Novel splicing variant and gonadal mosaicism in DYRK1A gene identified by whole-genome sequencing in multiplex autism spectrum disorder families.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with considerable genetic heterogeneity. The disorder is clinically diagnosed based on DSM-5 criteria, featuring deficits in social communication and interaction, along with restricted and repetitive behaviours. Here, we performed whole-genome sequencing (WGS) on four individuals with ASD from two multiplex families (MPX), where more than one individual is affected, to identify potential single nucleotide variants (SNVs) and structural variants (SVs) in coding and non-coding regions. A rigorous bioinformatics pipeline was employed for variant detection, followed by segregation analysis. Our investigation revealed an unreported splicing variant in the DYRK1A gene (c.-77 + 2T > C; IVS1 + 2T > C; NM_001396.5), in heterozygote form in two affected children in one of the families (family B), which was absent in the healthy parents and siblings. This finding suggests the presence of gonadal mosaicism in one of the parents, representing the first documented instance of such inheritance for a variant in the DYRK1A gene associated with ASD. Furthermore, we identified a 50 bp deletion in intron 9 of the DLG2 gene in two affected patients from the same family, confirmed by PCR and Sanger sequencing. In Family A, we identified potential candidate variants associated with ASD shared by the two patients. These findings enhance our understanding of the genetic landscape of ASD, particularly in MPX families, and highlight the utility of WGS in uncovering novel genetic contributions to neurodevelopmental disorders.

PIAS2-mediated blockade of IFN-ß signaling: a basis for sporadic Parkinson disease dementia.

Familial Parkinson disease (PD) is associated with rare genetic mutations, but the etiology in most patients with sporadic (s)PD is largely unknown, and the basis for its progression to dementia (sPDD) is poorly characterized. We have identified that loss of IFNß or IFNAR1, the receptor for IFNα/ß, causes pathological and behavioral changes resembling PDD, prompting us to hypothesize that dysregulated genes in IFNß-IFNAR signaling pathway predispose one to sPD. By transcriptomic analysis, we found defective neuronal IFNß-IFNAR signaling, including particularly elevated PIAS2 associated with sPDD. With meta-analysis of GWASs, we identified sequence variants in IFNß-IFNAR-related genes in sPD patients. Furthermore, sPDD patients expressed higher levels of PIAS2 mRNA and protein in neurons. To determine its function in brain, we overexpressed PIAS2 under a neuronal promoter, alone or with human α-synuclein, in the brains of mice, which caused motor and cognitive impairments and correlated with intraneuronal phosphorylated (p)α-synuclein accumulation and dopaminergic neuron loss. Ectopic expression of neuronal PIAS2 blocked mitophagy, increased the accumulation of senescent mitochondrial and oxidative stress, as evidenced by excessive oxDJ1 and 8OHdG, by inactivating ERK1/2-P53 signaling. Conversely, PIAS2 knockdown rescued the clinicopathological manifestations of PDD in Ifnb-/- mice on restoring mitochondrial homeostasis, oxidative stress, and pERK1/2-pP53 signaling. The regulation of JAK-STAT2-PIAS2 signaling was crucial for neurite outgrowth and neuronal survival and excitability and thus might prevent cognitive impairments. Our findings provide insights into the progression of sPD and dementia and have implications for new therapeutic approaches.

Temporal activation of LRH-1 and RAR-γ in human pluripotent stem cells induces a functional naïve-like state.

Naïve pluripotency can be established in human pluripotent stem cells (hPSCs) by manipulation of transcription factors, signaling pathways, or a combination thereof. However, differences exist in the molecular and functional properties of naïve hPSCs generated by different protocols, which include varying similarities with pre-implantation human embryos, differentiation potential, and maintenance of genomic integrity. We show here that short treatment with two chemical agonists (2a) of nuclear receptors, liver receptor homologue-1 (LRH-1) and retinoic acid receptor gamma (RAR-γ), along with 2i/LIF (2a2iL) induces naïve-like pluripotency in human cells during reprogramming of fibroblasts, conversion of pre-established hPSCs, and generation of new cell lines from blastocysts. 2a2iL-hPSCs match several defined criteria of naïve-like pluripotency and contribute to human-mouse interspecies chimeras. Activation of TGF-ß signaling is instrumental for acquisition of naïve-like pluripotency by the 2a2iL induction procedure, and transient activation of TGF-ß signaling substitutes for 2a to generate naïve-like hPSCs. We reason that 2a2iL-hPSCs are an easily attainable system to evaluate properties of naïve-like hPSCs and for various applications.

