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Background: In children with type 1 diabetes (T1D), diabetic ketoacidosis (DKA) triggers a significant inflammatory response; however, the specific effector proteins and signaling pathways involved remain largely unexplored. This pediatric case–control study utilized plasma proteomics to explore protein alterations associated with severe DKA and to identify signaling pathways that associate with clinical variables. Methods: We conducted a proteome analysis of plasma samples from 17 matched pairs of pediatric patients with T1D; one cohort with severe DKA and another with insulin-controlled diabetes. Proximity extension assays were used to quantify 3072 plasma proteins. Data analysis was performed using multivariate statistics, machine learning, and bioinformatics. Results: This study identified 214 differentially expressed proteins (162 upregulated, 52 downregulated; adj P < 0.05 and a fold change > 2), reflecting cellular dysfunction and metabolic stress in severe DKA. We characterized protein expression across various organ systems and cell types, with notable alterations observed in white blood cells. Elevated inflammatory pathways suggest an enhanced inflammatory response, which may contribute to the complications of severe DKA. Additionally, upregulated pathways related to hormone signaling and nitrogen metabolism were identified, consistent with increased hormone release and associated metabolic processes, such as glycogenolysis and lipolysis. Changes in lipid and fatty acid metabolism were also observed, aligning with the lipolysis and ketosis characteristic of severe DKA. Finally, several signaling pathways were associated with clinical biochemical variables. Conclusions: Our findings highlight differentially expressed plasma proteins and enriched signaling pathways that were associated with clinical features, offering insights into the pathophysiology of severe DKA. [ABSTRACT FROM AUTHOR]

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. Key points: RankCompV3 is a method for identifying differentially expressed genes (DEGs) in either bulk or single-cell RNA transcriptomics. It is based on the counts of relative expression orderings (REOs) of gene pairs in the two groups. The contingency tables are tested using McCullagh's method. RankCompV3 has comparable or better performance than that of other conventional methods. It has been shown to be effective in identifying DEGs in both single-cell and pseudo-bulk profiles. Pseudo-bulk method is implemented in RankCompV3, which allows the method to achieve higher computational efficiency and improves the concordance with the bulk ground-truth. RankCompV3 is effective in identifying functionally relevant DEGs in weak-signal datasets. The method is not biased towards highly expressed genes. [ABSTRACT FROM AUTHOR]

Non-coding RNA expression has shown to have cell type-specificity. The regulatory characteristics of these molecules are impacted by changes in their expression levels. We performed next-generation sequencing and examined small RNA-seq data obtained from 6 different types of blood cells separated by fluorescence-activated cell sorting of severe COVID−19 patients and healthy control donors. In addition to examining the behavior of piRNA in the blood cells of severe SARS-CoV−2 infected patients, our aim was to present a distinct piRNA differential expression portrait for each separate cell type. We observed that depending on the type of cell, different sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, and neutrophils) have altering piRNA expression patterns. After analyzing the expression of piRNAs in each set of sorted cells from patients with severe COVID−19, we observed 3 significantly elevated piRNAs - piR−33,123, piR−34,765, piR−43,768 and 9 downregulated piRNAs in erythrocytes. In lymphocytes, all 19 piRNAs were upregulated. Monocytes were presented with a larger amount of statistically significant piRNA, 5 upregulated (piR−49039 piR−31623, piR−37213, piR−44721, piR−44720) and 35 downregulated. It has been previously shown that piR−31,623 has been associated with respiratory syncytial virus infection, and taking in account the major role of piRNA in transposon silencing, we presume that the differential expression patterns which we observed could be a signal of indirect antiviral activity or a specific antiviral cell state. Additionally, in lymphocytes, all 19 piRNAs were upregulated. [ABSTRACT FROM AUTHOR]

Background: Extrachromosomal circular DNAs (eccDNAs) have been reported to play a key role in the occurrence and development of various diseases. However, the characterization and role of eccDNAs in pulmonary arterial hypertension (PAH) remain unclear. Methods: In the discovery cohort, we first explored eccDNA expression profiles by Circle-sequencing analysis. The candidate eccDNAs were validated by routine polymerase chain reaction (PCR), TOPO-TA cloning and Sanger sequencing. In the validation cohort, 30 patients with PAH and 10 healthy controls were recruited for qPCR amplification to detect the candidate eccDNAs. Datas at the baseline were collected, including clinical background, biochemical variables, echocardiography and hemodynamic factors. Receiver operating characteristic curve was used to investigate the diagnostic effect of the eccDNA. Results: We identified a total of 21,741 eccDNAs in plasma samples of 3 IPAH patients and 3 individuals in good health, and the expression frequency, GC content, length distribution, and genome distribution of the eccDNAs were thoroughly characterized and analyzed. In the validation cohort, 687 eccDNAs were differentially expressed in patients with IPAH compared with healthy controls (screening threshold: |FC|≥2 and P < 0.05). Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the specific eccDNAs in IPAH were significantly enriched in calcium channel activity, the mitogen-activated protein kinase pathway, and the wnt signaling pathway. Verification queue found that the expression of eccDNA-chr2:131208878–131,424,362 in PAH was considerably higher than that in healthy controls and exhibited a high level of accuracy in predicting PAH with a sensitivity of 86.67% and a specificity of 90%. Furthermore, correlation analysis disclosed a significant association between serum eccDNA-chr2:131208878–131,424,362 and mean pulmonary artery pressure (mPAP) (r = 0.396, P = 0.03), 6 min walking distance (6MWD) (r = -0.399, P = 0.029), N-terminal pro-B-type natriuretic peptide (NT-proBNP) (r = 0.685, P < 0.001) and cardiac index (CI) (r = − 0.419, P = 0.021). Conclusions: This is the first study to identify and characterize eccDNAs in patients with PAH. We revealed that serum eccDNA-chr2:131208878–131,424,362 is significantly overexpressed and can be used in the diagnosis of PAH, indicating its potential as a novel non-invasive biomarker. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. Results: We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. Conclusions: eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population. [ABSTRACT FROM AUTHOR]

Background: 17-Hydroxylase deficiency is the rarest form of congenital adrenal hyperplasia, a disorder that affects steroidogenesis, causing abnormal hormone levels. Studies have shown a clear association between 17-hydroxylase deficiency and primary infertility, but a definite protocol to treat the disorder has not been determined yet. Case presentation: Case I presents a 24-year-old Caucasian Israeli-Arab female who experienced 6 years of infertility. Before her initial visit to our clinic, she underwent three laparoscopic ovarian cystectomies, had an unsuccessful in vitro fertilization cycle, and was treated with combined oral contraceptives. Her hormonal profile was tested, and the results led to genetic counseling and the diagnosis of non-classical congenital adrenal hyperplasia. She was treated with estradiol, glucocorticoids, and transdermal testosterone. After hormonal levels were lowered, in vitro fertilization cycles were initiated, and the patient had a spontaneous ovulation. In case II, a 20-year-old Caucasian Israeli-Arab female presented for infertility evaluation owing to her oligomenorrhea. Her vitals and physical examination had normal results. The investigation of her abnormal hormonal profile led her to be referred to genetic testing, where the results showed the same genetic mutation as seen in case I. Conclusion: Both cases highlight the distinctiveness of the condition, where an identical mutation in the gene responsible for the same enzyme can bring about diverse phenotypes. Case I offers a potential treatment protocol for this rare disorder. [ABSTRACT FROM AUTHOR]

