pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis

Data on microRNA expression, predicted gene targets and pathway analysis in response to different concentrations of a cranberry proanthocyanidin-rich extract and its metabolite 3-(4-hydroxyphenyl)-propionic acid in intestinal Caco-2BBe1 cells

Cranberry-derived proanthocyanidin (PAC) is processed by the gut microbiota to produce 3-(4-hydroxyphenyl)-propionic acid (HPPA), among other metabolites. These data are in support of the article entitled, "Cranberry proanthocyanidin and its microbial metabolite 3,4-dihydroxyphenylacetic acid, but not 3-(4-hydroxyphenyl)-propionic acid, partially reverse pro-inflammatory microRNA responses in human intestinal epithelial cells," published in Molecular Nutrition and Food Research [1]. Here we describe data generated by nCounterⓇ Human v3 miRNA Expression Panel of RNA obtained from Caco-2BBe1 cells exposed to two different concentrations of cranberry extract rich in PAC (50 µg/ml or 100 µg/ml) or 3-(4-hydroxyphenyl)-propionic acid (5 µg/ml or 10 µg/ml) for 24 h, then stimulated with 1 ng/ml of IL-1ß or not (mock) for three hours. The raw data are publicly available at the NCBI GEO database GSE237078. This work also includes descriptive methodological procedures, treatment-responsive microRNA (miRNA) expression profiles in Caco-2BBe1 cells, and in silico mRNA gene target and pathway enrichment analyses of significantly differentially expressed miRNAs (q < 0.001). Cranberry and its components have recognized health benefits, particularly in relation to combatting inflammation and pathogenic bacterial adhesion. These data will be valuable as a reference to study the response of intestinal cells to other polyphenol-rich food sources, analyze gut microbial responses to cranberry and its metabolites in different cell lines and mammalian hosts to elucidate individualized effects, and to delineate the role of the gut microbiota in facilitating the benefits of cranberry. Moreover, these data will aid in expanding our knowledge on the mechanisms underlying the benefits of cranberry and its components.

Identification of metabolic pathways and key genes associated with atypical parkinsonism using a systems biology approach

Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes.

PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful

Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.

Prediction of Psoriatic Arthritis in Patients With Psoriasis Using DNA Methylation Profiles

OBJECTIVE

Psoriatic arthritis (PsA) is an immune-mediated inflammatory arthritis, associated with psoriasis, that significantly increases morbidity and mortality risk. We currently lack the means of predicting which patients with psoriasis will develop PsA, and a large number of patients remain undiagnosed. Regulation of gene expression through DNA methylation can potentially trigger and maintain PsA pathophysiological processes. We aimed to identify DNA methylation markers that can predict which patients with psoriasis will develop PsA prior to the onset of musculoskeletal symptoms.

METHODS

Genome-wide DNA methylation was assessed in blood samples from patients with psoriasis who went on to develop arthritis (converters) and patients with psoriasis who did not (biologic naive, matched for age, sex, psoriasis duration, and duration of follow-up). Methylation differences between converters and nonconverters were identified by a multivariate linear regression model including clinical covariates (age, sex, body mass index, smoking). Predictive performance of methylation markers was assessed by developing support vector machine classification models with and without the addition of clinical variables.

RESULTS

We identified a set of 36 highly relevant methylation markers (false discovery rate: adjusted P < 0.05 and a minimum change in methylation of 0.05) across 15 genes and several intergenic regions. A classification model relying on these markers identified converters and nonconverters with an area under the receiver operating characteristic curve of 0.9644.

CONCLUSION

This study shows that DNA methylation patterns at an early stage of psoriatic disease can distinguish between patients who will develop PsA from those who will not during the same follow-up.

Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits

Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile-quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues.

Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach

Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections and the induction of autoimmune disorders due to this phenomenon. Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid arthritis, psoriasis, Alzheimer's disease, etc., while infections included viral and bacterial infections like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions. Two sequence mimics (antigenic, non-allergenic, and immunogenic) of ≥10 amino acids from these proteins were used to design a vaccine construct to explore the possibility of eliciting an immune response. Docking analysis of the top vaccine construct C2 showed favorable interactions with HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also simulated, showing promising results for effective immunization against C. difficile infections. This study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and vaccine design based on sequence mimics that trigger a defensive response.

Molecular Mimicry Mapping in Streptococcus pneumoniae: Cues for Autoimmune Disorders and Implications for Immune Defense Activation

Streptococcus pneumoniae contributes to a range of infections, including meningitis, pneumonia, otitis media, and sepsis. Infections by this bacterium have been associated with the phenomenon of molecular mimicry, which, in turn, may contribute to the induction of autoimmunity. In this study, we utilized a bioinformatics approach to investigate the potential for S. pneumoniae to incite autoimmunity via molecular mimicry. We identified 13 S. pneumoniae proteins that have significant sequence similarity to human proteins, with 11 of them linked to autoimmune disorders such as psoriasis, rheumatoid arthritis, and diabetes. Using in silico tools, we predicted the sequence as well as the structural homology among these proteins. Database mining was conducted to establish links between these proteins and autoimmune disorders. The antigenic, non-allergenic, and immunogenic sequence mimics were employed to design and validate an immune response via vaccine construct design. Mimic-based vaccine construct can prove effective for immunization against the S. pneumoniae infections. Immune response simulation and binding affinity was assessed through the docking of construct C8 to human leukocyte antigen (HLA) molecules and TLR4 receptor, with promising results. Additionally, these mimics were mapped as conserved regions on their respective proteins, suggesting their functional importance in S. pneumoniae pathogenesis. This study highlights the potential for S. pneumoniae to trigger autoimmunity via molecular mimicry and the possibility of vaccine design using these mimics for triggering defense response.

The lncRNA MALAT1 is upregulated in urine of type 1 diabetes mellitus patients with diabetic kidney disease

Long non-coding RNAs (lncRNAs) are RNAs with >200 nucleotides that are unable to encode proteins and are involved in gene expression regulation. LncRNAs have a key role in many physiological and pathological processes and, consequently, they have been associated with several human diseases, including diabetes chronic complications, such as diabetes kidney disease (DKD). In this context, some studies have identified the dysregulation of the lncRNAs MALAT1 and TUG1 in patients with DKD; nevertheless, available data are still contradictory. Thus, the objective of this study was to compare MALAT1 and TUG1 expressions in urine of patients with type 1 diabetes mellitus (T1DM) categorized according to DKD presence. This study comprised 18 T1DM patients with DKD (cases) and 9 long-duration T1DM patients without DKD (controls). MALAT1 and TUG1 were analyzed using qPCR. Bioinformatics analyses were done to identify both lncRNA target genes and the signaling pathways under their regulation. The lncRNA MALAT1 was upregulated in urine of T1DM patients with DKD vs. T1DM controls (P = 0.007). The expression of lncRNA TUG1 did not differ between groups (P = 0.815). Bioinformatics analysis showed these two lncRNAs take part in metabolism-related pathways. The present study shows that the lncRNA MALAT1 is upregulated in T1DM patients presenting DKD.

