Small RNA-seq Data Research Articles

Reexamining the Mycovirome of Botrytis spp.

Botrytis species cause gray mold disease in more than 200 crops worldwide. To control this disease, chemical fungicides are usually applied. However, more sustainable control alternatives should be explored, such as the use of hypovirulent mycovirus-infected fungal strains. To determine the mycovirome of two Botrytis species, B. cinerea and B. prunorum, we reanalyzed RNA-Seq and small RNA-Seq data using different assembly programs and an updated viral database, aiming to identify new mycoviruses that were previously not described in the same dataset. New mycoviruses were identified, including those previously reported to infect or be associated with B. cinerea and Plasmopara viticola, such as Botrytis cinerea alpha-like virus 1 and Plasmopara viticola lesion-associated ourmia-like virus 80. Additionally, two novel narnaviruses, not previously identified infecting Botrytis species, have been characterized, tentatively named Botrytis cinerea narnavirus 1 and Botrytis narnavirus 1. The analysis of small RNAs suggested that all identified mycoviruses were targeted by the antiviral fungal mechanism, regardless of the viral genome type. In conclusion, the enlarged list of newly found viruses and the application of different bioinformatics approaches have enabled the identification of novel mycoviruses not previously described in Botrytis species, expanding the already extensive list.

Revealing differential expression patterns of piRNA in FACS blood cells of SARS-CoV−2 infected patients

Non-coding RNA expression has shown to have cell type-specificity. The regulatory characteristics of these molecules are impacted by changes in their expression levels. We performed next-generation sequencing and examined small RNA-seq data obtained from 6 different types of blood cells separated by fluorescence-activated cell sorting of severe COVID−19 patients and healthy control donors. In addition to examining the behavior of piRNA in the blood cells of severe SARS-CoV−2 infected patients, our aim was to present a distinct piRNA differential expression portrait for each separate cell type. We observed that depending on the type of cell, different sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, and neutrophils) have altering piRNA expression patterns. After analyzing the expression of piRNAs in each set of sorted cells from patients with severe COVID−19, we observed 3 significantly elevated piRNAs - piR−33,123, piR−34,765, piR−43,768 and 9 downregulated piRNAs in erythrocytes. In lymphocytes, all 19 piRNAs were upregulated. Monocytes were presented with a larger amount of statistically significant piRNA, 5 upregulated (piR−49039 piR−31623, piR−37213, piR−44721, piR−44720) and 35 downregulated. It has been previously shown that piR−31,623 has been associated with respiratory syncytial virus infection, and taking in account the major role of piRNA in transposon silencing, we presume that the differential expression patterns which we observed could be a signal of indirect antiviral activity or a specific antiviral cell state. Additionally, in lymphocytes, all 19 piRNAs were upregulated.

#111 RNAseq of microdissected collecting ducts revealing the early signaling mediating the loss of aquaporin 2 after K+ deprivation

Abstract Background and Aims Potassium (K+) deficiency could cause a reduction in urinary concentrating ability, resulting in nephrogenic diabetes insipidus (NDI), but the detailed mechanism remains unclear. Recently, transcriptomic and proteomic data from acquired NDI models reveal that oxidative stress, apoptosis, and inflammatory signaling are associated with AQP2 loss. We aim to explore the early signaling after K+ deprivation in collecting ducts. Method Immunoblotting of kidney lysate were performed at 0, 12, 24, and 48 hours after K+ deprivation in rats. Serum and urine biochemistry were also recorded. Based on immunoblotting, cortical collecting ducts (CCDs) were microdissected from rats at 6 hrs after K+ deprivation versus time controls. Small samples RNA-Seq was carried out independently in K+ deprivation rats versus controls (n = 4). Results Immunoblotting of bulk kidney showed a decreased in AQP2 protein abundance at 12 hours of K+ deprivation diet, and urine osmolality was significantly decreased at 24 hours, confirming the animal model of K+ deprivation-induced NDI. Small samples RNA-Seq data of CCDs at 6 hrs after K+ deprivation showed Aqp2, Aqp3, and Atp1a1 were significantly downregulated. It also revealed that chemokine transcripts (Ccl20 and Ccl28) were increased significantly. We also carried out analysis of Gene Ontology Biological Process terms that are statistically over-represented in the list of 88 “Increased Transcripts” at 6 hrs of K+ deprivation in CCDs, and many of the terms are related to glutathione metabolic process, cell chemotaxis, cellular response to lipopolysaccharide, consistent with an inflammatory response. Conclusion Our small samples RNA-Seq from microdissected CCDs in rats showed early cellular signaling changes in activation of oxidative stress and inflammatory signaling causing loss of aquaporin-2 in K+ deficiency induced NDI.

