Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears

Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images

Gene expression analysis is pivotal in cancer research and clinical practice. Although traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, RNAscope score, RT-droplet digital PCR, and automated QuantISH and QuPath were used for quantifying RNA-ISH expression values from formalin-fixed, paraffin-embedded samples. The methods were compared using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. The findings demonstrate good concordance between automated methods and RNAscope, with RT-droplet digital PCR showing less concordance. Additionally, QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.

Examining Transcriptomic Alterations in Rat Models of Intracerebral Hemorrhage and Severe Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a life-threatening condition associated with significant morbidity and mortality. This study investigates transcriptomic alterations in rodent models of ICH and severe ICH to shed light on the genetic pathways involved in hemorrhagic brain injury. We performed principal component analysis, revealing distinct principal component segments of normal rats compared to ICH and severe ICH rats. We employed heatmaps and volcano plots to identify differentially expressed genes and utilized bar plots and KEGG pathway analysis to elucidate the molecular pathways involved. We identified a multitude of differentially expressed genes in both the ICH and severe ICH models. Our results revealed 5679 common genes among the normal, ICH, and severe ICH groups in the upregulated genes group, and 1196 common genes in the downregulated genes, respectively. A volcano plot comparing these groups further highlighted common genes, including PDPN, TIMP1, SERPINE1, TUBB6, and CD44. These findings underscore the complex interplay of genes involved in inflammation, oxidative stress, and neuronal damage. Furthermore, pathway enrichment analysis uncovered key signaling pathways, including the TNF signaling pathway, protein processing in the endoplasmic reticulum, MAPK signaling pathway, and Fc gamma R-mediated phagocytosis, implicated in the pathogenesis of ICH.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

The molecular circadian rhythms regulating the cell cycle

The circadian clock controls the expression of a large proportion of protein-coding genes in mammals and can modulate a wide range of physiological processes. Recent studies have demonstrated that disruption or dysregulation of the circadian clock is involved in the development and progression of several diseases, including cancer. The cell cycle is considered to be the fundamental process related to cancer. Accumulating evidence suggests that the circadian clock can control the expression of a large number of genes related to the cell cycle. This article reviews the mechanism of cell cycle-related genes whose chromatin regulatory elements are rhythmically occupied by core circadian clock transcription factors, while their RNAs are rhythmically expressed. This article further reviews the identified oscillatory cell cycle-related genes in higher organisms such as baboons and humans. The potential functions of these identified genes in regulating cell cycle progression are also discussed. Understanding how the molecular clock controls the expression of cell cycle genes will be beneficial for combating and treating cancer.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

A systemic analysis of monocarboxylate transporters in ovarian cancer and possible therapeutic interventions

Monocarboxylate transporters (MCTs) play an immense role in metabolically active solid tumors by regulating concentration-dependent transport of different important monocarboxylates including pyruvate and lactate and are encoded by the SLC16A family of genes. Given the vast array of functions, these transporters play in oncogenesis, our objective was to look into the association of MCT1 (SLC16A1), MCT2 (SLC16A7), MCT3 (SLC16A8), and MCT4 (SLC16A3) with Epithelial ovarian cancer (EOC) pathophysiology by exploiting various publicly available databases and web resources. Few of the in silico findings were confirmed via in vitro experiments in EOC cell lines, SKOV3 and OAW-42. MCT1 and MCT4 were found to be upregulated at the mRNA level in OC tissues compared to normal. However, only higher level of MCT4 mRNA was found to be associated with poor patient survival. MCT4 was positively correlated with gene families responsible for invasion, migration, and immune modification, proving it to be one of the most important MCTs for therapeutic intervention. We compared the effects of MCT1/2 blocker SR13800 and a broad-spectrum MCT blocker α-Cyano Hydroxy Cinnamic Acid (α-CHCA) and discovered that α-CHCA has a greater effect on diminishing the invasive behavior of the cancer cells than MCT1/2 blocker SR13800. From our study, MCT4 has emerged as a prospective marker for predicting poor patient outcomes and a potential therapeutic target.

