Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Graph neural networks for clinical risk prediction based on electronic health records: A survey

OBJECTIVE

This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks.

METHODS

A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023.

RESULTS

Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource.

CONCLUSION

GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Brazilian women in Bioinformatics: Challenges and opportunities

Bioinformatics is a growing research field that received great notoriety in the years of the COVID-19 pandemic. It is a very integrative area, comprising professionals from science, technology, engineering, and mathematics (STEM). In agreement with the other STEM areas, several women have greatly contributed to bioinformatics ascension; however, they had to surpass prejudice and stereotypes to achieve recognition and leadership positions, a path that studies have demonstrated to be more comfortable to their male colleagues. In this review, we discuss the several difficulties that women in STEM, including bioinformatics, surpass during their careers. First, we present a historical context on bioinformatics and the main applications for this area. Then, we discuss gender disparity in STEM and present the challenges that still contribute to women's inequality in STEM compared to their male colleagues. We also present the opportunities and the transformation that we can start, acting in academia, inside the family and school environments, and as a society, hence contributing to gender equality in STEM. Finally, we discuss specific challenges in the bioinformatics field and how we can act to overcome them, especially in low and middle-income countries, such as Brazil.

Broken silence: 22,841 predicted deleterious synonymous variants identified in the human exome through computational analysis

Synonymous single nucleotide variants (sSNVs) do not alter the primary structure of a protein, thus it was previously accepted that they were neutral. Recently, several studies demonstrated their significance to a range of diseases. Still, variant prioritization strategies lack focus on sSNVs. Here, we identified 22,841 deleterious synonymous variants in 125,748 human exomes using two in silico predictors (SilVA and CADD). While 98.2% of synonymous variants are classified as neutral, 1.8% are predicted to be deleterious, yielding an average of 9.82 neutral and 0.18 deleterious sSNVs per exome. Further investigation of prediction features via Heterogeneous Ensemble Feature Selection revealed that impact on amino acid sequence and conservation carry the most weight for a deleterious prediction. Thirty nine detrimental sSNVs are not rare and are located on disease associated genes. Ten distinct putatively non-deleterious sSNVs are likely to be under positive selection in the North-Western European and East Asian populations. Taken together our analysis gives voice to the so-called silent mutations as we propose a robust framework for evaluating the deleteriousness of sSNVs in variant prioritization studies.

Bioinformatics Methods for Transcriptome Analysis on Teratogenesis Testing

Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.

Classification of Thyroid Tumors Based on DNA Methylation Patterns

Background: Alterations in DNA methylation are stable epigenetic events that can serve as clinical biomarkers. The aim of this study was to analyze methylation patterns among various follicular cell-derived thyroid neoplasms to identify disease subtypes and help understand and classify thyroid tumors. Methods: We employed an unsupervised machine learning method for class discovery to search for distinct methylation patterns among various thyroid neoplasms. Our algorithm was not provided with any clinical or pathological information, relying exclusively on DNA methylation data to classify samples. We analyzed 810 thyroid samples (n = 256 for discovery and n = 554 for validation), including benign and malignant tumors, as well as normal thyroid tissue. Results: Our unsupervised algorithm identified that samples could be classified into three subtypes based solely on their methylation profile. These methylation subtypes were strongly associated with histological diagnosis (p < 0.001) and were therefore named normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like. Follicular adenomas, follicular carcinomas, oncocytic adenomas, and oncocytic carcinomas clustered together forming the follicular-like methylation subtype. Conversely, classic papillary thyroid carcinomas (cPTC) and tall cell PTC clustered together forming the PTC-like subtype. These methylation subtypes were also strongly associated with genomic drivers: 98.7% BRAFV600E-driven cancers were PTC like, whereas 96.0% RAS-driven cancers had a follicular-like methylation pattern. Interestingly, unlike other diagnoses, follicular variant PTC (FVPTC) samples were split into two methylation clusters (follicular like and PTC like), indicating a heterogeneous group likely to be formed by two distinct diseases. FVPTC samples with a follicular-like methylation pattern were enriched for RAS mutations (36.4% vs. 8.0%; p < 0.001), whereas FVPTC- with PTC-like methylation patterns were enriched for BRAFV600E mutations (52.0% vs. 0%, Fisher exact p = 0.004) and RET fusions (16.0% vs. 0%, Fisher exact p = 0.003). Conclusions: Our data provide novel insights into the epigenetic alterations of thyroid tumors. Since our classification method relies on a fully unsupervised machine learning approach for subtype discovery, our results offer a robust background to support the classification of thyroid neoplasms based on methylation patterns.