The miRNA Content of Bone Marrow-Derived Extracellular Vesicles Contributes to Protein Pathway Alterations Involved in Ionising Radiation-Induced Bystander Responses

Extracellular vesicles (EVs), through their cargo, are important mediators of bystander responses in the irradiated bone marrow (BM). MiRNAs carried by EVs can potentially alter cellular pathways in EV-recipient cells by regulating their protein content. Using the CBA/Ca mouse model, we characterised the miRNA content of BM-derived EVs from mice irradiated with 0.1 Gy or 3 Gy using an nCounter analysis system. We also analysed proteomic changes in BM cells either directly irradiated or treated with EVs derived from the BM of irradiated mice. Our aim was to identify key cellular processes in the EV-acceptor cells regulated by miRNAs. The irradiation of BM cells with 0.1 Gy led to protein alterations involved in oxidative stress and immune and inflammatory processes. Oxidative stress-related pathways were also present in BM cells treated with EVs isolated from 0.1 Gy-irradiated mice, indicating the propagation of oxidative stress in a bystander manner. The irradiation of BM cells with 3 Gy led to protein pathway alterations involved in the DNA damage response, metabolism, cell death and immune and inflammatory processes. The majority of these pathways were also altered in BM cells treated with EVs from mice irradiated with 3 Gy. Certain pathways (cell cycle, acute and chronic myeloid leukaemia) regulated by miRNAs differentially expressed in EVs isolated from mice irradiated with 3 Gy overlapped with protein pathway alterations in BM cells treated with 3 Gy EVs. Six miRNAs were involved in these common pathways interacting with 11 proteins, suggesting the involvement of miRNAs in the EV-mediated bystander processes. In conclusion, we characterised proteomic changes in directly irradiated and EV-treated BM cells, identified processes transmitted in a bystander manner and suggested miRNA and protein candidates potentially involved in the regulation of these bystander processes.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Impact of miR-200b and miR-495 variants on the risk of large-artery atherosclerosis stroke

Single-nucleotide polymorphisms (SNPs) of microRNAs (miRNAs) may alter miRNA transcription, maturation and target specificity, thus affecting stroke susceptibility. We aimed to investigate whether miR-200b and miR-495 SNPs may be associated with ischemic stroke (IS) risk and further explore underlying mechanisms including related genes and pathways. MiR-200b rs7549819 and miR-495 rs2281611 polymorphisms were genotyped among 712 large-artery atherosclerosis (LAA) stroke patients and 1,076 controls in a case-control study. Bioinformatic analyses were performed to explore potential association of miR-200b/495 with IS and to examine the effects of these two SNPs on miR-200b/495. Furthermore, we evaluated the association between these two SNPs and stroke using the public GWAS datasets. In our case-control study, rs7549819 was significantly associated with a decreased risk of LAA stroke (OR = 0.73, 95% CI = 0.58-0.92; p = 0.007), while rs2281611 had no significant association with LAA stroke risk. These results were consistent with the findings in East Asians from the GIGASTROKE study. Combined effects analysis revealed that individuals with 2-4 protective alleles (miR-200bC and miR-495 T) exhibited lower risk of LAA stroke than those with 0-1 variants (OR = 0.76, 95% CI = 0.61-0.96; p = 0.021). Bioinformatic analyses showed that miR-200b and miR-495 were significantly associated with genes and pathways related to IS pathogenesis, and rs7549819 and rs2281611 markedly influenced miRNA expression and structure. MiR-200b rs7549819 polymorphism and the combined genotypes of miR-200b rs7549819 and miR-495 rs2281611 polymorphisms were associated with decreased risk of LAA stroke in Chinese population.

Longitudinal multi-omics alterations response to 8-week risperidone monotherapy: Evidence linking cortical thickness, transcriptomics and epigenetics

Background

Antipsychotic treatment-related alterations of cortical thickness (CT) and clinical symptoms have been previously corroborated, but less is known about whether the changes are driven by gene expression and epigenetic modifications.

Methods

Utilizing a prospective design, we recruited 42 treatment-naive first-episode schizophrenia patients (FESP) and 38 healthy controls. Patients were scanned by TI weighted imaging before and after 8-week risperidone monotherapy. CT estimation was automatically performed with the FreeSurfer software package. Participants' peripheral blood genomic DNA methylation (DNAm) status, quantified by using Infinium^® Human Methylation 450K BeadChip, was examined in parallel with T1 scanning. In total, CT measures from 118 subjects and genomic DNAm status from 114 subjects were finally collected. Partial least squares (PLS) regression was used to detect the spatial associations between longitudinal CT variations after treatment and cortical transcriptomic data acquired from the Allen Human Brain Atlas. Canonical correlation analysis (CCA) was then performed to identify multivariate associations between DNAm of PLS1 genes and patients' clinical improvement.

Results

We detected the significant PLS1 component (2,098 genes) related to longitudinal alterations of CT, and the PLS1 genes were significantly enriched in neurobiological processes, and dopaminergic- and cancer-related pathways. Combining Laplacian score and CCA analysis, we further linked DNAm of 33 representative genes from the 2,098 PLS1 genes with patients' reduction rate of clinical symptoms.

Conclusions

This study firstly revealed that changes of CT and clinical behaviors after treatment may be transcriptionally and epigenetically underlied. We define a "three-step" roadmap which represents a vital step toward the exploration of treatment- and treatment response-related biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture

Biological pathways are a broadly used formalism for representing and interpreting the cascade of biochemical reactions underlying cellular and biological mechanisms. Pathway representation provides an ontological link among biomolecules such as RNA, DNA, small molecules, proteins, protein complexes, hormones and genes. Frequently, pathway annotations are used to identify mechanisms linked to genes within affected biological contexts. This important role and the simplicity and elegance in representing complex interactions led to an explosion of pathway representations and databases. Unfortunately, the lack of overlap across databases results in inconsistent enrichment analysis results, unless databases are integrated. However, due to absence of consensus, guidelines or gold standards in pathway definition and representation, integration of data across pathway databases is not straightforward. Despite multiple attempts to provide consolidated pathways, highly related, redundant, poorly overlapping or ambiguous pathways continue to render pathways analysis inconsistent and hard to interpret. Ontology-based integration will promote unbiased, comprehensive yet streamlined analysis of experiments, and will reduce the number of enriched pathways when performing pathway enrichment analysis. Moreover, appropriate and consolidated pathways provide better training data for pathway prediction algorithms. In this manuscript, we describe the current methods for pathway consolidation, their strengths and pitfalls, and highlight directions for future improvements to this research area.

Nine quick tips for pathway enrichment analysis

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.

Circulating miRNAs in girls with abdominal obesity: miR-221-3p as a biomarker of response to weight loss interventions

BACKGROUND

Recent studies have associated several microRNAs (miRNAs) with childhood obesity and energy homeostasis, suggesting that an individual miRNA profile could be used as an early predictor to estimate the response to weight loss interventions in the design of precision nutrition.

OBJECTIVE

To investigate associations between the expression of circulating adiposity-related miRNAs and the response to a weight loss intervention.