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

Developmental programming: adverse sexually dimorphic transcriptional programming of gestational testosterone excess in cardiac left ventricle of fetal sheep

Adverse in-utero insults during fetal life alters offspring's developmental trajectory, including that of the cardiovascular system. Gestational hyperandrogenism is once such adverse in-utero insult. Gestational testosterone (T)-treatment, an environment of gestational hyperandrogenism, manifests as hypertension and pathological left ventricular (LV) remodeling in adult ovine offspring. Furthermore, sexual dimorphism is noted in cardiomyocyte number and morphology in fetal life and at birth. This study investigated transcriptional changes and potential biomarkers of prenatal T excess-induced adverse cardiac programming. Genome-wide coding and non-coding (nc) RNA expression were compared between prenatal T-treated (T propionate 100 mg intramuscular twice weekly from days 30 to 90 of gestation; Term: 147 days) and control ovine LV at day 90 fetus in both sexes. Prenatal T induced differential expression of mRNAs in the LV of female (2 down, 5 up) and male (3 down, 1 up) (FDR < 0.05, absolute log2 fold change > 0.5); pathways analysis demonstrated 205 pathways unique to the female, 382 unique to the male and 23 common pathways. In the male, analysis of ncRNA showed differential regulation of 15 lncRNAs (14 down, 1 up) and 27 snoRNAs (26 down and 1 up). These findings suggest sexual dimorphic modulation of cardiac coding and ncRNA with gestational T excess.

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

BACKGROUND

Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized.

METHODS

Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFA_renal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI.

RESULTS

A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFA_renal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFA_renal > 0 on Days 1 and 3, n = 62), transient AKI (SOFA_renal > 0 on Day 1 and SOFA_renal = 0 on Day 3, n = 50) and worsening AKI (SOFA_renal = 0 on Days 1 and SOFA_renal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong's test).

CONCLUSIONS

Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.

Cystathionine Gamma-Lyase Regulate Psilocybin Biosynthesis in Gymnopilus dilepis Mushroom via Amino Acid Metabolism Pathways

As a potential medicine for the treatment of depression, psilocybin has gradually attracted attention. To elucidate the molecular mechanism regulating psilocybin synthesis in Gymnopilus dilepis, ultra-performance liquid chromatography (UPLC) was used to detect the changes in psilocybin content after S-adenosyl-l-homocysteine (SAH) treatment and the changes of psilocybin content in different parts (stipe and pileus), and RNA-Seq was used to explore the mechanism of psilocybin content changes. In this study, the psilocybin content in G. dilepis mycelia treated with SAH was significantly lower than that in the control group, and the content of psilocybin in the stipe was significantly higher than that in the pileus. Transcriptome analysis revealed that differential expression genes (DEGs) were associated with cysteine and methionine metabolism. In particular, the transcription levels of genes encoding Cystathionine gamma-lyase (CTH) in different treatments and different parts were positively correlated with psilocybin content. In addition, we found that the exogenous addition of CTH activity inhibitor (DL-propargylglycine, PAG) could reduce the content of psilocybin and L-serine, and the content of psilocybin and L-serine returned to normal levels after L-cysteine supplementation, suggesting that psilocybin synthesis may be positively correlated with L-cysteine or CTH, and L-cysteine regulates the synthesis of psilocybin by affecting L-serine and 4-hydroxy-L-tryptophan. In conclusion, this study revealed a new molecular mechanism that affects psilocybin biosynthesis, which can provide a theoretical basis for improving psilocybin synthesis and the possibility for the development of biomedicine.

Progress and Challenges in China Turfgrass Abiotic Stress Resistance Research

Turfgrasses are an important vehicle for urban ecology and one of the most important indicators of economy and civilization. The biological characteristics of different turfgrass species affect the productivity and quality of the turf and its potential use in landscapes, slopes, and sports fields. Cultivation and management techniques can assist turfgrasses to meet the challenges of climate change, while the development of molecular breeding will provide a broader platform for the application of turfgrasses. The turfgrass industry of China has developed considerably in the last three decades; however, there is still an objective gap with developed countries. This manuscript reviewed the research progress of turfgrass resistance breeding, analyzed the bottlenecks in the development of turfgrass resistance breeding, and put forward the strategies to cope with the bottlenecks. Our review aims to promote research and utilization of turfgrasses.