Downregulation of Microcephaly-Causing Genes as a Mechanism for ZIKV Teratogenesis: A Meta-analysis of RNA-Seq Studies

Zika virus (ZIKV) is a neurotropic teratogen that causes congenital Zika syndrome (CZS), characterized by brain and eye anomalies. Impaired gene expression in neural cells after ZIKV infection has been demonstrated; however, there is a gap in the literature of studies comparing whether the differentially expressed genes in such cells are similar and how it can cause CZS. Therefore, the aim of this study was to compare the differential gene expression (DGE) after ZIKV infection in neural cells through a meta-analysis approach. Through the GEO database, studies that evaluated DGE in cells exposed to the Asian lineage of ZIKV versus cells, of the same type, not exposed were searched. From the 119 studies found, five meet our inclusion criteria. Raw data of them were retrieved, pre-processed, and evaluated. The meta-analysis was carried out by comparing seven datasets, from these five studies. We found 125 upregulated genes in neural cells, mainly interferon-stimulated genes, such as IFI6, ISG15, and OAS2, involved in the antiviral response. Furthermore, 167 downregulated, involved with cellular division. Among these downregulated genes, classic microcephaly-causing genes stood out, such as CENPJ, ASPM, CENPE, and CEP152, demonstrating a possible mechanism by which ZIKV impairs brain development and causes CZS.

A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors

Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.

Suicide risk classification with machine learning techniques in a large Brazilian community sample

Even though suicide is a relatively preventable poor outcome, its prediction remains an elusive task. The main goal of this study was to develop machine learning classifiers to identify increased suicide risk in Brazilians with common mental disorders. With the use of clinical and sociodemographic baseline data (n = 4039 adult participants) from a large Brazilian community sample, we developed several models (Elastic Net, Random Forests, Naïve Bayes, and ensemble) for the classification of increased suicide risk among individuals with common mental disorders. 1120 participants (27.7%) presented increased suicide risk. The Random Forests model achieved the best AUC ROC (0.814), followed by Naive Bayes (0.798) and Elastic Net (0.773). Sensitivity varied from 0.922 (Naive Bayes) to 0.630 (Random Forests), while specificity varied from 0.792 (Random Forests) to 0.473 (Naive Bayes). The ensemble model presented an AUC ROC of 0.811, sensitivity of 0.899, and specificity of 0.510. Features representing depression symptoms were the most relevant for the classification of increased suicide risk. Some of our models presented good performance metrics in the classification of increased suicide risk in the investigated sample, which can provide the means to early preventive interventions.

Transcriptome meta-analysis of valproic acid exposure in human embryonic stem cells

Valproic acid (VPA) is a widely used antiepileptic drug not recommended in pregnancy because it is teratogenic. Many assays have assessed the impact of the VPA exposure on the transcriptome of human embryonic stem-cells (hESC), but the molecular perturbations that VPA exerts in neurodevelopment are not completely understood. This study aimed to perform a transcriptome meta-analysis of VPA-exposed hESC to elucidate the main biological mechanisms altered by VPA effects on the gene expression. Publicly available microarray and RNA-seq transcriptomes were selected in the Gene Expression Omnibus (GEO) repository. Samples were processed according to the standard pipelines for each technology in the Galaxy server and R. Meta-analysis was performed using the Fisher-P method. Overrepresented genes were obtained by evaluating ontologies, pathways, and phenotypes' databases. The meta-analysis performed in seven datasets resulted in 61 perturbed genes, 54 upregulated. Ontology and pathway enrichments suggested neurodevelopment and neuroinflammatory effects; phenotype overrepresentation included epilepsy-related genes, such as SCN1A and GABRB2. The NDNF gene upregulation was also identified; this gene is involved in neuron migration and survival during development. Sub-network analysis proposed TGFβ and BMP pathways activation. These results suggest VPA exerts effects in epilepsy-related genes even in embryonic cells. Neurodevelopmental genes, such as NDNF were upregulated and VPA might also disturb several development pathways. These mechanisms might help to explain the spectrum of VPA-induced congenital anomalies and the molecular effects on neurodevelopment.