Magnaporthe oryzae is one of the most destructive pathogens that threaten rice production around the world. Previous studies mainly focus on pathogenic mechanism of M. oryzae during infection on rice at leaf stage. However, the pathogenic mechanism of M. oryzae infection on panicle tissue is not well understood. In the present study, we performed RNA sequencing (RNA-seq) to study gene expression patterns of M. oryzae during infection at leaf stage and at panicle stage, respectively. The differentially expressed genes (DEGs) of M. oryzae in the infected leaf and panicle tissues were analyzed. Gene ontology (GO) enrichment analysis of DEGs revealed that M. oryzae genes involved in the biological processes were different at leaf and panicle stages. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs indicates that genes related to individual and important pathways may function at different infection stages. In particular, CAZymes carbohydrate esterases (CEs), carbohydrate-binding modules (CBMs), and glycoside hydrolases (GHs) may play important roles during M. oryzae infection on rice leaves, while glycosyltransferases (GTs) and GHs may play important roles during infection at rice panicle stage. Further analysis of effectors (BAS3, BAS113, BAS162, MoCDIP4, and MoHEG13) and their homologous genes suggest that they are involved in host defense suppression. Our findings provide insights into understanding the infection mechanisms of M. oryzae for rice leaf blast and panicle blast disease. [ABSTRACT FROM AUTHOR]

Background: The role of high-risk human papillomaviruses (hr-HPVs) in cervical cancer (CC) pathogenesis has long been established. Knowledge about the involvement of hr-HPVs in the etiology of nasopharyngeal cancers (NPC) was not well appreciated until the early 2000s when a clear link began to emerge. However, it is not clear whether HPV oncogenesis in the different epithelial cancers is associated with L1 gene and long-control region (LCR) sequences variation. This study aimed to investigate the HPV18 L1 gene and LCR sequences variation in cervical and nasopharyngeal biopsies, and assessed E6 and E7 genes expression level in both cancers. Method: Four-hundred and three (403) formalin-fixed paraffin-embedded tissues originating from nasopharyngeal (NPC) (279) and cervical (CC) (124) sites were collected from a pathology laboratory, Pathologist Without Borders, Accra, Ghana. Haematoxylin and eosin staining was carried out to confirm the presence of cancer on prepared biopsy sections. DNA was extracted from the confirmed cancer biopsies, followed by PCR using MY09/GP5+ /6+ primers to detect the presence of HPV and specific primers for the amplification of L1 gene and LCR. Sanger sequencing was carried out to determine HPV genotypes, and L1 and LCR sequences variant of HPV18s in CC and NPC biopsies. The HPV18 E6/E7 mRNA expression pattern in both cancers was determined using RT-qPCR. Results: Most of the NPC (45%) and CC (55%) biopsies were HPV18 positive. Comparison of HPV18 L1 sequences obtained from cervical and nasopharyngeal cancer tissues, the L1 sequences from the NPC were highly dissimilar with a 59–100% variation among themselves, and in relation to the reference strains. However, the L1 sequences from the CC were more similar with a 91.0–100% variation among the amplified sequences. Also, the LCR sequences from CC were quite different relative to that of NPC. Results for the differential expression of E6/E7 in the two cancers showed a higher fold change in E6 expression in the CC tissues than the NPC tissues while a reverse expression pattern was found for E7 gene. Conclusion: The current study reports for the first-time variations in HPV18 L1 and LCR sequences, and differential expression of E6/E7 genes in NPC compared to CC, suggesting a possible adaptation mechanism of the virus at different cancer sites. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. Results: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. Conclusions: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects. [ABSTRACT FROM AUTHOR]

Objective: To explore the differential expression of placental specific gene 1 (PLAC1) and neurite guidance factor 1 (netrin-1) in colorectal cancer (CRC) liver metastasis and its predictive value. Methods: Paraffin specimens of primary CRC were selected, including 60 simple colorectal cancer specimens and 47 liver metastasis specimens. At the same time, 40 cases of normal colorectal mucosa were taken as the control group. The expression of PLAC1 and Netrin-1 in tissue was detected by immunohistochemistry (IHC). The correlation between PLAC1 and Netrin-1 expression and clinicopathological characteristics of patients with CRC liver metastases was analyzed. Logistic analysis was adopted to analyze the influencing factors of liver metastasis in CRC. A prediction model was established and ROC curve was used to detect the discrimination of the prediction model. The clinical value of PLAC1 and netrin-1 in predicting liver metastasis of CRC was analyzed using ROC curve. The relationship between the expression of PLAC1 and netrin-1 and the prognosis of CRC patients with liver metastasis was analyzed using Kaplan Meier survival curve. Results: The positive staining of PLAC1 and netrin-1 was mainly located in the cytoplasm by IHC detection. Positive expression of PLAC1 and netrin-1 in CRC tissues was markedly higher than that in normal colorectal mucosal epithelium (P < 0.05). Positive expression of PLAC1 in metastatic group was higher than that in non-metastatic group without significant difference (P > 0.05). The metastasis group had much higher positive expression of netrin-1 than the non-metastasis group (P < 0.05). The content of PLAC1 in the tissues of CRC with liver metastasis had a close relationship with differentiation degree and lymph node metastasis (P < 0.05). The expression of Netrin-1 in the tissues of CRC with liver metastasis was associated with Dukes stage, differentiation degree and lymph node metastasis (P < 0.05). Logistic regression analysis showed that Dukes stage, differentiation, lymph node metastasis, CEA, Alb and D-dimer were the independent risk factors for liver metastasis of CRC (P < 0.05). The model was constructed according to the regression coefficients and constant terms, and the discrimination of the prediction model was evaluated using ROC curve, with the AUC of 0.903 (95% CI 0.831 ~ 0.975), the sensitivity of 93.80%, the specificity of 80.00%, and the Jordan index of 0.738. The AUC of PLAC1 and netrin-1 alone and combined detection to predict liver metastasis of CRC were 0.805, 0.793 and 0.921, respectively. The survival time of patients with positive PLAC1 and netrin-1 expression were sharply shorter than that of the patients with negative expression (P < 0.05). Conclusions: The expression of PLAC1 and netrin-1 was strongly increased in CRC with liver metastasis, which had a certain clinical value in predicting liver metastasis of CRC. Dukes stage, differentiation degree, lymph node metastasis, CEA, Alb and D-dimer were independent risk factors for liver metastasis of CRC, and the model based on these indicators had good discrimination for effectively evaluating the risk of liver metastasis in CRC. [ABSTRACT FROM AUTHOR]

Introduction: Pediatric T-cell acute lymphoblastic leukemia (T-ALL) poses significant challenges due to its aggressive nature and resistance to standard treatments. Long non-coding RNAs (lncRNAs) have emerged as potential biomarkers and therapeutic targets in leukemia. This study aims to characterize the lncRNA landscape in pediatric T-ALL, identify specific lncRNAs signatures, and assess their clinical relevance. Methods: RNA sequencing was performed on T-ALL patient and control samples. Differential expression analysis identified dysregulated lncRNAs and mRNAs. Functional enrichment analysis revealed potential roles of these lncRNAs in cancer pathogenesis. Validation of candidate lncRNAs was conducted using real-time PCR. Clinical correlations were assessed, including associations with patients' clinical characteristics and survival outcomes. Results: Analysis identified 674 dysregulated lncRNAs in pediatric T-ALL, with LINC01221 and CRNDE showing the most interactions in cancer progression pathways. Functional enrichment indicated involvement in apoptosis, survival, proliferation, and metastasis. Top 10 lncRNAs based on adjusted p value < 0.05 and Fold Change > 2 were selected for validation. Seven lncRNAs LINC01221, PCAT18, LINC00977, RP11-620J15.3, RP11-472G21.2, CTD-2291D10.4, and CRNDE showed correlation with RNA sequencing data. RP11-472G21.2 and CTD-2291D10.4 were highly expressed in T-ALL patients, with RP11-620J15.3 correlating significantly with better overall survival (p = 0.0007) at a median follow up of 32 months. The identified lncRNAs were further analysed in B-ALL patients. Distinct lncRNAs signatures were noted, distinguishing T-ALL from B-ALL and healthy controls, with lineage-specific overexpression of LINC01221 (p < 0.0001), RP11-472G21.2 (p < 0.001) and CRNDE (p = 0.04) in T-ALL. Conclusion: This study provides insights into the lncRNA landscape of pediatric T-ALL, offering potential diagnostic and prognostic markers. RP11-620J15.3 emerges as a promising prognostic marker, and distinct lncRNAs signatures may aid in the differentiation of T-ALL subtypes. Further research with larger cohorts is warranted to validate these findings and advance personalized treatment strategies for pediatric T-ALL patients. [ABSTRACT FROM AUTHOR]