In-silico functional analyses identify TMPRSS15-mediated intestinal absorption of lithium as a modulator of lithium response in bipolar disorder

BackgroundThe therapeutic response to lithium in patients with bipolar disorder is highly variable and has a polygenic basis. Genome-wide association studies investigating lithium response have identified several relevant loci, though the precise mechanisms driving these associations are poorly understood. We aimed to prioritise the most likely effector gene and determine the mechanisms underlying an intergenic lithium response locus on chromosome 21 identified by the International Consortium on Lithium Genetics (ConLi+Gen). MethodsWe conducted in-silico functional analyses by integrating and synthesising information from several publicly available functional genetic datasets and databases including the Genotype-Tissue Expression (GTEx) project and HaploReg. ResultsThe findings from this study highlighted TMPRSS15 as the most likely effector gene at the ConLi+Gen lithium response locus. TMPRSS15 encodes enterokinase, a gastrointestinal enzyme responsible for converting trypsinogen into trypsin and thus aiding digestion. Convergent findings from gene-based lookups in human and mouse databases as well as co-expression network analyses of small intestinal RNA-seq data (GTEx) implicated TMPRSS15 in the regulation of intestinal nutrient absorption, including ions like sodium and potassium, which may extend to lithium. LimitationsAlthough the findings from this study indicated that TMPRSS15 was the most likely effector gene at the ConLi+Gen lithium response locus, the evidence was circumstantial. Thus, the conclusions from this study need to be validated in appropriately designed wet-lab studies. ConclusionsThe findings from this study are consistent with a model whereby TMPRSS15 impacts the efficacy of lithium treatment in patients with bipolar disorder by modulating intestinal lithium absorption.

Characterization and Function Analysis of miRNA Editing during Fat Deposition in Chinese Indigenous Ningxiang Pigs.

This study aimed to identify active miRNA editing sites during adipose development in Ningxiang pigs and analyze their characteristics and functions. Based on small RNA-seq data from the subcutaneous adipose tissues of Ningxiang pigs at four stages-30 days (piglet), 90 days (nursery), 150 days (early fattening), and 210 days (late fattening)-we constructed a developmental map of miRNA editing in the adipose tissues of Ningxiang pigs. A total of 505 miRNA editing sites were identified using the revised pipeline, with C-to-U editing types being the most prevalent, followed by U-to-C, A-to-G, and G-to-U. Importantly, these four types of miRNA editing exhibited base preferences. The number of editing sites showed obvious differences among age groups, with the highest occurrence of miRNA editing events observed at 90 days of age and the lowest at 150 days of age. A total of nine miRNA editing sites were identified in the miRNA seed region, with significant differences in editing levels (p < 0.05) located in ssc-miR-23a, ssc-miR-27a, ssc-miR-30b-5p, ssc-miR-15a, ssc-miR-497, ssc-miR-15b, and ssc-miR-425-5p, respectively. Target gene prediction and KEGG enrichment analyses indicated that the editing of miR-497 might potentially regulate fat deposition by inhibiting adipose synthesis via influencing target binding. These results provide new insights into the regulatory mechanism of pig fat deposition.

Identification of watermelon genes involved in the ZYMV interaction through a miRNA bio-informatics analysis and characterization of ATRIP and RBOHB

