Modeling incomplete penetrance in arrhythmogenic cardiomyopathy by human induced pluripotent stem cell derived cardiomyocytes

Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are commonly used to model arrhythmogenic cardiomyopathy (ACM), a heritable cardiac disease characterized by severe ventricular arrhythmias, fibrofatty myocardial replacement and progressive ventricular dysfunction. Although ACM is inherited as an autosomal dominant disease, incomplete penetrance and variable expressivity are extremely common, resulting in different clinical manifestations. Here, we propose hiPSC-CMs as a powerful in vitro model to study incomplete penetrance in ACM. Six hiPSC lines were generated from blood samples of three ACM patients carrying a heterozygous deletion of exon 4 in the PKP2 gene, two asymptomatic (ASY) carriers of the same mutation and one healthy control (CTR), all belonging to the same family. Whole exome sequencing was performed in all family members and hiPSC-CMs were examined by ddPCR, western blot, Wes™ immunoassay system, patch clamp, immunofluorescence and RNASeq. Our results show molecular and functional differences between ACM and ASY hiPSC-CMs, including a higher amount of mutated PKP2 mRNA, a lower expression of the connexin-43 protein, a lower overall density of sodium current, a higher intracellular lipid accumulation and sarcomere disorganization in ACM compared to ASY hiPSC-CMs. Differentially expressed genes were also found, supporting a predisposition for a fatty phenotype in ACM hiPSC-CMs. These data indicate that hiPSC-CMs are a suitable model to study incomplete penetrance in ACM.

Recent computational drug repositioning strategies against SARS-CoV-2

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We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data.

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We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19.

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Integrating different levels of data may improve the success rate for drug repositioning.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

Neuroprotective effect of selective hypothermic cerebral perfusion in extracorporeal cardiopulmonary resuscitation: A preclinical study

Objective

Neurologic complications seriously affect the survival rate and quality of life in patients with extracorporeal cardiopulmonary resuscitation (ECPR) undergoing cardiac arrest. This study aimed to repurpose selective hypothermic cerebral perfusion (SHCP) as a novel approach to protect the brains of these patients.

Methods

Rats were randomly allocated to Sham, ECPR, and SHCP combined ECPR (CP-ECPR) groups. In the ECPR group, circulatory resuscitation was performed at 6 minutes after asphyxial cardiac arrest by extracorporeal membrane oxygenation. The vital signs were monitored for 3 hours, and body and brain temperatures were maintained at the normal level. In the CP-ECPR group, the right carotid artery catheterization serving as cerebral perfusion was connected with the extracorporeal membrane oxygenation device to achieve selective brain cooling (26-28 °C). Serum markers of brain injury and pathomorphologic changes in the hippocampus were evaluated. Three biological replicates further received RNA sequencing in ECPR and CP-ECPR groups. Microglia activation and inflammatory cytokines in brain tissues and serum were detected.

Results

SHCP rapidly reduced the brain-targeted temperature and significantly alleviated nerve injury. This was evident from the reduced brain injury serum biomarker levels, lower pathologic scores, and more surviving neurons in the hippocampus in the CP-ECPR group. Furthermore, more differentially expressed genes for inflammatory responses were clustered functionally according to Kyoto Encyclopedia of Genes and Genomes pathway analysis. And SHCP reduced microglia activation and the release of proinflammatory mediators.

Conclusions

Our preliminary data indicate that SHCP may serve as a potential therapy to attenuate brain injury via downregulation of neuroinflammation in patients with ECPR.

MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning

In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.g., the low availability of samples for some diseases or limited research funding), small sample size is frequently used in experiments. Therefore, methods to screen reliable and stable features are urgently needed for analyses with limited sample size. In this study, MSPJ, a new machine learning approach for identifying DEGs was proposed to mitigate the reduced power and improve the stability of DEG identification in small gene expression datasets. This ensemble learning-based method consists of three algorithms: an improved multiple random sampling with meta-analysis, SVM-RFE (support vector machines-recursive feature elimination), and permutation test. MSPJ was compared with ten classical methods by 94 simulated datasets and large-scale benchmarking with 165 real datasets. The results showed that, among these methods MSPJ had the best performance in most small gene expression datasets, especially those with sample size below 30. In summary, the MSPJ method enables effective feature selection for robust DEG identification in small transcriptome datasets and is expected to expand research on the molecular mechanisms underlying complex diseases or phenotypes.