METHODS

A total of 51 Spanish girls (age 7-16 years) with abdominal obesity underwent 8 weeks of a multidisciplinary intervention for weight loss. Participants were stratified into two groups in accordance with changes in body mass index (BMI) standard deviation score: low-responders (LR) and high-responders (HR). The expression of 39 circulating miRNAs (c-miRNAs) was evaluated in plasma of all subjects before the intervention.

RESULTS

Six miRNAs were differentially expressed between LR and HR. However, after adjustment for Tanner stage, the association was maintained only for miR-126-3p and miR-221-3p with a higher expression in HR group compared to LR group. After the intervention, miR-221-3p expression decreased in all subjects with a significant difference in the change within groups. However, changes in miR-126-3p levels were not significant. The expression of miR-221-3p was positively correlated with body weight, BMI and waist circumference, and negatively correlated with quantitative insulin sensitivity check index.

CONCLUSIONS

Bioinformatic analysis evidenced that miR-221-3p participates in several obesity-related pathways, and more interestingly, this miRNA targets several candidate genes to childhood obesity according to DisGeNet database. Thus, miR-221-3p could be used for predicting the response to a multidisciplinary intervention for weight loss in young girls.

Identification of the ubiquitin–proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin–proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named “2DCNN-UPP” for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA.

An insight in Salmonella typhi associated autoimmunity candidates' prediction by molecular mimicry

Autoimmune diseases develop when the immune system targets healthy cells and tissues of an individual. In developing countries, S. typhi (a gram-negative pathogenic bacteria) remains a major public health issue. This study aimed to employ bioinformatics analyses to determine the 3D structural-based molecular mimicry and sequence of S. typhi and human host proteins. In addition, to classify possible antigenic microbial peptides homologous to human peptides and comprehend the molecular basis of S. typhi-related autoimmune disorders. Protein sequences were obtained from the NCBI database, and redundancy was removed using the CD-HIT tool. The BLASTp comparative sequence analysis was followed for molecular mimicry identification of human and S. typhi protein sequences. The PathDIP database was utilized to simulate essential physical relationships between proteins and curated pathways for metabolic processes. Subsequently, the IEDB database was used to find cross-reactive MHC class-II binding epitopes that could trigger an autoimmune reaction. SPARKS-X computational biology resource was also used to determine the structural homology between human and S. typhi peptides. The BLASTp study showed that S. typhi and the human host have several proteins holding considerable sequence similarities based on a set threshold of e ≤ 10-6 and bit score ≥100. The PathDIP putatively identified that these proteins enriched in a total of 68 metabolic pathways by a significant P-value (P < 0.005). The PSORTb analysis predicted that 26 out of these proteins are cytosolic, 1 predicted to be periplasmic protein, and 1 predicted to be localized in the cytoplasmic membrane. IEDB data analysis predicted many S.typhi and human homologs epitopes as a good binder of human HLA, i.e. DRB1*01:01, DPA1*03:01/DPB1*04:02, and DQA1*01:02/DQB1*06:02 with IC₅₀ < 50 nM. Finally, the docking data demonstrated that homolog lead epitopes promisingly interact with HLA and immune TLR4 receptors by exhibiting the best docking scores and molecular interactions. The analyses ultimately identified several potential candidate proteins and peptides that could cause S.typhi infection-mediated autoimmune diseases in humans.

Systems biology approach identifies key genes and related pathways in childhood obesity

BACKGROUND

Childhood obesity is triggered by a complex interplay of environmental, genetic, and epigenetic factors; however, the molecular mechanisms behind this disease are not completely elucidated. Thus, the aim of this study was to investigate molecular mechanisms involved in childhood obesity by implementing a systems biology approach.

METHODS

Experimentally validated and computationally predicted genes related to childhood obesity were downloaded from DisGeNET database. A protein-protein interaction (PPI) network was constructed using the STRING database and analyzed at Cytoscape web-tool. Hub-bottleneck genes and functional clusters were identified through CytoHubba and MCODE plugins, respectively. Functional enrichment analyses were performed based on Gene Ontology terms and KEGG Pathways.

RESULTS

The DisGeNET search retrieved 191 childhood obesity-related genes. The resulting PPI network contained 12 hub-bottleneck genes (INS, LEP, STAT3, POMC, ALB, TNF, BDNF, CAT, GCG, PPARG, VEGFA, and ADIPOQ) and 4 functional clusters, with cluster 1 showing the highest interaction score. Genes at this cluster were enriched at inflammation, carbohydrate, and lipid metabolism pathways. With exception of POMC, all hub-bottleneck genes were found in cluster 1, which contains highly connected genes that possibly play key roles in obesity-related pathways.

CONCLUSIONS

Our systems biology approach revealed a set of highly interconnected genes associated with childhood obesity, providing comprehensive information regarding genetic and molecular factors involved in the pathogenesis of this disease.

A molecular view of amyotrophic lateral sclerosis through the lens of interaction network modules

Background

Despite the discovery of familial cases with mutations in Cu/Zn-superoxide dismutase (SOD1), Guanine nucleotide exchange C9orf72, TAR DNA-binding protein 43 (TARDBP) and RNA-binding protein FUS as well as a number of other genes linked to Amyotrophic Lateral Sclerosis (ALS), the etiology and molecular pathogenesis of this devastating disease is still not understood. As proteins do not act alone, conducting an analysis of ALS at the system level may provide new insights into the molecular biology of ALS and put it into relationship to other neurological diseases.

Methods

A set of ALS-associated genes/proteins were collected from publicly available databases and text mining of scientific literature. We used these as seed proteins to build protein-protein interaction (PPI) networks serving as a scaffold for further analyses. From the collection of networks, a set of core modules enriched in seed proteins were identified. The molecular biology of the core modules was investigated, as were their associations to other diseases. To assess the core modules’ ability to describe unknown or less well-studied ALS biology, they were queried for proteins more recently associated to ALS and not involved in the primary analysis.

Results

We describe a set of 26 ALS core modules enriched in ALS-associated proteins. We show that these ALS core modules not only capture most of the current knowledge about ALS, but they also allow us to suggest biological interdependencies. In addition, new associations of ALS networks with other neurodegenerative diseases, e.g. Alzheimer’s, Huntington’s and Parkinson’s disease were found. A follow-up analysis of 140 ALS-associated proteins identified since 2014 reveals a significant overrepresentation of new ALS proteins in these 26 disease modules.

Conclusions

Using protein-protein interaction networks offers a relevant approach for broadening the understanding of the biological context of known ALS-associated genes. Using a bottom-up approach for the analysis of protein-protein interaction networks is a useful method to avoid bias caused by over-connected proteins. Our ALS-enriched modules cover most known biological functions associated with ALS. The presence of recently identified ALS-associated proteins in the core modules highlights the potential for using these as a scaffold for identification of novel ALS disease mechanisms.

