The ConsensusPathDB interaction database: 2013 update

A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer's Disease

Uncaria alkaloids are the major bioactive chemicals found in the Uncaria genus, which have a long history of clinical application in treating cardiovascular and mental diseases in traditional Chinese medicine (TCM). However, there are gaps in understanding the multiple targets, pathways, and biological activities of Uncaria alkaloids. By constructing the interactions among drug-targets-diseases, network pharmacology provides a systemic methodology and a novel perspective to present the intricate connections among drugs, potential targets, and related pathways. It is a valuable tool for studying TCM drugs with multiple indications, and how these multi-indication drugs are affected by complex interactions in the biological system. To better understand the mechanisms and targets of Uncaria alkaloids, we built an integrated analytical platform based on network pharmacology, including target prediction, protein-protein interaction (PPI) network, topology analysis, gene enrichment analysis, and molecular docking. Using this platform, we revealed the underlying mechanisms of Uncaria alkaloids' anti-hypertensive effects and explored the possible application of Uncaria alkaloids in preventing Alzheimer's disease. These results were further evaluated and refined using biological experiments. Our study provides a novel strategy for understanding the holistic pharmacology of TCM, as well as for exploring the multi-indication properties of TCM beyond its traditional applications.

Co-Expression Network Analysis of Spleen Transcriptome in Rock Bream (Oplegnathus fasciatus) Naturally Infected with Rock Bream Iridovirus (RBIV)

Rock bream iridovirus (RBIV) is a notorious agent that causes high mortality in aquaculture of rock bream (Oplegnathus fasciatus). Despite severity of this virus, no transcriptomic studies on RBIV-infected rock bream that can provide fundamental information on protective mechanism against the virus have been reported so far. This study aimed to investigate physiological mechanisms between host and RBIV through transcriptomic changes in the spleen based on RNA-seq. Depending on infection intensity and sampling time point, fish were divided into five groups: uninfected healthy fish at week 0 as control (0C), heavy infected fish at week 0 (0H), heavy mixed RBIV and bacterial infected fish at week 0 (0MH), uninfected healthy fish at week 3 (3C), and light infected fish at week 3 (3L). We explored clusters from 35,861 genes with Fragments Per Kilo-base of exon per Million mapped fragments (FPKM) values of 0.01 or more through signed co-expression network analysis using WGCNA package. Nine of 22 modules were highly correlated with viral infection (|gene significance (GS) vs. module membership (MM) |> 0.5, p-value < 0.05). Expression patterns in selected modules were divided into two: heavy infected (0H and 0MH) and control and light-infected groups (0C, 3C, and 3L). In functional analysis, genes in two positive modules (5448 unigenes) were enriched in cell cycle, DNA replication, transcription, and translation, and increased glycolysis activity. Seven negative modules (3517 unigenes) built in this study showed significant decreases in the expression of genes in lymphocyte-mediated immune system, antigen presentation, and platelet activation, whereas there was significant increased expression of endogenous apoptosis-related genes. These changes lead to RBIV proliferation and failure of host defense, and suggests the importance of blood cells such as thrombocytes and B cells in rock bream in RBIV infection. Interestingly, a hub gene, pre-mRNA processing factor 19 (PRPF19) showing high connectivity (kME), and expression of this gene using qRT-PCR was increased in rock bream blood cells shortly after RBIV was added. It might be a potential biomarker for diagnosis and vaccine studies in rock bream against RBIV. This transcriptome approach and our findings provide new insight into the understanding of global rock bream-RBIV interactions including immune and pathogenesis mechanisms.

Identification of TP53 mutation associated-immunotype and prediction of survival in patients with hepatocellular carcinoma

Background

Stratification of tumors is necessary to achieve better clinical outcomes. Hepatocellular carcinoma (HCC) is commonly associated with mutation of the TP53 gene and heterogeneity in immune cell content. However, TP53 mutation-associated immunotype of HCC has not been reported yet. This study aimed to identify the TP53 mutation-associated immunotype in HCC.

Methods

The mutation annotation format (MAF) document, mRNA expression data, and clinical data of HCC patients were downloaded from the publicly available The Cancer Genome Atlas (TCGA) data portal. Data from 332 HCC patients were analyzed in this study. Infiltrating immune cells were evaluated by the well-known CIBERSORT method. Additional mutation data of HCC patients were downloaded from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

Results

The TP53 gene harbored the highest frequency of mutations in HCC patients. Consequently, five lethal features, including TP53 mutations, were screened by least absolute shrinkage and selector operation (LASSO)-COX regression, according to TP53 mutations and 22 infiltrating immune cells. Two distinct subgroups of HCC were identified, namely, immunotypes A and B. Furthermore, the expression levels of co-inhibitory immune checkpoints were significantly upregulated, and the gene ontology (GO) terms or pathways to boost immune responses were found to be inhibited in the immunotype B subgroup compared to that in the immunotype A subgroup. Finally, we proved immunotype to be an independent adverse prognostic factor that contributed to improvement in the predictive accuracy of the immunotype-based model and helped in avoiding excessive medical treatment.

Conclusions

Two distinct immunotypes of HCC, in terms of prognosis, phenotype, and function, were identified and the traditional understanding of intratumoralCD8⁺ T cells was subverted. Moreover, the identified immunotypes contributed to improving the predictive accuracy of the immunotype-based model and helped in avoiding excessive medical treatment in some HCC patients.

Methylation‑driven genes PMPCAP1, SOWAHC and ZNF454 as potential prognostic biomarkers in lung squamous cell carcinoma

Of the different types of lung cancer, lung squamous cell cancer (LUSC) has the second highest rates of morbidity and mortality, which have been increasing in recent years. Epigenetic abnormalities may serve as potential biomarkers and diagnostic and/or therapeutic targets, which may help to monitor and improve the prognosis of patients with cancer. In the present study, data were obtained from The Cancer Genome Atlas database and survival and joint survival analyses were conducted using the R MethylMix package. Peptidase, mitochondrial processing a subunit pseudogene 1 (PMPCAP1), sosondowah ankyrin repeat domain family member C (SOWAHC) and zinc finger protein (ZNF) 454 were identified as independent prognosis‑related hub methylation‑driven genes (MDGs). Of these three genes, PMPCAP1 and SOWAHC, characterized by hypomethylation and high expression levels, were associated with poor prognosis in patients with LUSC, whilst ZNF454 was associated with an improved prognosis. In addition, pathway enrichment analysis suggested that PMPCAP1, SOWAHC and ZNF454 were primarily involved in gene expression or transcription pathways. Furthermore, 5, 1 and 10 key methylation sites of PMPCAP1, SOWAHC and ZNF454, respectively, were confirmed to be significantly relevant to gene expression, establishing a basis for further investigation into the mechanisms and more precise targets of these 3 genes. In conclusion, the MDGs PMPCAP1, SOWAHC and ZNF454 may be potential prognostic biomarkers of LUSC for guiding diagnosis and therapy options, as well as providing a theoretical basis for further investigation.