Identifies microtubule-binding protein CSPP1 as a novel cancer biomarker associated with ferroptosis and tumor microenvironment

Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.

miR-7/TGF-β2 axis sustains acidic tumor microenvironment-induced lung cancer metastasis

Acidosis, regardless of hypoxia involvement, is recognized as a chronic and harsh tumor microenvironment (TME) that educates malignant cells to thrive and metastasize. Although overwhelming evidence supports an acidic environment as a driver or ubiquitous hallmark of cancer progression, the unrevealed core mechanisms underlying the direct effect of acidification on tumorigenesis have hindered the discovery of novel therapeutic targets and clinical therapy. Here, chemical-induced and transgenic mouse models for colon, liver and lung cancer were established, respectively. miR-7 and TGF-β2 expressions were examined in clinical tissues (n = 184). RNA-seq, miRNA-seq, proteomics, biosynthesis analyses and functional studies were performed to validate the mechanisms involved in the acidic TME-induced lung cancer metastasis. Our data show that lung cancer is sensitive to the increased acidification of TME, and acidic TME-induced lung cancer metastasis via inhibition of miR-7-5p. TGF-β2 is a direct target of miR-7-5p. The reduced expression of miR-7-5p subsequently increases the expression of TGF-β2 which enhances the metastatic potential of the lung cancer. Indeed, overexpression of miR-7-5p reduces the acidic pH-enhanced lung cancer metastasis. Furthermore, the human lung tumor samples also show a reduced miR-7-5p expression but an elevated level of activated TGF-β2; the expressions of both miR-7-5p and TGF-β2 are correlated with patients' survival. We are the first to identify the role of the miR-7/TGF-β2 axis in acidic pH-enhanced lung cancer metastasis. Our study not only delineates how acidification directly affects tumorigenesis, but also suggests miR-7 is a novel reliable biomarker for acidic TME and a novel therapeutic target for non-small cell lung cancer (NSCLC) treatment. Our study opens an avenue to explore the pH-sensitive subcellular components as novel therapeutic targets for cancer treatment.

Differences in faecal microbiome composition between adult patients with UCD and PKU and healthy control subjects

Urea cycle disorders (UCDs) are a group of rare inherited metabolic diseases causing hyperammonemic encephalopathy. Despite intensive dietary and pharmacological therapy, outcome is poor in a subset of UCD patients. Reducing ammonia production by changing faecal microbiome in UCD is an attractive treatment approach. We compared faecal microbiome composition of 10 UCD patients, 10 healthy control subjects and 10 phenylketonuria (PKU) patients. PKU patients on a low protein diet were included to differentiate between the effect of a low protein diet and the UCD itself on microbial composition. Participants were asked to collect a faecal sample and to fill out a 24 h dietary journal. DNA was extracted from faecal material, taxonomy was assigned and microbiome data was analyzed, with a focus on microbiota involved in ammonia metabolism.In this study we show an altered faecal microbiome in UCD patients, different from both PKU and healthy controls. UCD patients on dietary and pharmacological treatment had a less diverse faecal microbiome, and the faecal microbiome of PKU patients on a protein restricted diet with amino acid supplementation showed reduced richness compared to healthy adults without a specific diet. The differences in the microbiome composition of UCD patients compared to healthy controls were in part related to lactulose use. Other genomic process encodings involved in ammonia metabolism, did not seem to differ. Since manipulation of the microbiome is possible, this could be a potential treatment modality. We propose as a first next step, to study the impact of these faecal microbiome alterations on metabolic stability.

TAKE HOME MESSAGE

The faecal microbiome of UCD patients was less diverse compared to PKU patients and even more compared to healthy controls.

