AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs).

The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

A comprehensive assessment of cell type-specific differential expression methods in bulk data.

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer.

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

Gene Expression Profiles of AHNAK2, DCSTAMP, FN1, and TERT Correlate With Mutational Status and Recurrence in Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC), the most common malignancy of follicular cell derivation, is generally associated with good prognosis. Nevertheless, it is important to identify patients with aggressive PTCs and unfavorable outcome. Molecular markers such as BRAFV600E mutation and TERT promoter mutations have been proposed for risk stratification. While TERT promoter mutations have been frequently associated with aggressive PTCs, the association of BRAFV600E mutation with increased recurrence and mortality is less clear and has been controversially discussed. The aim of the present study was to analyze whether differentially expressed genes can predict BRAFV600E mutations as well as TERT promoter mutations in PTCs. RNA sequencing identified a large number of differentially expressed genes between BRAFV600E and BRAFwildtype PTCs. Of those, AHNAK2, DCSTAMP, and FN1 could be confirmed in a larger cohort (n = 91) to be significantly upregulated in BRAFV600E mutant PTCs using quantitative RT-PCR. Moreover, individual PTC expression values of DCSTAMP and FN1 were able to predict the BRAFV600E mutation status with high sensitivity and specificity. The expression of TERT was detected in all PTCs harboring TERT promoter mutations and in 19% of PTCs without TERT promoter mutations. Tumors with both TERT expression and TERT promoter mutations were particularly associated with aggressive clinicopathological features and a shorter recurrence-free survival. Altogether, it will be interesting to explore the biological function of AHNAK2, DCSTAMP, and FN1 in PTC in more detail. The analysis of their expression patterns could allow the characterization of PTC subtypes and thus enabling a more individualized surgical and medical treatment.

Transcriptomic evidence of immune modulation in subjects with chronic Trypanosoma cruzi infection.

Chagas disease is a neglected tropical infection that affects millions of people. This study explores transcriptomic changes in T. cruzi-infected subjects before and after treatment. Using total RNA sequencing, gene transcription was analyzed in peripheral blood mononuclear cells from asymptomatic (n=19) and symptomatic (n=8) T. cruzi-infected individuals, and non-infected controls (n=15). Differential expression was compared across groups, and before/after treatment in infected subgroups. Untreated infection showed 12 upregulated and 206 downregulated genes in all T. cruzi-infected subjects, and 47 upregulated and 215 downregulated genes in the symptomatic group. Few differentially expressed genes were found after treatment and between the different infected groups. Gene set enrichment analysis highlighted immune-related pathways activated during infection, with therapy normalizing immune function. Changes in the kynurenine/tryptophan ratio, increased pre-treatment, suggested chronic immune fatigue, which was restored post-treatment. These differentially expressed genes offer insights for potential biomarkers and pathways associated with disease progression and treatment response.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.

BACKGROUND: Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. RESULTS: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . CONCLUSIONS: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Identification and validation of basement membrane-associated gene AGRN as prognostic and immune-associated biomarkers in colorectal cancer patients.

Colorectal cancer (COCA) has a poor prognosis, with growing evidence implicating basement membranes (BMs) in cancer progression. Our goal was to investigate the role and predictive significance of BMs in COCA patients. We obtained BMs-related genes from cutting-edge research and used TCGA and GTEx databases for mRNA expression and patient information. Cox regression and LASSO regression were used for prognostic gene selection and risk model construction. We compared prognosis using Kaplan-Meier analysis and examined drug sensitivity differences. The CMAP dataset identified potential small molecule drugs. In vitro tests involved suppressing a crucial gene to observe its impact on tumour metastasis. We developed a 12 BMs-based approach, finding it to be an independent prognostic factor. Functional analysis showed BMs concentrated in cancer-associated pathways, correlating with immune cell infiltration and immune checkpoint activation. High-risk individuals exhibited increased drug sensitivity. AGRN levels were linked to decreased progression-free survival (p < 0.001). AGRN knockdown suppressed tumour growth and metastasis. Our study offers new perspectives on BMs in COCA, concluding that AGRN is a dependable biomarker for patient survival and prognosis.

Identification and validation of N7 -methylguanosine-associated gene NCBP1 as prognostic and immune-associated biomarkers in breast cancer patients.