Prepregnant Obesity of Mothers in a Multiethnic Cohort Is Associated with Cord Blood Metabolomic Changes in Offspring

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal prepregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using a coupled untargeted and targeted metabolomic approach from the newborn cord blood metabolomes associated with a matched maternal prepregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multiethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using an elastic net regularization-based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age, ethnicity, parity, and gravidity). We validated the model results in a subsequent set of samples (N = 30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese and normal-weight groups. Interestingly, hydroxy-3-methylbutyric acid showed significantly higher levels in cord blood from the NHPI group compared to that from Asian and Caucasian groups. In summary, significant associations were observed between maternal prepregnant obesity and offspring metabolomic alternation at birth, revealing the intergenerational impact of maternal obesity.

Pan-cancer mapping of differential protein-protein interactions

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.

Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity

Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real imaging genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.

Mapping the Molecular Basis and Markers of Papillary Thyroid Carcinoma Progression and Metastasis Using Global Transcriptome and microRNA Profiling

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). In a subgroup of patients with PTC, the disease progresses to an invasive stage or in some cases to distant organ metastasis. At present, there is an unmet clinical and diagnostic need for early identification of patients with PTC who are at risk of disease progression or metastasis. In this study, we report several molecular leads and potential biomarker candidates of PTC metastasis for further translational research. The study design was based on comparisons of PTC in three different groups using cross-sectional sampling: Group 1, PTC localized to the thyroid (n = 20); Group 2, PTC with extrathyroidal progression (n = 22); and Group 3, PTC with distant organ metastasis (n = 20). Global transcriptome and microRNAs (miRNA) analyses were conducted using an initial screening set comprising nine formalin-fixed paraffin-embedded PTC samples obtained from three independent patients per study group. The findings were subsequently validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the abovementioned independent patient sample set (n = 62). Comparative analyses of differentially expressed miRNAs showed that miR-193-3p, miR-182-5p, and miR-3607-3p were novel miRNAs associated with PTC metastasis. These potential miRNA biomarkers were associated with TC metastasis and miRNA-target gene associations, which may provide important clinicopathological information on metastasis. Our findings provide new molecular leads for further translational biomarker research, which could facilitate the identification of patients at risk of PTC disease progression or metastasis.

Adaptive selection in the evolution of programmed cell death-1 and its ligands in vertebrates

Programmed cell death-1 (PD-1) and its ligands, particularly PD-L1 and PD-L2, are the most important proteins responsible for signaling T-cell inhibition and arbitrating immune homeostasis and tolerance mechanisms. However, the adaptive evolution of these genes is poorly understood. In this study, we aligned protein-coding genes from vertebrate species to evaluate positive selection constraints and evolution in the PD1, PD-L1 and PD-L2 genes conserved across up to 166 vertebrate species, with an average of 55 species per gene. We determined that although the positive selection was obvious, an average of 5.3% of codons underwent positive selection in the three genes across vertebrate lineages, and increased positive selection pressure was detected in both the Ig-like domains and transmembrane domains of the proteins. Moreover, the PD1, PD-L1 and PD-L2 genes were highly expressed in almost all tissues of the selected species indicating a distinct expression pattern in different tissues among most species. Our study reveals that adaptive selection plays a key role in the evolution of PD1 and its ligands in the majority of vertebrate species, which is in agreement with the contribution of these residues to the mechanisms of pathogen identification and coevolution in the complexity and novelties of vertebrate immune systems.

Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing

Clear-cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. VHL and PBRM1 are the top two significantly mutated genes in ccRCC specimens, while the genetic mechanism of the VHL/PBRM1-negative ccRCC remains to be elucidated. Here we carried out a comprehensive analysis of single-cell genomic copy number variations (CNVs) in VHL/PBRM1-negative ccRCC. Genomic CNVs were identified at the single-cell level, and the tumor cells showed widespread amplification and deletion across the whole genome. Functional enrichment analysis indicated that the amplified genes are significantly enriched in cancer-related signaling transduction pathways. Besides, receptor protein tyrosine kinase (RTK) genes also showed widespread copy number variations in cancer cells. Our studies indicated that the genomic CNVs in RTK genes and downstream signaling transduction pathways may be involved in VHL/PBRM1-negative ccRCC pathogenesis and progression, and highlighted the role of the comprehensive investigation of genomic CNVs at the single-cell level in both clarifying pathogenic mechanism and identifying potential therapeutic targets in cancers.

A review of omics approaches to study preeclampsia

Preeclampsia is a medical condition affecting 5-10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using network analysis on the biomarkers of all the literature reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.

Comparing DNA methylation profiles across different tissues associated with the diagnosis of pediatric asthma

DNA methylation (DNAm) profiles in central airway epithelial cells (AECs) may play a key role in pathological processes in asthma. The goal of the current study is to compare the diagnostic performance of DNAm markers across three tissues: AECs, nasal epithelial cells (NECs), and peripheral blood mononuclear cells (PBMCs). Additionally, we focused on the results using the machine learning algorithm in the context of multi-locus effects to evaluate the diagnostic performance of the optimal subset of CpG sites. We obtained 74 subjects with asthma and 41 controls from AECs, 15 subjects with asthma and 14 controls from NECs, 697 subjects with asthma and 97 controls from PBMCs. Epigenome-wide DNA methylation levels in AECs, NECs and PBMCs were measured using the Infinium Human Methylation 450 K BeadChip. Overlap analysis across the three different sample sources at the locus and pathway levels were studied to investigate shared or unique pathophysiological processes of asthma across tissues. Using the top 100 asthma-associated methylation markers as classifiers from each dataset, we found that both AEC- and NEC-based DNAm signatures exerted a lower classification error than the PBMC-based DNAm markers (p-value = 0.0002). The area-under-the-curve (AUC) analysis based on out-of-bag errors using the random forest classification algorithm revealed that PBMC-, NEC-, and AEC-based methylation data yielded 31 loci (AUC: 0.87), 8 loci (AUC: 0.99), and 4 loci (AUC: 0.97) from each optimal subset of tissue-specific markers, respectively. We also discovered the locus-locus interaction of DNAm levels of the CDH6 gene and RAPGEF3 gene might interact with each other to jointly predict the risk of asthma – which suggests the pivotal role of cell-cell junction in the pathological changes of asthma. Both AECs and NECs might provide better diagnostic accuracy and efficacy levels than PBMCs. Further research is warranted to evaluate how these tissue-specific DNAm markers classify and predict asthma risk.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

Differential expression of microRNAs in the saliva of patients with aggressive periodontitis: a pilot study of potential biomarkers for aggressive periodontitis

Purpose

The aim of this study was to compare microRNA (miRNA) gene expression in saliva using miRNA polymerase chain reaction (PCR) arrays in healthy and aggressive periodontitis (AP) patients.