Systematic analysis of potential targets of the curcumin analog pentagamavunon-1 (PGV-1) in overcoming resistance of glioblastoma cells to bevacizumab

Background

Glioblastoma is one of the most aggressive and deadliest malignant tumors. Acquired resistance decreases the effectiveness of bevacizumab in glioblastoma treatment and thus increases the mortality rate in patients with glioblastoma. In this study, the potential targets of pentagamavunone-1 (PGV-1), a curcumin analog, were explored as a complementary treatment to bevacizumab in glioblastoma therapy.

Methods

Target prediction, data collection, and analysis were conducted using the similarity ensemble approach (SEA), SwissTargetPrediction, STRING DB, and Gene Expression Omnibus (GEO) datasets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted using Webgestalt and DAVID, respectively. Hub genes were selected based on the highest degree scores using the CytoHubba. Analysis of genetic alterations and gene expression as well as Kaplan–Meier survival analysis of selected genes were conducted with cBioportal and GEPIA. Immune infiltration correlations between selected genes and immune cells were analyzed with database TIMER 2.0.

Results

We found 374 targets of PGV-1, 1139 differentially expressed genes (DEGs) from bevacizumab-resistant-glioblastoma cells. A Venn diagram analysis using these two sets of data resulted in 21 genes that were identified as potential targets of PGV-1 against bevacizumab resistance (PBR). PBR regulated the metabolism of xenobiotics by cytochrome P450. Seven potential therapeutic PBR, namely GSTM1, AKR1C3, AKR1C4, PTGS2, ADAM10, AKR1B1, and HSD17B110 were found to have genetic alterations in 1.2%–30% of patients with glioblastoma. Analysis using the GEPIA database showed that the mRNA expression of ADAM10, AKR1B1, and HSD17B10 was significantly upregulated in glioblastoma patients. Kaplan–Meier survival analysis showed that only patients with low mRNA expression of AKR1B1 had significantly better overall survival than the patients in the high mRNA group. We also found a correlation between PBR and immune cells and thus revealed the potential of PGV-1 as an immunotherapeutic agent via targeting of PBR.

Conclusion

This study highlighted seven PBR, namely, GSTM1, AKR1C3, AKR1C4, PTGS2, ADAM10, AKR1B1, and HSD17B110. This study also emphasized the potential of PBR as a target for immunotherapy with PGV-1. Further validation of the results of this study is required for the development of PGV-1 as an adjunct to immunotherapy for glioblastoma to counteract bevacizumab resistance.

LCCL peptide cleavage after noise exposure exacerbates hearing loss and is associated with the monocyte infiltration in the cochlea

Acoustic trauma induces an inflammatory response in the cochlea, resulting in debilitating hearing function. Clinically, amelioration of inflammation substantially prevents noise-induced hearing loss. The Limulus factor C, Cochlin, and Lgl1 (LCCL) peptide plays an important role in innate immunity during bacteria-induced inflammation in the cochlea. We aimed to investigate the LCCL-induced innate immune response to noise exposure and its impact on hearing function.

METHODS

We used Coch (encodes cochlin harboring LCCL peptide) knock-out and p.G88E knock-in mice to compare the immune responses before and after noise exposure. We explored their hearing function and hair cell degeneration. Moreover, we investigated distinct characteristics of immune responses upon noise exposure using flow cytometry and RNA sequencing.

RESULTS

One day after noise exposure, the LCCL peptide cleaved from cochlin increased over time in the perilymph space. Both Coch^-/- and Coch^G88E/G88E mutant mice revealed more preserved hearing following acoustic trauma compared to wild-type mice. The outer hair cells were more preserved in Coch^-/- than in wild-type mice upon noise exposure. The RNA sequencing data demonstrated significantly upregulated cell migration gene ontology in wild-type mice than in Coch^-/- mice following noise exposure, indicating that the infiltration of immune cells was dependent on cochlin. Notably, infiltrated monocytes from blood (C11b⁺/Ly6G^-/Ly6C⁺) were remarkably higher in wild-type mice than in Coch^-/- mice at 1 day after noise exposure.