We intend to evaluate the importance of N7 -methylguanosine (m7G) for the prognosis of breast cancer (BC). We gained 29 m7G-related genes from the published literature and among them, 16 m7G-related genes were found to have differential expression. Five differentially expressed genes (CYFIP1, EIF4E, EIF4E3, NCBP1 and WDR4) were linked to overall survival. This suggests that m7G-related genes might be prognostic or therapeutic targets for BC patients. We put the five genes to LASSO regression analysis to create a four-gene signature, including EIF4E, EIF4E3, WDR4 and NCBP1, that divides samples into two risky groups. Survival was drastically worsened in a high-risk group (p < 0.001). The signature's predictive capacity was demonstrated using ROC (10-year AUC 0.689; 10-year AUC 0.615; 3-year AUC 0.602). We found that immune status was significantly different between the two risk groups. In particular, NCBP1 also has a poor prognosis, with higher diagnostic value in ROC. NCBP1 also has different immune states according to its high or low expression. Meanwhile, knockdown of NCBP1 suppresses BC malignancy in vitro. Therefore, m7G RNA regulators are crucial participants in BC and four-gene mRNA levels are important predictors of prognosis. NCBP1 plays a critical target of m7G mechanism in BC.

Identification of hub genes and pathways associated with cellular senescence in diabetic foot ulcers via comprehensive transcriptome analysis.

This research aimed to find important genes and pathways related to cellular senescence (CS) in diabetic foot ulcers (DFU) and to estimate the possible pathways through which CS affects diabetic foot healing. The GSE80178 dataset was acquired from the Gene Expression Omnibus (GEO) database, containing six DFU and three diabetic foot skin (DFS) samples. The limma package was used to identify differentially expressed genes (DEGs). At the same time, DEGs associated with CS (CS-DEGs) were found using the CellAge database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the CS-DEGs. A protein-protein interaction (PPI) network was built using the String database, and the cytoHubba plug-in within Cytoscape helped identify hub genes. Lastly, the miRNA-TF-mRNA regulatory network for these hub genes was established. In total, 66 CS-DEGs were obtained. These genes mainly focus on CS, Kaposi sarcoma-associated herpesvirus infection and Toll-like receptor signalling pathway. Eight hub genes were identified to regulate cell senescence in DFU, including TP53, SRC, SIRT1, CCND1, EZH2, CXCL8, AR and CDK4. According to miRNA-TF-mRNA regulatory network, hsa-mir-132-3p/SIRT1/EZH2 axis is involved in senescence cell accumulation in DFU.

Ethanol extract of Vanilla planifolia stems reduces PAK6 expression and induces cell death in glioblastoma cells.

Glioblastoma multiforme (GBM) is a malignant tumour with a poor prognosis. Therefore, potential treatment strategies and novel therapeutic targets have gained increased attention. Our data showed that the ethanol extract of Vanilla planifolia stem (VAS) significantly decreased the viability and the colony formation of GBM cells. Moreover, VAS induced the cleavage of MAP1LC3, a marker of autophagy. Further RNA-seq and bioinformatic analysis revealed 4248 differentially expressed genes (DEGs) between VAS-treated GBM cells and the control cells. Protein-protein interactions between DEGs with fold changes less than -3 and more than 5 were further analysed, and we found that 16 and 9 hub DEGs, respectively, were correlated with other DEGs. Further qPCR experiments confirmed that 14 hub DEGs was significantly downregulated and 9 hub DEGs was significantly upregulated. In addition, another significantly downregulated DEG, p21-activated kinase 6 (PAK6), was correlated with the overall survival of GBM patients. Further validation experiments confirmed that VAS significantly reduced the mRNA and protein expression of PAK6, which led to the abolition of cell viability and colony formation. These findings demonstrated that VAS reduced cell viability, suppressed colony formation and induced autophagy and revealed PAK6 and other DEGs as potential therapeutic targets for GBM treatment.

Contribution of FOS in neutrophils to venous thromboembolism via miR-144 based on bioinformatic prediction and validation.