Objective: The class II transactivator (CIITA), encoded by the CIITA gene, controls expression of immune response regulators, which affect bone homeostasis. Previously, we investigated a functional CIITA polymorphism in elderly women. Women carrying the allele associated with lower CIITA levels displayed higher bone mineral density (BMD), but also higher bone loss. The present exploratory study in a rat model sought to investigate effects of differential expression of Ciita on bone structural integrity and strength. Two strains DA (normal-to-high expression) and DA.VRA4 (lower expression) underwent ovariectomy (OVX) or sham-surgery at ~ 14-weeks of age (DA OVX n = 8, sham n = 4; DA.VRA4 OVX n = 10, sham n = 2). After 16-weeks, femoral BMD and bone mineral content (BMC) were measured and morphometry and biomechanical testing performed. Results: In DA.VRA4 rats, BMD/BMC, cross-sectional area and biomechanical properties were lower. Ciita expression was accompanied by OVX-induced changes to cross-sectional area and femoral shaft strength; DA rats had lower maximum load-to-fracture. Thus, while lower Ciita expression associated with lower bone mass, OVX induced changes to structural and mechanical bone properties were less pronounced. Conclusion: The data tentatively suggests association between Ciita expression and structural and mechanical bone properties, and a possible role in bone changes resulting from estrogen deficiency. [ABSTRACT FROM AUTHOR]

Background: Identification of the causes of stroke of undetermined etiology, specifically cardioembolism (CE) and non-CE causes, can inform treatment planning and prognosis prediction. The objective of this study was to analyze the disparities in thrombus composition, particularly Semaphorin-7A (Sema7A) and CD163, between patients diagnosed with large-artery atherosclerosis (LAA) and those with CE, and to investigate their potential association with prognosis. Methods: Thrombi were collected from patients who underwent mechanical thrombectomy at two hospitals. The patients were categorized into two groups: LAA and CE. We compared the levels of Sema7A and CD163 between these groups and analyzed their relationships with stroke severity, hemorrhagic transformation and prognosis. Results: The study involved a total of 67 patients. Sema7A expression was found to be significantly higher in the CE group compared to LAA (p < 0.001). Conversely, no statistically significant differences were observed for CD163 between the groups. The presence of Sema7A/CD163 did not show any associations with stroke severity or hemorrhagic transformation (all p > 0.05). However, both Sema7A (OR, 2.017; 95% CI, 1.301–3.518; p = 0.005) and CD163 (OR, 2.283; 95% CI, 1.252–5.724; p = 0.03) were associated with the poor prognosis for stroke, after adjusting for stroke severity. Conclusion: This study highlights that CE thrombi exhibited higher levels of Sema7A expression compared to LAA thrombi. Moreover, we found a positive correlation between Sema7A/CD163 levels and the poor prognosis of patients with acute ischemic stroke. [ABSTRACT FROM AUTHOR]

Introduction: White Striping (WS) and Wooden Breast (WB) pectoral myopathies are relevant disorders for contemporary broiler production worldwide. Several studies aimed to elucidate the genetic components associated with the occurrence of these myopathies. However, epigenetic factors that trigger or differentiate these two conditions are still unclear. The aim of this study was to identify miRNAs differentially expressed (DE) between normal and WS and WB-affected broilers, and to verify the possible role of these miRNAs in metabolic pathways related to the manifestation of these pectoral myopathies in 28-day-old broilers. Results: Five miRNAs were DE in the WS vs control (gga-miR-375, gga-miR-200b-3p, gga-miR-429-3p, gga-miR-1769-5p, gga-miR-200a-3p), 82 between WB vs control and 62 between WB vs WS. Several known miRNAs were associated with WB, such as gga-miR-155, gga-miR-146b, gga-miR-222, gga-miR-146-5p, gga-miR- 29, gga-miR-21-5p, gga-miR-133a-3p and gga-miR-133b. Most of them had not previously been associated with the development of this myopathy in broilers. We also have predicted 17 new miRNAs expressed in the broilers pectoral muscle. DE miRNA target gene ontology analysis enriched 6 common pathways for WS and WB compared to control: autophagy, insulin signaling, FoxO signaling, endocytosis, and metabolic pathways. The WS vs control contrast had two unique pathways, ERBB signaling and the mTOR signaling, while WB vs control had 14 unique pathways, with ubiquitin-mediated proteolysis and endoplasmic reticulum protein processing being the most significant. Conclusions: We found miRNAs DE between normal broilers and those affected with breast myopathies at 28 days of age. Our results also provide novel evidence of the miRNAs role on the regulation of WS and in the differentiation of both WS and WB myopathies. Overall, our study provides insights into miRNA-mediated and pathways involved in the occurrence of WS and WB helping to better understand these chicken growth disorders in an early age. These findings can help developing new approaches to reduce these complex issues in poultry production possibly by adjustments in nutrition and management conditions. Moreover, the miRNAs and target genes associated with the initial stages of WS and WB development could be potential biomarkers to be used in selection to reduce the occurrence of these myopathies in broiler production. [ABSTRACT FROM AUTHOR]

Background: Use of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies. Results: The proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM. Conclusions: The proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease. [ABSTRACT FROM AUTHOR]

Background: The zinc-finger CCHC-type (ZCCHC) superfamily proteins are characterized by the shared sequence CX2-CX4-HX4-C and thought to own high affinity to single-stranded nucleic acids, particularly RNAs. In humans, a total of 24 ZCCHC proteins have been annotated in the HUGO Gene Nomenclature Committee (HGNC, https://www.genenames.org/) database with most of these members involved in multiple steps of RNA metabolism. Many studies have indicated that the ZCCHC genes play a regulatory role in the development and progression of solid tumors. To date, the expression pattern and prognostic value of ZCCHC factors in thyroid carcinomas have not been reported. Methods: Bioinformatics analyses on the functions of ZCCHC factors in thyroid carcinoma (THCA) patients were performed based on various databases, i.e., TCGA, GEPIA, Kaplan-Meier Plotter, and TIMER. Results: Compared with normal tissues, the expression of ZCCHC12 mRNA was significantly increased in THCA tissues. And it was associated with the overall survival of THCA patients, based on the Kaplan-Meier Plotter database. Furthermore, the expression levels of all ZCCHCs were correlated with tumor stages, implying its high relevance to THCA, specifically its immunity. Conclusion: The ZCCHC genes, represented by ZCCHC12, are differentially expressed in THCA staging. These genes are associated with immune infiltration of THCA and identified as the potential therapeutic targets for immunotherapy in THCA patients, which are possible novel biomarkers for the treatment of THCA. [ABSTRACT FROM AUTHOR]

Background: Severe COVID-19 is a disease characterized by profound dysregulation of the innate immune system. There is a need to identify highly reliable prognostic biomarkers that can be rapidly assessed in body fluids for early identification of patients at higher risk for hospitalization and/or death. This study aimed to assess whether differential gene expression of immune response molecules and cellular enzymes, detected in saliva samples of COVID-19 patients, occurs according to disease severity staging. Methods: In this cross-sectional study, subjects with a COVID-19 diagnosis were classified as having mild, moderate, or severe disease based on clinical features. Transcripts of genes encoding 6 biomarkers, IL-1β, IL-6, IL-10, C-reactive protein, IDO1 and ACE2, were measured by RT‒qPCR in saliva samples of patients and COVID-19-free individuals. Results: The gene expression levels of all 6 biomarkers in saliva were significantly increased in severe disease patients compared to mild/moderate disease patients and healthy controls. A significant strong inverse relationship between oxemia and the level of expression of the 6 biomarkers (Spearman's correlation coefficient between -0.692 and -0.757; p < 0.001) was found. Conclusions: Biomarker gene expression determined in saliva samples still needs to be validated as a potentially valuable predictor of severe clinical outcomes early at the onset of COVID-19 symptoms. [ABSTRACT FROM AUTHOR]