CONCLUSIONS

Noise-induced hearing loss was attributed to over-stimulated cochlin, and led to the cleavage and secretion of LCCL peptide in the cochlea. The LCCL peptide recruited more monocytes from the blood vessels upon noise stimulation, thus highlighting a novel therapeutic target for noise-induced hearing loss.

Machine learning and deep learning methods that use omics data for metastasis prediction

Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

A bioinformatics approach for identifying potential molecular mechanisms and key genes involved in COVID-19 associated cardiac remodeling

In 2019 coronavirus disease (COVID-19), whose main complication is respiratory involvement, different organs may also be affected in severe cases. However, COVID-19 associated cardiovascular manifestations are limited at present. The main purpose of this study was to identify potential candidate genes involved in COVID-19-associated heart damage by bioinformatics analysis. Differently expressed genes (DEGs) were identified using transcriptome profiles (GSE150392 and GSE4172) downloaded from the GEO database. After gene and pathway enrichment analyses, PPI network visualization, module analyses, and hub gene extraction were performed using Cytoscape software. A total of 228 (136 up and 92 downregulated) overlapping DEGs were identified at these two microarray datasets. Finally, the top hub genes (FGF2, JUN, TLR4, and VEGFA) were screened out as the critical genes among the DEGs from the PPI network. Identification of critical genes and mechanisms in any disease can lead us to better diagnosis and targeted therapy. Our findings identified core genes shared by inflammatory cardiomyopathy and SARS-CoV-2. The findings of the current study support the idea that these key genes can be used in understanding and managing the long-term cardiovascular effects of COVID-19.

Differential blood transcriptome modules predict response to corticosteroid therapy in alcoholic hepatitis

Background & Aims

In patients with severe alcoholic hepatitis (SAH), little is known about the profile of peripheral blood mononuclear cells (PBMCs) at baseline and during corticosteroid therapy, among those who can be treated successfully with steroids (steroid-responders [R] and those who cannot (steroid-non-responders [NR]); 2 groups with different outcomes.

Methods

We performed RNA-seq analysis in PBMCs from 32 patients with definite SAH, at baseline and after 7 days of corticosteroids. The data were sorted into R and NR (n = 16, each group) using the Lille model and 346 blood transcription modules (BTMs) were identified. BTMs are predefined modules of highly co-expressed PBMC genes, which can determine specific immune cell types and cellular functions. The activity of each BTM was taken as the mean value of its member genes.

Results

At baseline, 345 BTMs had higher activity (i.e. were upregulated) in NR relative to R. The 100 most upregulated BTMs in NR, included several modules related to lymphoid lineage (T, B, and natural killer [NK] cells), modules for cell division and mitochondrial respiratory electron transport chain (ETC, relating to energy production), but only a few modules of myeloid cells. Correlation studies of BTM activities found features of significantly greater activation/proliferation and differentiation for T and B cells in NR relative to R. After 7 days of corticosteroids, NR had no significant changes in BTM activities relative to baseline, whereas R had downregulation of BTMs related to innate and adaptive immunity.

Conclusions

At baseline and during corticosteroid therapy, increased activity in the PBMCs of gene modules related to activation/proliferation and differentiation of T and B cells, NK cells, and mitochondrial ETC, is a hallmark of SAH patients who are steroid-non-responders.

Lay summary

Patients with severe alcoholic hepatitis receive steroid therapy as the main line of treatment; however, this treatment is ineffective in some patients. This only becomes apparent after 7 days of steroid therapy. We have developed an approach where it can be estimated if a patient is going to respond or not to steroid therapy using the gene expression information of blood cells. This method will allow clinicians to assess the response of patients to steroids earlier, and will help them in adopting alternate strategies if the treatment is found to be ineffective in a particular patient.

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RNA-seq is an unprecedented tool for analysis of the bulk peripheral blood mononuclear cells (PBMCs) transcriptome.

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Co-expressed genes in the bulk PBMC transcriptome can be grouped as blood transcriptional modules (BTMs).

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Patients with severe alcoholic hepatitis, non-responsive to corticosteroids, have a distinct BTM profile at baseline.

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Deconvolution of RNA-seq data using CIBERSORTx showed increases in different populations of B cells, CD4 and CD8 T cells, and NK cells.

