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Expanded transcriptomic view of strawberry fruit ripening through meta-analysis.
Yi, G, Shin, H, Min, K, Lee, EJ
PloS one. 2021;(6):e0252685
Abstract
Strawberry is an important fruit crop and a model for studying non-climacteric fruit ripening. Fruit ripening and senescence influence strawberry fruit quality and postharvest storability, and have been intensively studied. However, genetic and physiological differences among cultivars preclude consensus understanding of these processes. We therefore performed a meta-analysis by mapping existing transcriptome data to the newly published and improved strawberry reference genome and extracted meta-differentially expressed genes (meta-DEGs) from six cultivars to provide an expanded transcriptomic view of strawberry ripening. We identified cultivar-specific transcriptome changes in anthocyanin biosynthesis-related genes and common changes in cell wall degradation, chlorophyll degradation, and starch metabolism-related genes during ripening. We also identified 483 meta-DEGs enriched in gene ontology categories related to photosynthesis and amino acid and fatty acid biosynthesis that had not been revealed in previous studies. We conclude that meta-analysis of existing transcriptome studies can effectively address fundamental questions in plant sciences.
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A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.
Long, G, Yang, C
Molecular medicine reports. 2020;(3):1561-1571
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Abstract
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms of PSS using four microarray datasets (GSE26378, GSE26440, GSE13904 and GSE4607) that were obtained from the Gene Expression Omnibus database. Based on the MetaDE package, the consistently differentially expressed genes (DEGs) in the four datasets were screened. Using the WGCNA package, the disease‑associated modules and genes were identified. Subsequently, the optimal feature genes were further selected using the caret package. Finally, a support vector machine (SVM) classifier based on the optimal feature genes was built using the e1071 package. Initially, there were 2,699 consistent DEGs across the four datasets. From the 10 significantly stable modules across the datasets, four stable modules (including the magenta, purple, turquoise and yellow modules), in which the consistent DEGs were significantly enriched (P<0.05), were further screened. Subsequently, six optimal feature genes (including cysteine rich transmembrane module containing 1, S100 calcium binding protein A9, solute carrier family 2 member 14, stomatin, uridine phosphorylase 1 and utrophin) were selected from the genes in the four stable modules. Additionally, an effective SVM classifier was constructed based on the six optimal genes. The SVM classifier based on the six optimal genes has the potential to be applied for PSS diagnosis. This may improve the accuracy of early PSS diagnosis and suggest possible molecular targets for interventions.
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Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer's disease.
Li, X, Long, J, He, T, Belshaw, R, Scott, J
Scientific reports. 2015;:12393
Abstract
Previous studies have evaluated gene expression in Alzheimer's disease (AD) brains to identify mechanistic processes, but have been limited by the size of the datasets studied. Here we have implemented a novel meta-analysis approach to identify differentially expressed genes (DEGs) in published datasets comprising 450 late onset AD (LOAD) brains and 212 controls. We found 3124 DEGs, many of which were highly correlated with Braak stage and cerebral atrophy. Pathway Analysis revealed the most perturbed pathways to be (a) nitric oxide and reactive oxygen species in macrophages (NOROS), (b) NFkB and (c) mitochondrial dysfunction. NOROS was also up-regulated, and mitochondrial dysfunction down-regulated, in healthy ageing subjects. Upstream regulator analysis predicted the TLR4 ligands, STAT3 and NFKBIA, for activated pathways and RICTOR for mitochondrial genes. Protein-protein interaction network analysis emphasised the role of NFKB; identified a key interaction of CLU with complement; and linked TYROBP, TREM2 and DOK3 to modulation of LPS signalling through TLR4 and to phosphatidylinositol metabolism. We suggest that NEUROD6, ZCCHC17, PPEF1 and MANBAL are potentially implicated in LOAD, with predicted links to calcium signalling and protein mannosylation. Our study demonstrates a highly injurious combination of TLR4-mediated NFKB signalling, NOROS inflammatory pathway activation, and mitochondrial dysfunction in LOAD.
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A comparative genomic study in schizophrenic and in bipolar disorder patients, based on microarray expression profiling meta-analysis.
Logotheti, M, Papadodima, O, Venizelos, N, Chatziioannou, A, Kolisis, F
TheScientificWorldJournal. 2013;:685917
Abstract
Schizophrenia affecting almost 1% and bipolar disorder affecting almost 3%-5% of the global population constitute two severe mental disorders. The catecholaminergic and the serotonergic pathways have been proved to play an important role in the development of schizophrenia, bipolar disorder, and other related psychiatric disorders. The aim of the study was to perform and interpret the results of a comparative genomic profiling study in schizophrenic patients as well as in healthy controls and in patients with bipolar disorder and try to relate and integrate our results with an aberrant amino acid transport through cell membranes. In particular we have focused on genes and mechanisms involved in amino acid transport through cell membranes from whole genome expression profiling data. We performed bioinformatic analysis on raw data derived from four different published studies. In two studies postmortem samples from prefrontal cortices, derived from patients with bipolar disorder, schizophrenia, and control subjects, have been used. In another study we used samples from postmortem orbitofrontal cortex of bipolar subjects while the final study was performed based on raw data from a gene expression profiling dataset in the postmortem superior temporal cortex of schizophrenics. The data were downloaded from NCBI's GEO datasets.
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Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR.
Zhou, K, Zhou, L, Lim, Q', Zou, R, Stephanopoulos, G, Too, HP
BMC molecular biology. 2011;:18
Abstract
BACKGROUND Accurate interpretation of quantitative PCR (qPCR) data requires normalization using constitutively expressed reference genes. Ribosomal RNA is often used as a reference gene for transcriptional studies in E. coli. However, the choice of reliable reference genes has not been systematically validated. The objective of this study is to identify a set of reliable reference genes for transcription analysis in recombinant protein over-expression studies in E. coli. RESULTS In this study, the meta-analysis of 240 sets of single-channel Affymetrix microarray data representing over-expressions of 63 distinct recombinant proteins in various E. coli strains identified twenty candidate reference genes that were stably expressed across all conditions. The expression of these twenty genes and two commonly used reference genes, rrsA encoding ribosomal RNA 16S and ihfB, was quantified by qPCR in E. coli cells over-expressing four genes of the 1-Deoxy-D-Xylulose 5-Phosphate pathway. From these results, two independent statistical algorithms identified three novel reference genes cysG, hcaT, and idnT but not rrsA and ihfB as highly invariant in two E. coli strains, across different growth temperatures and induction conditions. Transcriptomic data normalized by the geometric average of these three genes demonstrated that genes of the lycopene synthetic pathway maintained steady expression upon enzyme overexpression. In contrast, the use of rrsA or ihfB as reference genes led to the mis-interpretation that lycopene pathway genes were regulated during enzyme over-expression. CONCLUSION This study identified cysG/hcaT/idnT to be reliable novel reference genes for transcription analysis in recombinant protein producing E. coli.