RNAcentral: a comprehensive database of non-coding RNA sequences

An unbiased lncRNA dropout CRISPR-Cas9 screen reveals RP11-350G8.5 as a novel therapeutic target for multiple myeloma

ABSTRACT

Multiple myeloma (MM) is an incurable malignancy characterized by altered expression of coding and noncoding genes promoting tumor growth and drug resistance. Although the crucial role of long noncoding RNAs (lncRNAs) in MM is clearly established, the function of the noncoding RNAome, which might allow the design of novel therapeutics, is largely unknown. We performed an unbiased CRISPR-Cas9 loss-of-function screen of 671 lncRNAs in MM cells and their bortezomib (BZB)-resistant derivative. To rank functionally and clinically relevant candidates, we designed and used a bioinformatic prioritization pipeline combining functional data from cellular screens with prognostic and transcriptional data from patients with MM. With this approach, we unveiled and prioritized 8 onco-lncRNAs essential for MM cell fitness, associated with high expression and poor prognosis in patients with MM. The previously uncharacterized RP11-350G8.5 emerged as the most promising target, irrespective of BZB resistance. We (1) demonstrated the anti-tumoral effect obtained by RP11-350G8.5 inhibition in vitro and in vivo; (2) highlighted a modulation of the unfolded protein response and the induction of immunogenic cell death triggered by the RP11-350G8.5 knockout, via RNA sequencing and molecular studies; (3) characterized its cytoplasmic homing through RNA fluorescence in situ hybridization; and (4) predicted its 2-dimensional structure and identified 2 G-quadruplex and 3 hairpin-forming regions by biophysical assays, including thioflavin T, 1H nuclear magnetic resonance, and circular dichroism, to pave the way to the development of novel targeted therapeutics. Overall, we provided innovative insights about unexplored lncRNAs in MM and identified RP11-350G8.5 as an oncogenic target for treatment-naïve and BZB-resistant patients with MM.

Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

Long non-coding RNA variability in porcine skeletal muscle

Recently, numerous studies including various tissues have been carried out on long non-coding RNAs (lncRNAs), but still, its variability has not yet been fully understood. In this study, we characterised the inter-individual variability of lncRNAs in pigs, in the context of number, length and expression. Transcriptomes collected from muscle tissue belonging to six Polish Landrace boars (PL1-PL6), including half-brothers (PL1-PL3), were investigated using bioinformatics (lncRNA identification and functional analysis) and statistical (lncRNA variability) methods. The number of lncRNA ranged from 1289 to 3500 per animal, and the total number of common lncRNAs among all boars was 232. The number, length and expression of lncRNAs significantly varied between individuals, and no consistent pattern has been found between pairs of half-brothers. In detail, PL5 exhibits lower expression than the others, while PL4 has significantly higher expression than PL2-PL3 and PL5-PL6. Noteworthy, comparing the inter-individual variability of lncRNA and mRNA expression, they exhibited concordant patterns. The enrichment analysis for common lncRNA target genes determined a variety of biological processes that play fundamental roles in cell biology, and they were mostly related to whole-body homeostasis maintenance, energy and protein synthesis as well as dynamics of multiple nucleoprotein complexes. The high variability of lncRNA landscape in the porcine genome has been revealed in this study. The inter-individual differences have been found in the context of three aspects: the number, length and expression of lncRNAs, which contribute to a better understanding of its complex nature.

RiboVision2: A Web Server for Advanced Visualization of Ribosomal RNAs

RiboVision2 is a web server designed to visualize phylogenetic, structural, and evolutionary properties of ribosomal RNAs simultaneously at the levels of primary, secondary, and three-dimensional structure and in the context of full ribosomal complexes. RiboVision2 instantly computes and displays a broad variety of data; it has no login requirements, is open-source, free for all users, and available at https://ribovision2.chemistry.gatech.edu.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Puzzling out the role of MIAT LncRNA in hepatocellular carcinoma

A non-negligible part of our DNA has been proven to be transcribed into non-protein coding RNA and its intricate involvement in several physiological processes has been highly evidenced. The significant biological role of non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) has been variously reported. In the current review, the authors highlight the multifaceted role of myocardial infarction-associated transcript (MIAT), a well-known lncRNA, in hepatocellular carcinoma (HCC). Since its discovery, MIAT has been described as a regulator of carcinogenesis in several malignant tumors and its overexpression predicts poor prognosis in most of them. At the molecular level, MIAT is closely linked to the initiation of metastasis, invasion, cellular migration, and proliferation, as evidenced by several in-vitro and in-vivo models. Thus, MIAT is considered a possible theranostic agent and therapeutic target in several malignancies. In this review, the authors provide a comprehensive overview of the underlying molecular mechanisms of MIAT in terms of its downstream target genes, interaction with other classes of ncRNAs, and potential clinical implications as a diagnostic and/or prognostic biomarker in HCC.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

Sex- and region-specific cortical and hippocampal whole genome transcriptome profiles from control and APP/PS1 Alzheimer's disease mice

A variety of Alzheimer's disease (AD) mouse models has been established and characterized within the last decades. To get an integrative view of the sophisticated etiopathogenesis of AD, whole genome transcriptome studies turned out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex and hippocampus of age-matched, eight months old male and female APP/PS1 AD mice and control animals to perform sex- and brain region specific analysis of transcriptome profiles. The results of our studies reveal novel, detailed insight into differentially expressed signature genes and related fold changes in the individual APP/PS1 subgroups. Gene ontology and Venn analysis unmasked that intersectional, upregulated genes were predominantly involved in, e.g., activation of microglial, astrocytic and neutrophilic cells, innate immune response/immune effector response, neuroinflammation, phagosome/proteasome activation, and synaptic transmission. The number of (intersectional) downregulated genes was substantially less in the different subgroups and related GO categories included, e.g., the synaptic vesicle docking/fusion machinery, synaptic transmission, rRNA processing, ubiquitination, proteasome degradation, histone modification and cellular senescence. Importantly, this is the first study to systematically unravel sex- and brain region-specific transcriptome fingerprints/signature genes in APP/PS1 mice. The latter will be of central relevance in future preclinical and clinical AD related studies, biomarker characterization and personalized medicinal approaches.