Methods

PCR arrays of 84 miRNAs related to the human inflammatory response and autoimmunity from the saliva samples of 4 patients with AP and 4 healthy controls were performed. The functions and diseases related to the miRNAs were obtained using TAM 2.0. Experimentally validated targets of differentially expressed miRNAs were obtained from mirTarBase. Gene ontology terms and pathways were analyzed using ConsensusPathDB.

Results

Four downregulated miRNAs (hsa-let-7a-5p, hsa-let-7f-5p, hsa-miR-181b-5p, and hsa-miR-23b-3p) were identified in patients with AP. These miRNAs are associated with cell death and innate immunity, and they target genes associated with osteoclast development and function.

Conclusions

This study is the first analysis of miRNAs in the saliva of patients with AP. Identifying discriminatory human salivary miRNA biomarkers reflective of periodontal disease in a non-invasive screening assay is crucial for the development of salivary diagnostics. These data provide a first step towards the discovery of key salivary miRNA biomarkers for AP.

Differential alternative splicing regulation among hepatocellular carcinoma with different risk factors

Background

Hepatitis B virus (HBV), hepatitis C virus (HCV), and alcohol consumption are predominant causes of hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying how differently these causes are implicated in HCC development are not fully understood. Therefore, we investigated differential alternative splicing (AS) regulation among HCC patients with these risk factors.

Methods

We conducted a genome-wide survey of AS events associated with HCCs among HBV (n = 95), HCV (n = 47), or alcohol (n = 76) using RNA-sequencing data obtained from The Cancer Genome Atlas.

Results

In three group comparisons of HBV vs. HCV, HBV vs. alcohol, and HCV vs. alcohol for RNA seq (ΔPSI> 0.05, FDR < 0.05), 133, 93, and 29 differential AS events (143 genes) were identified, respectively. Of 143 AS genes, eight and one gene were alternatively spliced specific to HBV and HCV, respectively. Through functional analysis over the canonical pathways and gene ontologies, we identified significantly enriched pathways in 143 AS genes including immune system, mRNA splicing-major pathway, and nonsense-mediated decay, which may be important to carcinogenesis in HCC risk factors. Among eight genes with HBV-specific splicing events, HLA-A, HLA-C, and IP6K2 exhibited more differential expression of AS events (ΔPSI> 0.1). Intron retention of HLA-A was observed more frequently in HBV-associated HCC than HCV- or alcohol-associated HCC, and intron retention of HLA-C showed vice versa. Exon 3 (based on ENST00000432678) of IP6K2 was less skipped in HBV-associated in HCC compared to HCV- or alcohol-associated HCC.

Conclusion

AS may play an important role in regulating transcription differences implicated in HBV-, HCV-, and alcohol-related HCC development.

Altered microRNA and target gene expression related to Tetralogy of Fallot

MicroRNAs (miRNAs) play an important role in guiding development and maintaining function of the human heart. Dysregulation of miRNAs has been linked to various congenital heart diseases including Tetralogy of Fallot (TOF), which represents the most common cyanotic heart malformation in humans. Several studies have identified dysregulated miRNAs in right ventricular (RV) tissues of TOF patients. In this study, we profiled genome-wide the whole transcriptome and analyzed the relationship of miRNAs and mRNAs of RV tissues of a homogeneous group of 22 non-syndromic TOF patients. Observed profiles were compared to profiles obtained from right and left ventricular tissue of normal hearts. To reduce the commonly observed large list of predicted target genes of dysregulated miRNAs, we applied a stringent target prediction pipeline integrating probabilities for miRNA-mRNA interaction. The final list of disease-related miRNA-mRNA pairs comprises novel as well as known miRNAs including miR-1 and miR-133, which are essential to cardiac development and function by regulating KCNJ2, FBN2, SLC38A3 and TNNI1. Overall, our study provides additional insights into post-transcriptional gene regulation of malformed hearts of TOF patients.

Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology

Classifying subjects into clinically and biologically homogeneous subgroups will facilitate the understanding of disease pathophysiology and development of targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, but subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (e.g., those from GWASs) with clinical symptoms for disease subtyping. Here we proposed an analytic framework capable of finding complex diseases subgroups by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, so the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modeled. We also proposed various evaluation metrics for assessing clustering performance. Our framework was able to subtype schizophrenia subjects into diverse subgroups with different prognosis and treatment response. We also applied the framework to the Northern Finland Birth Cohort (NFBC) 1966 dataset and identified high and low cardiometabolic risk subgroups in a gender-stratified analysis. The prediction strength by cross-validation was generally greater than 80%, suggesting good stability of the clustering model. Our results suggest a more data-driven and biologically informed approach to defining metabolic syndrome and subtyping psychiatric disorders. Moreover, we found that the genes "blindly" selected by the algorithm are significantly enriched for known susceptibility genes discovered in GWASs of schizophrenia or cardiovascular diseases. The proposed framework opens up an approach to subject stratification.

Transcriptome changes in undifferentiated Caco-2 cells exposed to food-grade titanium dioxide (E171): contribution of the nano- and micro- sized particles

The food additive titanium dioxide (TiO2), or E171, is a white food colorant. Recent studies showed after E171 ingestion a significantly increased number of colorectal tumours in a colorectal cancer mouse model as well as inflammatory responses and dysregulation of the immune system in the intestine of rats. In the mouse colon, E171 induced gene expression changes related to oxidative stress, impairment of the immune system, activation of signalling and cancer-related processes. E171 comprises nanoparticles (NPs) and microparticles (MPs). Previous in vitro studies showed that E171, NPs and MPs induced oxidative stress responses, DNA damage and micronuclei formation. This study aimed to investigate the relative contribution of the NPs and MPs to effects of E171 at the transcriptome level in undifferentiated Caco-2 cells by genome wide microarray analysis. The results showed that E171, NPs, and MPs induce gene expression changes related to signalling, inflammation, immune system, transport and cancer. At the pathway level, metabolism of proteins with the insulin processing pathway and haemostasis were specific to E171 exposure. The gene expression changes associated with the immune system and inflammation induced by E171, MPs, and NPs suggest the creation of a favourable environment for colon cancer development.

Genome-wide screening of abberant methylated drivers combined with relative risk loci in bladder cancer

Background

To explore important methylation‐driven genes (MDGs) and risk loci to construct risk model for prognosis of bladder cancer (BCa).