Background: Our recent studies have demonstrated the crucial involvement of FOXA2 in the development of human pancreas. Reduction of FOXA2 expression during the differentiation of induced pluripotent stem cells (iPSCs) into pancreatic islets has been found to reduce α-and β-cell masses. However, the extent to which such changes are linked to alterations in the expression profile of long non-coding RNAs (lncRNAs) remains unraveled. Methods: Here, we employed our recently established FOXA2-deficient iPSCs (FOXA2−/− iPSCs) to investigate changes in lncRNA profiles and their correlation with dysregulated mRNAs during the pancreatic progenitor (PP) and pancreatic islet stages. Furthermore, we constructed co-expression networks linking significantly downregulated lncRNAs with differentially expressed pancreatic mRNAs. Results: Our results showed that 442 lncRNAs were downregulated, and 114 lncRNAs were upregulated in PPs lacking FOXA2 compared to controls. Similarly, 177 lncRNAs were downregulated, and 59 lncRNAs were upregulated in islet cells lacking FOXA2 compared to controls. At both stages, we observed a strong correlation between lncRNAs and several crucial pancreatic genes and TFs during pancreatic differentiation. Correlation analysis revealed 12 DE-lncRNAs that strongly correlated with key downregulated pancreatic genes in both PPs and islet cell stages. Selected DE-lncRNAs were validated using RT-qPCR. Conclusions: Our data indicate that the observed defects in pancreatic islet development due to the FOXA2 loss is associated with significant alterations in the expression profile of lncRNAs. Therefore, our findings provide novel insights into the role of lncRNA and mRNA networks in regulating pancreatic islet development, which warrants further investigations. 1-fWnpmZLux-XLhPse_mGh Video Abstract [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a progressive, neurodegenerative disorder with insidious onset. Some scholars believe that there is a close relationship between pyroptosis and AD. However, studies with evidence supporting this relationship are lacking. Materials and methods: The microarray data of AD were retrieved from the Gene Expression Omnibus (GEO) database with the datasets merged using the R package inSilicoMerging. R software package Limma was used to perform the differential expression analysis to identify the differentially expressed genes (DEGs). We further performed the enrichment analyses of the DEGs based on Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to identify the metabolic pathways with a significant difference. The Gene Set Enrichment Analysis (GSEA) was applied to identify the significant pathways. The protein-protein interaction (PPI) network was constructed based on the STRING database with the hub genes identified. Quantitative real-time PCR (qRT-PCR) analyses based on HT22 cells were performed to validate the findings based on the microarray analysis. Gene expression correlation heatmaps were generated to evaluate the relationships among the genes. Results: A new dataset was derived by merging 4 microarray datasets in the hippocampus of AD patients in the GEO database. Differential gene expression analysis yielded a volcano plot of a total of 20 DEGs (14 up-regulated and 6 down-regulated). GO analysis revealed a group of GO terms with a significant difference, e.g., cytoplasmic vesicle membrane, vesicle membrane, and monocyte chemotaxis. KEGG analysis detected the metabolic pathways with a significant difference, e.g., Rheumatoid arthritis and Fluid shear stress and atherosclerosis. The results of the Gene Set Enrichment Analysis of the microarray data showed that gene set ALZHEIMER_DISEASE and the gene set PYROPTOSIS were both up-regulated. PPI network showed that pyroptosis-related genes were divided into two groups. In the Aβ-induced HT22 cell model, three genes (i.e., BAX, IL18, and CYCS) were revealed with significant differences. Gene expression correlation heatmaps revealed strong correlations between pyroptotic genes and AD-related genes. Conclusion: The pyroptosis-related genes BAX, IL18, and CYCS were significantly different between AD patients and normal controls. [ABSTRACT FROM AUTHOR]

Background: Circular RNAs (circRNAs) are a class of covalenty closed non-coding RNAs that have garnered increased attention from the research community due to their stability, tissue-specific expression and role as transcriptional modulators via sequestration of miRNAs. Currently, multiple quantification tools capable of detecting circRNAs exist, yet none delineate circRNA–miRNA interactions, and only one employs differential expression analysis. Efforts have been made to bridge this gap by way of circRNA workflows, however these workflows are limited by both the types of analyses available and computational skills required to run them. Results: We present nf-core/circrna, a multi-functional, automated high-throughput pipeline implemented in nextflow that allows users to characterise the role of circRNAs in RNA Sequencing datasets via three analysis modules: (1) circRNA quantification, robust filtering and annotation (2) miRNA target prediction of the mature spliced sequence and (3) differential expression analysis. nf-core/circrna has been developed within the nf-core framework, ensuring robust portability across computing environments via containerisation, parallel deployment on cluster/cloud-based infrastructures, comprehensive documentation and maintenance support. Conclusion: nf-core/circrna reduces the barrier to entry for researchers by providing an easy-to-use, platform-independent and scalable workflow for circRNA analyses. Source code, documentation and installation instructions are freely available at https://nf-co.re/circrna and https://github.com/nf-core/circrna. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment is the main factor in the poor prognosis of schizophrenia, but its mechanism remains unclear. The inferior parietal lobule (IPL) is related to various clinical symptoms and cognitive impairment in schizophrenia. We aimed to explore the relationship between IPL-related functions and cognitive impairment in schizophrenia. Methods: 136 schizophrenia patients and 146 demographically matched healthy controls were enrolled for a cross-sectional study. High-spatial-resolution structural and resting-state functional images were acquired to demonstrate the alternations of brain structure and function. At the same time, the digit span and digit symbol coding tasks of the Chinese Wechsler Adult Intelligence Test Revised (WAIS-RC) were utilized in assessing the subjects' cognitive function. Patients were divided into cognitive impairment and normal cognitive groups according to their cognitive score and then compared whether there were differences between the three groups in fractional amplitude of low-frequency fluctuation (fALFF). In addition, we did a correlation analysis between cognitive function and the fALFF for the left IPL of patients and healthy controls. Based on the Allen Human Brain Atlas, we obtained genes expressed in the left IPL, which were then intersected with the transcriptome-wide association study results and differentially expressed genes in schizophrenia. Results: Grouping of patients by the backward digit span task and the digit symbol coding task showed differences in fALFF values between healthy controls and cognitive impairment patients (P < 0.05). We found a negative correlation between the backward digit span task score and fALFF of the left IPL in healthy controls (r = − 0.388, P = 0.003), which was not seen in patients (r = 0.203, P = 0.020). In addition, none of the other analyses were statistically significant (P > 0.017). In addition, we found that diacylglycerol kinase ζ (DGKζ) is differentially expressed in the left IPL and associated with schizophrenia. Conclusion: Our study demonstrates that the left IPL plays a vital role in cognitive impairment in schizophrenia. DGKζ may act as an essential regulator in the left IPL of schizophrenia patients with cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Gliomas are tumours arising mostly from astrocytic or oligodendrocytic precursor cells. These tumours are classified according to the updated WHO classification from 2021 in 4 grades depending on molecular and histopathological criteria. Despite novel multimodal therapeutic approaches, the vast majority of gliomas (WHO grade III and IV) are not curable. The circadian clock is an important regulator of numerous cellular processes and its dysregulation had been found during the progression of many cancers, including gliomas. Results: In this study, we explore expression patterns of clock-controlled genes in low-grade glioma (LGG) and glioblastoma multiforme (GBM) and show that a set of 45 clock-controlled genes can be used to distinguish GBM from normal tissue. Subsequent analysis identified 17 clock-controlled genes with a significant association with survival. The results point to a loss of correlation strength within elements of the circadian clock network in GBM compared to LGG. We further explored the progression patterns of mutations in LGG and GBM, and showed that tumour suppressor APC is lost late both in LGG and GBM. Moreover, HIF1A, involved in cellular response to hypoxia, exhibits subclonal losses in LGG, and TERT, involved in the formation of telomerase, is lost late in the GBM progression. By examining multi-sample LGG data, we find that the clock-controlled driver genes APC, HIF1A, TERT and TP53 experience frequent subclonal gains and losses. Conclusions: Our results show a higher level of disrgulation at the gene expression level in GBM compared to LGG, and indicate an association between the differentially expressed clock-regulated genes and patient survival in both LGG and GBM. By reconstructing the patterns of progression in LGG and GBM, our data reveals the relatively late gains and losses of clock-regulated glioma drivers. Our analysis emphasizes the role of clock-regulated genes in glioma development and progression. Yet, further research is needed to asses their value in the development of new treatments. [ABSTRACT FROM AUTHOR]