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Baseline FACS analysis of PBMCs can be reflective of immune cells identified by RNA-seq data analysis.

Application of "omics" sciences to the prediction of bone metastases from breast cancer: State of the art

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Breast cancer (BC) is the first cause of cancer-related death in women.

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Most patients with advanced BC develop bone metastases (BM).

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Omics technologies have been applied to identify putative BM “predicting” biomarkers.

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Prospective studies are needed before any clinical application of such biomarkers.

Breast cancer (BC) is the most frequent malignancy and the first cause of cancer-related death in women.

The majority of patients with advanced BC develop skeletal metastases which may ultimately lead to serious complications, termed skeletal-related events, that often dramatically impact on quality of life and survival.

Therefore, the identification of biomarkers able to stratify BC patient risk to develop bone metastases (BM) is fundamental to define personalized diagnostic and therapeutic strategies, possibly at the earliest stages of the disease.

In this regard, the advent of “omics” sciences boosted the investigation of several putative biomarkers of BC osteotropism, including deregulated genes, proteins and microRNAs.

The present review revisits the current knowledge on BM development in BC and the most recent studies exploring potential BM-predicting biomarkers, based on the application of omics sciences to the study of primary breast malignancies.

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.

Iron deficiency triggered transcriptome changes in bread wheat

A series of complex transport, storage and regulation mechanisms control iron metabolism and thereby maintain iron homeostasis in plants. Despite several studies on iron deficiency responses in different plant species, these mechanisms remain unclear in the allohexaploid wheat, which is the most widely cultivated commercial crop. We used RNA sequencing to reveal transcriptomic changes in the wheat flag leaves and roots, when subjected to iron limited conditions. We identified 5969 and 2591 differentially expressed genes (DEGs) in the flag leaves and roots, respectively. Genes involved in the synthesis of iron ligands i.e., nicotianamine (NA) and deoxymugineic acid (DMA) were significantly up-regulated during iron deficiency. In total, 337 and 635 genes encoding transporters exhibited altered expression in roots and flag leaves, respectively. Several genes related to MAJOR FACILITATOR SUPERFAMILY (MFS), ATP-BINDING CASSETTE (ABC) transporter superfamily, NATURAL RESISTANCE ASSOCIATED MACROPHAGE PROTEIN (NRAMP) family and OLIGOPEPTIDE TRANSPORTER (OPT) family were regulated, indicating their important roles in combating iron deficiency stress. Among the regulatory factors, the genes encoding for transcription factors of BASIC HELIX-LOOP-HELIX (bHLH) family were highly up-regulated in both roots and the flag leaves. The jasmonate biosynthesis pathway was significantly altered but with notable expression differences between roots and flag leaves. Homoeologs expression and induction bias analysis revealed subgenome specific differential expression. Our findings provide an integrated overview on regulated molecular processes in response to iron deficiency stress in wheat. This information could potentially serve as a guideline for breeding iron deficiency stress tolerant crops as well as for designing appropriate wheat iron biofortification strategies.

Early microRNA indicators of PPARα pathway activation in the liver

MicroRNAs (miRNAs) are short non-coding RNA species that play key roles in post-transcriptional regulation of gene expression. MiRNAs also serve as a promising source of early biomarkers for different environmental exposures and health effects, although there is limited information linking miRNA changes to specific target pathways. In this study, we measured liver miRNAs in male B6C3F1 mice exposed to a known chemical activator of the peroxisome proliferator-activated receptor alpha (PPARα) pathway, di(2-ethylhexyl) phthalate (DEHP), for 7 and 28 days at concentrations of 0, 750, 1500, 3000, or 6000 ppm in feed. At the highest dose tested, DEHP altered 61 miRNAs after 7 days and 171 miRNAs after 28 days of exposure, with 48 overlapping miRNAs between timepoints. Analysis of these 48 common miRNAs indicated enrichment in PPARα–related targets and other pathways related to liver injury and cancer. Four of the 10 miRNAs exhibiting a clear dose trend were linked to the PPARα pathway: mmu-miRs-125a-5p, -182−5p, -20a−5p, and -378a−3p. mmu-miRs-182−5p and -378a−3p were subsequently measured using digital drop PCR across a dose range for DEHP and two related phthalates with weaker PPARα activity, di-n-octyl phthalate and n-butyl benzyl phthalate, following 7-day exposures. Analysis of mmu-miRs-182−5p and -378a−3p by transcriptional benchmark dose analysis correctly identified DEHP as having the greatest potency. However, benchmark dose estimates for DEHP based on these miRNAs (average 163; range 126−202 mg/kg-day) were higher on average than values for PPARα target genes (average 74; range 29−183 mg/kg-day). These findings identify putative miRNA biomarkers of PPARα pathway activity and suggest that early miRNA changes may be used to stratify chemical potency.