A call for citizen science in pandemic preparedness and response: beyond data collection

The COVID-19 pandemic has underlined the need to partner with the community in pandemic preparedness and response in order to enable trust-building among stakeholders, which is key in pandemic management. Citizen science, defined here as a practice of public participation and collaboration in all aspects of scientific research to increase knowledge and build trust with governments and researchers, is a crucial approach to promoting community engagement. By harnessing the potential of digitally enabled citizen science, one could translate data into accessible, comprehensible and actionable outputs at the population level. The application of citizen science in health has grown over the years, but most of these approaches remain at the level of participatory data collection. This narrative review examines citizen science approaches in participatory data generation, modelling and visualisation, and calls for truly participatory and co-creation approaches across all domains of pandemic preparedness and response. Further research is needed to identify approaches that optimally generate short-term and long-term value for communities participating in population health. Feasible, sustainable and contextualised citizen science approaches that meaningfully engage affected communities for the long-term will need to be inclusive of all populations and their cultures, comprehensive of all domains, digitally enabled and viewed as a key component to allow trust-building among the stakeholders. The impact of COVID-19 on people’s lives has created an opportune time to advance people’s agency in science, particularly in pandemic preparedness and response.

EPGAT: Gene Essentiality Prediction With Graph Attention Networks

Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological information, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for Essentiality Prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs), operating on graph-structured data. Our model directly learns gene essentiality patterns from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with ROC AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform

There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.

Patterns of high-risk drinking among medical students: A web-based survey with machine learning

BACKGROUND

Prior studies have found increased rates of alcohol consumption among physicians and medical students. The present study aims to build machine learning (ML) models to identify patterns of high-risk drinking (HRD), including alcohol use disorder, within this population.

METHODS

We analyzed data collected through a web-based survey among Brazilian medical students. Variables included sociodemographic data, personal information, university status, and mental health. Stratification for HRD was carried out based on the AUDIT-C scores. Three ML algorithms were used to build classifiers to predict HRD among medical students: elastic net regularization, random forest, and artificial neural networks. Model interpretation techniques were adopted to assess the most influential predictors for models' decisions, which represent potential factors associated with HRD.

RESULTS

A total of 4840 medical students were included in the study. The prevalence of HRD was 53.03%. The three ML models built were able to distinguish individuals with HRD from low-risk drinking (LRD) with very similar performance. The average AUC scores in the cross-validation procedure were around 0.72, and this performance was replicated in the test set. The most important features for the ML models were the use of tobacco and cannabis, monthly family income, marital status, sexual orientation, and physical activities.

CONCLUSIONS

This study proposes that ML models may serve as tools for initial screening of students regarding their susceptibility for at-risk drinking or alcohol use disorder. In addition, we identified several key factors associated with HRD that could be further investigated and explored for preventive and assistance measures.

Comparative Genomics Identifies Putative Interspecies Mechanisms Underlying Crbn-Sall4-Linked Thalidomide Embryopathy

The identification of thalidomide–Cereblon-induced SALL4 degradation has brought new understanding for thalidomide embryopathy (TE) differences across species. Some questions, however, regarding species variability, still remain. The aim of this study was to detect sequence divergences between species, affected or not by TE, and to evaluate the regulated gene co-expression in a murine model. Here, we performed a comparative analysis of proteins experimentally established as affected by thalidomide exposure, evaluating 14 species. The comparative analysis, regarding synteny, neighborhood, and protein conservation, was performed in 42 selected genes. Differential co-expression analysis was performed, using a publicly available assay, GSE61306, which evaluated mouse embryonic stem cells (mESC) exposed to thalidomide. The comparative analyses evidenced 20 genes in the upstream neighborhood of NOS3, which are different between the species who develop, or not, the classic TE phenotype. Considering protein sequence alignments, RECQL4, SALL4, CDH5, KDR, and NOS2 proteins had the biggest number of variants reported in unaffected species. In co-expression analysis, Crbn was a gene identified as a driver of the co-expression of other genes implicated in genetic, non-teratogenic, limb reduction defects (LRD), such as Tbx5, Esco2, Recql4, and Sall4; Crbn and Sall4 were shown to have a moderate co-expression correlation, which is affected after thalidomide exposure. Hence, even though the classic TE phenotype is not identified in mice, a deregulatory Crbn-induced mechanism is suggested in this animal. Functional studies are necessary, especially evaluating the genes responsible for LRD syndromes and their interaction with thalidomide–Cereblon.