Methods

We utilized TCGA‐Assembler package to download 450K methylation data and corresponding transcriptome profiles. MethylMix package was used for identifying methylation‐driven genes and functional analysis was mainly performed based on ConsensusPathDB database. Then, Cox regression method was utilized to find prognostic MDGs, and we selected 17 hub genes via stepwise regression and multivariate Cox models. Kruskal‐Wallis test was implemented for comparisons between risk with other clinical variables. Moreover, we constructed the risk model and validated it in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507. Gene set enrichment analysis was performed using the levels of risk score as the phenotype. Additionally, we further screened out the relative methylation sites associated with the 17 hub genes. Cox regression and Survival analysis were conducted to find the specifically prognostic sites.

Results

Two hundred and twenty‐eight MDGs were chosen by ConsensusPathDB database. Results revealed that most conspicuous pathways were transcriptional mis‐regulation pathways in cancer and EMT. After Cox regression analysis, 17 hub epigenetic MDGs were identified. We calculated the risk score and found satisfactory predictive efficiency by ROC curve (AUC = 0.762). In the validation group from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507, 17 hub genes remained higher predictive value with AUC = 0.723 and patients in high‐risk group. Meanwhile, Kruskal‐Wallis test revealed that higher risk score correlated with a higher level of TNM stage, tumor grade, and advanced pathological stages. Then, identified 38 risk methylated loci that highly associated with prognosis. Last, gene set enrichment analysis revealed that high‐risk level of MDGs may correlate with several important pathways, including MAPK signaling pathway and so on.

Conclusion

Our study indicated several hub‐MDGs, calculated novel risk score and explored the prognostic value in BCa, which provided a promising approach to BCA prognosis assessment.

Monitoring methylation-driven genes as prognostic biomarkers in patients with lung squamous cell cancer

Aberrant DNA methylations have been reported to be significantly associated with lung squamous cell cancer (LUSC). The aim of this study was to investigate the DNA methylation-driven genes in LUSC by integrative bioinformatics analysis. In the present study, methylation-driven genes in LUSC were screened out, and survival analysis related to these genes was performed to confirm their value in prognostic assessment. Gene expression and methylation data were downloaded from The Cancer Genome Atlas (TCGA), and the MethylMix algorithm was used to identify methylation-driven genes. ConsensusPathDB was used to perform Gene Ontology and pathway enrichment analysis of methylation-driven genes. Survival analysis was performed to investigate the correlation with prognosis. In total, 52 differentially expressed methylation-driven genes were identified in LUSC and adjacent tissues. Survival analysis showed that DQX1, GPR75, STX12, and TRIM61 could serve as independent prognostic biomarkers. In addition, the combined methylation and gene expression survival analysis revealed that the combined expression level of the genes ALG1L, DQX1, and ZNF418 alone can be used as a prognostic marker or drug target. Methylation of four sites of gene ZNF418, four sites of ZNF701, two sites of DQX1, and four sites of DCAF4L2 was significantly associated with survival. The present study provides an important bioinformatic and relevant theoretical basis for subsequent early diagnosis and prognostic assessment of LUSC.

Membrane-type frizzled-related protein regulates lipidome and transcription for photoreceptor function

Molecular decision‐makers of photoreceptor (PRC) membrane organization and gene regulation are critical to understanding sight and retinal degenerations that lead to blindness. Using Mfrp^rd6mice, which develop PRC degeneration, we uncovered that membrane‐type frizzled‐related protein (MFRP) participates in docosahexaenoic acid (DHA, 22:6) enrichment in a manner similar to adiponectin receptor 1 (AdipoR1). Untargeted imaging mass spectrometry demonstrates cell‐specific reduction of phospholipids containing 22:6 and very long‐chain polyunsaturated fatty acids (VLC‐PUFAs) in Adipor1^−/−and Mfrp^rd6 retinas. Gene expression of pro‐inflammatory signaling pathways is increased and gene‐encoding proteins for PRC function decrease in both mutants. Thus, we propose that both proteins are necessary for retinal lipidome membrane organization, visual function, and to the understanding of the early pathology of retinal degenerative diseases.

Novel miR-29b target regulation patterns are revealed in two different cell lines

MicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene or protein expression by targeting mRNAs and triggering either translational repression or mRNA degradation. Distinct expression levels of miRNAs, including miR-29b, have been detected in various biological fluids and tissues from a large variety of disease models. However, how miRNAs "react" and function in different cellular environments is still largely unknown. In this study, the regulation patterns of miR-29b between human and mouse cell lines were compared for the first time. CRISPR/Cas9 gene editing was used to stably knockdown miR-29b in human cancer HeLa cells and mouse fibroblast NIH/3T3 cells with minimum off-targets. Genome editing revealed mir-29b-1, other than mir-29b-2, to be the main source of generating mature miR-29b. The editing of miR-29b decreased expression levels of its family members miR-29a/c via changing the tertiary structures of surrounding nucleotides. Comparing transcriptome profiles of human and mouse cell lines, miR-29b displayed common regulation pathways involving distinct downstream targets in macromolecular complex assembly, cell cycle regulation, and Wnt and PI3K-Akt signalling pathways; miR-29b also demonstrated specific functions reflecting cell characteristics, including fibrosis and neuronal regulations in NIH/3T3 cells and tumorigenesis and cellular senescence in HeLa cells.

Genomic and Genetic Approaches to Deciphering Acute Respiratory Distress Syndrome Risk and Mortality

Significance: Acute respiratory distress syndrome (ARDS) is a severe, highly heterogeneous critical illness with staggering mortality that is influenced by environmental factors, such as mechanical ventilation, and genetic factors. Significant unmet needs in ARDS are addressing the paucity of validated predictive biomarkers for ARDS risk and susceptibility that hamper the conduct of successful clinical trials in ARDS and the complete absence of novel disease-modifying therapeutic strategies. Recent Advances: The current ARDS definition relies on clinical characteristics that fail to capture the diversity of disease pathology, severity, and mortality risk. We undertook a comprehensive survey of the available ARDS literature to identify genes and genetic variants (candidate gene and limited genome-wide association study approaches) implicated in susceptibility to developing ARDS in hopes of uncovering novel biomarkers for ARDS risk and mortality and potentially novel therapeutic targets in ARDS. We further attempted to address the well-known health disparities that exist in susceptibility to and mortality from ARDS. Critical Issues: Bioinformatic analyses identified 201 ARDS candidate genes with pathway analysis indicating a strong predominance in key evolutionarily conserved inflammatory pathways, including reactive oxygen species, innate immunity-related inflammation, and endothelial vascular signaling pathways. Future Directions: Future studies employing a system biology approach that combines clinical characteristics, genomics, transcriptomics, and proteomics may allow for a better definition of biologically relevant pathways and genotype-phenotype connections and result in improved strategies for the sub-phenotyping of diverse ARDS patients via molecular signatures. These efforts should facilitate the potential for successful clinical trials in ARDS and yield a better fundamental understanding of ARDS pathobiology.