Background: Coagulation function monitoring is important for the occurrence and development of diabetes. A total of 16 related proteins are involved in coagulation, but how these proteins change in diabetic urine exosomes is unclear. To explore the expression changes of coagulation-related proteins in urine exosomes and their possible roles in the pathogenesis of diabetes, we performed proteomic analysis and finally applied them to the noninvasive monitoring of diabetes. Methods: Subject urine samples were collected. LC-MS/MS was used to collect the information on coagulation-related proteins in urine exosomes. ELISA, mass spectrometry and western blotting were used to further verify the differential protein expression in urine exosomes. Correlations with clinical indicators were explored, and receiver operating characteristic (ROC) curves were drawn to evaluate the value of differential proteins in diabetes monitoring. Results: Analyzing urine exosome proteomics data, eight coagulation-related proteins were found in this study. Among them, F2 was elevated in urine exosomes of diabetic patients compared with healthy controls. The results of ELISA, mass spectrometry and western blotting further verified the changes in F2. Correlation analysis showed that the expression of urine exosome F2 was correlated with clinical lipid metabolism indexes, and the concentration of F2 was strongly positively correlated with blood TG levels (P < 0.05). ROC curve analysis showed that F2 protein in urine exosomes had a good monitoring value for diabetes. Conclusion: Coagulation-related proteins were expressed in urine exosomes. Among them, F2 was increased in diabetic urine exosomes and may be a potential biomarker for monitoring diabetic changes. [ABSTRACT FROM AUTHOR]

Background: In male dogs, uroepithelial cancers include invasive urothelial carcinoma (iUC) and prostate carcinoma (PCA). The inability to distinguish iUC involving the prostate from PCA results in indiscriminate clinical management strategies that could be suboptimal as first-line chemotherapy for iUC (cisplatin) and PCA (docetaxel) differ in people. Prostate specific membrane antigen (PSMA) is a transmembrane protein, and its overexpression has been identified in human prostate carcinoma and neovasculature associated with solid tumor growth. This study investigates whether differential PSMA expression exists between presumptive canine iUC and PCA among cell lines and archived patient samples, which might allow for improved accuracy in disease-based stratification and optimal chemotherapy selection. Additionally, in vitro sensitivities of reported canine iUC and PCA cell lines to uroepithelial directed chemotherapeutic agents were characterized. Results: Normalized PSMA gene and protein expressions were not significantly different between 5 iUC and 4 PCA cell lines. PSMA protein expression was uniformly observed in uroepithelial cancers regardless of anatomic origin from archived patient samples, further confirming that PSMA cannot differentiate iUC from PCA. In vitro sensitivity of cell lines to uroepithelial directed chemotherapeutics revealed that vinblastine exerted the broadest cytotoxic activity. Conclusions: Differential expression of PSMA was not identified between canine iUC and PCA cell lines or archived patient samples, and PSMA alone cannot be used for disease stratification. Nonetheless given its conserved overexpression, PSMA may be a targetable surface marker for both canine iUC and PCA. Lastly, in uroepithelial carcinomas, vinblastine might exert the broadest anticancer activity regardless of cellular origin. [ABSTRACT FROM AUTHOR]

Background: In male dogs, uroepithelial cancers include invasive urothelial carcinoma (iUC) and prostate carcinoma (PCA). The inability to distinguish iUC involving the prostate from PCA results in indiscriminate clinical management strategies that could be suboptimal as first-line chemotherapy for iUC (cisplatin) and PCA (docetaxel) differ in people. Prostate specific membrane antigen (PSMA) is a transmembrane protein, and its overexpression has been identified in human prostate carcinoma and neovasculature associated with solid tumor growth. This study investigates whether differential PSMA expression exists between presumptive canine iUC and PCA among cell lines and archived patient samples, which might allow for improved accuracy in disease-based stratification and optimal chemotherapy selection. Additionally, in vitro sensitivities of reported canine iUC and PCA cell lines to uroepithelial directed chemotherapeutic agents were characterized. Results: Normalized PSMA gene and protein expressions were not significantly different between 5 iUC and 4 PCA cell lines. PSMA protein expression was uniformly observed in uroepithelial cancers regardless of anatomic origin from archived patient samples, further confirming that PSMA cannot differentiate iUC from PCA. In vitro sensitivity of cell lines to uroepithelial directed chemotherapeutics revealed that vinblastine exerted the broadest cytotoxic activity. Conclusions: Differential expression of PSMA was not identified between canine iUC and PCA cell lines or archived patient samples, and PSMA alone cannot be used for disease stratification. Nonetheless given its conserved overexpression, PSMA may be a targetable surface marker for both canine iUC and PCA. Lastly, in uroepithelial carcinomas, vinblastine might exert the broadest anticancer activity regardless of cellular origin. [ABSTRACT FROM AUTHOR]

Background: In male dogs, uroepithelial cancers include invasive urothelial carcinoma (iUC) and prostate carcinoma (PCA). The inability to distinguish iUC involving the prostate from PCA results in indiscriminate clinical management strategies that could be suboptimal as first-line chemotherapy for iUC (cisplatin) and PCA (docetaxel) differ in people. Prostate specific membrane antigen (PSMA) is a transmembrane protein, and its overexpression has been identified in human prostate carcinoma and neovasculature associated with solid tumor growth. This study investigates whether differential PSMA expression exists between presumptive canine iUC and PCA among cell lines and archived patient samples, which might allow for improved accuracy in disease-based stratification and optimal chemotherapy selection. Additionally, in vitro sensitivities of reported canine iUC and PCA cell lines to uroepithelial directed chemotherapeutic agents were characterized. Results: Normalized PSMA gene and protein expressions were not significantly different between 5 iUC and 4 PCA cell lines. PSMA protein expression was uniformly observed in uroepithelial cancers regardless of anatomic origin from archived patient samples, further confirming that PSMA cannot differentiate iUC from PCA. In vitro sensitivity of cell lines to uroepithelial directed chemotherapeutics revealed that vinblastine exerted the broadest cytotoxic activity. Conclusions: Differential expression of PSMA was not identified between canine iUC and PCA cell lines or archived patient samples, and PSMA alone cannot be used for disease stratification. Nonetheless given its conserved overexpression, PSMA may be a targetable surface marker for both canine iUC and PCA. Lastly, in uroepithelial carcinomas, vinblastine might exert the broadest anticancer activity regardless of cellular origin. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the expression of serum miR-363-3p in patients with polycystic ovary syndrome (PCOS) and its predictive value for pregnancy after ovulation induction therapy. Methods: The expression of serum miR-363-3p was detected by Reverse transcription quantitative polymerase chain reaction (RT-qPCR). PCOS patients were treated with ovulation induction therapy, and after the successful pregnancy was confirmed, they were followed up for 1 year in outpatient department to record the pregnancy outcomes of the patients. The Pearson correlation coefficient was used to evaluate the correlation between the expression level of miR-363-3p and biochemical indicators of PCOS patients. Logistic regression analysis was used to analyze the risk factors of pregnancy failure after ovulation induction therapy. Results: The serum level of miR-363-3p in PCOS group was significantly lower than that in control group. Compared with the control group, both pregnant and non-pregnant groups had lower miR-363-3p levels, while the non-pregnant group had a greater reduction in miR-363-3p levels than the pregnant group. Low levels of miR-363-3p showed high accuracy in distinguishing pregnant and non-pregnant patients. Logistic regression analysis showed that high levels of luteinizing hormone, testosterone (T), prolactin (PRL) and low level of miR-363-3p were independent risk factors for pregnancy failure after ovulation induction in PCOS patients. Additionally, compared with pregnancy outcomes of healthy women, the incidence of premature delivery, macrosomia, and gestational diabetes in PCOS patients increased. Conclusions: The expression of miR-363-3p in PCOS patients was reduced and correlated with abnormal hormone levels, suggesting that miR-363-3p may be involved in the occurrence and development of PCOS. [ABSTRACT FROM AUTHOR]