Transcriptional profiling of macrophages reveals distinct parasite stage-driven signatures during early infection by Leishmania donovani

Macrophages undergo swift changes in mRNA abundance upon pathogen invasion. Herein we describe early remodelling of the macrophage transcriptome during infection by amastigotes or promastigotes of Leishmania donovani. Approximately 10–16% of host mRNAs were differentially modulated in L. donovani-infected macrophages when compared to uninfected controls. This response was partially stage-specific as a third of changes in mRNA abundance were either exclusively driven by one of the parasite forms or significantly different between them. Gene ontology analyses identified categories associated with immune functions (e.g. antigen presentation and leukocyte activation) among significantly downregulated mRNAs during amastigote infection while cytoprotective-related categories (e.g. DNA repair and apoptosis inhibition) were enriched in upregulated transcripts. Interestingly a combination of upregulated (e.g. cellular response to IFNβ) and repressed (e.g. leukocyte activation, chemotaxis) immune-related transcripts were overrepresented in the promastigote-infected dataset. In addition, Ingenuity Pathway Analysis (IPA) associated specific mRNA subsets with a number of upstream transcriptional regulators predicted to be modulated in macrophages infected with L. donovani amastigotes (e.g. STAT1 inhibition) or promastigotes (e.g. NRF2, IRF3, and IRF7 activation). Overall, our results indicate that early parasite stage-driven transcriptional remodelling in macrophages contributes to orchestrate both protective and deleterious host cell responses during L. donovani infection.

Comparative Study of Classification Algorithms for Various DNA Microarray Data

Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN), and the resulting accuracies were compared. k-fold cross-validation was used in evaluating the performance and the result was analyzed by comparing the performances of the five machine learning methods. Through the experiments, it was observed that the two tree-based methods, DT and RF, showed similar trends in results and the remaining three methods, MLP, SVM, and DT, showed similar trends. DT and RF generally showed worse performance than other methods except for one dataset. This suggests that, for the effective classification of microarray data, selecting a classification algorithm that is suitable for data traits is crucial to ensure optimum performance.

Exploration of alcohol use disorder-associated brain miRNA-mRNA regulatory networks

Transcriptomic changes in specific brain regions can influence the risk of alcohol use disorder (AUD), but the underlying mechanism is not fully understood. We investigated AUD-associated miRNA-mRNA regulatory networks in multiple brain regions by analyzing transcriptomic changes in two sets of postmortem brain tissue samples and ethanol-exposed human embryonic stem cell (hESC)-derived cortical interneurons. miRNA and mRNA transcriptomes were profiled in 192 tissue samples (Set 1) from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control European Australians. Nineteen differentially expressed miRNAs (fold-change>2.0 & P < 0.05) and 97 differentially expressed mRNAs (fold-change>2.0 & P < 0.001) were identified in one or multiple brain regions of AUD subjects. AUD-associated miRNA-mRNA regulatory networks in each brain region were constructed using differentially expressed and negatively correlated miRNA-mRNA pairs. AUD-relevant pathways (including CREB Signaling, IL-8 Signaling, and Axonal Guidance Signaling) were potentially regulated by AUD-associated brain miRNA-mRNA pairs. Moreover, miRNA and mRNA transcriptomes were mapped in additional 96 tissue samples (Set 2) from six of the above eight brain regions of eight AUD and eight control European Australians. Some of the AUD-associated miRNA-mRNA regulatory networks were confirmed. In addition, miRNA and mRNA transcriptomes were analyzed in hESC-derived cortical interneurons with or without ethanol exposure, and ethanol-influenced miRNA-mRNA regulatory networks were constructed. This study provided evidence that alcohol could induce concerted miRNA and mRNA expression changes in reward-related or alcohol-responsive brain regions. We concluded that altered brain miRNA-mRNA regulatory networks might contribute to AUD development.

A stacking ensemble deep learning approach to cancer type classification based on TCGA data

Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p =  < 0.001, and p =  < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p =  < 0.001 and p =  < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p =  < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival.