StrongestPath: a Cytoscape application for protein-protein interaction analysis.

BACKGROUND: StrongestPath is a Cytoscape 3 application that enables the analysis of interactions between two proteins or groups of proteins in a collection of protein-protein interaction (PPI) network or signaling network databases. When there are different levels of confidence over the interactions, the application is able to process them and identify the cascade of interactions with the highest total confidence score. Given a set of proteins, StrongestPath can extract a set of possible interactions between the input proteins, and expand the network by adding new proteins that have the most interactions with highest total confidence to the current network of proteins. The application can also identify any activating or inhibitory regulatory paths between two distinct sets of transcription factors and target genes. This application can be used on the built-in human and mouse PPI or signaling databases, or any user-provided database for some organism. RESULTS: Our results on 12 signaling pathways from the NetPath database demonstrate that the application can be used for indicating proteins which may play significant roles in a pathway by finding the strongest path(s) in the PPI or signaling network. CONCLUSION: Easy access to multiple public large databases, generating output in a short time, addressing some key challenges in one platform, and providing a user-friendly graphical interface make StrongestPath an extremely useful application.

Downregulation of ITM2A Gene Expression in Macrophages of Patients with Ankylosing Spondylitis.

OBJECTIVES: Ankylosing spondylitis (AS) is a rheumatic disorder that is mostly determined by genetic and environmental factors. Given the known importance of macrophage in AS pathogenesis, we investigated the transcriptional profile of macrophage cells in the disease. METHODS AND RESULTS: Two approaches of differential expression and subsequently, weighted gene co-expression network analysis was utilized to analyze a publicly available microarray dataset of macrophages. Integral membrane protein 2A (ITM2A) was among the most significant genes with a decreased trend in the common results of both methods. In order to confirm the finding, the expression of ITM2A was evaluated in monocyte-derived (M2-like) and M1 macrophages obtained from 14 AS patients and 14 controls. Macrophages were differentiated from whole-blood separated monocytes by 7 days incubating with macrophage colony-stimulating factor and then macrophages specific markers were verified with the flow cytometer. M1 polarization was induced by IFN-γ and lipopolysaccharide. Finally, relative gene expression analysis by real-time polymerase chain reaction revealed a significant downregulation of the ITM2A gene in both M2 like and M1 macrophages of the AS group compared to the control. CONCLUSION: Since ITM2A plays a critical role in osteo- and chondrogenic cellular differentiation, our finding may provide new insights into AS pathogenesis.

A novel panel of blood-based microRNAs capable of discrimination between benign breast disease and breast cancer at early stages.

Breast cancer (BC) as a leading cause of cancer death among women, exhibits a wide range of genetic heterogeneity in affected individuals. Satisfactory management of BC depends on early diagnosis and proper monitoring of patients' response to therapy. In this study, we aimed to assess the relation between the expression patterns of blood-based microRNAs (miRNAs) with demographic characteristics of the patients with BC in an attempt to find novel diagnostic markers for BC with acceptable precision in clinical applications. To this end, we performed comprehensive statistical analysis of the data of the Cancer Genome Atlas (TCGA) database and the blood miRNome dataset (GSE31309). As a result, 21 miRNAs were selected for experimental verification by quantitative RT-PCR on blood samples of 70 BC patients and 60 normal individuals (without any lesions or benign breast diseases). Statistical one-way ANOVA revealed no significant difference in the blood levels of the selected miRNAs in BC patients compared to any lesions or benign breast diseases. However, the multi-marker panel consisting of hsa-miR-106b-5p, -126-3p, -140-3p, -193a-5p, and -10b-5p could detect early-stages of BC with 0.79 sensitivity, 0.86 specificity and 0.82 accuracy. Furthermore, this multi-marker panel showed the potential of detecting benign breast diseases from BC patients with 0.67 sensitivity, 0.80 specificity, and 0.74 accuracy. In conclusion, these data indicate that the present panel might be considered an asset in detecting benign breast disease and BC.

Dysregulation of ribosome-related genes in ankylosing spondylitis: a systems biology approach and experimental method.