Detecting Aedes aegypti mosquitoes through audio classification with convolutional neural networks

The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% (±0.55) and 94.56% (±0.77), respectively, whereas the multiclass had an accuracy of 78.12% (±2.09). The best sensitivity was observed in the ensemble approach (96.82% ± 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% ± 3.83) and the binary (88.49% ± 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% ± 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset.

Anticonvulsants and Chromatin-Genes Expression: A Systems Biology Investigation

Embryofetal development is a critical process that needs a strict epigenetic control, however, perturbations in this balance might lead to the occurrence of congenital anomalies. It is known that anticonvulsants potentially affect epigenetics-related genes, however, it is not comprehended whether this unbalance could explain the anticonvulsants-induced fetal syndromes. In the present study, we aimed to evaluate the expression of epigenetics-related genes in valproic acid, carbamazepine, or phenytoin exposure. We selected these three anticonvulsants exposure assays, which used murine or human embryonic stem-cells and were publicly available in genomic databases. We performed a differential gene expression (DGE) and weighted gene co-expression network analysis (WGCNA), focusing on epigenetics-related genes. Few epigenetics genes were differentially expressed in the anticonvulsants' exposure, however, the WGCNA strategy demonstrated a high enrichment of chromatin remodeling genes for the three drugs. We also identified an association of 46 genes related to Fetal Valproate Syndrome, containing SMARCA2 and SMARCA4, and nine genes to Fetal Hydantoin Syndrome, including PAX6, NEUROD1, and TSHZ1. The evaluation of stem-cells under drug exposure can bring many insights to understand the drug-induced damage to the embryofetal development. The candidate genes here presented are potential biomarkers that could help in future strategies for the prevention of congenital anomalies.

Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results.

METHODS

Ten public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each dataset, and the results were meta-analytically aggregated using the rth ordered p value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis.

RESULTS

The meta-analysis of transcriptomes resulted in 1197 differentially expressed genes, whose protein-protein interaction network presented 39 hubs-bottlenecks and four main identified functional modules. These modules were enriched for 39, 20, 64, and 10 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. The stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hub-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, HSPA5, ANK2, CTNNB1, and MAPK1 were identified.

CONCLUSION

Our approach based on transcriptome meta-analysis revealed a set of key genes that demonstrated consistent overall changes in expression patterns associated with AF despite data heterogeneity related, among others, to type of tissue. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets.

Decreased expression of the thyroid hormone-inactivating enzyme type 3 deiodinase is associated with lower survival rates in breast cancer

Thyroid hormones (THs) are critical regulators of cellular processes, while changes in their levels impact all the hallmarks of cancer. Disturbed expression of type 3 deiodinase (DIO3), the main TH-inactivating enzyme, occurs in several human neoplasms and has been associated with adverse outcomes. Here, we investigated the patterns of DIO3 expression and its prognostic significance in breast cancer. DIO3 expression was evaluated by immunohistochemistry in a primary cohort of patients with breast cancer and validated in a second cohort using RNA sequencing data from the TCGA database. DNA methylation data were obtained from the same database. DIO3 expression was present in normal and tumoral breast tissue. Low levels of DIO3 expression were associated with increased mortality in the primary cohort. Accordingly, low DIO3 mRNA levels were associated with an increased risk of death in a multivariate model in the validation cohort. DNA methylation analysis revealed that the DIO3 gene promoter is hypermethylated in tumors when compared to normal tissue. In conclusion, DIO3 is expressed in normal and tumoral breast tissue, while decreased expression relates to poor overall survival in breast cancer patients. Finally, loss of DIO3 expression is associated with hypermethylation of the gene promoter and might have therapeutic implications.