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

Isomer-Specific Effects of cis-9,trans-11- and trans-10,cis-12-CLA on Immune Regulation in Ruminal Epithelial Cells

Simple Summary

The significant contribution of rumen microbiota to the balance of the innate immunity of rumen epithelium has been extensively verified. As the natural rumen microbial metabolites, information regarding the immunoprotective effects of different conjugated linoleic acid (CLA) isomers on ruminal epithelial cells (RECs) is limited. In this study, the 100 μM trans-10,cis-12-CLA exerted better anti-inflammatory effects than the cis-9,trans-11-CLA by significantly downregulating the expression of genes related to inflammation, cell proliferation and migration in RECs upon lipopolysaccharide (LPS) stimulation. The trans-10,cis-12-CLA, but not cis-9,trans-11-CLA, significantly suppressed the biological signals of gene ontology (GO) terms’ response to lipopolysaccharide, the regulation of signal transduction and cytokine production and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways NF-κB, chemokine, NOD-like receptor, Hippo, PI3K-Akt, TGF-β and Rap1 signaling in RECs upon LPS stimulation. Furthermore, pretreatment with trans-10,cis-12-CLA significantly reduced the expression of lipogenic genes and the biosynthesis of the unsaturated fatty acid pathway in RECs compared with the LPS group, however, cis-9,trans-11-CLA exhibited the opposite results. These results suggest the distinct isomer differences of CLA in the regulation of inflammatory responses and adipocytokine signaling in RECs and will provide important references for determining their target use in the future.

Abstract

In this study, we used transcriptomics and qPCR to investigate the potential immunoprotective effects of different conjugated linoleic acid (CLA) isomers, the natural rumen microbial metabolites, on lipopolysaccharide (LPS)-induced inflammation of ruminal epithelial cells (RECs) in vitro. The results showed that 100 μM trans-10,cis-12-CLA exerted higher anti-inflammatory effects than cis-9,trans-11-CLA by significantly downregulating the expression of genes related to inflammation, cell proliferation and migration in RECs upon LPS stimulation. Transcriptomic analyses further indicated that pretreatment with trans-10,cis-12-CLA, but not cis-9,trans-11-CLA, significantly suppressed the biological signals of GO terms’ response to LPS, the regulation of signal transduction and cytokine production and KEGG pathways NF-κB, chemokine, NOD-like receptor, Hippo, PI3K-Akt, TGF-β and Rap1 signaling in RECs upon LPS stimulation. Furthermore, pretreatment with trans-10,cis-12-CLA significantly reduced the expression of lipogenic genes and the biosynthesis of the unsaturated fatty acid pathway in RECs compared with the LPS group, however, cis-9,trans-11-CLA exhibited the opposite results. These results suggest the distinct isomer differences of CLA in the regulation of inflammatory responses and adipocytokine signaling in RECs and will provide important references for determining their target use in the future.

RNA-Seq analysis reveals critical transcriptome changes caused by sodium butyrate in DN mouse models

Diabetic nephropathy (DN)—a common complication of diabetes—is the primary cause of end-stage renal disease. Sodium butyrate (NaB) is a short-chain fatty acid (SCFA) that is a metabolic product of intestinal bacterium, and its protective effect on the kidney has been reported in cases of DN. However, its underlying mechanism remains unclear. The aim of the present study was to investigate the effect of NaB on globe transcriptome changes in DN. In our study, 8-week-old male db/db mice suffering from DN were randomly divided into two groups: the DN+NaB group (DN mice treated with NaB, 5 g/kg/day) and the DN group (DN mice treated with saline). Further, normal db/m mice were used as the normal control (NC) group. The blood glucose, body weight, urinary microalbumin and urinary creatinine of mice were measured for all three groups. Whole-transcriptome analysis was performed by RNA sequencing (RNA-Seq) to evaluate the profiling of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs). Bioinformatics analysis was performed to predict the potential NaB-related lncRNAs and genes in DN. The expressions of lncRNAs and mRNAs were tested using the quantitative real-time polymerase chain reactions (qRT-PCRs) in renal tissues and mesangial cells treated with NaB. The results of the present study demonstrated that NaB ameliorated renal dysfunction in DN mice. Moreover, RNA-Seq results identified that some lncRNAs and mRNAs were reversely changed in the DN+NaB group in comparison to those in the DN group. Additionally, the integrated co-expression networks of NaB-related lncRNAs revealed that these lncRNAs interacted with 155 key mRNAs. Furthermore, the co-expression network of inflammation-related lncRNAs and mRNAs demonstrated that those reversed lncRNAs and mRNAs also play essential roles in the inflammatory response. In summary, the present study suggests that NaB ameliorates diabetes-induced renal dysfunction and regulates transcriptome changes in DN.