miRNAs in Heart Development and Disease

Cardiovascular diseases (CVD) are a group of disorders that affect the heart and blood vessels. They include conditions such as myocardial infarction, coronary artery disease, heart failure, arrhythmia, and congenital heart defects. CVDs are the leading cause of death worldwide. Therefore, new medical interventions that aim to prevent, treat, or manage CVDs are of prime importance. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the posttranscriptional level and play important roles in various biological processes, including cardiac development, function, and disease. Moreover, miRNAs can also act as biomarkers and therapeutic targets. In order to identify and characterize miRNAs and their target genes, scientists take advantage of computational tools such as bioinformatic algorithms, which can also assist in analyzing miRNA expression profiles, functions, and interactions in different cardiac conditions. Indeed, the combination of miRNA research and bioinformatic algorithms has opened new avenues for understanding and treating CVDs. In this review, we summarize the current knowledge on the roles of miRNAs in cardiac development and CVDs, discuss the challenges and opportunities, and provide some examples of recent bioinformatics for miRNA research in cardiovascular biology and medicine.

The Nucleic Acid Knowledgebase: a new portal for 3D structural information about nucleic acids

The Nucleic Acid Knowledgebase (nakb.org) is a new data resource, updated weekly, for experimentally determined 3D structures containing DNA and/or RNA nucleic acid polymers and their biological assemblies. NAKB indexes nucleic acid-containing structures derived from all major structure determination methods (X-ray, NMR and EM), including all held by the Protein Data Bank (PDB). As the planned successor to the Nucleic Acid Database (NDB), NAKB's design preserves all functionality of the NDB and provides novel nucleic acid-centric content, including structural and functional annotations, as well as annotations from and links to external resources. A variety of custom interactive tools have been developed to enable rapid exploration and drill-down of NAKB's content.

Ribosomal profiling of human endogenous retroviruses in healthy tissues

Human endogenous retroviruses (HERVs) are the germline embedded proviral fragments of ancient retroviral infections that make up roughly 8% of the human genome. Our understanding of HERVs in physiology primarily surrounds their non-coding functions, while their protein coding capacity remains virtually uncharacterized. Therefore, we applied the bioinformatic pipeline "hervQuant" to high-resolution ribosomal profiling of healthy tissues to provide a comprehensive overview of translationally active HERVs. We find that HERVs account for 0.1-0.4% of all translation in distinct tissue-specific profiles. Collectively, our study further supports claims that HERVs are actively translated throughout healthy tissues to provide sequences of retroviral origin to the human proteome.

microRNAs in Syrista parreyssi (Hymenoptera) and Lepisma saccharina (Zygentoma) possibly involved in the mitochondrial function

Mitochondria are essential organelles for maintaining vital cellular functions, and microRNAs (miRNAs) regulate gene expression posttranscriptionally. miRNAs exhibit tissue and time-specific patterns in mitochondria and specifically mitochondrial miRNAs (mitomiRs) can regulate the mRNA expression both originating from mitochondrial and nuclear transcription which affect mitochondrial metabolic activity and cell homeostasis. In this study, miRNAs of two insect species, Syrista parreyssi (Hymenoptera) and Lepisma saccharina (Zygentoma), were investigated for the first time. The known and possible novel miRNAs were predicted and characterized and their potential effects on mitochondrial transcription were investigated in these insect species using deep sequencing. The previously reported mitomiRs were also investigated and housekeeping miRNAs were characterized. miRNAs that are involved in mitochondrial processes such as apoptosis and signaling and that affect genes encoding the subunits of OXPHOS complexes have been identified in each species. Here, 81 and 161 novel mature miRNA candidates were bioinformatically predicted and 9 and 24 of those were aligned with reference mitogenomes of S. parreyssi and L. saccharina, respectively. As a result of RNAHybrid analysis, 51 and 69 potential targets of miRNAs were found in the mitogenome of S. parreyssi and L. saccharina, respectively. cox1 gene was the most targeted gene and cytB, rrnS, and rrnL genes were highly targeted in both of the species by novel miRNAs, hypothetically. We speculate that these novel miRNAs, originating from or targeting mitochondria, influence on rRNA genes or positively selected mitochondrial protein-coding genes. These findings may provide a new perspective in evaluating miRNAs for maintaining mitochondrial function and transcription.

The human genome contains over a million autonomous exons

Mammalian mRNA and lncRNA exons are often small compared to introns. The exon definition model predicts that exons splice autonomously, dependent on proximal exon sequence features, explaining their delineation within large introns. This model has not been examined on a genome-wide scale, however, leaving open the question of how often mRNA and lncRNA exons are autonomous. It is also unknown how frequently such exons can arise by chance. Here, we directly assayed large fragments (500-1000 bp) of the human genome by exon trapping, which detects exons spliced into a heterologous transgene, here designed with a large intron context. We define the trapped exons as "autonomous." We obtained ∼1.25 million trapped exons, including most known mRNA and well-annotated lncRNA internal exons, demonstrating that human exons are predominantly autonomous. mRNA exons are trapped with the highest efficiency. Nearly a million of the trapped exons are unannotated, most located in intergenic regions and antisense to mRNA, with depletion from the forward strand of introns. These exons are not conserved, suggesting they are nonfunctional and arose from random mutations. They are nonetheless highly enriched with known splicing promoting sequence features that delineate known exons. Novel autonomous exons are more numerous than annotated lncRNA exons, and computational models also indicate they will occur with similar frequency in any randomly generated sequence. These results show that most human coding exons splice autonomously, and provide an explanation for the existence of many unconserved lncRNAs, as well as a new annotation and inclusion levels of spliceable loci in the human genome.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

DNABERT-based explainable lncRNA identification in plant genome assemblies

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.