Our objective was to identify differentially expressed watermelon genes in its interaction with ZYMV through a bioinformatics analysis of differentially expressed miRNAs. Small RNAseq data analysis of healthy and ZYMV-infected watermelon (21 dpi) identified 353 miRNAs from which 22 known and 331 new miRNAs. Important information about their precursors, their length, the loci of which they originated on watermelon genome are provided. The ZYMV genome could be a target for mir396a-3p, miR8706a, and miR1886i-5p from the miRbase, but none of them was identified in the watermelon miRNAome. Furthermore, watermelon miRNAome does not contain a miRNA targeting ZYMV genome with an expectation score ≤3.5. In healthy watermelon bioinformatically predicted targets of 32 miRNAs were 34 resistance genes, as CC-NBS-LRR, TIR-NBS-LRR, TIR-NBS. For nine differentially expressed miRNAs the respective target genes (10 in total) were bioinformatically predicted. For cla-new_miR307 (upregulated upon ZYMV infection) and cla-miR166h-3p (downregulated upon ZYMV infection) the targets were predicted to be ClaATRIP and ClaRBOHB, respectively, with ClaATRIP downregulated and ClaRBOHB upregulated upon ZYMV infection. ALSV-mediated VIGS of ClaATRIP rendered watermelon plants more resistant to ZYMV, whereas VIGS of ClaRBOHB resulted in higher levels of ZYMV titer in watermelon. These data suggest that ClaATRIP and ClaRBOHB are a susceptibility and resistant gene, respectively. Our results provide new insights in watermelon miRNAome and could propose new strategies for generating resistant watermelon to ZYMV.

Identification and Potential Roles of Human MicroRNAs in Ebola Virus Infection and Disease Pathogenesis.

Ebola virus (EBOV) is a highly pathogenic virus that causes a severe illness called Ebola virus disease (EVD). EVD has a high mortality rate and remains a significant threat to public health. Research on EVD pathogenesis has traditionally focused on host transcriptional responses. Limited recent studies, however, have revealed some information on the significance of cellular microRNAs (miRNAs) in EBOV infection and pathogenic mechanisms, but further studies are needed. Thus, this study aimed to identify and validate additional known and novel human miRNAs in EBOV-infected adult retinal pigment epithelial (ARPE) cells and predict their potential roles in EBOV infection and pathogenic mechanisms. We analyzed previously available small RNA-Seq data obtained from ARPE cells and identified 23 upregulated and seven downregulated miRNAs in the EBOV-infected cells; these included two novel miRNAs and 17 additional known miRNAs not previously identified in ARPE cells. In addition to pathways previously identified by others, these miRNAs are associated with pathways and biological processes that include WNT, FoxO, and phosphatidylinositol signaling; these pathways were not identified in the original study. This study thus confirms and expands on the previous study using the same datasets and demonstrates further the importance of human miRNAs in the host response and EVD pathogenesis during infection.

Exploring miRNA-target gene pair detection in disease with coRmiT.

A wide range of approaches can be used to detect micro RNA (miRNA)-target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Unique tRNA Fragment Upregulation with SARS-CoV-2 but Not with SARS-CoV Infection.

Unlike other coronaviruses, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly infected the global population, with some suffering long-term effects. Thanks to extensive data on SARS-CoV-2 made available through global, multi-level collaborative research, investigators are getting closer to understanding the mechanisms of SARS-CoV-2 infection. Here, using publicly available total and small RNAseq data of Calu3 cell lines, we conducted a comparative analysis of the changes in tRNA fragments (tRFs; regulatory small noncoding RNAs) in the context of severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 infections. We found extensive upregulation of multiple tRFs in SARS-CoV-2 infection that was not present in SARS-CoV or other virus infections our group has studied. By comparing the total RNA changes in matching samples, we identified significant downregulation of TRDMT1 (tRNA methyltransferase), only in SARS-CoV-2 infection, a potential upstream event. We further found enriched neural functions among downregulated genes with SARS-CoV-2 infection. Interestingly, theoretically predicted targets of the upregulated tRFs without considering mRNA expression data are also enriched in neural functions such as axon guidance. Based on a combination of expression data and theoretical calculations, we propose potential targets for tRFs. For example, among the mRNAs downregulated with SARS-CoV-2 infection (but not with SARS-CoV infection), SEMA3C is a theoretically calculated target of multiple upregulated tRFs and a ligand of NRP1, a SARS-CoV-2 receptor. Our analysis suggests that tRFs contribute to distinct neurological features seen in SARS-CoV-2.