Repurposing Itraconazole and Hydroxychloroquine to Target Lysosomal Homeostasis in Epithelial Ovarian Cancer

Drug repurposing is an attractive option for oncology drug development. Itraconazole is an antifungal ergosterol synthesis inhibitor that has pleiotropic actions including cholesterol antagonism, inhibition of Hedgehog and mTOR pathways. We tested a panel of 28 epithelial ovarian cancer (EOC) cell lines with itraconazole to define its spectrum of activity. To identify synthetic lethality in combination with itraconazole, a whole-genome drop-out genome-scale clustered regularly interspaced short palindromic repeats sensitivity screen in two cell lines (TOV1946 and OVCAR5) was performed. On this basis, we conducted a phase I dose-escalation study assessing the combination of itraconazole and hydroxychloroquine in patients with platinum refractory EOC (NCT03081702). We identified a wide spectrum of sensitivity to itraconazole across the EOC cell lines. Pathway analysis showed significant involvement of lysosomal compartments, the trans-golgi network and late endosomes/lysosomes; similar pathways are phenocopied by the autophagy inhibitor, chloroquine. We then demonstrated that the combination of itraconazole and chloroquine displayed Bliss defined synergy in EOC cancer cell lines. Furthermore, there was an association of cytotoxic synergy with the ability to induce functional lysosome dysfunction, by chloroquine. Within the clinical trial, 11 patients received at least one cycle of itraconazole and hydroxychloroquine. Treatment was safe and feasible with the recommended phase II dose of 300 and 600 mg twice daily, respectively. No objective responses were detected. Pharmacodynamic measurements on serial biopsies demonstrated limited pharmacodynamic impact. In vitro, itraconazole and chloroquine have synergistic activity and exert a potent antitumor effect by affecting lysosomal function. The drug combination had no clinical antitumor activity in dose escalation.

Significance

The combination of the antifungal drug itraconazole with antimalarial drug hydroxychloroquine leads to a cytotoxic lysosomal dysfunction, supporting the rational for further research on lysosomal targeting in ovarian cancer.

JAK-STAT core cancer pathway: An integrative cancer interactome analysis

Through a comprehensive review and in silico analysis of reported data on STAT-linked diseases, we analysed the communication pathways and interactome of the seven STATs in major cancer categories and proposed rational targeting approaches for therapeutic intervention to disrupt critical pathways and addictions to hyperactive JAK/STAT in neoplastic states. Although all STATs follow a similar molecular activation pathway, STAT1, STAT2, STAT4 and STAT6 exert specific biological profiles associated with a more restricted pattern of activation by cytokines. STAT3 and STAT5A as well as STAT5B have pleiotropic roles in the body and can act as critical oncogenes that promote many processes involved in cancer development. STAT1, STAT3 and STAT5 also possess tumour suppressive action in certain mutational and cancer type context. Here, we demonstrated member-specific STAT activity in major cancer types. Through systems biology approaches, we found surprising roles for EGFR family members, sex steroid hormone receptor ESR1 interplay with oncogenic STAT function and proposed new drug targeting approaches of oncogenic STAT pathway addiction.

Machine learning-based predictions of dietary restriction associations across ageing-related genes

BACKGROUND

Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features.

RESULTS

This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments.

CONCLUSIONS

This work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.

Targeting uPA-uPAR interaction to improve intestinal epithelial barrier integrity in inflammatory bowel disease

Background

Loss of intestinal epithelial barrier integrity is a critical component of Inflammatory Bowel Disease (IBD) pathogenesis. Co-expression regulation of ligand-receptor pairs in IBD mucosa has not been systematically studied. Targeting ligand-receptor pairs which are induced in IBD mucosa and function in intestinal epithelial barrier integrity may provide novel therapeutics for IBD.

Methods

We performed transcriptomic meta-analysis on public IBD datasets combined with cell surface protein-protein-interaction (PPI) databases. We explored primary human/mouse intestinal organoids and Caco-2 cells for expression and function studies of uPA-uPAR (prime hits from the meta-analysis). Epithelial barrier integrity was measured by Trans-Epithelial Electrical Resistance (TEER), FITC-Dextran permeability and tight junction assessment. Genetic (CRISPR, siRNA and KO mice) and pharmacological (small molecules, neutralizing antibody and peptide inhibitors) approaches were applied. Mice deficient of uPAR were studied using the Dextran Sulfate Sodium (DSS)-induced colitis model.

Findings

The IBD ligand-receptor meta-analysis led to the discovery of a coordinated upregulation of uPA and uPAR in IBD mucosa. Both genes were significantly upregulated during epithelial barrier breakdown in primary intestinal organoids and decreased during barrier formation. Genetic inhibition of uPAR or uPA, or pharmacologically blocking uPA-uPAR interaction protects against cytokine-induced barrier breakdown. Deficiency of uPAR in epithelial cells leads to enhanced EGF/EGFR signalling, a known regulator of epithelial homeostasis and repair. Mice deficient of uPAR display improved intestinal barrier function in vitro and during DSS-induced colitis in vivo.

Interpretation

Our findings suggest that blocking uPA-uPAR interaction via pharmacological agents protects the epithelial barrier from inflammation-induced damage, indicating a potential therapeutic target for IBD.

Funding

The study was funded by Boehringer Ingelheim.

Estrogen Receptor 1 Inhibition of Wnt/β-Catenin Signaling Contributes to Sex Differences in Hepatocarcinogenesis

Background

Hepatocellular Carcinoma (HCC) is a sexually dimorphic cancer, with female sex being independently protective against HCC incidence and progression. The aim of our study was to understand the mechanism of estrogen receptor signaling in driving sex differences in hepatocarcinogenesis.

Methods

We integrated 1,268 HCC patient sample profiles from publicly available gene expression data to identify the most differentially expressed genes (DEGs). We mapped DEGs into a physical protein interaction network and performed network topology analysis to identify the most important proteins. Experimental validation was performed in vitro on HCC cell lines, in and in vivo, using HCC mouse model.

Results

We showed that the most central protein, ESR1, is HCC prognostic, as increased ESR1 expression was protective for overall survival, with HR=0.45 (95%CI 0.32-0.64, p=4.4E-06), and was more pronounced in women. Transfection of HCC cell lines with ESR1 and exposure to estradiol affected expression of genes involved in the Wnt/β-catenin signaling pathway. ER-α (protein product of ESR1) agonist treatment in a mouse model of HCC resulted in significantly longer survival and decreased tumor burden (p<0.0001), with inhibition of Wnt/β-Catenin signaling. In vitro experiments confirmed colocalization of β-catenin with ER-α, leading to inhibition of β-catenin-mediated transcription of target genes c-Myc and Cyclin D1.

Conclusion

Combined, the centrality of ESR1 and its inhibition of the Wnt/β-catenin signaling axis provide a biological rationale for protection against HCC incidence and progression in women.

Combined proteomic/transcriptomic signature of recurrence post-liver transplantation for hepatocellular carcinoma beyond Milan

Background and aims

Liver transplantation (LT) can be offered to patients with Hepatocellular carcinoma (HCC) beyond Milan criteria. However, there are currently limited molecular markers on HCC explant histology to predict recurrence, which arises in up to 20% of LT recipients. The goal of our study was to derive a combined proteomic/transcriptomic signature on HCC explant predictive of recurrence post-transplant using unbiased, high-throughput approaches.