Maximizing Heterologous Expression of Engineered Type I Polyketide Synthases: Investigating Codon Optimization Strategies

Type I polyketide synthases (T1PKSs) hold enormous potential as a rational production platform for the biosynthesis of specialty chemicals. However, despite great progress in this field, the heterologous expression of PKSs remains a major challenge. One of the first measures to improve heterologous gene expression can be codon optimization. Although controversial, choosing the wrong codon optimization strategy can have detrimental effects on the protein and product levels. In this study, we analyzed 11 different codon variants of an engineered T1PKS and investigated in a systematic approach their influence on heterologous expression in Corynebacterium glutamicum, Escherichia coli, and Pseudomonas putida. Our best performing codon variants exhibited a minimum 50-fold increase in PKS protein levels, which also enabled the production of an unnatural polyketide in each of these hosts. Furthermore, we developed a free online tool (https://basebuddy.lbl.gov) that offers transparent and highly customizable codon optimization with up-to-date codon usage tables. In this work, we not only highlight the significance of codon optimization but also establish the groundwork for the high-throughput assembly and characterization of PKS pathways in alternative hosts.

DeepRaccess: high-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes

Genes have the ability to produce transcript variants that perform specific cellular functions. However, accurately detecting all transcript variants remains a long-standing challenge, especially when working with poorly annotated genomes or without a known genome. To address this issue, we have developed a new computational method, TransIntegrator, which enables transcriptome-wide detection of novel transcript variants. For this, we determined 10 Illumina sequencing transcriptomes and a PacBio full-length transcriptome for consecutive embryo development stages of amphioxus, a species of great evolutionary importance. Based on the transcriptomes, we employed TransIntegrator to create a comprehensive transcript variant library, namely iTranscriptome. The resulting iTrancriptome contained 91 915 distinct transcript variants, with an average of 2.4 variants per gene. This substantially improved current amphioxus genome annotation by expanding the number of genes from 21 954 to 38 777. Further analysis manifested that the gene expansion was largely ascribed to integration of multiple Illumina datasets instead of involving the PacBio data. Moreover, we demonstrated an example application of TransIntegrator, via generating iTrancriptome, in aiding accurate transcriptome assembly, which significantly outperformed other hybrid methods such as IDP-denovo and Trinity. For user convenience, we have deposited the source codes of TransIntegrator on GitHub as well as a conda package in Anaconda. In summary, this study proposes an affordable but efficient method for reliable transcriptomic research in most species.

A New Understanding of Long Non-Coding RNA in Hepatocellular Carcinoma-From m6A Modification to Blood Biomarkers

With recent advancements in biological research, long non-coding RNAs (lncRNAs) with lengths exceeding 200 nucleotides have emerged as pivotal regulators of gene expression and cellular phenotypic modulation. Despite initial skepticism due to their low sequence conservation and expression levels, their significance in various biological processes has become increasingly apparent. We provided an overview of lncRNAs and discussed their defining features and modes of operation. We then explored their crucial function in the hepatocarcinogenesis process, elucidating their complex involvement in hepatocellular carcinoma (HCC). The influential role of lncRNAs within the HCC tumor microenvironment is emphasized, illustrating their potential as key modulators of disease dynamics. We also investigated the significant influence of N6-methyladenosine (m6A) modification on lncRNA function in HCC, enhancing our understanding of both their roles and their upstream regulators. Additionally, the potential of lncRNAs as promising biomarkers was discussed in liver cancer diagnosis, suggesting a novel avenue for future research and clinical application. Finally, our work underscored the dual potential of lncRNAs as both contributors to HCC pathogenesis and innovative tools for its diagnosis. Existing challenges and prospective trajectories in lncRNA research are also discussed, emphasizing their potential in advancing liver cancer research.

Abundance and prevalence of ESBL coding genes in patients undergoing first line eradication therapy for Helicobacter pylori

The spread of extended-spectrum beta-lactamases (ESBLs) in nosocomial and community-acquired enterobacteria is an important challenge for clinicians due to the limited therapeutic options for infections that are caused by these organisms. Here, we developed a panel of ESBL coding genes, evaluated the abundance and prevalence of ESBL encoding genes in patients undergoing H. pylori eradication therapy, and summarized the effects of eradication therapy on functional profiles of the gut microbiome. To assess the repertoire of known beta lactamase (BL) genes, they were divided into clusters according to their evolutionary relation. Primers were designed for amplification of cluster marker regions, and the efficiency of this amplification panel was assessed in 120 fecal samples acquired from 60 patients undergoing H. pylori eradication therapy. In addition, fecal samples from an additional 30 patients were used to validate the detection efficiency of the developed ESBL panel. The presence for majority of targeted clusters was confirmed by NGS of amplification products. Metagenomic sequencing revealed that the abundance of ESBL genes within the pool of microorganisms was very low. The global relative abundances of the ESBL-coding gene clusters did not differ significantly among treatment states. However, at the level of each cluster, classical ESBL producers such as Klebsiella sp. for blaOXY (p = 0.0076), Acinetobacter sp. for blaADC (p = 0.02297) and others, differed significantly with a tendency to decrease compared to the pre- and post-eradication states. Only 13 clusters were common across all three datasets, suggesting a patient-specific distribution profile of ESBL-coding genes. The number of AMR genes detected in the post-eradication state was higher than that in the pre-eradication state, which could be attributed, at least in part, to the therapy. This study demonstrated that the ESBL screening panel was effective in targeting ESBL-coding gene clusters from bacterial DNA and that minor differences exist in the abundance and prevalence of ESBL-coding gene levels before and after eradication therapy.