Inferring the Regulatory Network of miRNAs on Terpene Trilactone Biosynthesis Affected by Environmental Conditions

As a medicinal tree species, ginkgo (Ginkgo biloba L.) and terpene trilactones (TTLs) extracted from its leaves are the main pharmacologic activity constituents and important economic indicators of its value. The accumulation of TTLs is known to be affected by environmental stress, while the regulatory mechanism of environmental response mediated by microRNAs (miRNAs) at the post-transcriptional levels remains unclear. Here, we focused on grafted ginkgo grown in northwestern, southwestern, and eastern-central China and integrally analyzed RNA-seq and small RNA-seq high-throughput sequencing data as well as metabolomics data from leaf samples of ginkgo clones grown in natural environments. The content of bilobalide was highest among detected TTLs, and there was more than a twofold variation in the accumulation of bilobalide between growth conditions. Meanwhile, transcriptome analysis found significant differences in the expression of 19 TTL-related genes among ginkgo leaves from different environments. Small RNA sequencing and analysis showed that 62 of the 521 miRNAs identified were differentially expressed among different samples, especially the expression of miRN50, miR169h/i, and miR169e was susceptible to environmental changes. Further, we found that transcription factors (ERF, MYB, C3H, HD-ZIP, HSF, and NAC) and miRNAs (miR319e/f, miRN2, miRN54, miR157, miR185, and miRN188) could activate or inhibit the expression of TTL-related genes to participate in the regulation of terpene trilactones biosynthesis in ginkgo leaves by weighted gene co-regulatory network analysis. Our findings provide new insights into the understanding of the regulatory mechanism of TTL biosynthesis but also lay the foundation for ginkgo leaves’ medicinal value improvement under global change.

Integrating a microRNA signature as a liquid biopsy-based tool for the early diagnosis and prediction of potential therapeutic targets in pancreatic cancer

IntroductionPancreatic cancer is a highly aggressive cancer, and early diagnosis significantly improves patient prognosis due to the early implementation of curative-intent surgery. Our study aimed to implement machine-learning algorithms to aid in early pancreatic cancer diagnosis based on minimally invasive liquid biopsies.Materials and methodsThe analysis data were derived from nine public pancreatic cancer miRNA datasets and two sequencing datasets from 26 pancreatic cancer patients treated in our medical center, featuring small RNAseq data for patient-matched tumor and non-tumor samples and serum. Upon batch-effect removal, systematic analyses for differences between paired tissue and serum samples were performed. The robust rank aggregation (RRA) algorithm was used to reveal feature markers that were co-expressed by both sample types. The repeatability and real-world significance of the enriched markers were then determined by validating their expression in our patients’ serum. The top candidate markers were used to assess the accuracy of predicting pancreatic cancer through four machine learning methods. Notably, these markers were also applied for the identification of pancreatic cancer and pancreatitis. Finally, we explored the clinical prognostic value, candidate targets and predict possible regulatory cell biology mechanisms involved.ResultsOur multicenter analysis identified hsa-miR-1246, hsa-miR-205-5p, and hsa-miR-191-5p as promising candidate serum biomarkers to identify pancreatic cancer. In the test dataset, the accuracy values of the prediction model applied via four methods were 94.4%, 84.9%, 82.3%, and 83.3%, respectively. In the real-world study, the accuracy values of this miRNA signatures were 82.3%, 83.5%, 79.0%, and 82.2. Moreover, elevated levels of these miRNAs were significant indicators of advanced disease stage and allowed the discrimination of pancreatitis from pancreatic cancer with an accuracy rate of 91.5%. Elevated expression of hsa-miR-205-5p, a previously undescribed blood marker for pancreatic cancer, is associated with negative clinical outcomes in patients.ConclusionA panel of three miRNAs was developed with satisfactory statistical and computational performance in real-world data. Circulating hsa-miRNA 205-5p serum levels serve as a minimally invasive, early detection tool for pancreatic cancer diagnosis and disease staging and might help monitor therapy success.

Detection of a 7SL RNA-derived small non-coding RNA using Molecular Beacons invitro and in cells.

Small non-coding RNAs (sncRNA) are involved in many steps of the gene expression cascade and regulate processing and expression of mRNAs by the formation ofribonucleoprotein complexes (RNP) such as the RNA-induced silencing complex (RISC). By analyzing small RNA Seq data sets, we identified a sncRNA annotated as piR-hsa-1254, which is likely derived from the 3'-end of 7SL RNA2 (RN7SL2), herein referred to as snc7SL RNA. The 7SL RNA is an abundant long non-coding RNA polymerase III transcript and serves as structural component of the cytoplasmic signal recognition particle (SRP). To evaluate a potential functional role of snc7SL RNA, we aimed to define its cellular localization by live cell imaging. Therefore, a Molecular Beacon (MB)-based method was established to compare the subcellular localization of snc7SL RNA with its precursor 7SL RNA. We designed and characterized several MBs invitro and tested those by live cell fluorescence microscopy. Using a multiplex approach, we show that 7SL RNA localizes mainly to the endoplasmic reticulum (ER), as expected for the SRP, whereas snc7SL RNA predominately localizes to the nucleus. This finding suggests a fundamentally different function of 7SL RNA and its derivate snc7SL RNA.