Protective functions of alternative splicing transcripts (CdDHN4-L and CdDHN4-S) of CdDHN4 from bermudagrass under multiple abiotic stresses

Dehydrins (DHNs) play critical roles in plant adaptation to abiotic stresses. The objective of this study was to characterize DHNs in bermudagrass (Cynodon spp.). CdDHN4 gene was cloned from bermudagrass ‘Tifway’. Two CdDHN4 transcripts were detected due to alternative splicing (the nonspliced CdDHN4-L and the spliced CdDHN4-S) and both the CdDHN4-S and CdDHN4-L proteins are YSK₂-type DHNs, the Φ-segment is present in CdDHN4-L and absent in CdDHN4-S. Transgenic Arabidopsis thaliana expressing CdDHN4-L or CdDHN4-S exhibited improved tolerance to salt, osmotic, low temperature and drought stress compared to the wild type (WT). The two transgenic lines did not differ in salt or drought tolerance, while plants expressing CdDHN4-S grew better under osmotic stress than those expressing CdDHN4-L. Both transgenic lines exhibited reduced content of malondialdehyde (MDA) and reactive oxygen species (ROS); and higher antioxidant enzymatic activities than the wild type plants under salt or drought stress. CdDHN4-S exhibited a higher ROS-scavenging capacity than CdDHN4-L.

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Two CdDHN4 transcripts (CdDHN4-L and CdDHN4-S) were detected due to alternative splicing in bermudagrass ‘Tifway’.

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CdDHN4s transgenic Arabidopsis thaliana exhibited higher tolerance to multiple abiotic stress compared to the wild type.

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CdDHN4s transgenic lines has lower content of ROS than the wild type under salt or drought stress.

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CdDHN4-S had a higher ROS-scavenging capacity than CdDHN4-L.

RNA-seq data analysis of stimulated hepatocellular carcinoma cells treated with epigallocatechin gallate and fisetin reveals target genes and action mechanisms

Hepatocellular carcinoma (HCC) is an essentially incurable inflammation-related cancer. We have previously shown by network analysis of proteomic data that the flavonoids epigallocatechin gallate (EGCG) and fisetin (FIS) efficiently downregulated pro-tumor cytokines released by HCC through inhibition of Akt/mTOR/RPS6 phospho-signaling. However, their mode of action at the global transcriptome level remains unclear. Herein, we endeavor to compare gene expression alterations mediated by these compounds through a comprehensive transcriptome analysis based on RNA-seq in HEP3B, a responsive HCC cell line, upon perturbation with a mixture of prototypical stimuli mimicking conditions of tumor microenvironment or under constitutive state.

Analysis of RNA-seq data revealed extended changes on HEP3B transcriptome imposed by test nutraceuticals. Under stimulated conditions, EGCG and FIS significantly modified, compared to the corresponding control, the expression of 922 and 973 genes, respectively, the large majority of which (695 genes), was affected by both compounds. Hierarchical clustering based on the expression data of shared genes demonstrated an almost identical profile in nutraceutical-treated stimulated cells which was virtually opposite in cells exposed to stimuli alone. Downstream enrichment analyses of the co-modified genes uncovered significant associations with cancer-related transcription factors as well as terms of Gene Ontology/Reactome Pathways and highlighted ECM dynamics as a nodal modulation point by nutraceuticals along with angiogenesis, inflammation, cell motility and growth. RNA-seq data for selected genes were independently confirmed by RT-qPCR.