Regenerative capacity of trophoblast stem cell-derived extracellular vesicles on mesenchymal stem cells

BACKGROUND

Human mesenchymal stem cells (MSCs) are therapeutic for clinical applications because of their excellent immunomodulatory and multiple lineage differentiation abilities at tissue injury sites. However, insufficient number of cells and lack of regenerative properties during in vitro expansion still limit the clinical applicability of MSC therapies. Here, we demonstrated a preconditioning strategy with trophoblast stem cell-derived extracellular vesicles (TSC-EVs) to boost the proliferation and regenerative capacity of MSCs.

METHODS

We employed cell proliferation analyses such as CCK8 and BrdU assays to determine the proliferation-promoting role of TSC-EVs on MSCs. Osteogenic effects of TSC-EVs on MSCs were assessed by alkaline phosphatase (ALP) activity, calcium assays, and calvarial bone defect animal models. For skin regenerative effects, skin wound mice model was exploited to analyze wound-healing rate in this study, as well as immunofluorescence and histological staining evaluates. We also performed the small RNA profiling and RNA-sequencing analyzes to understand the cellular mechanism of TSC-EVs on MSCs.

RESULTS

TSC-EVs significantly promoted MSC proliferation under xeno-free conditions and facilitated the therapeutic effects of MSCs, including osteogenesis, anti-senescence, and wound healing. Transcriptomic analysis also provided evidence that specific microRNAs in TSC-EVs and differentially expressed genes (DEGs) in TSC-EV-treated MSCs showed the possibility of TSC-EVs triggering the regenerative abilities of MSCs with cytokine interaction. Hence, we found that NGF/Akt signaling mediated the regenerative effects of TSC-EVs on MSCs as a particular cellular signaling pathway.

CONCLUSION

The results of this study demonstrated the functional properties of TSC-EVs on MSCs for MSC-based therapeutic applications, suggesting that TSC-EVs may serve as a potential preconditioning source for MSC therapy in the clinical field of regenerative medicine.

Crosstalk between Noncoding RNAs and the Epigenetics Machinery in Pediatric Tumors and Their Microenvironment

According to the World Health Organization, every year, an estimated 400,000+ new cancer cases affect children under the age of 20 worldwide. Unlike adult cancers, pediatric cancers develop very early in life due to alterations in signaling pathways that regulate embryonic development, and environmental factors do not contribute much to cancer development. The highly organized complex microenvironment controlled by synchronized gene expression patterns plays an essential role in the embryonic stages of development. Dysregulated development can lead to tumor initiation and growth. The low mutational burden in pediatric tumors suggests the predominant role of epigenetic changes in driving the cancer phenotype. However, one more upstream layer of regulation driven by ncRNAs regulates gene expression and signaling pathways involved in the development. Deregulation of ncRNAs can alter the epigenetic machinery of a cell, affecting the transcription and translation profiles of gene regulatory networks required for cellular proliferation and differentiation during embryonic development. Therefore, it is essential to understand the role of ncRNAs in pediatric tumor development to accelerate translational research to discover new treatments for childhood cancers. This review focuses on the role of ncRNA in regulating the epigenetics of pediatric tumors and their tumor microenvironment, the impact of their deregulation on driving pediatric tumor progress, and their potential as effective therapeutic targets.

Small RNA sequencing of circulating small extracellular vesicles microRNAs in patients with amyotrophic lateral sclerosis

Dysregulation of microRNAs (miRNA) in small extracellular vesicles (sEV) such as exosomes have been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Although circulating cell-free miRNA have been extensively investigated in ALS, sEV-derived miRNAs have not been systemically explored yet. Here, we performed small RNA sequencing analysis of serum sEV and identified 5 differentially expressed miRNA in a discovery cohort of 12 patients and 11 age- and sex-matched healthy controls (fold change > 2, p < 0.05). Two of them (up- and down-regulation of miR-23c and miR192-5p, respectively) were confirmed in a separate validation cohort (18 patients and 15 healthy controls) by droplet digital PCR. Bioinformatic analysis revealed that these two miRNAs interact with distinct sets of target genes and involve biological processes relevant to the pathomechanism of ALS. Our results suggest that circulating sEV from ALS patients have distinct miRNA profiles which may be potentially useful as a biomarker of the disease.

Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction

Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.

Shade‐induced lncRNA PUAR promotes shade response by repressing PHYA expression

Shade avoidance syndrome (SAS) commonly occurs in plants experiencing vegetative shade, triggering a series of morphological and physiological changes for the plants to reach more light. A number of positive regulators, such as PHYTOCHROME-INTERACTING 7 (PIF7), and negative regulators, such as PHYTOCHROMES, are known to ensure appropriate SAS. Here, we identify 211 shade-regulated long non-coding RNAs (lncRNAs) in Arabidopsis. We further characterize PUAR (PHYA UTR Antisense RNA), a lncRNA produced from the intron of the 5' UTR of the PHYTOCHROME A (PHYA) locus. PUAR is induced by shade and promotes shade-induced hypocotyl elongation. PUAR physically associates with PIF7 and represses the shade-mediated induction of PHYA by blocking the binding of PIF7 to the 5' UTR of PHYA. Our findings highlight a role for lncRNAs in SAS and provide insight into the mechanism of PUAR in regulating PHYA gene expression and SAS.

Linear-Time Algorithms for RNA Structure Prediction

RNA secondary structure prediction is widely used to understand RNA function. Existing dynamic programming-based algorithms, both the classical minimum free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we designed linear-time heuristic algorithms, LinearFold and LinearPartition, to approximate the MFE structure, partition function and base pairing probabilities. These programs are orders of magnitude faster than Vienna RNAfold and CONTRAfold on long sequences. More interestingly, LinearFold and LinearPartition lead to more accurate predictions on the longest sequence families for which the structures are well established (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500 +  nucleotides apart). This chapter provides protocols for using LinearFold and LinearPartition for secondary structure prediction.