Deep annotation of long noncoding RNAs by assembling RNA-seq and small RNA-seq data

Long noncoding RNAs (lncRNAs) are increasingly being recognized as modulators in various biological processes. However, due to their low expression, their systematic characterization is difficult to determine. Here, we performed transcript annotation by a newly developed computational pipeline, termed RNA-seq and small RNA-seq combined strategy (RSCS), in a wide variety of cellular contexts. Thousands of high-confidence potential novel transcripts were identified by the RSCS, and the reliability of the transcriptome was verified by analysis of transcript structure, base composition, and sequence complexity. Evidenced by the length comparison, the frequency of the core promoter and the polyadenylation signal motifs, and the locations of transcription start and end sites, the transcripts appear to be full length. Furthermore, taking advantage of our strategy, we identified a large number of endogenous retrovirus-associated lncRNAs, and a novel endogenous retrovirus-lncRNA that was functionally involved in control of Yap1 expression and essential for early embryogenesis was identified. In summary, the RSCS can generate a more complete and precise transcriptome, and our findings greatly expanded the transcriptome annotation for the mammalian community.

Abstract 6224: Exploring the integration of CMS subtyping and other omics in colorectal cancer classification and its reflection in stool miRNome and metagenome for non-invasive precision medicine

Abstract Background: The Consensus Molecular Subtypes (CMS) is a subtyping system used for colorectal cancer (CRC) to facilitate traditional tumor classification and clinical translation. Relying on RNA-seq data, the system classifies patients into four molecular subtypes (CMS 1-4). Each subtype is enriched with several mutations, but no genetic aberrations are peculiar to a single CMS. A more in-depth characterization of each subtype's genomic and epigenomic background by integrating different omics may help in refining this classification, including the missing assignment of a subgroup of tumors. The possible translation of this improved classification to surrogate tissues, such as stool, has never been investigated. Aims: We aim to comprehensively characterize the CMS by the integrative analysis of target genomic sequencing, RNA-Seq, small RNA-Seq, and immune infiltrate profiling data of a cohort of CRC patients. These data will be used to identify integrative CMS-specific signatures and to characterize those tumors not assigned to any CMS. In addition, differences in stool miRNome and metagenomic profiles will be explored to identify potential specific signatures characterizing each subtype. Methods: Tumor tissue and adjacent mucosa were collected from 87 CRC patients and preserved in RNA later. mRNA and other non-coding RNA expression levels were evaluated with ribosomal depleted RNA-seq and small RNA-seq. Tumors were assigned to a CMS subtype using the CMScaller algorithm and genomically characterized with the TruSight Oncology 500 (TSO500) cancer panel. Differential expression analyses were performed using the DESeq2 package for the R software to identify CMS-specific expression signatures. Small RNA-seq and shotgun metagenomic analyses were performed on stool samples collected from the same patients. Preliminary Results: Tumors were assigned to CMS1 (n=9), CMS2 (n=24), CMS3 (n=26), and CMS4 (n=22), except for six samples. Differential expression analyses between paired tumor samples and adjacent mucosa within each CMS revealed 1096 differentially expressed genes in CMS1, 4218 in CMS2, 27 in CMS3 and 190 in CMS4. From the genomic point of view, CMS3 cases showed 384 unique missense variants, while CMS1 included almost all samples with microsatellite instability. Small RNA-seq on the same tissue sample identified 380 dysregulated miRNAs (215 up and 165 downregulated) in the tumor compared to adjacent mucosa. Conclusions: A more in-depth CMS subtyping by integrating different omics analyses could help refine this classification system. Moreover, the possible CMS reflection in stool miRNome and metagenomic could shed new light on the host gut-microbiota interactions and allow a non-invasive classification applicable for a routine personalized clinical management of CRC. Citation Format: Amedeo Gagliardi, Giulia Francesca, Giulio Ferrero, Sonia Tarallo, Giulia Piaggeschi, Antonio Francavilla, Carla Di Battista, Enrico Berrino, Caterina Marchiò, Francesca Cordero, Barbara Pardini, Alessio Naccarati. Exploring the integration of CMS subtyping and other omics in colorectal cancer classification and its reflection in stool miRNome and metagenome for non-invasive precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6224.