Methods

Patients who received a LT for HCC beyond Milan criteria in the context of hepatitis B cirrhosis were identified. Tumor explants from patients with post-transplant HCC recurrence (N = 7) versus those without recurrence (N = 4) were analyzed by mass spectrometry and gene expression array. Univariate analysis was used to generate a combined proteomic/transcriptomic signature linked to recurrence. Significantly predictive genes and proteins were verified and internally validated by immunoblotting and immunohistochemistry.

Results

Seventy-nine proteins and 636 genes were significantly differentially expressed in HCC tumors with subsequent recurrence (p < 0.05). Univariate survival analysis identified Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1) gene (HR = 0.084, 95%CI 0.01–0.68, p = 0.0152), ALDH1A1 protein (HR = 0.039, 95%CI 0.16–0.91, p = 0.03), Galectin 3 Binding Protein (LGALS3BP) gene (HR = 7.14, 95%CI 1.20–432.96, p = 0.03), LGALS3BP protein (HR = 2.6, 95%CI 1.1–6.1, p = 0.036), Galectin 3 (LGALS3) gene (HR = 2.89, 95%CI 1.01–8.3, p = 0.049) and LGALS3 protein (HR = 2.6, 95%CI 1.2–5.5, p = 0.015) as key dysregulated analytes in recurrent HCC. In concordance with our proteome findings, HCC recurrence was linked to decreased ALDH1A1 and increased LGALS3 protein expression by Western Blot. LGALS3BP protein expression was validated in 29 independent HCC samples.

Conclusions

Significantly increased LGALS3 and LGALS3BP gene and protein expression on explant were associated with post-transplant recurrence, whereas increased ALDH1A1 was associated with absence of recurrence in patients transplanted for HCC beyond Milan criteria. This combined proteomic/transcriptomic signature could help in predicting HCC recurrence risk and guide post-transplant surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09333-x.

Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

BACKGROUND

Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools.

METHODS

PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context.

RESULTS

We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer.

CONCLUSION

As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

Involvement of miR-126 rs4636297 and miR-146a rs2910164 polymorphisms in the susceptibility for diabetic retinopathy: a case-control study in a type 1 diabetes population

BACKGROUND AND PURPOSE

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. MiRNA-126 and miRNA-146a have been described as having abnormal expressions in diabetic retinopathy (DR) patients. Polymorphisms in genes codifying miRNAs (miRSNPs) may alter the expression of the corresponding miRNA and, thus, interfere with susceptibility to DR. Therefore, miRSNPs in miR-126 and miR-146a genes could be associated with DR susceptibility. The purpose of this study was to investigate the association between miR-126 rs4636297 (G/A) and miR-146a rs2910164 (G/C) miRSNPs and DR.

METHODS

This case-control study included 195 type 1 diabetes mellitus (T1DM) patients with DR (cases) and 215 patients without DR and with ≥10 years of T1DM (controls). MiRSNPs were genotyped by real-time PCR.

RESULTS

Genotype distributions of two analysed miRSNPs were in Hardy-Weinberg equilibrium in controls (p > 0.050). Frequencies of the miR-126 rs4636297 miRSNP were not significantly different between case and control groups (p = 0.169). However, after adjustment for age, glycated haemoglobin, triglycerides, estimated glomerular filtration rate and ethnicity, the A allele of this miRSNP was associated with protection for DR under additive [OR: 0.444 (95% CI: 0.211-0.936), p = 0.033] and dominant [OR: 0.512 (95% CI: 0.303-0.865), p = 0.012] inheritance models. Genotype and allele frequencies of miR-146a rs2910164 miRSNP did not differ between groups (p = 0.368 and p = 0.957), and this polymorphism was not associated with DR when assuming different inheritance models.

CONCLUSION

Our results suggest an association between the A allele of miR-126 rs4636297 miRSNP and protection for DR in a Southern Brazilian population.

Reactivation of Multiple Fetal miRNAs in Lung Adenocarcinoma

MicroRNAs (miRNAs) play vital roles in the regulation of normal developmental pathways. However, cancer cells can co-opt these miRNAs, and the pathways that they regulate, to drive pro-tumourigenic phenotypes. Characterization of the miRNA transcriptomes of fetal organs is essential for identifying these oncofetal miRNAs, but it has been limited by fetal sample availability. As oncofetal miRNAs are absent from healthy adult lungs, they represent ideal targets for developing diagnostic and therapeutic strategies. We conducted small RNA sequencing of a rare collection of 25 human fetal lung (FL) samples and compared them to two independent cohorts (n = 140, n = 427), each comprised of adult non-neoplastic lung (ANL) and lung adenocarcinoma (LUAD) samples. We identified 13 oncofetal miRNAs that were expressed in FL and LUAD but not in ANL. These oncofetal miRNAs are potential biomarkers for LUAD detection (AUC = 0.963). Five of these miRNAs are derived from the imprinted C14MC miRNA cluster at the 14q32 locus, which has been associated with cancer development and abnormal fetal and placental development. Additionally, we observed the pulmonary expression of 44 previously unannotated miRNAs. The sequencing of these fetal lung samples also provides a baseline resource against which aberrant samples can be compared.

Normothermic Ex-vivo Kidney Perfusion in a Porcine Auto-Transplantation Model Preserves the Expression of Key Mitochondrial Proteins: An Unbiased Proteomics Analysis

Normothermic ex-vivo kidney perfusion (NEVKP) results in significantly improved graft function in porcine auto-transplant models of donation after circulatory death injury compared with static cold storage (SCS); however, the molecular mechanisms underlying these beneficial effects remain unclear. We performed an unbiased proteomics analysis of 28 kidney biopsies obtained at three time points from pig kidneys subjected to 30 min of warm ischemia, followed by 8 h of NEVKP or SCS, and auto-transplantation. 70/6593 proteins quantified were differentially expressed between NEVKP and SCS groups (false discovery rate < 0.05). Proteins increased in NEVKP mediated key metabolic processes including fatty acid ß-oxidation, the tricarboxylic acid cycle, and oxidative phosphorylation. Comparison of our findings with external datasets of ischemia-reperfusion and other models of kidney injury confirmed that 47 of our proteins represent a common signature of kidney injury reversed or attenuated by NEVKP. We validated key metabolic proteins (electron transfer flavoprotein subunit beta and carnitine O-palmitoyltransferase 2, mitochondrial) by immunoblotting. Transcription factor databases identified members of the peroxisome proliferator-activated receptors (PPAR) family of transcription factors as the upstream regulators of our dataset, and we confirmed increased expression of PPARA, PPARD, and RXRA in NEVKP with reverse transcription polymerase chain reaction. The proteome-level changes observed in NEVKP mediate critical metabolic pathways. These effects may be coordinated by PPAR-family transcription factors and may represent novel therapeutic targets in ischemia-reperfusion injury.