An animal model study on the gene expression profile of meniscal degeneration

Meniscal degeneration is a very common condition in elderly individuals, but the underlying mechanisms of its occurrence are not completely clear. This study examines the molecular mechanisms of meniscal degeneration. The anterior cruciate ligament (ACL) and lateral collateral ligament (LCL) of the right rear limbs of seven Wuzhishan mini-pigs were resected (meniscal degeneration group), and the left rear legs were sham-operated (control group). After 6 months, samples were taken for gene chip analysis, including differentially expressed gene (DEG) analysis, gene ontology (GO) analysis, clustering analysis, and pathway analysis. The selected 12 DEGs were validated by real time reverse transcription-polymerase chain reaction (RT-PCR). The two groups showed specific and highly clustered DEGs. A total of 893 DEGs were found, in which 537 are upregulated, and 356 are downregulated. The GO analysis showed that the significantly affected biological processes include nitric oxide metabolic process, male sex differentiation, and mesenchymal morphogenesis, the significantly affected cellular components include the endoplasmic reticulum membrane, and the significantly affected molecular functions include transition metal ion binding and iron ion binding. The pathway analysis showed that the significantly affected pathways include type II diabetes mellitus, inflammatory mediator regulation of TRP channels, and AMPK signaling pathway. The results of RT-PCR indicate that the microarray data accurately reflects the gene expression patterns. These findings indicate that several molecular mechanisms are involved in the development of meniscal degeneration, thus improving our understanding of meniscal degeneration and provide molecular therapeutic targets in the future.

Transcriptomic Analyses Reveal the Protective Immune Regulation of Conjugated Linoleic Acids in Sheep Ruminal Epithelial Cells

The ruminal epithelium is continuously challenged by antigens released by the lysis of dead microbial cells within the rumen. However, the innate immune system of the ruminal epithelium can almost always actively respond to these challenges. The cross talk between the ruminal microbiota and innate immune cells in the ruminal epithelium has been suggested to play an important role in sustaining the balance of immune tolerance and inflammatory response in the rumen. We hypothesized that conjugated linoleic acid (CLA), a functional microbial metabolite in the rumen, may contribute to the immune regulation in rumen epithelial cells (RECs); therefore, we first established an immortal REC line and then investigated the regulatory effects of CLA on the immune responses in these RECs. The results showed that long-term REC cultures were successfully established via SV40T-induced immortalization. Transcriptome analysis showed that a 100 μM CLA mixture consisting of 50:50 cis-9, trans-11:trans-10, cis-12 CLA significantly downregulated the expression of the inflammatory response-related genes TNF-α, IL-6, CX3CL1, IRF1, ICAM1 and EDN1, and upregulated the expression of the cell proliferation-related genes FGF7, FGF21, EREG, AREG and HBEGF and the lipid metabolism-related genes PLIN2, CPT1A, ANGPTL4, ABHD5 and SREBF1 in the RECs upon LPS stimulation. Correspondingly, the GO terms regulation of cell adhesion, response to stimulus and cytokine production and KEGG pathways TNF and HIF-1 signaling, ECM-receptor interaction and cell adhesion molecules were identified for the significantly downregulated genes, while the GO terms epithelial cell proliferation and regulation of epithelial cell migration and the KEGG pathways PPAR, ErbB and adipocytokine signaling were identified for the RECs with significantly upregulated CLA-pretreated genes upon LPS stimulation. These findings revealed that CLA conferred protective immunity onto the RECs by inhibiting proinflammatory processes, promoting cell proliferation and regulating lipid metabolism related to the immune response.

Metabolism and Toxicity of Emodin: Genome-Wide Association Studies Reveal Hepatocyte Nuclear Factor 4α Regulates UGT2B7 and Emodin Glucuronidation