Significant random signatures reveals new biomarker for breast cancer

BACKGROUND

In 2012, Venet et al. proposed that at least in the case of breast cancer, most published signatures are not significantly more associated with outcome than randomly generated signatures. They suggested that nominal p-value is not a good estimator to show the significance of a signature. Therefore, one can reasonably postulate that some information might be present in such significant random signatures.

METHODS

In this research, first we show that, using an empirical p-value, these published signatures are more significant than their nominal p-values. In other words, the proposed empirical p-value can be considered as a complimentary criterion for nominal p-value to distinguish random signatures from significant ones. Secondly, we develop a novel computational method to extract information that are embedded within significant random signatures. In our method, a score is assigned to each gene based on the number of times it appears in significant random signatures. Then, these scores are diffused through a protein-protein interaction network and a permutation procedure is used to determine the genes with significant scores. The genes with significant scores are considered as the set of significant genes.

RESULTS

First, we applied our method on the breast cancer dataset NKI to achieve a set of significant genes in breast cancer considering significant random signatures. Secondly, prognostic performance of the computed set of significant genes is evaluated using DMFS and RFS datasets. We have observed that the top ranked genes from this set can successfully separate patients with poor prognosis from those with good prognosis. Finally, we investigated the expression pattern of TAT, the first gene reported in our set, in malignant breast cancer vs. adjacent normal tissue and mammospheres.

CONCLUSION

Applying the method, we found a set of significant genes in breast cancer, including TAT, a gene that has never been reported as an important gene in breast cancer. Our results show that the expression of TAT is repressed in tumors suggesting that this gene could act as a tumor suppressor in breast cancer and could be used as a new biomarker.

GenCLiP 3: mining human genes' functions and regulatory networks from PubMed based on co-occurrences and natural language processing

SUMMARY

We present a web server, GenCLiP 3, which is an updated version of GenCLiP 2.0 to enhance analysis of human gene functions and regulatory networks, with the following improvements: i) accurate recognition of molecular interactions with polarity and directionality from the entire PubMed database; ii) support for Boolean search to customize multiple-term search and to quickly retrieve function related genes; iii) strengthened association between gene and keyword by a new scoring method; and iv) daily updates following literature release at PubMed FTP.

AVAILABILITY

The server is freely available for academic use at: http://ci.smu.edu.cn/genclip3/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bone Marrow Mesenchymal Stem Cells' Secretome Exerts Neuroprotective Effects in a Parkinson's Disease Rat Model

Parkinson's disease (PD) is characterized by a selective loss of dopamine (DA) neurons in the human midbrain causing motor dysfunctions. The exact mechanism behind dopaminergic cell death is still not completely understood and, so far, no cure or neuroprotective treatment for PD is available. Recent studies have brought attention to the variety of bioactive molecules produced by mesenchymal stem cells (MSCs), generally referred to as the secretome. Herein, we evaluated whether human MSCs-bone marrow derived (hBMSCs) secretome would be beneficial in a PD pre-clinical model, when compared directly with cell transplantation of hBMSCs alone. We used a 6-hydroxydpomanie (6-OHDA) rat PD model, and motor behavior was evaluated at different time points after treatments (1, 4, and 7 weeks). The impact of the treatments in the recovery of DA neurons was estimated by determining TH-positive neuronal densities in the substantia nigra and fibers in the striatum, respectively, at the end of the behavioral characterization. Furthermore, we determined the effect of the hBMSCs secretome on the neuronal survival of human neural progenitors in vitro, and characterized the secretome through proteomic-based approaches. This work demonstrates that the injection of hBMSCs secretome led to the rescue of DA neurons, when compared to transplantation of hBMSCs themselves, which can explain the recovery of secretome-injected animals' behavioral performance in the staircase test. Moreover, we observed that hBMSCs secretome induces higher levels of in vitro neuronal differentiation. Finally, the proteomic analysis revealed that hBMSCs secrete important exosome-related molecules, such as those related with the ubiquitin-proteasome and histone systems. Overall, this work provided important insights on the potential use of hBMSCs secretome as a therapeutic tool for PD, and further confirms the importance of the secreted molecules rather than the transplantation of hBMSCs for the observed positive effects. These could be likely through normalization of defective processes in PD, namely proteostasis or altered gene transcription, which lately can lead to neuroprotective effects.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

BACKGROUND

The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

OBJECTIVE

Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

METHODS

We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

RESULTS

We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

CONCLUSION

PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.

Hepatic gene expression variations in response to high-fat diet-induced impaired glucose tolerance using RNAseq analysis in collaborative cross mouse population

Hepatic gene expression is known to differ between healthy and type 2 diabetes conditions. Identifying these variations will provide better knowledge to the development of gene-targeted therapies. The aim of this study is to assess diet-induced hepatic gene expression of susceptible versus resistant CC lines to T2D development. Next-generation RNA-sequencing was performed for 84 livers of diabetic and non-diabetic mice of 41 different CC lines (both sexes) following 12 weeks on high-fat diet (42% fat). Data analysis revealed significant variations of hepatic gene expression in diabetic versus non-diabetic mice with significant sex effect, where 601 genes were differentially expressed (DE) in overall population (males and females), 718 genes in female mice, and 599 genes in male mice. Top prioritized DE candidate genes were Lepr, Ins2, Mb, Ckm, Mrap2, and Ckmt2 for the overall population; for females-only group were Hdc, Serpina12, Socs1, Socs2, and Mb, while for males-only group were Serpine1, Mb, Ren1, Slc4a1, and Atp2a1. Data analysis for sex differences revealed 193 DE genes in health (Top: Lepr, Cav1, Socs2, Abcg2, and Col5a3), and 389 genes DE between diabetic females versus males (Top: Lepr, Clps, Ins2, Cav1, and Mrap2). Furthermore, integrating gene expression results with previously published QTL, we identified significant variants mapped at chromosomes at positions 36-49 Mb, 62-71 Mb, and 79-99 Mb, on chromosomes 9, 11, and 12, respectively. Our findings emphasize the complexity of T2D development and that significantly controlled by host complex genetic factors. As well, we demonstrate the significant sex differences between males and females during health and increasing to extent levels during disease/diabetes. Altogether, opening the venue for further studies targets the discovery of effective sex-specific and personalized preventions and therapies.

Selective Genetic Overlap Between Amyotrophic Lateral Sclerosis and Diseases of the Frontotemporal Dementia Spectrum

Importance

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by loss of upper and lower motor neurons. Although novel ALS genetic variants have been identified, the shared genetic risk between ALS and other neurodegenerative disorders remains poorly understood.

Objectives

To examine whether there are common genetic variants that determine the risk for ALS and other neurodegenerative diseases and to identify their functional pathways.

Design, Setting, and Participants

In this study conducted from December 1, 2016, to August 1, 2017, the genetic overlap between ALS, sporadic frontotemporal dementia (FTD), FTD with TDP-43 inclusions, Parkinson disease (PD), Alzheimer disease (AD), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP) were systematically investigated in 124 876 cases and controls. No participants were excluded from this study. Diagnoses were established using consensus criteria.