BioPAX-Parser: parsing and enrichment analysis of BioPAX pathways

SUMMARY

Biological pathways are fundamental for learning about healthy and disease states. Many existing formats support automatic software analysis of biological pathways, e.g. BioPAX (Biological Pathway Exchange). Although some algorithms are available as web application or stand-alone tools, no general graphical application for the parsing of BioPAX pathway data exists. Also, very few tools can perform pathway enrichment analysis (PEA) using pathway encoded in the BioPAX format. To fill this gap, we introduce BiP (BioPAX-Parser), an automatic and graphical software tool aimed at performing the parsing and accessing of BioPAX pathway data, along with PEA by using information coming from pathways encoded in BioPAX.

AVAILABILITY AND IMPLEMENTATION

BiP is freely available for academic and non-profit organizations at https://gitlab.com/giuseppeagapito/bip under the LGPL 2.1, the GNU Lesser General Public License.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies

BACKGROUND

The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC). 

AIM

To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect. 

METHODS

We collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.

RESULTS

We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and β1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately.

CONCLUSION

By analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, β1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and β1-integrin are master regulators that could serve as potential therapeutic targets in HCC.

Frequency of Pathogenic Germline Variants in Cancer-Susceptibility Genes in Patients With Osteosarcoma

Importance

Osteosarcoma, the most common malignant bone tumor in children and adolescents, occurs in a high number of cancer predisposition syndromes that are defined by highly penetrant germline mutations. The germline genetic susceptibility to osteosarcoma outside of familial cancer syndromes remains unclear.

Objective

To investigate the germline genetic architecture of 1244 patients with osteosarcoma.

Design, Setting, and Participants

Whole-exome sequencing (n = 1104) or targeted sequencing (n = 140) of the DNA of 1244 patients with osteosarcoma from 10 participating international centers or studies was conducted from April 21, 2014, to September 1, 2017. The results were compared with the DNA of 1062 individuals without cancer assembled internally from 4 participating studies who underwent comparable whole-exome sequencing and 27 173 individuals of non-Finnish European ancestry who were identified through the Exome Aggregation Consortium (ExAC) database. In the analysis, 238 high-interest cancer-susceptibility genes were assessed followed by testing of the mutational burden across 736 additional candidate genes. Principal component analyses were used to identify 732 European patients with osteosarcoma and 994 European individuals without cancer, with outliers removed for patient-control group comparisons. Patients were subsequently compared with individuals in the ExAC group. All data were analyzed from June 1, 2017, to July 1, 2019.

Main Outcomes and Measures

The frequency of rare pathogenic or likely pathogenic genetic variants.

Results

Among 1244 patients with osteosarcoma (mean [SD] age at diagnosis, 16 [8.9] years [range, 2-80 years]; 684 patients [55.0%] were male), an analysis restricted to individuals with European ancestry indicated a significantly higher pathogenic or likely pathogenic variant burden in 238 high-interest cancer-susceptibility genes among patients with osteosarcoma compared with the control group (732 vs 994, respectively; P = 1.3 × 10-18). A pathogenic or likely pathogenic cancer-susceptibility gene variant was identified in 281 of 1004 patients with osteosarcoma (28.0%), of which nearly three-quarters had a variant that mapped to an autosomal-dominant gene or a known osteosarcoma-associated cancer predisposition syndrome gene. The frequency of a pathogenic or likely pathogenic cancer-susceptibility gene variant was 128 of 1062 individuals (12.1%) in the control group and 2527 of 27 173 individuals (9.3%) in the ExAC group. A higher than expected frequency of pathogenic or likely pathogenic variants was observed in genes not previously linked to osteosarcoma (eg, CDKN2A, MEN1, VHL, POT1, APC, MSH2, and ATRX) and in the Li-Fraumeni syndrome-associated gene, TP53.

Conclusions and Relevance

In this study, approximately one-fourth of patients with osteosarcoma unselected for family history had a highly penetrant germline mutation requiring additional follow-up analysis and possible genetic counseling with cascade testing.

The Impact of lncRNAs in Diabetes Mellitus: A Systematic Review and In Silico Analyses

Long non-coding RNAs (lncRNAs) are non-coding transcripts that have emerged as one of the largest and diverse RNA families that regulate gene expression. Accumulating evidence has suggested a number of lncRNAs are involved in diabetes mellitus (DM) pathogenesis. However, results about lncRNA expressions in DM patients are still inconclusive. Thus, we performed a systematic review of the literature on the subject followed by bioinformatics analyses to better understand which lncRNAs are dysregulated in DM and in which pathways they act. Pubmed, Embase, and Gene Expression Omnibus (GEO) repositories were searched to identify studies that investigated lncRNA expression in cases with DM and non-diabetic controls. LncRNAs consistently dysregulated in DM patients were submitted to bioinformatics analysis to retrieve their target genes and identify potentially affected signaling pathways under their regulation. Fifty-three eligible articles were included in this review after the application of the inclusion and exclusion criteria. Six hundred and thirty-eight lncRNAs were differentially expressed between cases and controls in at least one study. Among them, six lncRNAs were consistently dysregulated in patients with DM (Anril, Hotair, Malat1, Miat, Kcnq1ot1, and Meg3) compared to controls. Moreover, these six lncRNAs participate in several metabolism-related pathways, evidencing their importance in DM. This systematic review suggests six lncRNAs are dysregulated in DM, constituting potential biomarkers of this disease.

Comprehensive Analysis Reveals Novel Interactions between Circulating MicroRNAs and Gut Microbiota Composition in Human Obesity

Background: The determinants that mediate the interactions between microRNAs and the gut microbiome impacting on obesity are scarcely understood. Thus, the aim of this study was to investigate possible interactions between circulating microRNAs and gut microbiota composition in obesity. Method: The sample comprised 78 subjects with obesity (cases, body mass index (BMI): 30–40 kg/m²) and 25 eutrophic individuals (controls, BMI ≤ 25 kg/m²). The expression of 96 microRNAs was investigated in plasma of all individuals using miRCURY LNA miRNA Custom PCR Panels. Bacterial DNA sequencing was performed following the Illumina 16S protocol. The FDR correction was used for multiple comparison analyses. Results: A total of 26 circulating microRNAs and 12 bacterial species were found differentially expressed between cases and controls. Interestingly, an interaction among three miRNAs (miR-130b-3p, miR-185-5p and miR-21-5p) with Bacteroides eggerthi and BMI levels was evidenced (r² = 0.148, p = 0.004). Moreover, these microRNAs regulate genes that participate in metabolism-related pathways, including fatty acid degradation, insulin signaling and glycerolipid metabolism. Conclusions: This study characterized an interaction between the abundance of 4 bacterial species and 14 circulating microRNAs in relation to obesity. Moreover, the current study also suggests that miRNAs may serve as a communication mechanism between the gut microbiome and human hosts.

