Human plasma pregnancy-associated miRNAs and their temporal variation within the first trimester of pregnancy

Background During pregnancy, maternal metabolism undergoes substantial changes to support the developing fetus. Such changes are finely regulated by different mechanisms carried out by effectors such as microRNAs (miRNAs). These small non-coding RNAs regulate numerous biological functions, mostly through post-transcriptional repression of gene expression. miRNAs are also secreted in circulation by numerous organs, such as the placenta. However, the complete plasmatic microtranscriptome of pregnant women has still not been fully described, although some miRNA clusters from the chromosome 14 (C14MC) and the chromosome 19 (C19MC and miR-371-3 cluster) have been proposed as being specific to pregnancy. Our aims were thus to describe the plasma microtranscriptome during the first trimester of pregnancy, by assessing the differences with non-pregnant women, and how it varies between the 4th and the 16th week of pregnancy. Methods Plasmatic miRNAs from 436 pregnant (gestational week 4 to 16) and 15 non-pregnant women were quantified using Illumina HiSeq next-generation sequencing platform. Differentially abundant miRNAs were identified using DESeq2 package (FDR q-value ≤ 0.05) and their targeted biological pathways were assessed with DIANA-miRpath. Results A total of 2101 miRNAs were detected, of which 191 were differentially abundant (fold change < 0.05 or > 2, FDR q-value ≤ 0.05) between pregnant and non-pregnant women. Of these, 100 miRNAs were less and 91 miRNAs were more abundant in pregnant women. Additionally, the abundance of 57 miRNAs varied according to gestational age at first trimester, of which 47 were positively and 10 were negatively associated with advancing gestational age. miRNAs from the C19MC were positively associated with both pregnancy and gestational age variation during the first trimester. Biological pathway analysis revealed that these 191 (pregnancy-specific) and 57 (gestational age markers) miRNAs targeted genes involved in fatty acid metabolism, ECM-receptor interaction and TGF-beta signaling pathways. Conclusion We have identified circulating miRNAs specific to pregnancy and/or that varied with gestational age in first trimester. These miRNAs target biological pathways involved in lipid metabolism as well as placenta and embryo development, suggesting a contribution to the maternal metabolic adaptation to pregnancy and fetal growth.

Contribution of Transcriptome to Elucidate the Biology of Plasmodium spp

In the present review, we discuss some of the new technologies that have been applied to elucidate how Plasmodium spp escape from the immune system and subvert the host physiology to orchestrate the regulation of its biological pathways. Our manuscript describes how techniques such as microarray approaches, RNA-Seq and single-cell RNA sequencing have contributed to the discovery of transcripts and changed the concept of gene expression regulation in closely related malaria parasite species. Moreover, the text highlights the contributions of high-throughput RNA sequencing for the current knowledge of malaria parasite biology, physiology, vaccine target and the revelation of new players in parasite signaling.

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.

Disparities in Lung Cancer: miRNA Isoform Characterization in Lung Adenocarcinoma

Despite the development of targeted therapeutics, immunotherapy, and strategies for early detection, lung cancer carries a high mortality. Further, significant racial disparities in outcomes exist for which the molecular drivers have yet to be fully elucidated. The growing field of Epitranscriptomics has introduced a new layer of complexity to the molecular pathogenesis of cancer. RNA modifications can occur in coding and non-coding RNAs, such as miRNAs, possibly altering their gene regulatory function. The potential role for such modifications as clinically informative biomarkers remains largely unknown. Here, we concurrently profiled canonical miRNAs, shifted isomiRs (templated and non-templated), miRNAs with single-point modification events (RNA and DNA) in White American (W) and Black or African American (B/AA) lung adenocarcinoma (LUAD) patients. We found that while most deregulated miRNA isoforms were similar in W and B/AA LUAD tissues compared to normal adjacent tissues, there was a subgroup of isoforms with deregulation according to race. We specifically investigated an edited miRNA, miR-151a-3p with an A-to-I editing event at position 3, to determine how its altered expression may be associated with activation of divergent biological pathways between W and B/AA LUAD patients. Finally, we identified distinct race-specific miRNA isoforms that correlated with prognosis for both Ws and B/AAs. Our results suggest that concurrently profiling canonical and non-canonical miRNAs may have potential as a strategy for identifying additional distinct biological pathways and biomarkers in lung cancer.