Overall, the present systems approach provides novel evidence stepping up the mechanistic understanding of test nutraceuticals, thus rationalizing their clinical exploitation in new preventive/therapeutic modalities against HCC.

Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure

In order to explore the molecular mechanisms behind the pathogenesis of acute liver failure (ALF) associated with hepatitis B virus (HBV) infection, the present study aimed to identify potential key genes and pathways involved using samples from patients with HBV-associated ALF. The GSE38941 array dataset was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between 10 liver samples from 10 healthy donors and 17 liver specimens from 4 patients with HBV-associated ALF were analyzed using the Linear Models for Microarray Data package. Gene Ontology and KEGG pathway enrichment analyses of the DEGs were performed, followed by functional annotation of the genes and construction of a protein–protein interaction (PPI) network. Subnetwork modules were subsequently identified and analyzed. In total, 3142 DEGs were identified, of which 1755 were upregulated and 1387 were downregulated. The extracellular exosome, immune response, and inflammatory response pathways may potentially be used as biomarkers of ALF pathogenesis. In total, 17 genes (including CCR5, CXCR4, ALB, C3, VGEFA, and IGF1) were identified as hub genes in the PPI network and may therefore be potential marker genes for HBV-associated ALF.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Type I interferon related genes are common genes on the early stage after vaccination by meta-analysis of microarray data

The objective of this study was to find common immune mechanism across different kinds of vaccines. A meta-analysis of microarray datasets was performed using publicly available microarray Gene Expression Omnibus (GEO) and Array Express data sets of vaccination records. Seven studies (out of 35) were selected for this meta-analysis. A total of 447 chips (145 pre-vaccination and 302 post-vaccination) were included. Significance analysis of microarrays (SAM) program was used for screening differentially expressed genes (DEGs). Functional pathway enrichment for the DEGs was conducted in DAVID Gene Ontology (GO) database. Twenty DEGs were identified, of which 10 up-regulated genes involved immune response. Six of which were type I interferon (IFN) related genes, including LY6E, MX1, OAS3, IFI44L, IFI6 and IFITM3. Ten down-regulated genes mainly mediated negative regulation of cell proliferation and cell motion. Results of a subgroup analysis showed that although the kinds of genes varied widely between days 3 and 7 post vaccination, the pathways between them are basically the same, such as immune response and response to viruses, etc. For an independent verification of these 6 type I IFN related genes, peripheral blood mononuclear cells (PBMCs) were collected at baseline and day 3 after the vaccination from 8 Enterovirus 71(EV71) vaccinees and were assayed by RT-PCR. Results showed that the 6 DEGs were also upregulated in EV71 vaccinees. In summary, meta-analysis methods were used to explore the immune mechanism of vaccines and results indicated that the type I IFN related genes and corresponding pathways were common in early immune responses for different kinds of vaccines.

Deciphering the RNA landscape by RNAome sequencing

Current RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species in an unperturbed manner. We report strand-specific RNAome sequencing that determines expression of small and large RNAs from rRNA-depleted total RNA in a single sequence run. Since current analysis pipelines cannot reliably analyze small and large RNAs simultaneously, we developed TRAP, Total Rna Analysis Pipeline, a robust interface that is also compatible with existing RNA sequencing protocols. RNAome sequencing quantitatively preserved all RNA classes, allowing cross-class comparisons that facilitates the identification of relationships between different RNA classes. We demonstrate the strength of RNAome sequencing in mouse embryonic stem cells treated with cisplatin. MicroRNA and mRNA expression in RNAome sequencing significantly correlated between replicates and was in concordance with both existing RNA sequencing methods and gene expression arrays generated from the same samples. Moreover, RNAome sequencing also detected additional RNA classes such as enhancer RNAs, anti-sense RNAs, novel RNA species and numerous differentially expressed RNAs undetectable by other methods. At the level of complete RNA classes, RNAome sequencing also identified a specific global repression of the microRNA and microRNA isoform classes after cisplatin treatment whereas all other classes such as mRNAs were unchanged. These characteristics of RNAome sequencing will significantly improve expression analysis as well as studies on RNA biology not covered by existing methods.