Emodin is the main toxic component in Chinese medicinal herbs such as rhubarb. Our previous studies demonstrated that genetic polymorphisms of UDP-glucuronosyltransferase 2B7 (UGT2B7) had an effect on the glucuronidation and detoxification of emodin. This study aimed to reveal the transcriptional regulation mechanism of UGT2B7 on emodin glucuronidation and its effect on toxicity. Emodin glucuronic activity and genome and transcriptome data were obtained from 36 clinical human kidney tissues. The genome-wide association studies (GWAS) identified that four single nucleotide polymorphisms (SNPs) (rs6093966, rs2868094, rs2071197, and rs6073433), which were located on the hepatocyte nuclear factor 4α (HNF4A) gene, were significantly associated with the emodin glucuronidation (p < 0.05). Notably, rs2071197 was significantly associated with the gene expression of HNF4A and UGT2B7 and the glucuronidation of emodin. The gene expression of HNF4A showed a high correlation with UGT2B7 (R² = 0.721, p = 5.83 × 10^-11). The luciferase activity was increased 7.68-fold in 293T cells and 2.03-fold in HepG2 cells, confirming a significant transcriptional activation of UGT2B7 promoter by HNF4A. The knockdown of HNF4A in HepG2 cells (36.6%) led to a significant decrease of UGT2B7 (19.8%) and higher cytotoxicity (p < 0.05). The overexpression of HNF4A in HepG2 cells (31.2%) led to a significant increase of UGT2B7 (24.4%) and improved cell viability (p < 0.05). Besides, HNF4A and UGT2B7 were both decreased in HepG2 cells and rats after treatment with emodin. In conclusion, emodin used long term or in high doses could inhibit the expression of HNF4A, thereby reducing the expression of UGT2B7 and causing hepatotoxicity.

Transcriptional profiling to identify the key genes and pathways of pterygium

Purpose

Pterygium results from a variety of biological pathways that are involved in the formation of ocular surface diseases. However, the exact pathogenesis of pterygium is still unclear. Our study focused on gene expression profiles to better understand the potential mechanisms of pterygium.

Methods

RNA sequencing experiments were performed on clinical pterygium tissues and normal conjunctival tissues. To identify the hub genes for the development of pterygium, we further conducted weighted gene co-expression network analysis (WGCNA). qRT-PCR was utilized to validate the dysregulation of the most significant differentially expressed genes (DEGs) and key hub genes in the independent subjects.

Results

A total of 339 DEGs (P-adjusted < 0.05 and log2 fold change [log2FC] ≥ 1.0) were obtained that reached statistical significance with p-values < 0.05. Among them, 200 DEGs were upregulated; these genes were mainly associated with the extracellular matrix and with cell adhesion or migration. In contrast, the 139 downregulated genes were enriched for endocrine and inflammation pathways. With regard to WGCNA, five modules were assigned based on the DEG profiles, and the biological functions of each module were verified with previously published GO terms. The functions included ECM-receptor interactions, the PI3K-Akt signalling pathway and an endoplasmic reticulum (ER)-related pathway. The five hub genes with the highest connectivity in each module and the five most significant DEGs showed dysregulated expression in the independent cohort samples.

Conclusions

RNA sequencing and WGCNA provided novel insights into the potential regulatory mechanisms of pterygium. The identified DEGs and hub genes, which were classified into two groups according to different functions or signalings, may provide important references for further research on the molecular biology of pterygium.

Cytokine Gene Expression Alterations in Human Macrophages Infected by Leishmania major

Objective

Leishmaniasis is caused by members of the Leishmania species and constitute a group of infective diseases that range from cutaneous lesions to lethal visceral forms. In infected persons, macrophages recognize and eliminate the parasites via phagocytosis. In order to change a hostile environment into an environment adequate for survival and reproduction, the engulfed Leishmania species needs to modulate the function of its host macrophage. The expression patterns of cytokine genes such as interleukin-12 (IL-12), tumour necrosis factor-alpha (TNF-α), IL-1, and interferon-gamma (IFNγ) represent the immune response. In this study, we employed an RNA-seq approach for human monocyte-derived macrophages infected with Leishmania major (L. major) to decipher cytokine gene expression alterations in host macrophages.

Materials and Methods

In this descriptive study, human monocytes were isolated by magnetic activated cell sorting (MACS) and cultured in the presence of monocyte colony stimulating factor (M-CSF) to obtain the macrophages. Monocyte-derived macrophages were then co-cultured with metacyclic promastigotes of L. major for 4 hours. RNA isolation was performed using TRIzol reagent. RNA sequencing was performed using the Illumina sequencing platforms. Gene expression analysis was performed using a Bioconductor DESeq2 package.