Calcium Signaling Alterations Caused by Epigenetic Mechanisms in Pancreatic Cancer: From Early Markers to Prognostic Impact

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with high mortality rates. PDAC initiation and progression are promoted by genetic and epigenetic dysregulation. Here, we aimed to characterize the PDAC DNA methylome in search of novel altered pathways associated with tumor development. We examined the genome-wide DNA methylation profile of PDAC in an exploratory cohort including the comparative analyses of tumoral and non-tumoral pancreatic tissues (PT). Pathway enrichment analysis was used to choose differentially methylated (DM) CpGs with potential biological relevance. Additional samples were used in a validation cohort. DNA methylation impact on gene expression and its association with overall survival (OS) was investigated from PDAC TCGA (The Cancer Genome Atlas) data. Pathway analysis revealed DM genes in the calcium signaling pathway that is linked to the key pathways in pancreatic carcinogenesis. DNA methylation was frequently correlated with expression, and a subgroup of calcium signaling genes was associated with OS, reinforcing its probable phenotypic effect. Cluster analysis of PT samples revealed that some of the methylation alterations observed in the Calcium signaling pathway seemed to occur early in the carcinogenesis process, a finding that may open new insights about PDAC tumor biology.

CRL4-Cereblon complex in Thalidomide Embryopathy: a translational investigation

The Cereblon-CRL4 complex has been studied predominantly with regards to thalidomide treatment of multiple myeloma. Nevertheless, the role of Cereblon-CRL4 in Thalidomide Embryopathy (TE) is still not understood. Not all embryos exposed to thalidomide develop TE, hence here we evaluate the role of the CRL4-Cereblon complex in TE variability and susceptibility. We sequenced CRBN, DDB1, CUL4A, IKZF1, and IKZF3 in individuals with TE. To better interpret the variants, we suggested a score and a heatmap comprising their regulatory effect. Differential gene expression after thalidomide exposure and conservation of the CRL4-Cereblon protein complex were accessed from public repositories. Results suggest a summation effect of Cereblon variants on pre-axial longitudinal limb anomalies, and heatmap scores identify the CUL4A variant rs138961957 as potentially having an effect on TE susceptibility. CRL4-Cereblon gene expression after thalidomide exposure and CLR4-Cereblon protein conservation does not explain the difference in Thalidomide sensitivity between species. In conclusion, we suggest that CRL4-Cereblon variants act through several regulatory mechanisms, which may influence CRL4-Cereblon complex assembly and its ability to bind thalidomide. Human genetic variability must be addressed not only to further understand the susceptibility to TE, but as a crucial element in therapeutics, including in the development of pharmacogenomics strategies.

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common. Here we provide a guide to the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus. Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years. When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.

Synergistic effects between ADORA2A and DRD2 genes on anxiety disorders in children with ADHD

The prevalence of anxiety disorders in patients with Attention Deficit/Hyperactivity Disorder (ADHD) is around 15-40%, three times higher than in the general population. The dopaminergic system, classically associated with ADHD, interacts directly with the adenosinergic system through adenosine A_2A receptors (A_2A) and dopamine D₂ receptors (D₂) forming A_2A-D₂ heterodimers. Both dopaminergic and adenosinergic systems are implicated in anxiety disorders. Therefore, the aims of this study were: a) to investigate the main effects of ADORA2A and DRD2 gene variants on anxiety disorders in an ADHD sample of children and adolescents; b) to test potential synergism between ADORA2A and DRD2 genes on the same outcome; c) to explore ADORA2A variants functionality using an in silico approach. The sample consists of 478 children and adolescents with ADHD and their parents, totalizing 1.239 individuals. An association between the ADORA2A rs2298383 TT genotype with the presence of anxiety disorders (P = .004) and an interaction between ADORA2A-DRD2 risk haplotypes with the same outcome (P = .005) was detected. The in silico analyses showed that rs2298383 has the highest score for regulatory function among all variants in the ADORA2A gene described up to date. Altogether, the present findings suggested that the ADORA2A gene and the interaction of ADORA2A and DRD2 genes may play a role in anxiety disorders in children and adolescents with ADHD.

Abstract 4926: Loss of deiodinase type 3 expression distinguishes patients with poor prognosis in breast cancer

Background: Breast cancer is a highly heterogeneous disease and the identification of biomarkers that predict tumor biological behavior is warranted in improving patient survival. Thyroid hormones (THs) are critical regulators of cellular processes, and TH status alterations are known to contribute to cancer progression through all the hallmarks of cancer. Clinical studies associate THs levels with breast cancer mortality, and THs have been shown to influence breast cancer proliferation, apoptosis, and migration in in vitro models. Type 3 deiodinase (DIO3) is the main enzyme responsible for TH inactivation and disturbed DIO3 expression has been demonstrated in several human neoplasias. Here, we aimed to evaluate expression patterns and the prognostic significance of DIO3 in breast cancer.