The Role of Drug Resistance Causing lncRNAs in Breast Cancer: In-silico Analysis

Background: Breast cancer is the most common cancer in women globally. LncRNAs are non-coding RNAs that play an essential role in biological pathways. Many lncRNAs have been discovered to influence cancer medication resistance. As a result, identifying how lncRNAs may cause drug resistance is vital.Method: Breast cancer TCGA RNA-seq data was applied in this study. We used the PharmacoGX package to explore lncRNAs with drug resistance or sensitivity effect through GDSC and CCLE data. Differential gene expression analysis (DGE) was used to find dysregulated lncRNAs (P<0.01). Survival analysis was performed to identify lncRNAs associated with patient survival, and a model based on them was developed. Multivariate cox regression analysis and ROC curve analysis were applied to assess the model. The TCGA-BRCA and two independent datasets (GSE21653 and GSE20685) were used to study the relevance of lncRNAs in biological pathways. lncRNA-miRNA-mRNA interaction network was investigated. The connections of lncRNAs with MRPs were analysed through the correlation test. Finally, lncRNA and MRP mRNAs attachment sites were analysed through the LncRRisearch tool.Result: According to our data, thirty-eight lncRNAs were associated with cell drug response in breast cancer cells. IL12A-AS1, AC137723.1, LINC00667, SVIL-AS1, CYTOR, and MIR4435-2HG linked to patient survival (P<0.05). AC137723.1 and LINC00667 were identified as good prognostic genes, while the others were discovered to have poor prognostic effects. Moreover, the risk score model separated patients perfectly, in which about 45% of high-risk patients were dead; by contrast, around 95% of low-risk patients could survive. ROC curve results proved that CYTOR, MIR4435-2HG, and LINC00667 are potential biomarkers in breast cancer with AUC >0.8. Pathway analysis revealed that CYTOR and MIR4435-2HG are highly correlated with the Epithelial-Mesenchymal transition pathway, while AC137723.1 and LINC00667 were negatively correlated with the pathway. AC128688.2, CYTOR, TDRKH-AS1 and LINC00667 can participate in lncRNA-miRNA-mRNA networks. Also, MIR22HG might influence drug resistance by attaching to MRP mRNAs.Conclusion: Our findings revealed 38 lncRNAs involved in cancer cell treatment resistance and sensitivity. They can participate in patients’ prognosis, diagnosis and cellular pathways. Also, they may influence cell drug response through connections with CSPs, lncRNA-miRNA-mRNA networks and MRPs.

Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

Development of Biomarker Signatures Associated with Anoikis to Predict Prognosis in Endometrial Carcinoma Patients

Purpose. To generate a signature based on anoikis-related genes (ARGs) for endometrial carcinoma (EC) patients and elucidate the molecular mechanisms in EC. Methods. On the basis of TCGA-UCEC dataset, we identified specific anoikis-related genes (ARGs) in EC. Cox-relative regression methods were used to generate an anoikis-related signature (ARS). The possible biological pathways of ARS-related genes were analyzed by GSEA. The clinical potency and immune status of ARS were analyzed by CIBERSORT method, ssGSEA algorithm, Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Moreover, the expression patterns of ARS genes were verified by HPA database. Results. Seven anoikis genes (CDKN2A, E2F1, ENDOG, EZH2, HMGA1, PLK1, and SLC2A1) were determined to develop a prognostic ARS. Both genes of ARS were closely bound up with the prognosis of EC patients. The ARS could accurately classify EC cases with different clinical outcome and mirror the specific immune status of EC. We observed that ARS-high patients could not benefit from immunotherapy. Finally, all the hub genes of ARS were proved to be upregulated in EC tissues by immunohistology. Conclusion. ARS can be used to stratify the risk and forecast the survival outcome of EC patients and provide prominent reference for individualized treatment in EC.

Alzheimer: A Complex Genetic Background

Alzheimer is a complex, multifactorial disease with an ever increasing impact in modern medicine. Research in this area has revealed a lot about the biological and environmental underpinnings of this disease, especially its correlation with Β-Amyloid and Tau related mechanics; however, the precise biological pathways behind the disease are yet to be discovered. Recent studies evidenced how several mechanisms, including neuroinflammation, oxidative stress, autophagy failure and energy production impairments in the brain, −--- have been proposed to contribute to this pathology. In this section we will focus on the role of these molecular pathways and their potential link with Alzheimer Disease.

Theranostics – present and future

Theragnostics in nuclear medicine constitute an essential element of precision medicine. This notion integrates radionuclide diagnostics procedures and radionuclide therapies using appropriate radiopharmaceutics and treatment targeting specific biological pathways or receptors. The term theragnostics should also include another aspect of treatment: not only whether a given radioisotopic drug can be used, but also in what dose it ought to be used. Theragnostic procedures also allow predicting the effects of treatment based on the assessment of specific receptor density or the metabolic profile of neoplastic cells. The future of theragnostics depends not only on the use of new radiopharmaceuticals, but also on new gamma cameras. Modern theragnostics already require unambiguous pharmacokinetic and pharmacodynamic measurements based on absolute values. Only dynamic studies provide such a possibility. The introduction of the dynamic total-body PET-CT will enable this type of measurements characterizing metabolic processes and receptor expression on the basis of Patlak plot.