The Role of Non-Coding RNAs in Liver Disease, Injury, and Regeneration

Non-coding RNAs (ncRNAs) have diverse functions in health and pathology in many tissues, including the liver. This review highlights important microRNAs (miRs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) in liver disease and regeneration. Greater attention is given to more prevalent and well characterized RNAs, including: miR-122, miR-21, the let-7 family of miRs, miR-451a, miR-144, and MALAT1.

Grad-seq analysis of Enterococcus faecalis and Enterococcus faecium provides a global view of RNA and protein complexes in these two opportunistic pathogens

Enterococcus faecalis and Enterococcus faecium are major nosocomial pathogens. Despite their relevance to public health and their role in the development of bacterial antibiotic resistance, relatively little is known about gene regulation in these species. RNA-protein complexes serve crucial functions in all cellular processes associated with gene expression, including post-transcriptional control mediated by small regulatory RNAs (sRNAs). Here, we present a new resource for the study of enterococcal RNA biology, employing the Grad-seq technique to comprehensively predict complexes formed by RNA and proteins in E. faecalis V583 and E. faecium AUS0004. Analysis of the generated global RNA and protein sedimentation profiles led to the identification of RNA-protein complexes and putative novel sRNAs. Validating our data sets, we observe well-established cellular RNA-protein complexes such as the 6S RNA-RNA polymerase complex, suggesting that 6S RNA-mediated global control of transcription is conserved in enterococci. Focusing on the largely uncharacterized RNA-binding protein KhpB, we use the RIP-seq technique to predict that KhpB interacts with sRNAs, tRNAs, and untranslated regions of mRNAs, and might be involved in the processing of specific tRNAs. Collectively, these datasets provide departure points for in-depth studies of the cellular interactome of enterococci that should facilitate functional discovery in these and related Gram-positive species. Our data are available to the community through a user-friendly Grad-seq browser that allows interactive searches of the sedimentation profiles (https://resources.helmholtz-hiri.de/gradseqef/).

LazySampling and LinearSampling: fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2

Many RNAs fold into multiple structures at equilibrium, and there is a need to sample these structures according to their probabilities in the ensemble. The conventional sampling algorithm suffers from two limitations: (i) the sampling phase is slow due to many repeated calculations; and (ii) the end-to-end runtime scales cubically with the sequence length. These issues make it difficult to be applied to long RNAs, such as the full genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address these problems, we devise a new sampling algorithm, LazySampling, which eliminates redundant work via on-demand caching. Based on LazySampling, we further derive LinearSampling, an end-to-end linear time sampling algorithm. Benchmarking on nine diverse RNA families, the sampled structures from LinearSampling correlate better with the well-established secondary structures than Vienna RNAsubopt and RNAplfold. More importantly, LinearSampling is orders of magnitude faster than standard tools, being 428× faster (72 s versus 8.6 h) than RNAsubopt on the full genome of SARS-CoV-2 (29 903 nt). The resulting sample landscape correlates well with the experimentally guided secondary structure models, and is closer to the alternative conformations revealed by experimentally driven analysis. Finally, LinearSampling finds 23 regions of 15 nt with high accessibilities in the SARS-CoV-2 genome, which are potential targets for COVID-19 diagnostics and therapeutics.

Temperature-sensitive male sterility in rice determined by the roles of AGO1d in reproductive phasiRNA biogenesis and function

Phased secondary siRNAs (phasiRNAs) are broadly present in the reproductive tissues of flowering plants, with spatial-temporal specificity. However, the ARGONAUTE (AGO) proteins associated with phasiRNAs and their miRNA triggers remain elusive. Here, through histological and high-throughput sequencing analyses, we show that rice AGO1d, which is specifically expressed in anther wall cells before and during meiosis, associates with both miR2118 and miR2275 to mediate phasiRNA biogenesis. AGO1d preferentially binds to miR2118-triggered 21-nucleotide (nt) phasiRNAs with a 5'-terminal uridine, suggesting a dual role in phasiRNA biogenesis and function. Depletion of AGO1d causes a reduction of 21- and 24-nt phasiRNAs and temperature-sensitive male sterility. At lower temperatures, anthers of the ago1d mutant predominantly show excessive tapetal cells with little starch accumulation during pollen formation, possibly caused by the dysregulation of cell metabolism. These results uncover an essential role of AGO1d in rice anther development at lower temperatures and demonstrate coordinative roles of AGO proteins during reproductive phasiRNA biogenesis and function.

A pilot study of microRNA assessment as a means to identify novel biomarkers of spontaneous osteoarthritis in dogs

MicroRNAs (miRNAs) are important regulators of intercellular signaling and are promising biomarkers in osteoarthritis (OA). In this study, comprehensive analysis was performed to identify miRNAs involved in the pathogenesis of spontaneous OA in dogs. Dogs diagnosed with OA based on radiography and arthroscopy of the stifle joint were included in the OA group. Dogs without any evidence of orthopedic disease were included in the unaffected group. To investigate miRNA expression levels, RNA sequencing analysis (RNA-seq) was performed in synovial tissue (OA group: n = 3, Unaffected group: n = 3) and RT-qPCR was performed in synovial tissue, synovial fluid and serum (OA group: n = 17, Unaffected group: n = 6), and compared between the two groups. The RNA-seq results showed that 57 miRNAs were significantly upregulated and 42 were significantly downregulated in the OA group. Specifically, miR-542 and miR-543 expression levels in the synovial tissue, synovial fluid, and serum were consistently higher in the OA group than in the unaffected group, suggesting that these miRNAs may be used as biomarkers for detecting canine OA. This is the first report to comprehensively analyze the expression patterns of miRNAs in the synovial tissue of dogs with spontaneous OA.