BACKGROUND: Ankylosing spondylitis (AS) is an autoimmune rheumatic disease. Few candidate gene associations have been reported for AS and the current understanding of its pathogenesis remains still poor. Thus, the exact mechanism of AS is needed to urgently be disclosed. The purpose of this study was to identify candidate genes involving in AS disease. METHODS AND RESULTS: GSE25101 publicly available microarray and GSE117769 RNA-seq datasets of AS patients were obtained for bioinformatics analyses. Gene set enrichment analysis showed that in the microarray dataset, the ribosome pathway was significantly up-regulated in AS compared with controls. Furthermore, some ribosomal components demonstrated overexpression in patients in the RNA-seq dataset. To confirm the findings, 20 AS patients and 20 matching controls were selected from the Rheumatology Research Center clinic, Shariati Hospital. PBMCs were separated from whole blood and RNA contents were extracted. Following the results of datasets analysis, the expression level of rRNA5.8S pseudogene, rRNA18S pseudogene, RPL23, RPL7, and RPL17 genes were measured through real-time PCR. Our findings showed dysregulation of rRNA5.8S and rRNA18S pseudogenes, and also the RPL17 gene in patients. CONCLUSION: Considering that genes involved in ribosome biogenesis contributed to some AS-associated biological processes as well as diseases that have comorbidities with AS, our results might advance our understanding of the pathological mechanisms of ankylosing spondylitis.

Expression and Function of C1orf132 Long-Noncoding RNA in Breast Cancer Cell Lines and Tissues.

miR-29b2 and miR-29c play a suppressive role in breast cancer progression. C1orf132 (also named MIR29B2CHG) is the host gene for generating both microRNAs. However, the region also expresses longer transcripts with unknown functions. We employed bioinformatics and experimental approaches to decipher C1orf132 expression and function in breast cancer tissues. We also used the CRISPR/Cas9 technique to excise a predicted C1orf132 distal promoter and followed the behavior of the edited cells by real-time PCR, flow cytometry, migration assay, and RNA-seq techniques. We observed that C1orf132 long transcript is significantly downregulated in triple-negative breast cancer. We also identified a promoter for the longer transcripts of C1orf132 whose functionality was demonstrated by transfecting MCF7 cells with a C1orf132 promoter-GFP construct. Knocking-out the promoter by means of CRISPR/Cas9 revealed no alterations in the expression of the neighboring genes CD46 and CD34, while the expression of miR-29c was reduced by half. Furthermore, the promoter knockout elevated the migration ability of the edited cells. RNA sequencing revealed many up- and downregulated genes involved in various cellular pathways, including epithelial to mesenchymal transition and mammary gland development pathways. Altogether, we are reporting here the existence of an additional/distal promoter with an enhancer effect on miR-29 generation and an inhibitory effect on cell migration.

Enhanced Waddington landscape model with cell-cell communication can explain molecular mechanisms of self-organization.

MOTIVATION: The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured in particular micropatterns, can self-organize and mimic the early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential and can enhance understanding of the phenomenon. RESULTS: Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation. We have shown the sufficiency of a simple mechanism by using a minimal number of parameters in the model, in order to address a variety of experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters and unexpected spotted forms of the germ layers under certain conditions. Moreover, we have tested different boundary conditions as well as various shapes, observing that the pattern is initiated from the boundary and gradually spreads towards the center. This model provides a basis for in-silico modeling of self-organization. AVAILABILITY AND IMPLEMENTATION: https://github.com/HFooladi/Self_Organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The assessment of efficient representation of drug features using deep learning for drug repositioning.

BACKGROUND: De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited number of candidate pairs of drugs and diseases. In other words, they are not scalable to a large number of drugs and diseases. Most of the in-silico methods mainly focus on linear approaches while non-linear models are still scarce for new indication predictions. Therefore, applying non-linear computational approaches can offer an opportunity to predict possible drug repositioning candidates. RESULTS: In this study, we present a non-linear method for drug repositioning. We extract four drug features and two disease features to find the semantic relations between drugs and diseases. We utilize deep learning to extract an efficient representation for each feature. These representations reduce the dimension and heterogeneity of biological data. Then, we assess the performance of different combinations of drug features to introduce a pipeline for drug repositioning. In the available database, there are different numbers of known drug-disease associations corresponding to each combination of drug features. Our assessment shows that as the numbers of drug features increase, the numbers of available drugs decrease. Thus, the proposed method with large numbers of drug features is as accurate as small numbers. CONCLUSION: Our pipeline predicts new indications for existing drugs systematically, in a more cost-effective way and shorter timeline. We assess the pipeline to discover the potential drug-disease associations based on cross-validation experiments and some clinical trial studies.

