Transcriptomic Analysis of the Rat Dorsal Root Ganglion After Fracture.

In fractures, pain signals are transmitted from the dorsal root ganglion (DRG) to the brain, and the DRG generates efferent signals to the injured bone to participate in the injury response. However, little is known about how this process occurs. We analyzed DRG transcriptome at 3, 7, 14, and 28 days after fracture. We identified the key pathways through KEGG and GO enrichment analysis. We then used IPA analysis to obtain upstream regulators and disease pathways. Finally, we compared the sequencing results with those of nerve injury to identify the unique transcriptome changes in DRG after fracture. We found that the first 14 days after fracture were the main repair response period, the 3rd day was the peak of repair activity, the 14th day was dominated by the stimulus response, and on the 28th day, the repair response had reached a plateau. ECM-receptor interaction, protein digestion and absorption, and the PI3K-Akt signaling pathway were most significantly enriched, which may be involved in repair regeneration, injury response, and pain transmission. Compared with the nerve injury model, DRG after fracture produced specific alterations related to bone repair, and the bone density function was the most widely activated bone-related function. Our results obtained some important genes and pathways in DRG after fracture, and we also summarized the main features of transcriptome function at each time point through functional annotation clustering of GO pathway, which gave us a deeper understanding of the role played by DRG in fracture.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.

Background: To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment.

Transcriptome-wide association studies (TWAS) have identified several genes that are associated with qualitative traits. In this work, we performed TWAS using quantitative traits and predicted gene expressions in six brain subcortical structures in 286 mild cognitive impairment (MCI) samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The six brain subcortical structures were in the limbic region, basal ganglia region, and cerebellum region. We identified 9, 15, and 6 genes that were stably correlated longitudinally with quantitative traits in these three regions, of which 3, 8, and 6 genes have not been reported in previous Alzheimer's disease (AD) or MCI studies. These genes are potential drug targets for the treatment of early-stage AD. Single-Nucleotide Polymorphism (SNP) analysis results indicated that cis-expression Quantitative Trait Loci (cis-eQTL) SNPs with gene expression predictive abilities may affect the expression of their corresponding genes by specific binding to transcription factors or by modulating promoter and enhancer activities. Further, baseline structure volumes and cis-eQTL SNPs from correlated genes in each region were used to predict the conversion risk of MCI patients. Our results showed that limbic volumes and cis-eQTL SNPs of correlated genes in the limbic region have effective predictive abilities.

Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype.

Alzheimer's disease (AD) is a neurodegenerative brain disease in the elderly. Identifying patients with mild cognitive impairment (MCI) who are more likely to progress to AD is a key step in AD prevention. Recent studies have shown that AD is a heterogeneous disease. In this study, we propose a subtyping-based prediction strategy to predict the conversion from MCI to AD in three years according to MCI patient subtypes. Structural magnetic resonance imaging (sMRI) data and multi-omics data, including genotype data and gene expression profiling derived from peripheral blood samples, from 125 MCI patients were used in the Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 dataset and from 98 MCI patients in the ADNI-GO/2 dataset. A variational Bayes approximation model based on the multiple kernel learning method was constructed to predict whether an MCI patient will progress to AD within three years. In internal fivefold cross-validation within ADNI-1, we achieved an overall AUC of 0.83 (79.20% accuracy, 81.25% sensitivity, 77.92% specificity) compared to the model without subtyping, which achieved an AUC of 0.78 (76.00% accuracy, 77.08% sensitivity, 75.32% specificity). In external validation using ADNI-1 as a training set and ADNI-GO/2 as an independent test set, we attained an AUC of 0.78 (74.49% accuracy, 74.19% sensitivity, 74.63% specificity). Identifying MCI patient subtypes with omics data would improve the accuracy of predicting the conversion from MCI to AD. In addition to evaluating statistics, obtaining the significant sMRI, single nucleotide polymorphism (SNP) and mRNA expression data from peripheral blood of MCI patients is noninvasive and cost-effective for predicting conversion from MCI to AD.

Classification of Mild Cognitive Impairment With Multimodal Data Using Both Labeled and Unlabeled Samples.

Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.

Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease.

The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.

Constitutive hyperproduction of sorbicillinoids in Trichoderma reesei ZC121.