LncPheDB: a genome-wide lncRNAs regulated phenotypes database in plants

LncPheDB (https://www.lncphedb.com/) is a systematic resource of genome-wide long non-coding RNAs (lncRNAs)-phenotypes associations for multiple species. It was established to display the genome-wide lncRNA annotations, target genes prediction, variant-trait associations, gene-phenotype correlations, lncRNA-phenotype correlations, and the similar non-coding regions of the queried sequence in multiple species. LncPheDB sorted out a total of 203,391 lncRNA sequences, 2000 phenotypes, and 120,271 variants of nine species (Zea mays L., Gossypium barbadense L., Triticum aestivum L., Lycopersicon esculentum Mille, Oryza sativa L., Hordeum vulgare L., Sorghum bicolor L., Glycine max L., and Cucumis sativus L.). By exploring the relationship between lncRNAs and the genomic position of variants in genome-wide association analysis, a total of 68,862 lncRNAs were found to be related to the diversity of agronomic traits. More importantly, to facilitate the study of the functions of lncRNAs, we analyzed the possible target genes of lncRNAs, constructed a blast tool for performing similar fragmentation studies in all species, linked the pages of phenotypic studies related to lncRNAs that possess similar fragments and constructed their regulatory networks. In addition, LncPheDB also provides a user-friendly interface, a genome visualization platform, and multi-level and multi-modal convenient data search engine. We believe that LncPheDB plays a crucial role in mining lncRNA-related plant data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-022-00084-3.

A high-resolution map of human RNA translation

Translated small open reading frames (smORFs) can have important regulatory roles and encode microproteins, yet their genome-wide identification has been challenging. We determined the ribosome locations across six primary human cell types and five tissues and detected 7,767 smORFs with translational profiles matching those of known proteins. The human genome was found to contain highly cell-type- and tissue-specific smORFs and a subset that encodes highly conserved amino acid sequences. Changes in the translational efficiency of upstream-encoded smORFs (uORFs) and the corresponding main ORFs predominantly occur in the same direction. Integration with 456 mass-spectrometry datasets confirms the presence of 603 small peptides at the protein level in humans and provides insights into the subcellular localization of these small proteins. This study provides a comprehensive atlas of high-confidence translated smORFs derived from primary human cells and tissues in order to provide a more complete understanding of the translated human genome.

Extending applications of AFM to fluidic AFM in single living cell studies

In this article, a review of a series of applications of atomic force microscopy (AFM) and fluidic Atomic Force Microscopy (fluidic AFM, hereafter fluidFM) in single-cell studies is presented. AFM applications involving single-cell and extracellular vesicle (EV) studies, colloidal force spectroscopy, and single-cell adhesion measurements are discussed. FluidFM is an offshoot of AFM that combines a microfluidic cantilever with AFM and has enabled the research community to conduct biological, pathological, and pharmacological studies on cells at the single-cell level in a liquid environment. In this review, capacities of fluidFM are discussed to illustrate (1) the speed with which sequential measurements of adhesion using coated colloid beads can be done, (2) the ability to assess lateral binding forces of endothelial or epithelial cells in a confluent cell monolayer in an appropriate physiological environment, and (3) the ease of measurement of vertical binding forces of intercellular adhesion between heterogeneous cells. Furthermore, key applications of fluidFM are reviewed regarding to EV absorption, manipulation of a single living cell by intracellular injection, sampling of cellular fluid from a single living cell, patch clamping, and mass measurements of a single living cell.

Complete Genome Sequence of Emiliania huxleyi Virus Strain M1, Isolated from an Induced E. huxleyi Bloom in Bergen, Norway

Emiliania huxleyi virus strain M1 (EhVM1), a large double-stranded DNA virus from the family Phycodnaviridae, was isolated from an Emiliania huxleyi bloom during a mesocosm experiment in Raunefjorden, Bergen, Norway. Here, we report its complete genome, composed of one full contig.

Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure-function relations of RNAs.

Thoughts on how to think (and talk) about RNA structure

Recent events have pushed RNA research into the spotlight. Continued discoveries of RNA with unexpected diverse functions in healthy and diseased cells, such as the role of RNA as both the source and countermeasure to a severe acute respiratory syndrome coronavirus 2 infection, are igniting a new passion for understanding this functionally and structurally versatile molecule. Although RNA structure is key to function, many foundational characteristics of RNA structure are misunderstood, and the default state of RNA is often thought of and depicted as a single floppy strand. The purpose of this perspective is to help adjust mental models, equipping the community to better use the fundamental aspects of RNA structural information in new mechanistic models, enhance experimental design to test these models, and refine data interpretation. We discuss six core observations focused on the inherent nature of RNA structure and how to incorporate these characteristics to better understand RNA structure. We also offer some ideas for future efforts to make validated RNA structural information available and readily used by all researchers.

SwissBioPics—an interactive library of cell images for the visualization of subcellular location data

SwissBioPics (www.swissbiopics.org) is a freely available resource of interactive, high-resolution cell images designed for the visualization of subcellular location data. SwissBioPics provides images describing cell types from all kingdoms of life—from the specialized muscle, neuronal and epithelial cells of animals, to the rods, cocci, clubs and spirals of prokaryotes. All cell images in SwissBioPics are drawn in Scalable Vector Graphics (SVG), with each subcellular location tagged with a unique identifier from the controlled vocabulary of subcellular locations and organelles of UniProt (https://www.uniprot.org/locations/). Users can search and explore SwissBioPics cell images through our website, which provides a platform for users to learn more about how cells are organized. A web component allows developers to embed SwissBioPics images in their own websites, using the associated JavaScript and a styling template, and to highlight subcellular locations and organelles by simply providing the web component with the appropriate identifier(s) from the UniProt-controlled vocabulary or the ‘Cellular Component’ branch of the Gene Ontology (www.geneontology.org), as well as an organism identifier from the National Center for Biotechnology Information taxonomy (https://www.ncbi.nlm.nih.gov/taxonomy). The UniProt website now uses SwissBioPics to visualize the subcellular locations and organelles where proteins function. SwissBioPics is freely available for anyone to use under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Database URL

www.swissbiopics.org

Developing Community Resources for Nucleic Acid Structures

In this review, we describe the creation of the Nucleic Acid Database (NDB) at Rutgers University and how it became a testbed for the current infrastructure of the RCSB Protein Data Bank. We describe some of the special features of the NDB and how it has been used to enable research. Plans for the next phase as the Nucleic Acid Knowledgebase (NAKB) are summarized.

