSC samples can predict for subsequent development of t-MDS. Importantly, perturbation in genes determining response to genotoxic stress and regulating stem cell growth are observed in CD34+ cells from patients who subsequently develop t-MDS.

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