Methods: The expression of DIO3 was assessed by immunohistochemistry in a primary cohort of 53 samples of breast tissue and was validated in a second cohort using the RNA-Seq data from TCGA database (BRCA study). We assessed DIO3 expression in both populations according to retrieved clinicopathological information. For methylation analysis, DNA methylation data from the TCGA-BRCA study were obtained. Regulation of DIO3 gene was investigated in MCF-7 and MDA-MB-231 cell lines.

Results: DIO3 expression was present in normal and tumoral breast glandular tissue. DIO3 mRNA expression was found to be reduced in tumor samples when compared to normal tissue (p&lt;0.0001). Unexpectedly, loss of DIO3 expression was associated with increased mortality (HR 4.29; 95% CI 1.24-14.7) in the primary cohort. We then validated this finding in the secondary cohort. Patients with low DIO3 expression exhibited reduced overall survival (OS) when compared to those higher levels of DIO3 expression. Five-year OS rates were 90.4% and 77.4% in the high and low expression groups, respectively (p=0.0002). Among patients with metastasis, five-year OS rate was 70% in the high DIO3 group and 0% in the low DIO3 group (p=0.038). Additional experiments were performed to determine DIO3 regulation in breast cancer. In MCF-7 and MDA-MD-231 cells, DIO3 was subject to T3 stimulation and was under the regulation of MAPK pathway, as MAPK inhibition resulted in a 50% reduction of DIO3 expression (p=0.004). Methylation analysis revealed that DIO3 gene promoter is hypermethylated in tumoral cells when compared to normal tissue (p &lt;0.0001).

Conclusions: DIO3 is expressed in normal and tumoral breast tissue. Here, we demonstrate a role for DIO3 as a prognostic marker in breast cancer, as loss of DIO3 expression distinguishes breast cancer patients with poor overall survival. Moreover, our fındings suggest the utility of DIO3 expression for stratifıcation of patients with metastasis. The finding that DIO3 gene is regulated by the MAPK pathway and that gene promoter is hypermethylated in breast cancer might have therapeutic implications.

Citation Format: Iuri M. Goemann, Vicente R. Marczyk, Mariana Recamonde-Mendoza, Marcia S. Graudenz, Simone M. Wajner, Ana Luiza Maia. Loss of deiodinase type 3 expression distinguishes patients with poor prognosis in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4926.

Circulating miRNAs in diabetic kidney disease: case-control study and in silico analyses

AIMS

The aim of this study was to investigate a miRNA expression profile in type 1 diabetes mellitus (T1DM) patients with DKD (cases) or without this complication (controls).

METHODS

Expression of 48 miRNAs was screened in plasma of 58 T1DM patients (23 controls, 18 with moderate DKD, and 17 with severe DKD) using TaqMan Low Density Array cards (Thermo Fisher Scientific). Then, five of the dysregulated miRNAs were selected for validation in an independent sample of 10 T1DM controls and 19 patients with DKD (10 with moderate DKD and 9 with severe DKD), using RT-qPCR. Bioinformatic analyses were performed to explore the putative target genes and biological pathways regulated by the validated miRNAs.

RESULTS

Among the 48 miRNAs investigated in the screening analysis, 9 miRNAs were differentially expressed between DKD cases and T1DM controls. Among them, the five most dysregulated miRNAs were chosen for validation in an independent sample. In the validation sample, miR-21-3p and miR-378-3p were confirmed to be upregulated in patients with severe DKD, while miR-16-5p and miR-29a-3p were downregulated in this group compared to T1DM controls and patients with moderate DKD. MiR-503-3p expression was not validated. Bioinformatic analyses indicate that the four validated miRNAs regulate genes from PI3K/Akt, fluid shear stress and atherosclerosis, AGE-RAGE, TGF-β1, and relaxin signaling pathways.

CONCLUSIONS

Our study found four miRNAs differentially expressed in patients with severe DKD, providing significant information about the biological pathways in which they are involved.