The Finkel-Biskis-Jinkins Osteosarcoma (c-Fos; encoded by FOS) plays an important role in several cardiovascular diseases, including atherosclerosis and stroke. However, the relationship between FOS and venous thromboembolism (VTE) remains unknown. We identified differentially expressed genes in Gene Expression Omnibus dataset, GSE48000, comprising VTE patients and healthy individuals, and analysed them using CIBERSORT and weighted co-expression network analysis (WGCNA). FOS and CD46 expressions were significantly downregulated (FOS p = 2.26E-05, CD64 p = 8.83E-05) and strongly linked to neutrophil activity in VTE. We used GSE19151 and performed PCR to confirm that FOS and CD46 had diagnostic potential for VTE; however, only FOS showed differential expression by PCR and ELISA in whole blood samples. Moreover, we found that hsa-miR-144 which regulates FOS expression was significantly upregulated in VTE. Furthermore, FOS expression was significantly downregulated in neutrophils of VTE patients (p = 0.03). RNA sequencing performed on whole blood samples of VTE patients showed that FOS exerted its effects in VTE via the leptin-mediated adipokine signalling pathway. Our results suggest that FOS and related genes or proteins can outperform traditional clinical markers and may be used as diagnostic biomarkers for VTE.

Effect of supplementing lysins and methionine to low-protein diets on growth performance, hepatic antioxidant capacity, immune status, and glycolytic activity of tibetan sheep.

Reducing the levels of dietary protein is an effective nutritional approach in lowering feed cost and nitrogen emissions in ruminants. The purpose of this study was to evaluate the effects of dietary Lys/Met ratio in a low protein diet (10%, dry matter basis) on the growth performance and hepatic function (antioxidant capacity, immune status, and glycolytic activity) in Tibetan lambs. Ninety two-month-old rams with an average weight of 15.37 ± 0.92 kg were randomly assigned to LP-L (dietary Lys/Met = 1:1), LP-M (dietary Lys/Met = 2:1) and LP-H (dietary Lys/Met = 3:1) treatments. The trial was conducted over 100 d, including 10 d of adaption to the diets. Hepatic phenotypes, antioxidant capacity, immune status, glycolytic activity and gene expression profiling was detected after the conclusion of the feeding trials. The results showed that the body weight was higher in the LP-L group when compared to those on the LP-M group (P < 0.05). In addition, the activities of the catalase (CAT) and glutathione peroxidase (GSH-Px) in the LP-L group were significantly increased compared with the LP-M group (P < 0.05), while the malondialdehyde (MDA) levels in LP-H group were significantly decreased (P < 0.05). Compared with LP-H group, both hepatic glycogen (P < 0.01) and lactate dehydrogenase (LDH) (P < 0.05) were significantly elevated in LP-L group. For the LP-L group, the hepatocytes were arranged radially with the central vein in the center, and hepatic plates exhibited tight arrangement. Transcriptome analysis identified 29, 179, and 129 differentially expressed genes (DEGs) between the LP-M vs. LP-L, LP-H vs. LP-M, and LP-H vs. LP-L groups, respectively (Q-values < 0.05 and |log2Fold Change| > 1). Gene Ontology (GO) and correlation analyses showed that in the LP-L group, core genes (C1QA and JUNB) enriched in oxidoreductase activity were positively correlated with antioxidant indicators, while the MYO9A core gene enriched in the immune response was positively associated with immune indicators, and core genes enriched in molecular function (PDK3 and PDP2) were positively correlated with glycolysis indicators. In summary, low-protein diet with a low Lys/Met ratio (1:1) could reduce the hepatic oxidative stress and improve the glycolytic activity by regulating the expression of related genes of Tibetan sheep.

Comparative transcriptomic profiles of Paulownia catalpifolia under different degrees of chilling stress during the seedling stage.

BACKGROUND: Paulownia, an ecologically and economically valuable plant species native to China, is notable for its excellent timber quality and strong adaptability. Among them, Paulownia catalpifolia displays the ability to survive in cold climate, a trait associated with northern China. Yet, the molecular information for its cold-tolerance has not been explored. This study was to investigate the changes in physiological indices and transcript levels of P. catalpifolia following cold exposure, which could provide evidence for revealing whether there were differences in the genetic basis of inducing physiological perturbations between moderate low temperature (MLT) and extreme low temperature (ELT). RESULTS: The detection of physiological indices under diverse degrees of chilling stress showed similar patterns of alteration. Enhanced accumulation of osmoregulatory substances, such as soluble sugar and soluble protein, were more conducive under ELT compared to MLT in P. catalpifolia. Moreover, we observed leaf wilting symptoms distinctly after exposure to ELT for 48 h, while this effect was not obvious after MLT exposure for 48 h. Comparative transcriptomic analysis between MLT and ELT demonstrated 13,688 differentially expressed genes (DEGs), most of them appeared after 12 h and 48 h of treatment. GO and KEGG analyses elucidated prominent enrichment in aromatic-L-amino-acid decarboxylase activity term and carbohydrate metabolism pathways. Therefore, it was speculated that the DEGs involved in the above processes might be related to the difference in the contents of soluble protein and soluble sugar between MLT and ELT. Time series clustering analyses further highlighted several key genes engaged in the 'Glycosyltransferases', 'Galactose metabolism' and 'Starch and sucrose metabolism' pathways as well as the 'tyrosine decarboxylase activity' term. For instance, cellulose synthase-like A (CLSA2/9), raffinose synthase (RafS2), ß-amylase (BAM1) and tyrosine/DOPA decarboxylase (TYDC1/2/5) genes, diverging in their expression trends between MLT and ELT, might significantly affect the soluble sugar and soluble protein abundance within P. catalpifolia. CONCLUSION: Between MLT and ELT treatments, partial overlaps in response pathways of P. catalpifolia were identified, while several genes regulating the accumulation of osmotic adjustment substances had disparate expression patterns. These findings could provide a novel physiological and molecular perspective for P. catalpifolia to adapt to complex low temperature habitats.