Systematic identification and characterization of viral small RNAs from small RNA-Seq data.

Virus-encoded small RNAs (vsRNAs) have been reported to play an important role in viral infections. Unfortunately, there is still a lack of a systematic characterization and resource of vsRNAs. Herein, we identified a total of 19 734 high-confidence vsRNAs including 2746 microRNAs (miRNAs) in 64 viral species from more than 800 samples of public small RNA-Seq data. The number of vsRNAs identified in viruses varied from 1 to 2489 with a median of 170. The length distribution of vsRNAs peaked at 21 and 22 nt. Plant viruses were found to express larger number and higher levels of vsRNAs than those of animal viruses. Besides, the number of vsRNAs identified increased as the viral infection persisted. Interestingly, the vsRNA showed strong expression specificity as little overlap was observed among vsRNAs identified in different strains of a virus, or in different hosts, cells, or tissues infected by the same virus. Little conservation was observed among vsRNAs of different viruses. The viral miRNAs were found to interact with host genes involved in multiple biological processes related to organization, development, action potential, polarity establishment, methylation, immune response, gene regulation, localization, and so on. To facilitate the usage of vsRNAs, a database named vsRNAdb was built for organizing and storing vsRNAs which is available at http://www.computationalbiology.cn/vsRNAdb/#/vsRNAdb/#/. Overall, the study deepens our understanding about the diversity and complexity of vsRNAs and provides a rich resource for further studies of vsRNAs.

Tiny-count: a counting tool for hierarchical classification and quantification of small RNA-seq reads with single-nucleotide precision.

tiny-count is a highly flexible counting tool that allows for hierarchical classification and quantification of small RNA reads from high-throughput sequencing data. Selection rules can be used to filter reads by 5' nucleotide, length, position of alignments in relation to reference features, and by the number of mismatches to reference sequences. tiny-count can quantify reads aligned to a genome or directly to small RNA or transcript sequences. With tiny-count, users can quantify a single class of small RNAs or multiple classes in parallel. tiny-count can resolve distinct classes of small RNAs, for example, piRNAs and siRNAs, produced from the same locus. It can distinguish small RNA variants, such as miRNAs and isomiRs, with single-nucleotide precision. tRNA, rRNA, and other RNA fragments can also be quantified. tiny-count can be run alone or as part of tinyRNA, a workflow that provides a basic all-in-one command line-based solution for small RNA-seq data analysis, with documentation and statistics generated at each step for accurate and reproducible results. tiny-count and other tinyRNA tools are implemented in Python, C++, Cython, and R, and the workflow is coordinated with CWL. tiny-count and tinyRNA are free and open-source software distributed under the GPLv3 license. tiny-count can be installed via Bioconda (https://anaconda.org/bioconda/tiny-count) and both tiny-count and tinyRNA documentation and software downloads are available at https://github.com/MontgomeryLab/tinyRNA. Reference data, including genome and feature information, for certain species can be found at https://www.MontgomeryLab.org.

Small RNA sequencing and profiling of serum-derived exosomes from African swine fever virus-infected pigs.

African swine fever (ASF) virus (ASFV) is responsible for one of the most severe swine diseases worldwide, with a morbidity rate of up to 100%; no vaccines or antiviral medicines are available against the virus. Exosomal miRNAs from individual cells can regulate the immune response to infectious diseases. In this study, pigs were infected with an ASFV Pig/HN/07 strain that was classified as acute form, and exosomal miRNA expression in the serum of infected pigs was analyzed using small RNA sequencing (small RNA-seq). Twenty-seven differentially expressed (DE) miRNAs were identified in the ASFV-infected pigs compared to that in the uninfected controls. Of these, 10 were upregulated and 17 were downregulated in the infected pigs. All DE miRNAs were analyzed using gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the DE miRNAs were found to be highly involved in T-cell receptor signaling, cGMP-PKG signaling, Toll-like receptor, MAPK signaling, and mTOR signaling pathways. Furthermore, the Cytoscape network analysis identified the network of interactions between DE miRNAs and target genes. Finally, the transcription levels of four miRNA genes (ssc-miR-24-3p, ssc-miR-130b-3p, ssc-let-7a, and ssc-let-7c) were examined using quantitative real-time PCR (qRT-PCR) and were found to be consistent with the small RNA-seq data. These DE miRNAs were associated with cellular genes involved in the pathways related to immune response, virus-host interactions, and several viral genes. Overall, our findings provide an important reference and improve our understanding of ASF pathogenesis and the immune or protective responses during an acute infection in the host.