Background: A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. Results: In this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 35 significant eQTLs (P < 5 × 10–8) following the ANOVA model and 39 significant eQTLs (P < 5 × 10–8) following the additive model. Using a differential gene expression framework, we identified 930 and six significant eQTLs (P < 5 × 10–8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. Conclusions: Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed. Our proposed approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution. [ABSTRACT FROM AUTHOR]

Background: Trichoderma asperellum and T. harzianum were assessed in this study as a potential biological control against Colletotrichum truncatum. C. truncatum is a hemibiotrophic fungus that causes anthracnose disease in chilli thereby affecting plant growth and fruit yield. Scanning electron microscope (SEM) technique showed the beneficial interaction between chilli root-Trichoderma spp. inducing the plant growth promotion, mechanical barrier, and defense network under C. truncatum challenged conditions. Methods: Seeds bio-primed with T. asperellum, T. harzianum, and T. asperellum + T. harzianum promoted the plant growth parameters and strengthening of physical barrier via lignification on the wall of vascular tissues. Seed primed with bioagents were used for exploring the molecular mechanism of defense response in pepper against anthracnose to assess the temporal expression of six defense genes in the Surajmukhi variety of Capsicum annuum. QRT-PCR demonstrated induction of defense responsive genes in chilli pepper bioprimed with Trichoderma spp. such as plant defensin 1.2 (CaPDF1.2), superoxide dismutase (SOD), ascorbate peroxidase (APx), guaiacol peroxidase (GPx), pathogenesis related proteins PR-2 and PR-5. Results: The results showed that bioprimed seeds were assessed for T. asperellum, T. harzianum, and T. asperellum + T. harzianum-chilli root colonization interaction under in vivo conditions. The results of the scanning electron microscope revealed that T. asperellum, T. harzianum and T. asperellum + T. harzianum interact with chilli roots directly via the development of plant-Trichoderma interaction system. Seeds bio-primed with bioagents promoted the plant growth parameters, fresh and dry weight of shoot and root, plant height, leaf area index, number of leaves, stem diameter and strengthening of physical barrier via lignification on the wall of vascular tissues and expression of six defense related genes in pepper against anthracnose. Conclusions: Application of T. asperellum and T. harzianum and in combination of treatments enhanced the plant growth. Further, as seeds bioprimed with T. asperellum, T. harzianum and in combination with treatment of T. asperellum + T. harzianum induced the strengthening of the cell wall by lignification and expression of six defense related genes CaPDF1.2, SOD, APx, GPx, PR-2 and PR-5 in pepper against C. truncatum. Our study contributed for better disease management through biopriming with T. asperellum, T. harzianum and T. asperellum + T. harzianum. The biopriming possess enormous potential to promote plant growth, modulate the physical barrier, and induced the defense related genes in chilli pepper against anthracnose. [ABSTRACT FROM AUTHOR]

Background: RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. Results: The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. Conclusions: MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required. [ABSTRACT FROM AUTHOR]

Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear. Results: Using "mappability", a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically. Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis. [ABSTRACT FROM AUTHOR]

IFN-γ is critical for both thyroid and ovarian function, while thyroxine, secreted from the thyroid gland, regulates the ovarian function via the hypothalamus-pituitary -ovary axis. However, the effect of thyroxine on INF-γ involved in the regulation of hypothalamic pituitary ovarian axis ovarian function is hitherto unknown. Therefore, we set up three groups including a sham-operated group, an experimental thyroidectomized group, and an experimental thyroidectomized group treated with T4 to reveal the IFN-γ expression levels in the in the hypothalamus, pituitary gland, and ovary by immunohistochemical staining, RT-PCR, and Western blotting. IFN-γ-like immunoreactive-positive substances were visualized in the hypothalamus, pituitary gland, and ovary, which were located mainly in the cytoplasm of the hypothalamic neurons anterior pituitary cells, luteal cells, and theca cells in the ovary of hypothyroidism rats, respectively. RT-PCR and Western blotting showed that the rats in the experimental thyroidectomized group treated with T4 had significantly elevated expression of IFN-γ at both the mRNA and protein levels. Thyroxine affects the expression of IFN-γ in the thalamus-pituitary-ovarian axis, which may influence the secretion of IFN-γ to regulate ovarian function during hypothyroidism. This work highlights the potential effect of thyroxine on the involvement of INF-γ in the modulation of the ovarian function in the hypothalamic-pituitary-ovarian axis. [ABSTRACT FROM AUTHOR]

Background: Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks to increase statistical power to identify differentially expressed genes (DEGs). Results: We develop MRFscRNAseq, which is based on a Markov random field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DEGs. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DEGs than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls. Conclusions: The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method increases statistical power to detect differentially expressed genes from scRNA-seq data. [ABSTRACT FROM AUTHOR]

Background: Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes. Results: In this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset. Conclusions: Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets. [ABSTRACT FROM AUTHOR]

Background: β-Glucosidase is the rate-limiting enzyme of cellulose degradation. It has been stipulated and established that β-glucosidase-producing microbial communities differentially regulate the expression of glucose/non-glucose tolerant β-glucosidase genes. However, it is still unknown if this differential expression of functional microbial community happens accidentally or as a general regulatory mechanism, and of what biological significance it has. To investigate the composition and function of microbial communities and how they respond to different carbon metabolism pressures and the transcriptional regulation of functional genes, the different carbon metabolism pressure was constructed by setting up the static chamber during composting. Results: The composition and function of functional microbial communities demonstrated different behaviors under the carbon metabolism pressure. Functional microbial community up-regulated glucose tolerant β-glucosidase genes expression to maintain the carbon metabolism rate by enhancing the transglycosylation activity of β-glucosidase to compensate for the decrease of hydrolysis activity under carbon catabolite repression (CCR). Micrococcales play a vital role in the resistance of functional microbial community under CCR. The transcription regulation of GH1 family β-glucosidase genes from Proteobacteria showed more obvious inhibition than other phyla under CCR. Conclusion: Microbial functional communities differentially regulate the expression of glucose/non-glucose tolerant β-glucosidase genes under CCR, which is a general regulatory mechanism, not accidental. Furthermore, the differentially expressed β-glucosidase gene exhibited species characteristics at the phylogenetic level. [ABSTRACT FROM AUTHOR]

Background: The predominant genotype of Toxoplasma in China is the Chinese 1 (ToxoDB#9) lineage. TgCtwh3 and TgCtwh6 are two representative strains of Chinese 1, exhibiting high and low virulence to mice, respectively. Little is known regarding the virulence mechanism of this non-classical genotype. Our previous RNA sequencing data revealed differential mRNA levels of TgMIC1 in TgCtwh3 and TgCtwh6. We aim to further confirm the differential expression of TgMIC1 and its significance in this atypical genotype. Methods: Quantitative real-time PCR was used to verify the RNA sequencing data; then, polyclonal antibodies against TgMIC1 were prepared and identified. Moreover, the invasion and proliferation of the parasite in HFF cells were observed after treatment with TgMIC1 polyclonal antibody or not. Results: The data showed that the protein level of TgMIC1 was significantly higher in high-virulence strain TgCtwh3 than in low-virulence strain TgCtwh6 and that the invasion and proliferation of TgCtwh3 were inhibited by TgMIC1 polyclonal antibody. Conclusion: Differential expression of TgMIC1 in TgCtwh3 and TgCtwh6 may explain, at least partly, the virulence mechanism of this atypical genotype. [ABSTRACT FROM AUTHOR]