An integrated analysis to predict micro-RNAs targeting both stemness and metastasis in breast cancer stem cells.

Several evidences support the idea that a small population of tumour cells representing self-renewal potential are involved in initiation, maintenance, metastasis, and outcomes of cancer therapy. Elucidation of microRNAs/genes regulatory networks activated in cancer stem cells (CSCs) is necessary for the identification of new targets for cancer therapy. The aim of the present study was to predict the miRNAs pattern, which can target both metastasis and self-renewal pathways using integration of literature and data mining. For this purpose, mammospheres derived from MCF-7, MDA-MB231, and MDA-MB468 were used as breast CSCs model. They had higher migration, invasion, and colony formation potential, with increasing in stemness- and EMT-related genes expression. Our results determined that miR-204, -200c, -34a, and -10b contemporarily could target both self-renewal and EMT pathways. This core regulatory of miRNAs could increase the survival rate of breast invasive carcinoma via up-regulation of OCT4, SOX2, KLF4, c-MYC, NOTCH1, SNAI1, ZEB1, and CDH2 and down-regulation of CDH1. The majority of those target genes were involved in the regulation of pluripotency, MAPK, WNT, Hedgehog, p53, and transforming growth factor ß pathways. Hence, this study provides novel insights for targeting core regulatory of miRNAs in breast CSCs to target both self-renewal and metastasis potential and eradication of breast cancer.

Genome-wide DNA methylation profiling in ectopic and eutopic of endometrial tissues.

PURPOSE: Endometriosis is a gynecological disease that causes the uterine lining to appear in other organs outside the uterus. As DNA methylation has an important role in this disorder, its profiling can reveal new information to improve the diagnosis and treatment of endometriosis patients. METHODS: We conducted a genome-wide methylation profiling of ectopic and eutopic endometrial tissues from women with and without endometriosis using Infinium Human Methylation 450K BeadChip arrays. DNA methylation samples were collected from nine ectopic and nine eutopic endometrial tissues of endometriosis and six endometrial tissues of healthy controls. RESULTS: Correlation heatmaps and the principal component analysis divided the samples into two clusters, one consisting of all ectopic samples and the other consisting of both eutopic and control samples unexpectedly without segregation between them. The assay identified a group of methylated genes that were overrepresented in biological processes, including abnormality in signaling, development, and adhesion of cells. Pathway analysis revealed disruption in HTLV infection pathways, PI3K-Akt, oxytocin, and relaxin signaling. Moreover, we found eutopic lesions are strongly associated with autoimmune disease. CONCLUSIONS: Our results confirmed the role of DNA methylation alternations in endometriosis development and pathogenesis. Our finding suggests aberrant DNA methylation can activate several signaling pathways including PI3k-AKT signaling, relaxin, and oxytocin which are associated with the pathogenesis of endometriosis.

DNA methylation regulates discrimination of enhancers from promoters through a H3K4me1-H3K4me3 seesaw mechanism.

BACKGROUND: DNA methylation at promoters is largely correlated with inhibition of gene expression. However, the role of DNA methylation at enhancers is not fully understood, although a crosstalk with chromatin marks is expected. Actually, there exist contradictory reports about positive and negative correlations between DNA methylation and H3K4me1, a chromatin hallmark of enhancers. RESULTS: We investigated the relationship between DNA methylation and active chromatin marks through genome-wide correlations, and found anti-correlation between H3K4me1 and H3K4me3 enrichment at low and intermediate DNA methylation loci. We hypothesized "seesaw" dynamics between H3K4me1 and H3K4me3 in the low and intermediate DNA methylation range, in which DNA methylation discriminates between enhancers and promoters, marked by H3K4me1 and H3K4me3, respectively. Low methylated regions are H3K4me3 enriched, while those with intermediate DNA methylation levels are progressively H3K4me1 enriched. Additionally, the enrichment of H3K27ac, distinguishing active from primed enhancers, follows a plateau in the lower range of the intermediate DNA methylation level, corresponding to active enhancers, and decreases linearly in the higher range of the intermediate DNA methylation. Thus, the decrease of the DNA methylation switches smoothly the state of the enhancers from a primed to an active state. We summarize these observations into a rule of thumb of one-out-of-three methylation marks: "In each genomic region only one out of these three methylation marks {DNA methylation, H3K4me1, H3K4me3} is high. If it is the DNA methylation, the region is inactive. If it is H3K4me1, the region is an enhancer, and if it is H3K4me3, the region is a promoter". To test our model, we used available genome-wide datasets of H3K4 methyltransferases knockouts. Our analysis suggests that CXXC proteins, as readers of non-methylated CpGs would regulate the "seesaw" mechanism that focuses H3K4me3 to unmethylated sites, while being repulsed from H3K4me1 decorated enhancers and CpG island shores. CONCLUSIONS: Our results show that DNA methylation discriminates promoters from enhancers through H3K4me1-H3K4me3 seesaw mechanism, and suggest its possible function in the inheritance of chromatin marks after cell division. Our analyses suggest aberrant formation of promoter-like regions and ectopic transcription of hypomethylated regions of DNA. Such mechanism process can have important implications in biological process in where it has been reported abnormal DNA methylation status such as cancer and aging.