MicroRNAs and diabetic kidney disease: Systematic review and bioinformatic analysis

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. Emerging evidence has suggested a role for miRNAs in the development of diabetic kidney disease (DKD), indicating that miRNAs may represent potential biomarkers of this disease. However, results are still inconclusive. Therefore, we performed a systematic review of the literature on the subject, followed by bioinformatic analysis. PubMed and EMBASE were searched to identify all studies that compared miRNA expressions between patients with DKD and diabetic patients without this complication or healthy subjects. MiRNA expressions were analyzed in kidney biopsies, urine/urinary exosomes or total blood/plasma/serum. MiRNAs consistently dysregulated in DKD patients were submitted to bioinformatic analysis to retrieve their putative target genes and identify potentially affected pathways under their regulation. As result, twenty-seven studies were included in the systematic review. Among 151 dysregulated miRNAs reported in these studies, 6 miRNAs were consistently dysregulated in DKD patients compared to controls: miR-21-5p, miR-29a-3p, miR-126-3p, miR-192-5p, miR-214-3p, and miR-342-3p. Bioinformatic analysis indicated that these 6 miRNAs are involved in pathways related to DKD pathogenesis, such as apoptosis, fibrosis, and extracellular matrix accumulation. In conclusion, six miRNAs seem to be dysregulated in patients with different stages of DKD, constituting potential biomarkers of this disease.

MicroRNA expression profile in plasma from type 1 diabetic patients: Case-control study and bioinformatic analysis

AIMS

To investigate a miRNA expression profile in plasma of type 1 diabetes (T1DM) patients and control subjects and analyze the putative pathways involved.

METHODS

Expressions of 48 miRNAs were analyzed in plasma of 33 T1DM patients and 26 age-and-gender-matched controls using Stem-loop RT-PreAmp PCR and TaqMan Low Density Arrays (Thermo Fisher Scientific). Five dysregulated miRNAs were then chosen for validation in an independent sample of 27 T1DM patients and 14 controls, using RT-qPCR. Bioinformatic analyses were performed to determine in which pathways these miRNAs are involved.

RESULTS

Nine miRNAs were differentially expressed between recently-diagnosed T1DM patients (<5 years of diagnosis) and controls. No differences were observed between patients with ≥5 years of diagnosis and controls. After validation in an independent sample of T1DM patients, miR-103a-3p, miR-155-5p, miR-200a-3p, and miR-210-3p were confirmed as being upregulated in recently-diagnosed T1DM patients compared with controls or patients with ≥5 years of diagnosis. Moreover, miR-146a-5p was downregulated in recently-diagnosed T1DM patients compared with the other groups. These five miRNAs regulate several genes from innate immune system-, MAPK-, apoptosis-, insulin- and cancer-related pathways.

CONCLUSION

Five miRNAs are dysregulated in recently-diagnosed T1DM patients and target several genes involved in pathways related to T1DM pathogenesis, thus representing potential T1DM biomarkers.

MicroRNA expression profiles and type 1 diabetes mellitus: systematic review and bioinformatic analysis

Growing evidence indicates that microRNAs (miRNAs) have a key role in processes involved in type 1 diabetes mellitus (T1DM) pathogenesis, including immune system functions and beta-cell metabolism and death. Although dysregulated miRNA profiles have been identified in T1DM patients, results are inconclusive; with only few miRNAs being consistently dysregulated among studies. Thus, we performed a systematic review of the literature on the subject, followed by bioinformatic analysis, to point out which miRNAs are dysregulated in T1DM-related tissues and in which pathways they act. PubMed and EMBASE were searched to identify all studies that compared miRNA expressions between T1DM patients and non-diabetic controls. Search was completed in August, 2017. Those miRNAs consistently dysregulated in T1DM-related tissues were submitted to bioinformatic analysis, using six databases of miRNA-target gene interactions to retrieve their putative targets and identify potentially affected pathways under their regulation. Thirty-three studies were included in the systematic review: 19 of them reported miRNA expressions in human samples, 13 in murine models and one in both human and murine samples. Among 278 dysregulated miRNAs reported in these studies, 25.9% were reported in at least 2 studies; however, only 48 of them were analyzed in tissues directly related to T1DM pathogenesis (serum/plasma, pancreas and peripheral blood mononuclear cells (PBMCs)). Regarding circulating miRNAs, 11 were consistently dysregulated in T1DM patients compared to controls: miR-21-5p, miR-24-3p, miR-100-5p, miR-146a-5p, miR-148a-3p, miR-150-5p, miR-181a-5p, miR-210-5p, miR-342-3p, miR-375 and miR-1275. The bioinformatic analysis retrieved a total of 5867 validated and 2979 predicted miRNA-target interactions for human miRNAs. In functional enrichment analysis of miRNA target genes, 77 KEGG terms were enriched for more than one miRNA. These miRNAs are involved in pathways related to immune system function, cell survival, cell proliferation and insulin biosynthesis and secretion. In conclusion, eleven circulating miRNAs seem to be dysregulated in T1DM patients in different studies, being potential circulating biomarkers of this disease.