Are miRNAs Dynamic Biomarkers in Keratoconus? A Review of the Literature

Aim: A review of miRNA (microRNA) profiling studies in keratoconus. Methods: Literature search strategy—PubMed central database, using miRNA or microRNA and keratoconus as keywords. Results: Eleven experimental or clinical studies on humans regarding miRNA and keratoconus, published in English between 2009 and 2020 were retrieved. Conclusion: The publications regarding the role of miRNAs in keratoconus are scarce and diverse but provide some valuable information about potential new mechanisms of keratoconus development and progression. The cornea expresses almost 300 different miRNAs, 18 of which are specific, and miR-184 is by far the most abundant, with expression restricted to central basal and suprabasal epithelial cells. Mutations in the seed region of MIR184 were proved to be rare and nonspecific in patients with isolated keratoconus. Overall, in keratoconus, a total of 29 miRNAs were upregulated, and 11 were downregulated. It appeared that miR-143-3p, miR-182-5p, and miR-92a-3p were highly expressed, while the miRNAs connected to cell–cell junction, cell division, and motor activity were downregulated. In less advanced forms, altered expression of four miRNAs—miR-151a-3p, miR-194-5p, miR-195-5p, miR-185-5p—was proved in the cone epithelium; in contrast, in advanced keratoconus, the expression of miR-151a-3p and miR-194-5p remained altered, changes in the expression of miR-195 and miR-185 were not reported, and the expression of miR-138-5p, miR-146b-5p, miR-28-5p, and miR-181a-2-3p was also altered in the corneal epithelium. Keratoconus is a dynamic process of corneal stromal thinning that might result from a dynamic miRNA expression in the corneal epithelium exposed to environmental and behavioral factors causing repetitive traumas. Further experimental studies are needed to prove this hypothesis.

Towards SINEUP-based therapeutics: Design of an in vitro synthesized SINEUP RNA

SINEUPs are a novel class of natural and synthetic non-coding antisense RNA molecules able to increase the translation of a target mRNA. They present a modular organization comprising an unstructured antisense target-specific domain, which sets the specificity of each individual SINEUP, and a structured effector domain, which is responsible for the translation enhancement. In order to design a fully functional in vitro transcribed SINEUP for therapeutics applications, SINEUP RNAs were synthesized in vitro with a variety of chemical modifications and screened for their activity on endogenous target mRNA upon transfection. Three combinations of modified ribonucleotides-2'O methyl-ATP (Am), N6 methyl-ATP (m6A), and pseudo-UTP (ψ)-conferred SINEUP activity to naked RNA. The best combination tested in this study was fully modified with m6A and ψ. Aside from functionality, this combination conferred improved stability upon transfection and higher thermal stability. Common structural determinants of activity were identified by circular dichroisms, defining a core functional structure that is achieved with different combinations of modifications.

Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types

Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3′UTR, there are relatively few studies examining the changes in these regulatory mechanisms specific to single cancer types or shared between different cancer types.

We analyzed samples where both miRNA and mRNA expression had been measured and performed a thorough correlation analysis on 7494 experimentally validated human miRNA-mRNA target-gene pairs in both healthy and tumoral samples.

We show how more than 90% of these miRNA-mRNA interactions show a loss of regulation in the tumoral samples compared with their healthy counterparts.

As expected, we found shared miRNA-mRNA dysregulated pairs among different tumors of the same tissue. However, anatomically different cancers also share multiple dysregulated interactions, suggesting that some cancer-related mechanisms are not tumor-specific. 2865 unique miRNA-mRNA pairs were identified across 13 cancer types, ≈ 40% of these pairs showed a loss of correlation in the tumoral samples in at least 2 out of the 13 analyzed cancers. Specifically, miR-200 family, miR-155 and miR-1 were identified, based on the computational analysis described below, as the miRNAs that potentially lose the highest number of interactions across different samples (only literature-based interactions were used for this analysis).

Moreover, the miR-34a/ALDH2 and miR-9/MTHFD2 pairs show a switch in their correlation between healthy and tumor kidney samples suggesting a possible change in the regulation exerted by the miRNAs. Interestingly, the expression of these mRNAs is also associated with the overall survival. The disruption of miRNA regulation on its target, therefore, suggests the possible involvement of these pairs in cell malignant functions.

The analysis reported here shows how the regulation of miRNA-mRNA interactions strongly differs between healthy and tumoral cells, based on the strong correlation variation between miRNA and its target that we obtained by analyzing the expression data of healthy and tumor tissue in highly reliable miRNA-target pairs. Finally, a go term enrichment analysis shows that the critical pairs identified are involved in cellular adhesion, proliferation, and migration.

Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning

Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-training algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this ‘informative base embedding’ and use it to achieve accuracies superior to those of existing state-of-the-art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman–Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of O(n²) instead of the O(n⁶) time complexity of the naive implementation of Sankoff-style algorithm for input RNA sequence of length n.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

Altered microRNAs in C3H10T1/2 cells induced by p.E95K mutant IHH signaling

Background

Indian Hedgehog (IHH), an important cell signaling protein, plays a key regulatory role in development of cartilage and chondrogenesis. Earlier studies have shown that heterozygous missense mutations in IHH gene may cause brachydactyly type A1 (BDA1), an autosomal dominant inheritance disease characterized by apparent shortness or absence of the middle phalanges of all digits. MicroRNAs (miRNAs) have been found to be significant post-transcriptional regulators of gene expression and significantly influence the process of bone-development. Therefore, it is possible that miRNAs are involved in the mechanism underlying the development of BDA1. However, the relationship between miRNAs and the pathogenesis of BDA1 remains unclear.