ARYANA: Aligning Reads by Yet Another Approach.

MOTIVATION: Although there are many different algorithms and software tools for aligning sequencing reads, fast gapped sequence search is far from solved. Strong interest in fast alignment is best reflected in the $10(6) prize for the Innocentive competition on aligning a collection of reads to a given database of reference genomes. In addition, de novo assembly of next-generation sequencing long reads requires fast overlap-layout-concensus algorithms which depend on fast and accurate alignment. CONTRIBUTION: We introduce ARYANA, a fast gapped read aligner, developed on the base of BWA indexing infrastructure with a completely new alignment engine that makes it significantly faster than three other aligners: Bowtie2, BWA and SeqAlto, with comparable generality and accuracy. Instead of the time-consuming backtracking procedures for handling mismatches, ARYANA comes with the seed-and-extend algorithmic framework and a significantly improved efficiency by integrating novel algorithmic techniques including dynamic seed selection, bidirectional seed extension, reset-free hash tables, and gap-filling dynamic programming. As the read length increases ARYANA's superiority in terms of speed and alignment rate becomes more evident. This is in perfect harmony with the read length trend as the sequencing technologies evolve. The algorithmic platform of ARYANA makes it easy to develop mission-specific aligners for other applications using ARYANA engine. AVAILABILITY: ARYANA with complete source code can be obtained from http://github.com/aryana-aligner.

Revealing the grammar of small RNA secretion using interpretable machine learning.

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.

qPCR is still the gold standard for gene expression quantification. However, its accuracy is highly dependent on the normalization procedure. The conventional method involves using the geometric mean of multiple study-specific reference genes (RGs) expression for cross-sample normalization. While research on selecting stably expressed RGs is extensive, scant literature exists regarding the optimal approach for aggregating multiple RGs into a unified RG. In this paper, we introduce a family of scale-invariant functions as an alternative to the geometric mean aggregation. Our candidate method (weighted geometric mean minimizing standard deviation) demonstrated significantly better results compared to other proposed methods. We provide theoretical and experimental support for this finding using real data from solid tumors and liquid biopsies. Moreover, the closed form and regression-based solution enable efficient computation and straightforward adoption on various platforms. All the proposed methods have been implemented within an easy-to-use R package with graphics processing unit (GPU) acceleration.

Time-resolved Small-RNA Sequencing Identifies MicroRNAs Critical for Formation of Embryonic Stem Cells from the Inner Cell Mass of Mouse Embryos.

Cells of the inner cell mass (ICM) acquire a unique ability for unlimited self-renewal during transition into embryonic stem cells (ESCs) in vitro, while preserving their natural multi-lineage differentiation potential. Several different pathways have been identified to play roles in ESC formation but the function of non-coding RNAs in this process is poorly understood. Here, we describe several microRNAs (miRNAs) that are crucial for efficient generation of mouse ESCs from ICMs. Using small-RNA sequencing, we characterize dynamic changes in miRNA expression profiles during outgrowth of ICMs in a high-resolution, time-course dependent manner. We report several waves of miRNA transcription during ESC formation, to which miRNAs from the imprinted Dlk1-Dio3 locus contribute extensively. In silico analyses followed by functional investigations reveal that Dlk1-Dio3 locus-embedded miRNAs (miR-541-5p, miR-410-3p, and miR-381-3p), miR-183-5p, and miR-302b-3p promote, while miR-212-5p and let-7d-3p inhibit ESC formation. Collectively, these findings offer new mechanistic insights into the role of miRNAs during ESC derivation.

Pan-cancer analysis of microRNA expression profiles highlights microRNAs enriched in normal body cells as effective suppressors of multiple tumor types: A study based on TCGA database.