Emergence of non-canonical parvalbumin-containing interneurons in hippocampus of a murine model of type I lissencephaly

Type I lissencephaly is a neuronal migration disorder caused by haploinsuffiency of the PAFAH1B1 (mouse: Pafah1b1) gene and is characterized by brain malformation, developmental delays, and epilepsy. Here, we investigate the impact of Pafah1b1 mutation on the cellular migration, morphophysiology, microcircuitry, and transcriptomics of mouse hippocampal CA1 parvalbumin-containing inhibitory interneurons (PV+INTs). We find that WT PV+INTs consist of two physiological subtypes (80% fast-spiking (FS), 20% non-fast-spiking (NFS)) and four morphological subtypes. We find that cell-autonomous mutations within interneurons disrupts morphophysiological development of PV+INTs and results in the emergence of a non-canonical 'intermediate spiking (IS)' subset of PV+INTs. We also find that now dominant IS/NFS cells are prone to entering depolarization block, causing them to temporarily lose the ability to initiate action potentials and control network excitation, potentially promoting seizures. Finally, single-cell nuclear RNAsequencing of PV+INTs revealed several misregulated genes related to morphogenesis, cellular excitability, and synapse formation.

GSOAP: a tool for visualization of gene set over-representation analysis

Motivation

Gene sets over-representation analysis (GSOA) is a common technique of enrichment analysis that measures the overlap between a gene set and selected instances (e.g. pathways). Despite its popularity, there is currently no established standard for visualization of GSOA results.

Results

Here, we propose a visual exploration of the GSOA results by showing the relationships among the enriched instances, while highlighting important instance attributes, such as significance, closeness (centrality) and clustering.

Availability and implementation

GSOAP is implemented as an R package and is available at https://github.com/tomastokar/gsoap.

Diabetogenic Effects of Immunosuppression: An Integrative Analysis

BACKGROUND

Posttransplant diabetes mellitus (PTDM) affects up to 50% of solid organ transplant recipients and compromises long-term outcomes. The goal of this study was to investigate how immunosuppressants affect gene expression in a manner that increases diabetes risk, by performing integrative analysis on publicly available, high-throughput gene expression data.

METHODS

All high-throughput gene expression datasets of solid organ transplant recipients were retrieved from the Gene Expression Omnibus. Significantly dysregulated genes and pathways were determined, and those in common with type 2 diabetes were identified. THP-1 and HepG2 cells were exposed in vitro to tacrolimus, and validation of genes involved in insulin signaling and glucose metabolism was performed using specific arrays. These cells were then treated with the hypoglycemic agents, metformin, and insulin to assess for appropriate reversion of specific diabetogenic genes.

RESULTS

Insulin signaling and secretion were the most commonly dysregulated pathways that overlapped with diabetes in transplant recipients. KRAS, GRB2, PCK2, BCL2L1, INSL3, DOK3, and PTPN1 were among the most significantly upregulated genes in both immunosuppression and diabetes subsets and were appropriately reverted by metformin as confirmed in vitro.

CONCLUSIONS

We discovered that the significantly dysregulated genes in the context of immunosuppression are implicated in insulin signaling and insulin secretion, as a manifestation of pancreatic β-cell function. In vitro validation confirmed key diabetes-related genes in the context of immunosuppression. Further analysis and in vitro validation revealed that metformin optimally reverts diabetogenic genes dysregulated in the context of immunosuppression. The optimal therapeutic management of posttransplant diabetes mellitus needs to be further investigated, taking into account the mechanistic impact of immunosuppressants.

Identifying host microRNAs in bronchoalveolar lavage samples from lung transplant recipients infected with Aspergillus

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs of ∼22 nucleotides that play a crucial role in post-transcriptional regulation of gene expression. Dysregulation of miRNA expression has been shown during microbial infections. We sought to identify miRNAs that distinguish invasive aspergillosis (IA) from non-IA in lung transplant recipients (LTRs).

METHODS

We used NanoString nCounter Human miRNA, version 3, panel to measure miRNAs in bronchoalveolar lavage (BAL) samples from LTRs with Aspergillus colonization (ASP group) (n = 10), those with Aspergillus colonization and chronic lung allograft dysfunction (CLAD) (ASPCLAD group) (n = 7), those with IA without CLAD (IA group) (n = 10), those who developed IA with CLAD (IACLAD group) (n = 9), and control patients (controls) (n = 9). The miRNA profile was compared using the permutation test of 100,000 trials for each of the comparisons. We used mirDIP to obtain their gene targets and pathDIP to determine the pathway enrichment.

RESULTS

We performed pairwise comparisons between patient groups to identify differentially expressed miRNAs. A total of 5 miRNAs were found to be specific to IA, including 4 (miR-145-5p, miR-424-5p, miR-99b-5p, and miR-4488) that were upregulated and the pair (miR-4454 + miR-7975) that was downregulated in IA group vs controls. The expression change for these miRNAs was specific to patients with IA; they were not significantly differentiated between IACLAD and IA groups. Signaling pathways associated with an immunologic response to IA were found to be significantly enriched.

CONCLUSIONS

We report a set of 5 differentially expressed miRNAs in the BAL of LTRs with IA that might help in the development of diagnostic and prognostic tools for IA in LTRs. However, further investigation is needed in a larger cohort to validate the findings.

Crosstalk between circulating microRNAs and chronotypical features in subjects with metabolic syndrome

Circulating microRNAs (miRNAs) are valuable biomarkers that may provide important insight into the pathogenesis of metabolic syndrome (MetS). Moreover, there is an association between chronotypical characteristics and MetS predisposition. Considering that expression of some miRNAs is circadian-rhythm-dependent, the aim of this study was to investigate the circulating miRNA profile in subjects with and without MetS in association with chronotype. The expression of 86 metabolic syndrome-related miRNAs was investigated in the plasma of 21 subjects with MetS and in 82 subjects without MetS using miRCURY LNA miRNA PCR System technology. Chronotype was assessed using the Horne and Östberg Morningness-Eveningness Questionnaire. Bioinformatic analyses were performed to explore the target genes and biological pathways regulated by the selected miRNAs. Subjects with MetS were more often evening chronotype compared to non-MetS controls. Additionally, four miRNAs (miR-140-3p, miR-150-5p, miR-375, and miR-29 c-3p) demonstrated interaction with MetS and chronotype. Interestingly, the target genes of these four miRNAs participate in pathways related to the circadian clock. In conclusion, we identified four circulating miRNAs whose circulating levels could interact with MetS and chronotype.

A comparative pharmacogenomic analysis of three classic TCM prescriptions for coronary heart disease based on molecular network modeling

Traditional Chinese medicine (TCM) has evolved over several thousands of years, which has been shown to be efficacious in the treatment of ischemic heart disease. Three classical TCM prescriptions, namely Xuefu Zhuyu Decoction, Zhishi Xiebai Guizhi Decoction, and Gualou Xiebai Banxia Decoction, have been extensively used in the treatment of coronary heart disease (CHD). Based on molecular network modeling, we performed a comparative pharmacogenomic analysis to systematically determine the drug-targeting spectrum of the three prescriptions at molecular level. Wide-area target molecules of CHD were covered, which was a common feature of the three decoctions, demonstrating their therapeutic functions. Meanwhile, collective signaling involved metabolic/pro-metabolic pathways, driving and transferring pathways, neuropsychiatric pathways, and exocrine or endocrine pathways. These organized pharmacological disturbance was mainly focused on almost all stages of CHD intervention, such as anti-atherosclerosis, lipid metabolism, inflammation, vascular wall function, foam cells formation, platelets aggregation, thrombosis, arrhythmia, and ischemia-reperfusion injury. In addition, heterogeneity analysis of the global pharmacological molecular spectrum revealed that signaling crosstalk, cascade convergence, and key targets were tendentious among the three decoctions. After all, it is unadvisable to rank the findings on targeting advantages of the three decoctions. Comparative pharmacological evidence may provide an appropriate decoction scheme for individualized intervention of CHD.