Apelin-13 modulates the endometrial transcriptome of the domestic pig during implantation.

BACKGROUND: The peri-implantation period is a critical time during pregnancy that mostly defines the overall litter size. Most authors agree that the highest percentage of embryo mortality occurs during this time. Despite the brevity of the peri-implantation period, it is the most dynamic part of pregnancy in which the sequential and uninterrupted course of several processes is essential to the animal's reproductive success. Also then, the maternal uterine tissues undergo an intensive remodelling process, and their energy demand dramatically increases. It is believed that apelin, a member of the adipokine family, is involved in the control of female reproductive functions in response to the current metabolic state. The verified herein hypothesis assumed the modulatory effect of apelin on the endometrial tissue transcriptome on days 15 to 16 of gestation (beginning of implantation). RESULTS: The analysis of data obtained during RNA-seq (Illumina HiSeq2500) of endometrial slices treated and untreated with apelin (n = 4 per group) revealed changes in the expression of 68 genes (39 up-regulated and 29 down-regulated in the presence of apelin), assigned to 240 gene ontology terms. We also revealed changes in the frequency of alternative splicing events (397 cases), as well as single nucleotide variants (1,818 cases) in the presence of the adipokine. The identified genes were associated, among others, with the composition of the extracellular matrix, apoptosis, and angiogenesis. CONCLUSIONS: The obtained results indicate a potential role of apelin in the regulation of uterine tissue remodelling during the peri-implantation period.

Functional annotation and meta-analysis of maize transcriptomes reveal genes involved in biotic and abiotic stress.

BACKGROUND: Environmental stress factors, such as biotic and abiotic stress, are becoming more common due to climate variability, significantly affecting global maize yield. Transcriptome profiling studies provide insights into the molecular mechanisms underlying stress response in maize, though the functions of many genes are still unknown. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with an emphasis on identifying genes and traits related to biotic and abiotic stress. RESULTS: We mapped high-quality RNA-Seq expression reads from 24 different publicly available datasets (17 abiotic and seven biotic studies) generated from the B73 cultivar to the recent version of the reference genome B73 (B73v5) and deduced stress-related functional annotation of maize gene models. We conducted a robust meta-analysis of the transcriptome profiles from the datasets to identify maize loci responsive to stress, identifying 3,230 differentially expressed genes (DEGs): 2,555 DEGs regulated in response to abiotic stress, 408 DEGs regulated during biotic stress, and 267 common DEGs (co-DEGs) that overlap between abiotic and biotic stress. We discovered hub genes from network analyses, and among the hub genes of the co-DEGs we identified a putative NAC domain transcription factor superfamily protein (Zm00001eb369060) IDP275, which previously responded to herbivory and drought stress. IDP275 was up-regulated in our analysis in response to eight different abiotic and four different biotic stresses. A gene set enrichment and pathway analysis of hub genes of the co-DEGs revealed hormone-mediated signaling processes and phenylpropanoid biosynthesis pathways, respectively. Using phylostratigraphic analysis, we also demonstrated how abiotic and biotic stress genes differentially evolve to adapt to changing environments. CONCLUSIONS: These results will help facilitate the functional annotation of multiple stress response gene models and annotation in maize. Data can be accessed and downloaded at the Maize Genetics and Genomics Database (MaizeGDB).

Comparative physiological and transcriptome analysis between potassium-deficiency tolerant and sensitive sweetpotato genotypes in response to potassium-deficiency stress.