Abstract A030: Deciphering the micro-RNA landscape of AKT isoforms and its clinical impact in patients with lung adenocarcinoma

Abstract Over the last 25 years, AKT kinase has been thoroughly investigated and characterized as a crucial regulator of cellular homeostasis, affecting a plethora of essential molecular functions. The three identified isoforms of this kinase, namely AKT1, AKT2 and AKT3, often display opposing and antagonizing cellular functions. One of these important cellular functions is the expression and activation of micro-RNAs (miRNAs), an important RNA process that epigenetically regulates the human transcriptome. In the context of human cancer and based on several reports, AKT kinase has been associated with aberrant expression of oncogenic miRNAs, that in turn hijack oncogenic pathways, leading to tumor progression, metastasis and resistance to therapy, especially in lung adenocarcinoma (LUAD). Nevertheless, a comprehensive analysis of the impact of each AKT isoform on the regulation and enrichment of a specific miRNA signature in lung adenocarcinoma has yet to be performed. Here, we were willing to elucidate the miRNA epigenetic landscape and downstream targets, driven by each AKT isoform, and its clinical importance in patients with lung adenocarcinoma. To this extent, we utilized RNA-Seq and Small RNA-Seq data from The Cancer Genome Atlas (TCGA-LUAD) to identify the miRNA signatures related with the expression of each AKT isoform in LUAD. Gene Set Enrichment Analysis from RNA-Seq and miRNA levels analysis from Small RNA-Seq, identified a unique miRNA signature for each AKT isoform, demonstrating their largely opposing effect in miRNA profiling. In parallel analysis, in silico target analysis of the top 50 regulated miRNAs, demonstrated another set of unique targets for each isoform-dependent miRNA signature, which is involved in metabolism, DNA damage, RNA processing, cell survival, immune response and metastasis. More importantly, clinical and survival analysis revealed that the top 50 Differentially regulated miRNAs, ranked by q-value, for each isoform correlated with poor progression free survival in patients with LUAD. Collectively, our atlas of AKT isoform-driven miRNA landscapes implicates AKT as a global effector of miRNA landscape in lung adenocarcinoma. Due to the lack of reliable isoform-specific AKT inhibitors, the identification of the oncogenic miRNA networks regulated by each isoform, acts as an important transnational resource for the design and testing of inhibitors that bypass AKT inhibition toxicity, sparing its impactful physiological functions. Citation Format: George Laliotis, Pauline Lin. Deciphering the micro-RNA landscape of AKT isoforms and its clinical impact in patients with lung adenocarcinoma. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr A030.

VsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data.

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.

SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data.

MicroRNAs (miRNAs) are small non-coding RNAs (sncRNAs) that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of translation and typically destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by crosslinking and ligation of sncRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric sncRNA:target RNAs. Despite the strength of these direct profiling approaches, standardized pipelines for effectively analyzing the resulting chimeric sncRNA:target RNA sequencing data are not readily available. Here we present SCRAP, a robust Small Chimeric RNA Analysis Pipeline for the bioinformatic processing of chimeric sncRNA:target RNA sequencing data. SCRAP consists of two parts, each of which are specifically optimized for the distinctive characteristics of chimeric small RNA sequencing reads: first, read processing and alignment and second, peak calling and annotation. We apply SCRAP to benchmark chimeric sncRNA:target RNA sequencing datasets generated by distinct molecular approaches, and compare SCRAP to existing chimeric RNA analysis pipelines. SCRAP has minimal hardware requirements, is cross-platform, and contains extensive annotation to broaden accessibility for processing small chimeric RNA sequencing data and enable insights about the targets of small non-coding RNAs in regulating diverse biological systems.