Methods

In this study, we used microarray-based miRNA profiling to investigate the role of miRNAs in BDA1 by characterization of differentially expressed miRNAs in C3H10T1/2 cell line induced by wild type (WT) and p.E95K mutant (MT) IHH signaling.

Results

Our results identified 6 differentially expressed miRNAs between WT and control (CT) group and 5 differentially expressed miRNAs between MT and CT groups. In particular, miR-135a-1-3p was found to be a significantly differentially expressed miRNA between WT and CT group. Results of dual-luciferase reporter gene experiment successfully discovered Hoxd10 was one of the target gene of miR-135a-1-3p. Additionally, our pathway analysis revealed that the targets of these miRNAs of interest were highly involved with Runx1/2, Notch and collagen-related pathways.

Conclusions

Taken together, our findings provided important clue for future study of the process of miRNA-regulation in IHH signaling and novel insights into the regulatory role of miRNA in pathogenesis of BDA1.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00207-8.

LazySampling and LinearSampling: Fast Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2

Many RNAs fold into multiple structures at equilibrium. The classical stochastic sampling algorithm can sample secondary structures according to their probabilities in the Boltzmann ensemble, and is widely used. However, this algorithm, consisting of a bottom-up partition function phase followed by a top-down sampling phase, suffers from three limitations: (a) the formulation and implementation of the sampling phase are unnecessarily complicated; (b) the sampling phase repeatedly recalculates many redundant recursions already done during the partition function phase; (c) the partition function runtime scales cubically with the sequence length. These issues prevent stochastic sampling from being used for very long RNAs such as the full genomes of SARS-CoV-2. To address these problems, we first adopt a hypergraph framework under which the sampling algorithm can be greatly simplified. We then present three sampling algorithms under this framework, among which the LazySampling algorithm is the fastest by eliminating redundant work in the sampling phase via on-demand caching. Based on LazySampling, we further replace the cubic-time partition function by a linear-time approximate one, and derive LinearSampling, an end-to-end linear-time sampling algorithm that is orders of magnitude faster than the standard one. For instance, LinearSampling is 176Ã- faster (38.9s vs. 1.9h) than Vienna RNAsubopt on the full genome of Ebola virus (18,959 nt ). More importantly, LinearSampling is the first RNA structure sampling algorithm to scale up to the full-genome of SARS-CoV-2 without local window constraints, taking only 69.2 seconds on its reference sequence (29,903 nt ). The resulting sample correlates well with the experimentally-guided structures. On the SARS-CoV-2 genome, LinearSampling finds 23 regions of 15 nt with high accessibilities, which are potential targets for COVID-19 diagnostics and drug design. See code: https://github.com/LinearFold/LinearSampling.

A Survey of Spontaneous Antibiotic-Resistant Mutants of the Halophilic, Thermophilic Bacterium Rhodothermus marinus

Rhodothermus marinus is a halophilic extreme thermophile, with potential as a model organism for studies of the structural basis of antibiotic resistance. In order to facilitate genetic studies of this organism, we have surveyed the antibiotic sensitivity spectrum of R. marinus and identified spontaneous antibiotic-resistant mutants. R. marinus is naturally insensitive to aminoglycosides, aminocylitols and tuberactinomycins that target the 30S ribosomal subunit, but is sensitive to all 50S ribosomal subunit-targeting antibiotics examined, including macrolides, lincosamides, streptogramin B, chloramphenicol, and thiostrepton. It is also sensitive to kirromycin and fusidic acid, which target protein synthesis factors. It is sensitive to rifampicin (RNA polymerase inhibitor) and to the fluoroquinolones ofloxacin and ciprofloxacin (DNA gyrase inhibitors), but insensitive to nalidixic acid. Drug-resistant mutants were identified using rifampicin, thiostrepton, erythromycin, spiramycin, tylosin, lincomycin, and chloramphenicol. The majority of these were found to have mutations that are similar or identical to those previously found in other species, while several novel mutations were identified. This study provides potential selectable markers for genetic manipulations and demonstrates the feasibility of using R. marinus as a model system for studies of ribosome and RNA polymerase structure, function, and evolution.

Multiomics Landscape Uncovers the Molecular Mechanism of the Malignant Evolution of Lung Adenocarcinoma Cells to Chronic Low Dose Cadmium Exposure

Cadmium (Cd) from cigarette smoke and polluted air can lead to lung adenocarcinoma after long-term inhalation. However, most studies are based on short-term exposure to this toxic metal at high concentrations. Here, we investigate the effects of long-term exposure of A549 cells (lung adenocarcinoma) to cadmium at low concentrations using morphological and multiomics analyses. First, we treated A549 cells continuously with CdCl2 at 1μM for 8 months and found that CdCl2 promoted cellular migration and invasion. After that, we applied transmission electron and fluorescence microscopies and did not observe significant morphological changes in Golgi apparatus, endoplasmic reticulum, lysosomes, or mitochondria on Cd treated cells; microfilaments, in contrast, accumulated in lamellipodium and adhesion plaques, which suggested that Cd enhanced cellular activity. Second, by using whole-exome sequencing (WES) we detected 4222 unique SNPs in Cd-treated cells, which included 382 unique non-synonymous mutation sites. The corresponding mutated genes, after GO and KEGG enrichments, were involved mainly in cell adhesion, movement, and metabolic pathways. Third, by RNA-seq analysis, we showed that 1250 genes (784 up and 466 down), 1623 mRNAs (1023 up and 591 down), and 679 lncRNAs (375 up and 304 down) were expressed differently. Furthermore, GO enrichment of these RNA-seq results suggested that most differentially expressed genes were related to cell adhesion and organization of the extracellular matrix in biological process terms; KEGG enrichment revealed that the differentially expressed genes took part in 26 pathways, among which the metabolic pathway was the most significant. These findings could be important for unveiling mechanisms of Cd-related cancers and for developing cancer therapies in the future.