Main Outcomes and Measures

The primary outcomes were a list of novel loci and their functional pathways in ALS, FTD, PSP, and ALS mouse models.

Results

Among 124 876 cases and controls, genome-wide conjunction analyses of ALS, FTD, PD, AD, CBD, and PSP revealed significant genetic overlap between ALS and FTD at known ALS loci: rs13302855 and rs3849942 (nearest gene, C9orf72; P = .03 for rs13302855 and P = .005 for rs3849942) and rs4239633 (nearest gene, UNC13A; P = .03). Significant genetic overlap was also found between ALS and PSP at rs7224296, which tags the MAPT H1 haplotype (nearest gene, NSF; P = .045). Shared risk genes were enriched for pathways involving neuronal function and development. At a conditional FDR P < .05, 22 novel ALS polymorphisms were found, including rs538622 (nearest gene, ERGIC1; P = .03 for ALS and FTD), which modifies BNIP1 expression in human brains (35 of 137 females; mean age, 59 years; P = .001). BNIP1 expression was significantly reduced in spinal cord motor neurons from patients with ALS (4 controls: mean age, 60.5 years, mean [SE] value, 3984 [760.8] arbitrary units [AU]; 7 patients with ALS: mean age, 56 years, mean [SE] value, 1999 [274.1] AU; P = .02), in an ALS mouse model (mean [SE] value, 13.75 [0.09] AU for 2 SOD1 WT mice and 11.45 [0.03] AU for 2 SOD1 G93A mice; P = .002) and in brains of patients with PSP (80 controls: 39 females; mean age, 82 years, mean [SE] value, 6.8 [0.2] AU; 84 patients with PSP: 33 females, mean age 74 years, mean [SE] value, 6.8 [0.1] AU; β = -0.19; P = .009) or FTD (11 controls: 4 females; mean age, 67 years; mean [SE] value, 6.74 [0.05] AU; 17 patients with FTD: 10 females; mean age, 69 years; mean [SE] value, 6.53 [0.04] AU; P = .005).

Conclusions and Relevance

This study found novel genetic overlap between ALS and diseases of the FTD spectrum, that the MAPT H1 haplotype confers risk for ALS, and identified the mitophagy-associated, proapoptotic protein BNIP1 as an ALS risk gene. Together, these findings suggest that sporadic ALS may represent a selectively pleiotropic, polygenic disorder.

Chromatin profiling of cortical neurons identifies individual epigenetic signatures in schizophrenia

Both heritability and environment contribute to risk for schizophrenia. However, the molecular mechanisms of interactions between genetic and non-genetic factors remain unclear. Epigenetic regulation of neuronal genome may be a presumable mechanism in pathogenesis of schizophrenia. Here, we performed analysis of open chromatin landscape of gene promoters in prefrontal cortical (PFC) neurons from schizophrenic patients. We cataloged cell-type-based epigenetic signals of transcriptional start sites (TSS) marked by histone H3-K4 trimethylation (H3K4me3) across the genome in PFC from multiple schizophrenia subjects and age-matched control individuals. One of the top-ranked chromatin alterations was found in the major histocompatibility (MHC) locus on chromosome 6 highlighting the overlap between genetic and epigenetic risk factors in schizophrenia. The chromosome conformation capture (3C) analysis in human brain cells revealed the architecture of multipoint chromatin interactions between the schizophrenia-associated genetic and epigenetic polymorphic sites and distantly located HLA-DRB5 and BTNL2 genes. In addition, schizophrenia-specific chromatin modifications in neurons were particularly prominent for non-coding RNA genes, including an uncharacterized LINC01115 gene and recently identified BNRNA_052780. Notably, protein-coding genes with altered epigenetic state in schizophrenia are enriched for oxidative stress and cell motility pathways. Our results imply the rare individual epigenetic alterations in brain neurons are involved in the pathogenesis of schizophrenia.

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro

Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.

Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with statistical learning methods that do not use biological knowledge. In this review, we survey current knowledge-guided statistical learning methods, including both supervised learning and unsupervised learning, and their applications to precision oncology, and we discuss future research directions.

Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

Identifying Crohn's disease signal from variome analysis

Background

After years of concentrated research efforts, the exact cause of Crohn’s disease (CD) remains unknown. Its accurate diagnosis, however, helps in management and preventing the onset of disease. Genome-wide association studies have identified 241 CD loci, but these carry small log odds ratios and are thus diagnostically uninformative.

Methods

Here, we describe a machine learning method—AVA,Dx (Analysis of Variation for Association with Disease)—that uses exonic variants from whole exome or genome sequencing data to extract CD signal and predict CD status. Using the person-specific coding variation in genes from a panel of only 111 individuals, we built disease-prediction models informative of previously undiscovered disease genes. By additionally accounting for batch effects, we were able to accurately predict CD status for thousands of previously unseen individuals from other panels.

Results

AVA,Dx highlighted known CD genes including NOD2 and new potential CD genes. AVA,Dx identified 16% (at strict cutoff) of CD patients at 99% precision and 58% of the patients (at default cutoff) with 82% precision in over 3000 individuals from separately sequenced panels.

Conclusions

Larger training panels and additional features, including other types of genetic variants and environmental factors, e.g., human-associated microbiota, may improve model performance. However, the results presented here already position AVA,Dx as both an effective method for revealing pathogenesis pathways and as a CD risk analysis tool, which can improve clinical diagnostic time and accuracy. Links to the AVA,Dx Docker image and the BitBucket source code are at https://bromberglab.org/project/avadx/.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0670-6) contains supplementary material, which is available to authorized users.

Hypermethylation-associated downregulation of microRNA-4456 in hypersexual disorder with putative influence on oxytocin signalling: A DNA methylation analysis of miRNA genes

Hypersexual disorder (HD) was proposed as a diagnosis in the DSM-5 and the classification ‘Compulsive Sexual Behavior Disorder’ is now presented as an impulse-control disorder in ICD-11. HD incorporates several pathophysiological mechanisms; including impulsivity, compulsivity, sexual desire dysregulation and sexual addiction. No previous study investigated HD in a methylation analysis limited to microRNA (miRNA) associated CpG-sites. The genome wide methylation pattern was measured in whole blood from 60 subjects with HD and 33 healthy volunteers using the Illumina EPIC BeadChip. 8,852 miRNA associated CpG-sites were investigated in multiple linear regression analyses of methylation M-values to a binary independent variable of disease state (HD or healthy volunteer), adjusting for optimally determined covariates. Expression levels of candidate miRNAs were investigated in the same individuals for differential expression analysis. Candidate methylation loci were further studied for an association with alcohol dependence in an independent cohort of 107 subjects. Two CpG-sites were borderline significant in HD – cg18222192 (MIR708)(p < 10E-05,p_FDR = 5.81E-02) and cg01299774 (MIR4456)(p < 10E-06, p_FDR = 5.81E-02). MIR4456 was significantly lower expressed in HD in both univariate (p < 0.0001) and multivariate (p < 0.05) analyses. Cg01299774 methylation levels were inversely correlated with expression levels of MIR4456 (p < 0.01) and were also differentially methylated in alcohol dependence (p = 0.026). Gene target prediction and pathway analysis revealed that MIR4456 putatively targets genes preferentially expressed in brain and that are involved in major neuronal molecular mechanisms thought to be relevant for HD, e.g., the oxytocin signalling pathway. In summary, our study implicates a potential contribution of MIR4456 in the pathophysiology of HD by putatively influencing oxytocin signalling.