BACKGROUND: Sweetpotato is a typical ''potassium (K+) favoring'' food crop, which root differentiation process needs a large supply of potassium fertilizer and determine the final root yield. To further understand the regulatory network of the response to low potassium stress, here we analyze physiological and biochemical characteristics, and investigated root transcriptional changes in two sweetpotato genotypes, namely, - K tolerant "Xu32" and - K susceptible"NZ1". RESULT: We found Xu32 had the higher capability of K+ absorption than NZ1 with better growth performance, higher net photosynthetic rate and higher chlorophyll contents under low potassium stress, and identified 889 differentially expressed genes (DEGs) in Xu32, 634 DEGs in NZ1, 256 common DEGs in both Xu32 and NZ1. The Gene Ontology (GO) term in molecular function enrichment analysis revealed that the DEGs under low K+ stress are predominately involved in catalytic activity, binding, transporter activity and antioxidant activity. Moreover, the more numbers of identified DEGs in Xu32 than that in NZ1 responded to K+-deficiency belong to the process of photosynthesis, carbohydrate metabolism, ion transport, hormone signaling, stress-related and antioxidant system may result in different ability to K+-deficiency tolerance. The unique genes in Xu32 may make a great contribution to enhance low K+ tolerance, and provide useful information for the molecular regulation mechanism of K+-deficiency tolerance in sweetpotato. CONCLUSIONS: The common and distinct expression pattern between the two sweetpotato genotypes illuminate a complex mechanism response to low potassium exist in sweetpotato. The study provides some candidate genes, which can be used in sweetpotato breeding program for improving low potassium stress tolerance.

Transcriptome analysis of long non-coding RNA and mRNA Profiles in VSV-infected BHK-21 Cells.

BACKGROUND: Vesicular stomatitis virus (VSV) is a typical non-segmented negative-sense RNA virus of the genus Vesiculovirus in the family Rhabdoviridae. VSV can infect a wide range of animals, including humans, with oral blister epithelial lesions. VSV is an excellent model virus with a wide range of applications as a molecular tool, a vaccine vector, and an oncolytic vector. To further understand the interaction between VSV and host cells and to provide a theoretical basis for the application prospects of VSV, we analyzed the expression of host differentially expressed genes (DEGs) during VSV infection using RNA-Seq. RESULTS: Our analyses found a total of 1015 differentially expressed mRNAs and 161 differentially expressed LncRNAs in BHK-21 cells infected with VSV for 24 h compared with controls. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment showed that the differentially expressed lncRNAs and their target genes were mainly concentrated in pathways related to apoptosis, cancer, disease, and immune system activation, including the TNF, P53, MAPK, and NF-kappaB signaling pathways. The differentially expressed lncRNA can modulate immune processes by regulating genes involved in these signaling transmissions. Ten randomly selected DEGs, namely, Il12rb2, F2, Masp2, Mcl1, FGF18, Ripk1, Fas, BMF, POLK, and JAG1, were validated using RT-qPCR. As predicted through RNA-Seq analysis, these DEGs underwent either up- or downregulation, suggesting that they may play key regulatory roles in the pathways mentioned previously. CONCLUSIONS: Our study showed that VSV infection alters the host metabolic network and activates immune-related pathways, such as MAPK and TNF. The above findings provide unique insights for further study of the mechanism of VSV-host interactions and, more importantly, provide a theoretical basis for VSV as an excellent vaccine carrier.

Comparative transcriptomic analysis primarily explores the molecular mechanism of compound eye formation in Neocaridina denticulata sinensis.

Compound eyes formation in decapod crustaceans occurs after the nauplius stage. However, the key genes and regulatory mechanisms of compound eye development during crustacean embryonic development have not yet been clarified. In this study, RNA-seq was used to investigate the gene expression profiles of Neocaridina denticulata sinensis from nauplius to zoea stage. Based on RNA-seq data analysis, the phototransduction and insect hormone biosynthesis pathways were enriched, and molting-related neuropeptides were highly expressed. There was strong cell proliferation in the embryo prior to compound eye development. The formation of the visual system and the hormonal regulation of hatching were the dominant biological events during compound eye development. The functional analysis of DEGs across all four developmental stages showed that cuticle formation, muscle growth and the establishment of immune system occurred from nauplius to zoea stage. Key genes related to eye development were discovered, including those involved in the determination and differentiation of the eye field, eye-color formation, and visual signal transduction. In conclusion, the results increase the understanding of the molecular mechanism of eye formation in crustacean embryonic stage.