Gene expression profiles identify both MyD88-independent and MyD88-dependent pathways involved in the maturation of dendritic cells mediated by heparan sulfate: a novel adjuvant

The traditional vaccine adjuvant research is mainly based on the trial and error method, and the mechanisms underlying the immune system stimulation remaining largely unknown. We previously demonstrated that heparan sulfate (HS), a TLR-4 ligand and endogenous danger signal, effectively enhanced humoral and cellular immune responses in mice immunized by HBsAg. This study aimed to evaluate whether HS induces better humoral immune responses against inactivated Hepatitis A or Rabies Vaccines, respectively, compared with traditional adjuvants (e.g. Alum and complete Freund's adjuvant). In order to investigate the molecular mechanisms of its adjuvanticity, the gene expression pattern of peripheral blood monocytes derived DCs (dendritic cells) stimulated with HS was analyzed at different times points. Total RNA was hybridized to Agilent SurePrint G3 Human Gene Expression 8×60 K one-color oligo-microarray. Through intersection analysis of the microarray results, we found that the Toll-like receptor signaling pathway was significantly activated, and NF-kB, TRAF3 and IRF7 were activated as early as 12 h, and MyD88 was activated at 48 h post-stimulation. Furthermore, the expression of the surface marker CD83 and the co-stimulatory molecules CD80 and CD86 was up-regulated as early as 24 h. Therefore, we speculated that HS-induced human monocyte-derived DC maturation may occur through both MyD88-independent and dependent pathways, but primarily through the former (TRIF pathway). These data provide an important basis for understanding the mechanisms underlying HS enhancement of the immune response.

Insights in the etiopathology of galactosyltransferase II (GalT-II) deficiency from transcriptome-wide expression profiling of skin fibroblasts of two sisters with compound heterozygosity for two novel B3GALT6 mutations

Mutations in B3GALT6, encoding the galactosyltransferase II (GalT-II) involved in the synthesis of the glycosaminoglycan (GAG) linkage region of proteoglycans (PGs), have recently been associated with a spectrum of connective tissue disorders, including spondyloepimetaphyseal dysplasia with joint laxity type 1 (SEMDJL1) and Ehlers–Danlos-like syndrome. Here, we report on two sisters compound heterozygous for two novel B3GALT6 mutations that presented with severe short stature and progressive kyphoscoliosis, joint hypermobility and laxity, hyperextensible skin, platyspondyly, short ilia, and elbow malalignment. Microarray-based transcriptome analysis revealed the differential expression of several genes encoding extracellular matrix (ECM) structural components, including COMP, SPP1, COL5A1, and COL15A1, enzymes involved in GAG synthesis and in ECM remodeling, such as CSGALNACT1, CHPF, LOXL3, and STEAP4, signaling transduction molecules of the TGFβ/BMP pathway, i.e., GDF6, GDF15, and BMPER, and transcription factors of the HOX and LIM families implicated in skeletal and limb development. Immunofluorescence analyses confirmed the down-regulated expression of some of these genes, in particular of the cartilage oligomeric matrix protein and osteopontin, encoded by COMP and SPP1, respectively, and showed the predominant reduction and disassembly of the heparan sulfate specific GAGs, as well as of the PG perlecan and type III and V collagens. The key role of GalT-II in GAG synthesis and the crucial biological functions of PGs are consistent with the perturbation of many physiological functions that are critical for the correct architecture and homeostasis of various connective tissues, including skin, bone, cartilage, tendons, and ligaments, and generates the wide phenotypic spectrum of GalT-II-deficient patients.

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Clinical features/molecular characterization of two patients with spondyloepimetaphyseal dysplasia with joint laxity type 1

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Identification of two novel B3GALT6 mutations

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First report of transcriptome-wide gene expression profiling on GalT-II-deficient fibroblasts

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Immunofluorescence studies of several ECM structural components in GalT-II-deficient cells

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Enlargement of the knowledge on the GalT-II deficiency’s molecular pathogenesis