Background: Porcine Epidemic Diarrhea Virus (PEDV) is a coronavirus that seriously affects the swine industry. MicroRNAs and long noncoding RNAs are two relevant non-coding RNAs (ncRNAs) class and play crucial roles in a variety of physiological processes. Increased evidence indicates a complex interaction between mRNA and ncRNA. However, our understanding of the function of ncRNA involved in host-PEDV interaction is limited. Results: A total of 1,197 mRNA transcripts, 539 lncRNA transcripts, and 208 miRNA transcripts were differentially regulated at 24 h and 48 h post-infection. Gene ontology (GO) and KEGG pathway enrichment analysis showed that DE mRNAs and DE lncRNAs were mainly involved in biosynthesis, innate immunity, and lipid metabolism. Moreover, we constructed a miRNA-mRNA-pathway network using bioinformatics, including 12 DE mRNAs, 120 DE miRNAs, and 11 pathways. Finally, the target genes of DE miRNAs were screened by bioinformatics, and we constructed immune-related lncRNA-miRNA-mRNA ceRNA networks. Then, the selected DE genes were validated by qRT-PCR, which were consistent with the results from RNA-Seq data. Conclusions: This study provides the comprehensive analysis of the expression profiles of mRNAs, lncRNAs, and miRNAs during PEDV infection. We characterize the ceRNA networks which can provide new insights into the pathogenesis of PEDV. [ABSTRACT FROM AUTHOR]

Background: Duck plague virus (DPV), belonging to herpesviruses, is a linear double-stranded DNA virus. There are many reports about the outbreak of the duck plague in a variety of countries, which caused huge economic losses. Recently, increasing reports revealed that multiple long non-coding RNAs (lncRNAs) can possess great potential in the regulation of host antiviral immune response. Furthermore, it remains to be determined which specific molecular mechanisms are responsible for the DPV-host interaction in host immunity. Here, lncRNAs and mRNAs in DPV infected duck embryonic fibroblast (DEF) cells were identified by high-throughput RNA-sequencing (RNA-seq). And we predicted target genes of differentially expressed genes (DEGs) and formed a complex regulatory network depending on in-silico analysis and prediction. Result: RNA-seq analysis results showed that 2921 lncRNAs were found at 30 h post-infection (hpi). In our study, 218 DE lncRNAs and 2840 DE mRNAs were obtained in DEF after DPV infection. Among these DEGs and target genes, some have been authenticated as immune-related molecules, such as a Macrophage mannose receptor (MR), Anas platyrhynchos toll-like receptor 2 (TLR2), leukocyte differentiation antigen, interleukin family, and their related regulatory factors. Furthermore, according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, we found that the target genes may have important effects on biological development, biosynthesis, signal transduction, cell biological regulation, and cell process. Also, we obtained, the potential targeting relationship existing in DEF cells between host lncRNAs and DPV-encoded miRNAs by software. Conclusions: This study revealed not only expression changes, but also the possible biological regulatory relationship of lncRNAs and mRNAs in DPV infected DEF cells. Together, these data and analyses provide additional insight into the role of lncRNAs and mRNAs in the host's immune response to DPV infection. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking. Results: We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility. Conclusion: ideal is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand. [ABSTRACT FROM AUTHOR]

Background: The giant panda (Ailuropoda melanoleuca) is an endangered mammalian species native to China. Fewer than 2500 giant pandas are known to exist, many of which are bred in captivity as a means to preserve and repopulate the species. Like other captive mammals, giant pandas acquire age-related cataracts, reducing their quality of life. Recent comparative genome-wide methylation analysis revealed 110 differentially methylated genes associated with cataract formation including six also associated with the formation of age-related cataracts in humans. Results: To investigate the pathological pathway in greater detail, here we used RNA-Seq analysis to investigate the differential expression profiles of genes in three giant pandas with cataracts and three healthy controls. We identified more than 700 differentially expressed genes, 29 of which were selected for further analysis based on their low q-value. We found that many of the genes encoded regulatory and signaling proteins associated with the control of cell growth, migration, differentiation and apoptosis, supporting previous research indicating a key role for apoptosis in cataract formation. Conclusion: The identification of genes involved in the formation of age-related cataracts could facilitate the development of predictive markers, preventative measures and even new therapies to improve the life of captive animals. [ABSTRACT FROM AUTHOR]

Background: Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis. Results: We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method. Conclusions: The new method for the differential expression analysis of proteomic data is available as an easy to use Python package. [ABSTRACT FROM AUTHOR]

Background: Wound healing is a representative phenomenon of potato tubers subjected to mechanical injuries. Our previous results found that benzo-(1,2,3)-thiadiazole-7-carbothioic acid S-methyl ester (BTH) promoted the wound healing of potato tubers. However, the molecular mechanism related to inducible wound healing remains unknown. Results: Transcriptomic evaluation of healing tissues from potato tubers at three stages, namely, 0 d (nonhealing), 5 d (wounded tubers healed for 5 d) and 5 d (BTH-treated tubers healed for 5 d) using RNA-Seq and differentially expressed genes (DEGs) analysis showed that more than 515 million high-quality reads were generated and a total of 7665 DEGs were enriched, and 16 of these DEGs were selected by qRT-PCR analysis to further confirm the RNA sequencing data. Gene ontology (GO) enrichment analysis indicated that the most highly DEGs were involved in metabolic and cellular processes, and KEGG enrichment analysis indicated that a large number of DEGs were associated with plant hormones, starch and sugar metabolism, fatty acid metabolism, phenylpropanoid biosynthesis and terpenoid skeleton biosynthesis. Furthermore, a few candidate transcription factors, including MYB, NAC and WRKY, and genes related to Ca2+-mediated signal transduction were also found to be differentially expressed during wound healing. Most of these enriched DEGs were upregulated after BTH treatment. Conclusion: This comparative expression profile provided useful resources for studies of the molecular mechanism via these promising candidates involved in natural or elicitor-induced wound healing in potato tubers. [ABSTRACT FROM AUTHOR]

Background: Existing tools for the aggregation and visualization of differential expression data have discrete functionality and require that end-users rely on multiple software packages with complex dependencies or manually manipulate data for analysis and interpretation. Furthermore, at present, data aggregation and visualization are laborious, time consuming, and subject to human error. This is a serious limitation on the current state of differential transcriptomic analysis, which makes it necessary to expend extensive time and resources to reach the point where biological meaning can be interpreted. Such an approach for analysis also leads to scattered and non-standardized code, unsystematic project management and non-reproducible result sets. Results: Here, we present a differential expression analysis toolkit, DEvis, that provides a powerful, integrated solution for the analysis of differential expression data with a rapid turnaround time. DEvis has simple installation requirements and provides a convenient, user-friendly R package that addresses the issues inherent to complex multi-factor experiments, such as multiple contrast aggregation and integration, result sorting and selection, visualization, project management, and reproducibility. This tool increases the capabilities of differential expression analysis while reducing workload and the potential for manual error. Furthermore, it provides a much-needed encapsulation of scattered functionality, making large and complex analysis more efficient and reproducible. Conclusion: DEvis provides a wide range of powerful visualization, data aggregation, and project management tools that provide flexibility and speed in analysis. The functionality provided by DEVis increases efficiency of analysis and supplies researchers with new and relevant means for the analysis of large and complicated transcriptomic experiments. DEvis furthermore incorporates automatic project management capabilities, which standardizes analysis and ensures the reproducibility of results. After the establishment of statistical frameworks that identify differentially expressed genes, this package is the next logical step for differential transcriptomic analysis, establishing the critical framework necessary to manipulate, explore, and extract biologically relevant meaning from differential expression data. [ABSTRACT FROM AUTHOR]