The role of protein intrinsic disorder in major psychiatric disorders

Although new candidate genes for Autism Spectrum Disorder (ASD), Schizophrenia (SCZ), Attention-Deficit/Hyperactivity Disorder (ADHD), and Bipolar Disorder (BD) emerged from genome-wide association studies (GWAS), their underlying molecular mechanisms remain poorly understood. Evidences of the involvement of intrinsically disordered proteins in diseases have grown in the last decade. These proteins lack tridimensional structure under physiological conditions and are involved in important cellular functions such as signaling, recognition and regulation. The aim of the present study was to identify the role and abundance of intrinsically disordered proteins in a set of psychiatric diseases and to test whether diseases are different regarding protein intrinsic disorder. Our hypothesis is that differences across psychiatric illnesses phenotypes and symptoms may arise from differences in intrinsic protein disorder content and properties of each group. A bioinformatics prediction of intrinsic disorder was performed in proteins retrieved based on top findings from GWAS, Copy Number Variation and candidate gene investigations for each disease. This approach revealed that about 80% of studied proteins presented long stretches of disorder. This amount was significantly higher than that observed in general eukaryotic proteins, and those involved in cardiovascular diseases. These results suggest that proteins with intrinsic disorder are a common feature of neurodevelopment and synaptic transmission processes which are potentially involved in the etiology of psychiatric diseases. Moreover, we identified differences between ADHD and ASD when the binary prediction of structure and putative binding sites were compared. These differences may be related to variation in symptom complexity between both diseases. © 2016 Wiley Periodicals, Inc.

Abstract 318: Vitamin E Affects Pathological Cardiac Hypertrophy and MicroRNAs Expression in Mice

Aims: Vitamin E is a usual antioxidant, but little is known about its effects on cardiac hypertrophy and microRNAs (miRs) expression induced by transverse aortic constriction (TAC) in mice.

Methods and Results: Male Balb/c mice were randomly divided into four cohorts: SHAM ( n= 22), TAC ( n =34), SHAM supplemented with vitamin E (SHAM+VIT, n =22), and TAC supplemented with vitamin E (TAC+VIT, n =34). VIT groups received 200 mg/kg of α-tocopherol once a day, and the other groups received placebo, both by gavage. After 7 and 35 days of surgery analysis of cardiac hypertrophy, fibrosis, miRs and gene expression and carbonyl concentrations in cardiac tissue were performed. Left ventricle mass increased 23% and 35% in 7 and 35 days, respectively, in TAC group, similar data were observed in TAC+VIT group (all p <0.05 vs. SHAM groups). Cardiac fibrosis was increased by TAC surgery as early as 7 days and remained high after 35 days. Still, TAC mice exhibit higher levels of protein damage at 35 days. This pathological phenotype was not seen in animals of the TAC+VIT group. Vitamin E seemed to inhibit cardiac fibrosis and oxidative damage. Moreover, cardiac hypertrophy was followed by increased miR-21 and -499 expression in TAC group, mainly 35 days ( miR-21 : 2.9 ± 0.6 fold vs . SHAM: 1 ± 0.1 fold , miR 499 : 3 ± 0.4 fold vs. 1.1 ± 0.1 fold , p <0.05). However, TAC+VIT mice displayed a different miR expression profile, with decreased miR-21 and - 499 expression ( miR-21 : 0.5 ± 0.1 fold , miR-499 : 0.4 ± 0.1 fold ; TAC vs. p <0.05) and increased miR-210 expression (3.2 ± 0.5 fold vs. TAC: 1.9 ± 0.2 fold, p =0.034). Computational target prediction of these miRs demonstrated that they can be involved in the control of major pathways in the heart disease scenario such as MAPK, mTOR, PI3K-AKT, among others.

Conclusion: TAC model induced pathological cardiac hypertrophy, fibrosis and protein damage followed by changes in miRs expression. Vitamin E supplementation was associated with a different miR expression profile and mitigated the pathological phenotype.