BACKGROUND: MicroRNAs (miRNAs) are frequently deregulated in various types of cancer. While antisense oligonucleotides are used to block oncomiRs, delivery of tumour-suppressive miRNAs holds great potential as a potent anti-cancer strategy. Here, we aim to determine, and functionally analyse, miRNAs that are lowly expressed in various types of tumour but abundantly expressed in multiple normal tissues. METHODS: The miRNA sequencing data of 14 cancer types were downloaded from the TCGA dataset. Significant differences in miRNA expression between tumor and normal samples were calculated using limma package (R programming). An adjusted p value < 0.05 was used to compare normal versus tumor miRNA expression profiles. The predicted gene targets were obtained using TargetScan, miRanda, and miRDB and then subjected to gene ontology analysis using Enrichr. Only GO terms with an adjusted p < 0.05 were considered statistically significant. All data from wet-lab experiments (cell viability assays and flow cytometry) were expressed as means ± SEM, and their differences were analyzed using GraphPad Prism software (Student's t test, p < 0.05). RESULTS: By compiling all publicly available miRNA profiling data from The Cancer Genome Atlas (TCGA) Pan-Cancer Project, we reveal a small set of tumour-suppressing miRNAs (which we designate as 'normomiRs') that are highly expressed in 14 types of normal tissues but poorly expressed in corresponding tumour tissues. Interestingly, muscle-enriched miRNAs (e.g. miR-133a/b and miR-206) and miRNAs from DLK1-DIO3 locus (e.g. miR-381 and miR-411) constitute a large fraction of the normomiRs. Moreover, we define that the CCCGU motif is absent in the oncomiRs' seed sequences but present in a fraction of tumour-suppressive miRNAs. Finally, the gain of function of candidate normomiRs across several cancer cell types indicates that miR-206 and miR-381 exert the most potent inhibition on multiple cancer types in vitro. CONCLUSION: Our results reveal a pan-cancer set of tumour-suppressing miRNAs and highlight the potential of miRNA-replacement therapies for targeting multiple types of tumour.

Whole genome sequencing of SARS-CoV2 strains circulating in Iran during five waves of pandemic.

PURPOSE: Whole genome sequencing of SARS-CoV2 is important to find useful information about the viral lineages, variants of interests and variants of concern. As there are not enough data about the circulating SARS-CoV2 variants in Iran, we sequenced 54 SARS-CoV2 genomes during the 5 waves of pandemic in Iran. METHODS: After viral RNA extraction from clinical samples collected during the COVID-19 pandemic, next generation sequencing was performed using the Nextseq platform. The sequencing data were analyzed and compared with reference sequences. RESULTS: During the 1st wave, V and L clades were detected. The second wave was recognized by G, GH and GR clades. Circulating clades during the 3rd wave were GH and GR. In the fourth wave GRY (alpha variant), GK (delta variant) and one GH clade (beta variant) were detected. All viruses in the fifth wave were in clade GK (delta variant). There were different mutations in all parts of the genomes but Spike-D614G, NSP12-P323L, N-R203K and N-G204R were the most frequent mutants in these studied viruses. CONCLUSIONS: These findings display the significance of SARS-CoV2 monitoring to help on time detection of possible variants for pandemic control and vaccination plans.

DNA-RNA Hybrid (R-Loop): From a Unified Picture of the Mammalian Telomere to the Genome-Wide Profile.

Local three-stranded DNA/RNA hybrid regions of genomes (R-loops) have been detected either by binding of a monoclonal antibody (DRIP assay) or by enzymatic recognition by RNaseH. Such a structure has been postulated for mouse and human telomeres, clearly suggested by the identification of the complementary RNA Telomeric repeat-containing RNA "TERRA". However, the tremendous disparity in the information obtained with antibody-based technology drove us to investigate a new strategy. Based on the observation that DNA/RNA hybrids in a triplex complex genome co-purify with the double-stranded chromosomal DNA fraction, we developed a direct preparative approach from total protein-free cellular extract without antibody that allows their physical isolation and determination of their RNA nucleotide sequence. We then define in the normal mouse and human sperm genomes the notion of stable DNA associated RNA terminal R-loop complexes, including TERRA molecules synthesized from local promoters of every chromosome. Furthermore, the first strong evidence of all telomeric structures, applied additionally to the whole murine sperm genome compared to the testes, showed reproducible R-loop complexes of the whole genome and suggesting a defined profile in the sperm genome for the next generation.