BACKGROUND: In addition to its outstanding cellulase production ability, Trichoderma reesei produces a wide variety of valuable secondary metabolites, the production of which has not received much attention to date. Among them, sorbicillinoids, a large group of hexaketide secondary metabolites derived from polyketides, are drawing a growing interest from researchers because they exhibit a variety of important biological functions, including anticancer, antioxidant, antiviral, and antimicrobial properties. The development of fungi strains with constitutive, hyperproduction of sorbicillinoids is thus desired for future industry application but is not well-studied. Moreover, although T. reesei has been demonstrated to produce sorbicillinoids with the corresponding gene cluster and biosynthesis pathway proposed, the underlying molecular mechanism governing sorbicillinoid biosynthesis remains unknown. RESULTS: Recombinant T. reesei ZC121 was constructed from strain RUT-C30 by the insertion of the gene 12121-knockout cassette at the telomere of T. reesei chromosome IV in consideration of the off-target mutagenesis encountered during the unsuccessful deletion of gene 121121. Strain ZC121, when grown on cellulose, showed a sharp reduction of cellulase production, but yet a remarkable enhancement of sorbicillinoids production as compared to strain RUT-C30. The hyperproduction of sorbicillinoids is a constitutive process, independent of culture conditions such as carbon source, light, pH, and temperature. To the best of our knowledge, strain ZC121 displays record sorbicillinoid production levels when grown on both glucose and cellulose. Sorbicillinol and bisvertinolone are the two major sorbicillinoid compounds produced. ZC121 displayed a different morphology and markedly reduced sporulation compared to RUT-C30 but had a similar growth rate and biomass. Transcriptome analysis showed that most genes involved in cellulase production were downregulated significantly in ZC121 grown on cellulose, whereas remarkably all genes in the sorbicillinoid gene cluster were upregulated on both cellulose and glucose. CONCLUSION: A constitutive sorbicillinoid-hyperproduction strain T. reesei ZC121 was obtained by off-target mutagenesis, displaying an overwhelming shift from cellulase production to sorbicillinoid production on cellulose, leading to a record for sorbicillinoid production. For the first time, T. reesei degraded cellulose to produce platform chemical compounds other than protein in high yield. We propose that the off-target mutagenesis occurring at the telomere region might cause chromosome remodeling and subsequently alter the cell structure and the global gene expression pattern of strain ZC121, as shown by phenotype profiling and comparative transcriptome analysis of ZC121. Overall, T. reesei ZC121 holds great promise for the industrial production of sorbicillinoids and serves as a good model to explore the regulation mechanism of sorbicillinoids' biosynthesis.

Transcriptome analysis of Cd-treated switchgrass root revealed novel transcripts and the importance of HSF/HSP network in switchgrass Cd tolerance.

KEY MESSAGE: Transcriptome analysis of Cd-treated switchgrass roots not only revealed novel switchgrass transcripts and gene structures but also highlighted the indispensable role of HSF/HSP network in switchgrass Cd tolerance. Switchgrass (Panicum virgatum L.), a C4 perennial tall grass, can be used for revegetation of Cd-contaminated soil. In the present study, a comparative transcriptome analysis of Cd-treated switchgrass roots was conducted. The result revealed a total of 462 novel transcripts and refined gene structures of 2337 transcripts. KEGG pathway and Gene Ontology analyses of the differentially expressed genes (DEGs) suggested that activation of redox homeostasis and oxidation-related metabolic processes were the primary response to Cd stress in switchgrass roots. In particular, 21 out of 23 differentially expressed shock transcription factor genes (HSFs), and 22 out of 23 differentially expressed heat shock protein genes (HSPs) had increased expression levels after Cd treatment. Furthermore, over-expressing one HSP-encoding gene in Arabidopsis significantly improved plant Cd tolerance. The result highlighted the activation of the redox homeostasis and the involvement of the HSF/HSP network in re-establishing normal protein conformation and thus cellular homeostasis in switchgrass upon Cd stress. These DEGs, especially those of the HSF/HSP network, could be used as candidate genes for further functional studies toward improved plant Cd tolerance in switchgrass and related species.

Comprehensive analysis of CCCH-type zinc finger family genes facilitates functional gene discovery and reflects recent allopolyploidization event in tetraploid switchgrass.

BACKGROUND: In recent years, dozens of Arabidopsis and rice CCCH-type zinc finger genes have been functionally studied, many of which confer important traits, such as abiotic and biotic stress tolerance, delayed leaf senescence and improved plant architecture. Switchgrass (Panicum virgatum) is an important bioenergy crop. Identification of agronomically important genes and/or loci is an important step for switchgrass molecular breeding. Annotating switchgrass CCCH genes using translational genomics methods will help further the goal of understanding switchgrass genetics and creating improved varieties. RESULTS: Taking advantage of the publicly-available switchgrass genomic and transcriptomic databases, we carried out a comprehensive analysis of switchgrass CCCH genes (PvC3Hs). A total of 103 PvC3Hs were identified and divided into 21 clades according to phylogenetic analysis. Genes in the same clade shared similar gene structure and conserved motifs. Chromosomal location analysis showed that most of the duplicated PvC3H gene pairs are in homeologous chromosomes. Evolution analysis of 19 selected PvC3H pairs showed that 42.1% of them were under diversifying selection. Expression atlas of the 103 PvC3Hs in 21 different organs, tissues and developmental stages revealed genes with higher expression levels in lignified cells, vascular cells, or reproductive tissues/organs, suggesting the potential function of these genes in development. We also found that eight PvC3Hs in Clade-XIV were orthologous to ABA- or stress- responsive CCCH genes in Arabidopsis and rice with functions annotated. Promoter and qRT-PCR analyses of Clade-XIV PvC3Hs showed that these eight genes were all responsive to ABA and various stresses. CONCLUSIONS: Genome-wide analysis of PvC3Hs confirmed the recent allopolyploidization event of tetraploid switchgrass from two closely-related diploid progenitors. The short time window after the polyploidization event allowed the existence of a large number of PvC3H genes with a high positive selection pressure onto them. The homeologous pairs of PvC3Hs may contribute to the heterosis of switchgrass and its wide adaptation in different ecological niches. Phylogenetic and gene expression analyses provide informative clues for discovering PvC3H genes in some functional categories. Particularly, eight PvC3Hs in Clade-XIV were found involved in stress responses. This information provides a foundation for functional studies of these genes in the future.