Results

Our data revealed significant changes in immune response gene expressions in macrophages infected with L. major, with an up-regulation of cytokines and mostly down-regulation of their receptors.

Conclusion

The obtained data could shed more light on the biology of L. major and how the host cell responds to leishmaniasis.

RNA-seq analysis of chondrocyte transcriptome reveals genetic heterogeneity in LG/J and SM/J murine strains

OBJECTIVE

To investigate the transcriptomic differences in chondrocytes obtained from LG/J (large, healer) and SM/J (small, non-healer) murine strains in an attempt to discern the molecular pathways implicated in cartilage regeneration and susceptibility to osteoarthritis (OA).

DESIGN

We performed RNA-sequencing on chondrocytes derived from LG/J (n = 16) and SM/J (n = 16) mice. We validated the expression of candidate genes and compared single nucleotide polymorphisms (SNPs) between the two mouse strains. We also examined gene expression of positional candidates for ear pinna regeneration and long bone length quantitative trait loci (QTLs) that display differences in cartilaginous expression.

RESULTS

We observed a distinct genetic heterogeneity between cells derived from LG/J and SM/J mouse strains. We found that gene ontologies representing cell development, cartilage condensation, and regulation of cell differentiation were enriched in LG/J chondrocytes. In contrast, gene ontologies enriched in the SM/J chondrocytes were mainly related to inflammation and degeneration. Moreover, SNP analysis revealed that multiple validated genes vary in sequence between LG/J and SM/J in coding and highly conserved noncoding regions. Finally, we showed that most QTLs have 20-30% of their positional candidates displaying differential expression between the two mouse strains.

CONCLUSIONS

While the enrichment of pathways related to cell differentiation, cartilage development and cartilage condensation infers superior healing potential of LG/J strain, the enrichment of pathways related to cytokine production, immune cell activation and inflammation entails greater susceptibility of SM/J strain to OA. These data provide novel insights into chondrocyte transcriptome and aid in identification of the quantitative trait genes and molecular differences underlying the phenotypic differences associated with individual QTLs.

Transcriptome Analysis of Ochratoxin A-Induced Apoptosis in Differentiated Caco-2 Cells

Ochratoxin A (OTA), an important mycotoxin that occurs in food and animal feed, has aroused widespread concern in recent years. Previous studies have indicated that OTA causes nephrotoxicity, hepatotoxicity, genotoxicity, immunotoxicity, cytotoxicity, and neurotoxicity. The intestinal toxicity of OTA has gradually become a focus of research, but the mechanisms underlying this toxicity have not been described. Here, differentiated Caco-2 cells were incubated for 48 h with different concentrations of OTA and transcriptome analysis was used to estimate damage to the intestinal barrier. Gene expression profiling was used to compare the characteristics of differentially expressed genes (DEGs). There were altogether 10,090 DEGs, mainly clustered into two downregulation patterns. The Search Tool for Retrieval of Interacting Genes (STRING), which was used to analyze the protein-protein interaction network, indicated that 24 key enzymes were mostly responsible for regulating cell apoptosis. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis was used to validate eight genes, three of which were key genes (CASP3, CDC25B, and EGR1). The results indicated that OTA dose-dependently induces apoptosis in differentiated Caco-2 cells. Transcriptome analysis showed that the impairment of intestinal function caused by OTA might be partly attributed to apoptosis, which is probably associated with downregulation of murine double minute 2 (MDM2) expression and upregulation of Noxa and caspase 3 (CASP3) expression. This study has highlighted the intestinal toxicity of OTA and provided a genome-wide view of biological responses, which provides a theoretical basis for enterotoxicity and should be useful in establishing a maximum residue limit for OTA.

RNA-seq in Skeletal Biology

PURPOSE OF REVIEW

The goal of this paper is to review state-of-the-art transcriptome profiling methods and their recent applications in the field of skeletal biology.

RECENT FINDINGS

Next-generation sequencing of mRNA (RNA-seq) methods have been established and routinely used in skeletal biology research. RNA-seq has led to the identification of novel genes and transcription factors involved in skeletal development and disease, through its application in small and large animal models, as well as human tissue and cells. With the availability of advanced techniques such as single-cell RNA-seq, novel cell types in skeletal tissues are being identified. As the sequencing technologies are rapidly evolving, the exciting discoveries supported by transcriptomics will continue to emerge and improve our understanding of the biology of the skeleton.