POT1 and Damage Response Malfunction Trigger Acquisition of Somatic Activating Mutations in the VEGF Pathway in Cardiac Angiosarcomas

Background Mutations in the POT1 gene explain abnormally long telomeres and multiple tumors including cardiac angiosarcomas (CAS). However, the link between long telomeres and tumorigenesis is poorly understood. Methods and Results Here, we have studied the somatic landscape of 3 different angiosarcoma patients with mutations in the POT1 gene to further investigate this tumorigenesis process. In addition, the genetic landscape of 7 CAS patients without mutations in the POT1 gene has been studied. Patients with CAS and nonfunctional POT1 did not repress ATR (ataxia telangiectasia RAD3-related)-dependent DNA damage signaling and showed a constitutive increase of cell cycle arrest and somatic activating mutations in the VEGF (vascular endothelial growth factor)/angiogenesis pathway (KDR gene). The same observation was made in POT1 mutation carriers with tumors different from CAS and also in CAS patients without mutations in the POT1 gene but with mutations in other genes involved in DNA damage signaling. Conclusions Inhibition of POT1 function and damage-response malfunction activated DNA damage signaling and increased cell cycle arrest as well as interfered with apoptosis, which would permit acquisition of somatic mutations in the VEGF/angiogenesis pathway that drives tumor formation. Therapies based on the inhibition of damage signaling in asymptomatic carriers may diminish defects on cell cycle arrest and thus prevent the apoptosis deregulation that leads to the acquisition of driver mutations.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Early complement genes are associated with visual system degeneration in multiple sclerosis

Multiple sclerosis is a heterogeneous disease with an unpredictable course and a wide range of severity; some individuals rapidly progress to a disabled state whereas others experience only mild symptoms. Though genetic studies have identified variants that are associated with an increased risk of developing multiple sclerosis, no variants have been consistently associated with multiple sclerosis severity. In part, the lack of findings is related to inherent limitations of clinical rating scales; these scales are insensitive to early degenerative changes that underlie disease progression. Optical coherence tomography imaging of the retina and low-contrast letter acuity correlate with and predict clinical and imaging-based outcomes in multiple sclerosis. Therefore, they may serve as sensitive phenotypes to discover genetic predictors of disease course. We conducted a set of genome-wide association studies of longitudinal structural and functional visual pathway phenotypes in multiple sclerosis. First, we assessed genetic predictors of ganglion cell/inner plexiform layer atrophy in a discovery cohort of 374 patients with multiple sclerosis using mixed-effects models adjusting for age, sex, disease duration, optic neuritis and genetic ancestry and using a combination of single-variant and network-based analyses. For candidate variants identified in discovery, we conducted a similar set of analyses of ganglion cell/inner plexiform layer thinning in a replication cohort (n = 376). Second, we assessed genetic predictors of sustained loss of 5-letters in low-contrast letter acuity in discovery (n = 582) using multivariable-adjusted Cox proportional hazards models. We then evaluated candidate variants/pathways in a replication cohort. (n = 253). Results of both studies revealed novel subnetworks highly enriched for connected genes in early complement activation linked to measures of disease severity. Within these networks, C3 was the gene most strongly associated with ganglion cell/inner plexiform layer atrophy (P = 0.004) and C1QA and CR1 were top results in analysis of sustained low-contrast letter acuity loss. Namely, variant rs158772, linked to C1QA, and rs61822967, linked to CR1, were associated with 71% and 40% increases in risk of sustained LCLA loss, respectively, in meta-analysis pooling discovery and replication cohorts (rs158772: hazard ratio: 1.71; 95% confidence interval 1.30-2.25; P = 1.3 × 10-4; rs61822967: hazard ratio: 1.40; 95% confidence interval: 1.16-1.68; P = 4.1 × 10-4). In conclusion, early complement pathway gene variants were consistently associated with structural and functional measures of multiple sclerosis severity. These results from unbiased analyses are strongly supported by several prior reports that mechanistically implicated early complement factors in neurodegeneration.

What went wrong with variant effect predictor performance for the PCM1 challenge

The recent years have seen a drastic increase in the amount of available genomic sequences. Alongside this explosion, hundreds of computational tools were developed to assess the impact of observed genetic variation. Critical Assessment of Genome Interpretation (CAGI) provides a platform to evaluate the performance of these tools in experimentally relevant contexts. In the CAGI-5 challenge assessing the 38 missense variants affecting the human Pericentriolar material 1 protein (PCM1), our SNAP-based submission was the top performer, although it did worse than expected from other evaluations. Here, we compare the CAGI-5 submissions, and 24 additional commonly used variant effect predictors, to analyze the reasons for this observation. We identified per residue conservation, structural, and functional PCM1 characteristics, which may be responsible. As expected, predictors had a hard time distinguishing effect variants in nonconserved positions. They were also better able to call effect variants in a structurally rich region than in a less-structured one; in the latter, they more often correctly identified benign than effect variants. Curiously, most of the protein was predicted to be functionally robust to mutation-a feature that likely makes it a harder problem for generalized variant effect predictors.

Hemimethylation Patterns in Breast Cancer Cell Lines

DNA methylation is an epigenetic event that involves adding a methyl group to the cytosine (C) site, especially the one that pairs with a guanine (G) site (ie, CG or CpG site), in a human genome. This event plays an important role in both cancerous and normal cell development. Previous studies often assume symmetric methylation on both DNA strands. However, asymmetric methylation, or hemimethylation (methylation that occurs only on 1 DNA strand), does exist and has been reported in several studies. Due to the limitation of previous DNA methylation sequencing technologies, researchers could only study hemimethylation on specific genes, but the overall genomic hemimethylation landscape remains relatively unexplored. With the development of advanced next-generation sequencing techniques, it is now possible to measure methylation levels on both forward and reverse strands at all CpG sites in an entire genome. Analyzing hemimethylation patterns may potentially reveal regions related to undergoing tumor growth. For our research, we first identify hemimethylated CpG sites in breast cancer cell lines using Wilcoxon signed rank tests. We then identify hemimethylation patterns by grouping consecutive hemimethylated CpG sites based on their methylation states, methylation "M" or unmethylation "U." These patterns include regular (or consecutive) hemimethylation clusters (eg, "MMM" on one strand and "UUU" on another strand) and polarity (or reverse) clusters (eg, "MU" on one strand and "UM" on another strand). Our results reveal that most hemimethylation clusters are the polarity type, and hemimethylation does occur across the entire genome with notably higher numbers in the breast cancer cell lines. The lengths or sizes of most hemimethylation clusters are very short, often less than 50 base pairs. After mapping hemimethylation clusters and sites to corresponding genes, we study the functions of these genes and find that several of the highly hemimethylated genes may influence tumor growth or suppression. These genes may also indicate a progressing transition to a new tumor stage.