UCP2, IL18, and miR-133a-3p are dysregulated in subcutaneous adipose tissue of patients with obesity

The aim of this study was to compare the expression of UCP2, NLRP3, IL1B, IL18, and miR-133a-3p in subcutaneous adipose tissue (SAT) of 61 patients divided according to BMI: Group 1 (n = 8; BMI<25.0 kg/m²), Group 2 (n = 24; BMI 30.0-39.9 kg/m²), and Group 3 (n = 29; BMI≥40.0 kg/m²). SAT biopsies were obtained from individuals who underwent bariatric surgery or elective abdominal surgery. Gene expressions were quantified using qPCR. Bioinformatics analyses were employed to investigate target genes and pathways related to miR-133a-3p. UCP2 and miR-133a-3p expressions were decreased in SAT of Groups 2 and 3 while IL18 was increased compared to Group 1. NLRP3 and IL1B expressions did not differ between groups; however, NLRP3 was positively correlated with waist circumference and excess weight. Bioinformatics analysis demonstrated that UCP2 and NLRP3 are targets of miR-133a-3p. In conclusion, UCP2 and miR-133a-3p expressions are downregulated in patients with obesity, while IL18 is upregulated. NRLP3 is correlated with waist circumference and weight excess.

DNA methylation in genes of longevity-regulating pathways: association with obesity and metabolic complications

Aging is the main risk factor for most chronic diseases. Epigenetic mechanisms, such as DNA methylation (DNAm) plays a pivotal role in the regulation of physiological responses that can vary along lifespan. The aim of this research was to analyze the association between leukocyte DNAm in genes involved in longevity and the occurrence of obesity and related metabolic alterations in an adult population. Subjects from the MENA cohort (n=474) were categorized according to age (<45 vs 45>) and the presence of metabolic alterations: increased waist circumference, hypercholesterolemia, insulin resistance, and metabolic syndrome. The methylation levels of 58 CpG sites located at genes involved in longevity-regulating pathways were strongly correlated (FDR-adjusted< 0.0001) with BMI. Fifteen of them were differentially methylated (p<0.05) between younger and older subjects that exhibited at least one metabolic alteration. Six of these CpG sites, located at MTOR (cg08862778), ULK1 (cg07199894), ADCY6 (cg11658986), IGF1R (cg01284192), CREB5 (cg11301281), and RELA (cg08128650), were common to the metabolic traits, and CREB5, RELA, and ULK1 were statistically associated with age. In summary, leukocyte DNAm levels of several CpG sites located at genes involved in longevity-regulating pathways were associated with obesity and metabolic syndrome traits, suggesting a role of DNAm in aging-related metabolic alterations.

Complexities in Genetics of Psoriatic Arthritis

PURPOSE OF THE REVIEW

To provide a general overview and current challenges regarding the genetics of psoriatic disease. With the use of integrative medicine, multiple candidate loci identified to date in psoriatic disease will be annotated, summarized, and visualized. Recent studies reporting differences in genetic architecture between psoriatic arthritis and cutaneous-only psoriasis will be highlighted.

RECENT FINDINGS

Focusing on functional pathways that connect previously identified genetic variants can increase our understanding of psoriatic diseases. The genetic architecture differs between psoriatic arthritis and cutaneous-only psoriasis with arthritis-specific signals in linkage disequilibrium independent of the published psoriasis signals. Integrative medicine is helpful in understanding cellular mechanisms of psoriatic diseases. Careful selection of the psoriatic disease cohort has translated into mechanistic differences among psoriatic arthritis and cutaneous psoriasis.

A 4-gene signature from histologically normal surgical margins predicts local recurrence in patients with oral carcinoma: clinical validation

Prognostic biomarkers for recurrence of Oral Squamous Cell Carcinoma (OSCC) are urgently needed. We aimed to independently validate a 4-gene expression signature (MMP1, COL4A1, P4HA2, THBS2) predictive of OSCC recurrence risk. Gene expression was measured using Nanostring nCounter^® in 245 histologically normal surgical resection margins from 62 patients. Association between risk scores for individual patients and recurrence was assessed by Kaplan-Meier analysis. Signature performance was quantified by concordance index (CI), hazard ratio (HR) and the area under receiver operating characteristics (AUC). Risk scores for recurrence were significantly higher than recurrence-free patients (p = 9.58e-7, Welch’s t-test). A solid performance of the 4-gene signature was determined: CI = 0.64, HR = 3.38 (p = 1.4E-4; log-rank test), AUC = 0.71. We showed that three margins per patient are sufficient to preserve predictive performance (CI = 0.65; HR = 2.92; p = 2.94e-3; AUC = 0.71). Association between the predicted risk scores and recurrence was assessed and showed HR = 2.44 (p = 9.6E-3; log-rank test, N = 62). Signature performance analysis was repeated using an optimized threshold (70^th percentile of risks), resulting in HR = 3.38 (p = 1.4E-4; log-rank test, N = 62). The 4-gene signature was validated as predictive of recurrence risk in an independent cohort of patients with resected OSCC and histologically negative margins, and is potentially applicable for clinical decision making on adjuvant treatment and disease monitoring.

Circulating adiposity-related microRNAs as predictors of the response to a low-fat diet in subjects with obesity

Recent studies have revealed the critical role of several microRNAs (miRNAs) in energy homeostasis and metabolic processes and suggest that circulating miRNAs can be used as early predictors of weight loss in the design of precision nutrition. Thus, the aim of this study was to investigate circulating adiposity‐related miRNAs as biomarkers of the response to two specific weight loss dietary treatments. The expression of 86 miRNAs was investigated in plasma of 78 subjects with obesity randomized to two different diets [moderately high‐protein diet (n = 38) and low‐fat diet (n = 40)] and in 25 eutrophic controls (BMI ≤ 25 kg/m²). Bioinformatic analyses were performed to explore the target genes and biological pathways regulated by the dysregulated miRNAs. As results, 26 miRNAs were found differently expressed in eutrophic and volunteers with obesity. Moreover, 7 miRNAs (miR‐130a‐3p, miR‐142‐5p, miR‐144‐5p, miR‐15a‐5p, miR‐22‐3p, miR‐221‐3p and miR‐29c‐3p) were differentially expressed between responders and non‐responders to a low‐fat diet. Furthermore, after adjustment for basal glucose levels, 1‐SD increase in miR‐22‐3p expression was associated with reduction in the risk of non‐response to low‐fat diet [OR = 0.181, 95% CI (0.084‐0.947), P = .043]. Bioinformatic analyses evidenced that these 7 miRNAs regulate the expression of genes participating in important metabolic pathways. Conclusively, 7 circulating miRNAs related to adiposity could be used for predicting the response to a low‐fat diet intervention prescribed to lose weight.

pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein-protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.