Gestational binge alcohol-induced alterations in maternal uterine artery transcriptome

Binge alcohol exposure during pregnancy results in diminished vessel function and altered proteome in the maternal uterine artery. We aimed to utilize high throughput RNA-seq deep-sequencing to characterize specific effects of binge alcohol exposure during pregnancy on the uterine artery transcriptome, and gain insight into mechanisms underlying alcohol-mediated uterine artery dysfunction. Pregnant Sprague-Dawley rats assigned to Pair-Fed Control or Alcohol groups, received a once-daily orogastric gavage in a binge paradigm. RNA-sequencing using Illumina NextSeq 500, identified 13,941 genes; 40 significantly altered genes were altered by log2(fold change) > 2; 27 genes were upregulated and 13 were downregulated in the Alcohol group. Transcripts altered included those which encode for aldehyde dehydrogenases, matrix metalloproteases, and molecules vital for vasodilation and vascular remodeling. Biological pathways that were disproportionally altered by alcohol were proline and citrulline biosynthesis/metabolism. Disruption of these pathways suggests candidate mechanism(s) for alcohol-mediated impairments to the proteome and vascular function.

Distinct expression pattern of periostin splice variants in chondrocytes and ligament progenitor cells

Periostin (POSTN), a secretory matricellular matrix protein, plays a multitude of biologic functions. Various splice variants of POSTN have been described; however, their expression pattern and functional implications are not completely understood. This study was undertaken to decipher the differential expression pattern of POSTN and its splice variants in various tissues and cell types. We show that POSTN was more highly expressed in anterior cruciate ligament (ACL) remnants compared with articular cartilage at the cellular and tissue level. Isoforms 1 and 8 were highly expressed only in articular chondrocytes, suggesting their splice-specific regulation in chondrocytes. To discern the role of total POSTN and full-length human POSTN isoform 1 (hPOSTN-001), we stably transfected human chondrosarcoma 1 (hCh-1) cell line with hPOSTN-001 using a pcDNA3.1-hPOSTN-001 construct. RNA-sequencing analysis of hCh-1 cells identified differentially expressed genes with a known role in chondrocyte function and osteoarthritis. Similar expression of a subset of candidate genes was revealed in ACL progenitor cells and chondrocytes as well as in ACL progenitor cells in which POSTN activity was altered by overexpression and by small interfering RNA gene knockdown. Cells expressing total POSTN, not isoform 1, exhibited increased cell adhesion potential. These findings suggest an important role for POSTN in the knee.-Cai, L., Brophy, R. H., Tycksen, E. D., Duan, X., Nunley, R. M., Rai, M. F. Distinct expression pattern of periostin splice variants in chondrocytes and ligament progenitor cells.

Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species

Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats.

Next generation sequencing applications for cardiovascular disease

The Human Genome Project (HGP), as the primary sequencing of the human genome, lasted more than one decade to be completed using the traditional Sanger's method. At present, next-generation sequencing (NGS) technology could provide the genome sequence data in hours. NGS has also decreased the expense of sequencing; therefore, nowadays it is possible to carry out both whole-genome (WGS) and whole-exome sequencing (WES) for the variations detection in patients with rare genetic diseases as well as complex disorders such as common cardiovascular diseases (CVDs). Finding new variants may contribute to establishing a risk profile for the pathology process of diseases. Here, recent applications of NGS in cardiovascular medicine are discussed; both Mendelian disorders of the cardiovascular system and complex genetic CVDs including inherited cardiomyopathy, channelopathies, stroke, coronary artery disease (CAD) and are considered. We also state some future use of NGS in clinical practice for increasing our information about the CVDs genetics and the limitations of this new technology. Key messages Traditional Sanger's method was the mainstay for Human Genome Project (HGP); Sanger sequencing has high fidelity but is slow and costly as compared to next generation methods. Within cardiovascular medicine, NGS has been shown to be successful in identifying novel causative mutations and in the diagnosis of Mendelian diseases which are caused by a single variant in a single gene. NGS has provided the opportunity to perform parallel analysis of a great number of genes in an unbiased approach (i.e. without knowing the underlying biological mechanism) which probably contribute to advance our knowledge regarding the pathology of complex diseases such as CVD.