Global extracellular vesicle proteomic signature defines U87-MG glioma cell hypoxic status with potential implications for non-invasive diagnostics

Purpose

Glioblastoma multiforme (GBM) is the most common and lethal of primary malignant brain tumors. Hypoxia constitutes a major determining factor for the poor prognosis of high-grade glioma patients, and is known to contribute to the development of treatment resistance. Therefore, new strategies to comprehensively profile and monitor the hypoxic status of gliomas are of high clinical relevance. Here, we have explored how the proteome of secreted extracellular vesicles (EVs) at the global level may reflect hypoxic glioma cells.

Methods

We have employed shotgun proteomics and label free quantification to profile EVs isolated from human high-grade glioma U87-MG cells cultured at normoxia or hypoxia. Parallel reaction monitoring was used to quantify the identified, hypoxia-associated EV proteins. To determine the potential biological significance of hypoxia-associated proteins, the cumulative Z score of identified EV proteins was compared with GBM subtypes from HGCC and TCGA databases.

Results

In total, 2928 proteins were identified in EVs, out of which 1654 proteins overlapped with the ExoCarta EV-specific database. We found 1034 proteins in EVs that were unique to the hypoxic status of U87-MG cells. We subsequently identified an EV protein signature, “HYP_SIGNATURE”, encompassing nine proteins that strongly represented the hypoxic situation and exhibited close proximity to the mesenchymal GBM subtype.

Conclusions

We propose, for the first time, an EV protein signature that could comprehensively reflect the hypoxic status of high-grade glioma cells. The presented data provide proof-of-concept for targeted proteomic profiling of glioma derived EVs, which should motivate future studies exploring its utility in non-invasive diagnosis and monitoring of brain tumor patients.

Electronic supplementary material

The online version of this article (10.1007/s11060-019-03262-4) contains supplementary material, which is available to authorized users.

DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks

Background

The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem.

Results

Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database.

Conclusions

DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

Small extracellular vesicles and their miRNA cargo are anti-apoptotic members of the senescence-associated secretory phenotype

Loss of functionality during aging of cells and organisms is caused and accompanied by altered cell-to-cell communication and signalling. One factor thereby is the chronic accumulation of senescent cells and the concomitant senescence-associated secretory phenotype (SASP) that contributes to microenvironment remodelling and a pro-inflammatory status. While protein based SASP factors have been well characterized, little is known about small extracellular vesicles (sEVs) and their miRNA cargo. Therefore, we analysed secretion of sEVs from senescent human dermal fibroblasts and catalogued the therein contained miRNAs. We observed a four-fold increase of sEVs, with a concomitant increase of >80% of all cargo miRNAs. The most abundantly secreted miRNAs were predicted to collectively target mRNAs of pro-apoptotic proteins, and indeed, senescent cell derived sEVs exerted anti-apoptotic activity. In addition, we identified senescence-specific differences in miRNA composition of sEVs, with an increase of miR-23a-5p and miR-137 and a decrease of miR-625-3p, miR-766-3p, miR-199b-5p, miR-381-3p, miR-17-3p. By correlating intracellular and sEV-miRNAs, we identified miRNAs selectively retained in senescent cells (miR-21-3p and miR-17-3p) or packaged specifically into senescent cell derived sEVs (miR-15b-5p and miR-30a-3p). Therefore, we suggest sEVs and their miRNA cargo to be novel, members of the SASP that are selectively secreted or retained in cellular senescence.

Biomarker Identification Through Integrating fMRI and Epigenetics

OBJECTIVE

Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor to fill the gap by combining epigenetics (e.g., DNA methylation) with imaging data (e.g., fMRI) to identify biomarkers for schizophrenia (SZ).

METHODS

We propose to combine linear regression with canonical correlation analysis (CCA) in a relaxed yet coupled manner to extract discriminative features for SZ that are co-expressed in the fMRI and DNA methylation data.

RESULT

After validation through simulations, we applied our method to real imaging epigenetics data of 184 subjects from the Mental Illness and Neuroscience Discovery Clinical Imaging Consortium. After significance test, we identified 14 brain regions and 44 cytosine-phosphate-guanine(CpG) sites. Average classification accuracy is [Formula: see text]. By linking the CpG sites to genes, we identified pathways Guanosine ribonucleotides de novo biosynthesis and Guanosine nucleotides de novo biosynthesis, and a GO term Perikaryon.

CONCLUSION

This imaging epigenetics study has identified both brain regions and genes that are associated with neuron development and memory processing. These biomarkers contribute to a good understanding of the mechanism underlying SZ but are overlooked by previous imaging genetics studies.

SIGNIFICANCE

Our study sheds light on the understanding and diagnosis of SZ with a imaging epigenetics approach, which is demonstrated to be effective in extracting novel biomarkers associated with SZ.

The pivotal role of sampling recurrent tumors in the precision care of patients with tumors of the central nervous system

Effective management of brain and spine tumors relies on a multidisciplinary approach encompassing surgery, radiation, and systemic therapy. In the era of personalized oncology, the latter is complemented by various molecularly targeting agents. Precise identification of cellular targets for these drugs requires comprehensive profiling of the cancer genome coupled with an efficient analytic pipeline, leading to an informed decision on drug selection, prognosis, and confirmation of the original pathological diagnosis. Acquisition of optimal tumor tissue for such analysis is paramount and often presents logistical challenges in neurosurgery. Here, we describe the experience and results of the Personalized OncoGenomics (POG) program with a focus on tumors of the central nervous system (CNS). Patients with recurrent CNS tumors were consented and enrolled into the POG program prior to accrual of tumor and matched blood followed by whole-genome and transcriptome sequencing and processing through the POG bioinformatic pipeline. Sixteen patients were enrolled into POG. In each case, POG analyses identified genomic drivers including novel oncogenic fusions, aberrant pathways, and putative therapeutic targets. POG has highlighted that personalized oncology is truly a multidisciplinary field, one in which neurosurgeons must play a vital role if these programs are to succeed and benefit our patients.