Background: Spinal Muscular Atrophy (SMA), a neuromuscular disorder that leads to weakness in the muscles due to degeneration of motor neurons. Mutations in the survival motor neuron 1 (SMN1) gene leads to the deficiency of SMN protein that causes SMA. The molecular alterations associated with SMA extends across the transcriptome and proteome. Although several studies have examined the transcriptomic profile of SMA, the difference in experimental settings across these studies highlight the need for a comparative meta-analysis to better understand these differences. Methods and data: We conducted a systematic comparative meta-analysis of publicly available gene expression data from six selected studies to elucidate variations in the transcriptomic landscape across different experimental conditions, including tissue types and mouse models. We used both microarray and RNA-seq datasets, retrieved from Gene Expression Omnibus (GEO) and ArrayExpress (AE). Methods included normalization, differential expression analysis, gene-set enrichment analysis (GSEA), network reconstruction and co-expression analysis. Results: Differential expression analysis revealed varying numbers of differentially expressed genes ranging between zero and 1,655 across the selected studies. Notably, the Metallothionein gene Mt2 was common in several of the eight comparisons. This highlights its role in oxidative stress and detoxification. Additionally, genes such as Hspb1, St14 and Sult1a1 were among the top ten differentially expressed genes in more than one comparison. The Snrpa1 gene, involved in pre-mRNA splicing, was upregulated in the spinal cord and has a strong correlation with other differentially expressed genes from other comparisons in our network reconstruction analysis. Gene-set enrichment analysis identified significant GO terms such as contractile fibers and myosin complexes in more than one comparison which highlights its significant role in SMA. Conclusions: Our comparative meta-analysis identified only few genes and pathways that were consistently dysregulated in SMA across different tissues and experimental settings. Conversely, many genes and pathways appeared to play a tissue-specific role in SMA. In comparison with the original studies, reproducibility was rather weak. [ABSTRACT FROM AUTHOR]

Background: The predominant genotype of Toxoplasma in China is the Chinese 1 (ToxoDB#9) lineage. TgCtwh3 and TgCtwh6 are two representative strains of Chinese 1, exhibiting high and low virulence to mice, respectively. Little is known regarding the virulence mechanism of this non-classical genotype. Our previous RNA sequencing data revealed differential mRNA levels of TgMIC1 in TgCtwh3 and TgCtwh6. We aim to further confirm the differential expression of TgMIC1 and its significance in this atypical genotype. Methods: Quantitative real-time PCR was used to verify the RNA sequencing data; then, polyclonal antibodies against TgMIC1 were prepared and identified. Moreover, the invasion and proliferation of the parasite in HFF cells were observed after treatment with TgMIC1 polyclonal antibody or not. Results: The data showed that the protein level of TgMIC1 was significantly higher in high-virulence strain TgCtwh3 than in low-virulence strain TgCtwh6 and that the invasion and proliferation of TgCtwh3 were inhibited by TgMIC1 polyclonal antibody. Conclusion: Differential expression of TgMIC1 in TgCtwh3 and TgCtwh6 may explain, at least partly, the virulence mechanism of this atypical genotype. [ABSTRACT FROM AUTHOR]

Background: Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power. Results: Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. Conclusions: Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility. [ABSTRACT FROM AUTHOR]

Background: MiRNAs (microRNA) are 18–24 nt endogenous noncoding RNAs that regulate gene expression at the post-transcriptional level, including tissue-specific, developmental timing and evolutionary conservation gene expression. Results: This study used high-throughput sequencing technology for the first time in Larix olgensis, predicted 78 miRNAs, including 12,229,003 reads sRNA, screened differentially expressed miRNAs. Predicting target genes was helpful for understanding the miRNA regulation function and obtained 333 corresponding target genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation were analysed, mostly including nucleic acid binding, plant hormone signal transduction, pantothenate and CoA biosynthesis, and cellulose synthase. This study will lay the foundation for clarifying the complex miRNA-mediated regulatory network for growth and development. In view of this, spatio-temporal expression of miR396, miR950, miR164, miR166 and miR160 were analysed in Larix olgensis during the growth stages of not lignified, beginning of lignification, and completely lignified in different tissues (root, stem, and leaf) by quantitative real-time PCR (qRT-PCR). There were differences in the expression of miRNAs in roots, stems and leaves in the same growth period. At 60 days, miR160, miR166 and miR396–2 exhibited the highest expression in leaves. At 120 days, most miRNAs in roots and stems decreased significantly. At 180 days, miRNAs were abundantly expressed in roots and stems. Meanwhile, analysis of the expression of miRNAs in leaves revealed that miR396–2 was reduced as time went on, whereas other miRNAs increased initially and then decreased. On the other hand, in the stems, miR166–1 was increase, whereas other miRNAs, especially miR160, miR164, miR396 and miR950–1, first decreased and then increased. Similarly, in the roots, miR950–2 first decreased and then increased, whereas other miRNAs exhibited a trend of continuous increase. Conclusions: The present investigation included rapid isolation and identification of miRNAs in Larix olgensis through construction of a sRNA library using Solexa and predicted 78 novel miRNAs, which showed differential expression levels in different tissues and stages. These results provided a theoretical basis for further revealing the genetic regulation mechanism of miRNA in the growth and development of conifers and the verification of function in target genes. [ABSTRACT FROM AUTHOR]

Background: High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named “SCBN”. [ABSTRACT FROM AUTHOR]

Background: Electronic-cigarette (e-cig) usage, particularly in the youth population, is a growing concern. It is known that e-cig causes endothelial dysfunction, which is a risk factor for the development of cardiovascular diseases; however, the mechanisms involved remain unclear. We hypothesized that long noncoding RNAs (lncRNAs) may play a role in e-cig-induced endothelial dysfunction. Methods: Here, we identified lncRNAs that are dysregulated in human induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) following 24 h of e-cig aerosol extract treatment via microarray analysis. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analyses of the dysregulated mRNAs following e-cig exposure and constructed co-expression networks of the top 5 upregulated lncRNAs and the top 5 downregulated lncRNAs and the mRNAs that are correlated with them. Furthermore, the functional effects of knocking down lncRNA lung cancer-associated transcript 1 (LUCAT1) on EC phenotypes were determined as it was one of the significantly upregulated lncRNAs following e-cig exposure based on our profiling. Results: 183 lncRNAs and 132 mRNAs were found to be upregulated, whereas 297 lncRNAs and 413 mRNAs were found to be downregulated after e-cig exposure. We also observed that e-cig caused dysregulation of endothelial metabolism resulting in increased FAO activity, higher mitochondrial membrane potential, and decreased glucose uptake and glycolysis. These results suggest that e-cig alters EC metabolism by increasing FAO to compensate for energy deficiency in ECs. Finally, the knockdown of LUCAT1 prevented e-cig-induced EC dysfunction by maintaining vascular barrier, reducing reactive oxygen species level, and increasing migration capacity. Conclusion: This study identifies an expression profile of differentially expressed lncRNAs and several potential regulators and pathways in ECs exposed to e-cig, which provide insights into the regulation of lncRNAs and mRNAs and the role of lncRNA and mRNA networks in ECs associated e-cig exposure. [ABSTRACT FROM AUTHOR]

Background: Theoretically, paralogous genes generated through whole genome duplications should share identical expression levels due to their identical sequences and chromatin environments. However, functional divergences and expression differences have arisen due to selective pressures throughout evolution. A comprehensive investigation of the expression patterns of paralogous gene pairs in response to various stresses and a study of correlations between the expression levels and sequence divergences of the paralogs are needed. Results: In this study, we analyzed the expression patterns of paralogous genes under different types of stress and investigated the correlations between the expression levels and sequence divergences of the paralogs. We analyzed the differential expression patterns of the paralogs under four different types of stress (drought, cold, infection, and herbivory) and classified them into three main types according to their expression patterns. We then further analyzed the differential expression patterns under various degrees of stress and constructed corresponding co-expression networks of differentially expressed paralogs and transcription factors. Finally, we investigated the correlations between the expression levels and sequence divergences of the paralogs and identified positive correlations between expression level and sequence divergence. With regard to sequence divergence, we identified correlations between selective pressures and phylogenetic relationships. Conclusions: These results shed light on differential expression patterns of paralogs in response to environmental stresses and are helpful for understanding the relationships between expression levels and sequences divergences. [ABSTRACT FROM AUTHOR]