The Exosome-like Vesicles of Giardia Assemblages A, B, and E Are Involved in the Delivering of Distinct Small RNA from Parasite to Parasite.

The genetically related assemblages of the intestinal protozoa parasite Giardia lamblia are morphologically indistinguishable and are often derived from specific hosts. The Giardia assemblages are separated by large genetic distances, which might account for their relevant biological and pathogenic differences. In this work, we analyzed the RNAs cargo released into exosomal-like vesicles (ElVs) by the assemblages A and B, which differentially infect humans, and the assemblage E, which infects hoofed animals. The RNA sequencing analysis revealed that the ElVs of each assemblage contained distinct small RNA (sRNA) biotypes, suggesting a preference for specific packaging in each assemblage. These sRNAs were classified into three categories, ribosomal-small RNAs (rsRNAs), messenger-small RNAs (msRNAs), and transfer-small RNAs (tsRNAs), which may play a regulatory role in parasite communication and contribute to host-specificity and pathogenesis. Uptake experiments showed, for the first time, that ElVs were successfully internalized by the parasite trophozoites. Furthermore, we observed that the sRNAs contained inside these ElVs were first located below the plasma membrane but then distributed along the cytoplasm. Overall, the study provides new insights into the molecular mechanisms underlying the host-specificity and pathogenesis of G. lamblia and highlights the potential role of sRNAs in parasite communication and regulation.

rRNA-Derived Small RNA rsRNA-28S Regulates the Chemoresistance of Prostate Cancer Cells by Targeting PTGIS.

BACKGROUND: rRNA-derived small RNAs (rsRNAs) represent a novel class of small non-coding RNAs (sncRNAs), produced by the specific cleavage of rRNAs; however, their roles in tumor development are unclear. In the present study, we explored the effect of a kind of rsRNA-28S, which originates from 28S rRNA, on the chemoresistance of prostate cancer cells and the mechanisms underlying its effect. METHODS: Quantitative reverse transcription PCR (RT-PCR) was performed to quantify rsRNA-28S levels in serum samples taken from prostate cancer patients. DU-145R cells, which are resistant to both paclitaxel and docetaxel, were generated from parental DU-145 cells. Northern blot was conducted to detect cellular rsRNA-28S levels following drug treatments. To verify the effect of rsRNAs-28S on chemoresistance, antisense oligonucleotides were utilized to block rsRNA-28S functions, and a series of assays were further performed, such as cell viability, cell proliferation, colony formation and tumor sphere formation. The target gene of rsRNA-28S was explored using dual-luciferase reporter gene assay. RESULTS: The rsRNA-28S level was reduced in the serum samples of patients who received chemotherapy compared to that of patients who did not. Furthermore, the rsRNA-28S level was remarkably declined in DU-145R cells, and drug treatments decreased the levels of rsRNA-28S in DU-145 and DU-145R cells. Moreover, rsRNA-28S inhibition enhanced the chemoresistance of prostate cancer cells as well as their cancer stem cell characteristics. Mechanistically, the prostaglandin I2 synthase (PTGIS) gene transcript was verified as a target of rsRNA-28S, as rsRNA-28S inhibited the translation of PTGIS mRNA by directly binding the 3' untranslated region of PTGIS mRNA. rsRNA-28S inhibition was also found to increase PTGIS abundance, and PTGIS overexpression significantly enhanced prostate cancer cell chemoresistance. CONCLUSIONS: Our findings indicate that rsRNA-28S attenuates prostate cancer cell chemoresistance by downregulating its target gene PTGIS. This study not only greatly contributes to systematic identification and functional elucidation of chemoresistance relevant rsRNAs, but also promotes rsRNA-included combinatorial therapeutic regimens for cancer.

Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia.

Background: Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults; AML is highly heterogeneous and involves abnormalities at multiple omics levels. Small non-coding RNAs (sncRNAs) present in body fluids are important regulatory molecules and considered promising non-invasive clinical diagnostic biomarkers for disease. However, the signature of sncRNA profile alteration in AML patient serum and bone marrow supernatant is still under exploration. Methods: We examined data for blood and bone marrow samples from 80 consecutive, newly-diagnosed patients with AML and 12 healthy controls for high throughput small RNA-sequencing. Differentially expressed sncRNAs were analysed to reveal distinct patterns between AML patients and controls. Machine learning methods were used to evaluate the efficiency of specific sncRNAs in discriminating individuals with AML from controls. The altered expression level of individual sncRNAs was evaluated by RT-PCR, Q-PCR, and northern blot. Correlation analysis was employed to assess sncRNA patterns between serum and bone marrow supernatant. Results: We identified over 20 types of sncRNA categories beyond miRNAs in both serum and bone marrow supernatant, with highly coordinated expression patterns between them. Non-classical sncRNAs, including rsRNA (62.86%), ysRNA (14.97%), and tsRNA (4.22%), dominated among serum sncRNAs and showed sensitive alteration patterns in AML patients. According to machine learning-based algorithms, the tsRNA-based signature robustly discriminated subjects with AML from controls and was more reliable than that comprising miRNAs. Our data also showed that serum tsRNAs to be closely associated with AML prognosis, suggesting the potential application of serum tsRNAs as biomarkers to assist in AML diagnosis. Conclusions: We comprehensively characterized the expression pattern of circulating sncRNAs in blood and bone marrow and their alteration signature between healthy controls and AML patients. This study enriches research of sncRNAs in the regulation of AML, and provides insights into the role of sncRNAs in AML.

Prediction of RNA secondary structure based on stem region replacement using the RSRNA algorithm.

RNA functions, including the regulation of various cellular activities, seem to be closely related to its structure. However, accurately predicting RNA secondary structures can be difficult. Structural prediction can be achieved by selecting stem areas that are suitable and compatible from stem pools. Here, we propose a method for predicting the secondary structure of non-coding RNA based on stem region substitution, which we named RSRNA. This method is compatible with nested RNA secondary structures, while reducing any randomness. Our algorithm had higher performance and prediction accuracy than other algorithms, which deems it more effective for future RNA structure studies.

Peripheral blood non-canonical small non-coding RNAs as novel biomarkers in lung cancer.

One unmet challenge in lung cancer diagnosis is to accurately differentiate lung cancer from other lung diseases with similar clinical symptoms and radiological features, such as pulmonary tuberculosis (TB). To identify reliable biomarkers for lung cancer screening, we leverage the recently discovered non-canonical small non-coding RNAs (i.e., tRNA-derived small RNAs [tsRNAs], rRNA-derived small RNAs [rsRNAs], and YRNA-derived small RNAs [ysRNAs]) in human peripheral blood mononuclear cells and develop a molecular signature composed of distinct ts/rs/ysRNAs (TRY-RNA). Our TRY-RNA signature precisely discriminates between control, lung cancer, and pulmonary TB subjects in both the discovery and validation cohorts and outperforms microRNA-based biomarkers, which bears the diagnostic potential for lung cancer screening.

SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs.

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users' input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.

SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs / 基因组蛋白质组与生物信息学报·英文版

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users' input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.