RNA Pairs Research Articles

REDalign: accurate RNA structural alignment using residual encoder-decoder network

BackgroundRNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O(L6)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(L^6)$$\\end{document} for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities.ResultsIn this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency.ConclusionREDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

Genome wide association studies are enriched for interacting genes.

Background : With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk. Results : We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies. Conclusions : Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

Impacts of the feedback loop between sense-antisense RNAs in regulating circadian rhythms

Antisense transcripts are a unique group of non-coding RNAs and play regulatory roles in a variety of biological processes, including circadian rhythms. Per2AS is an antisense transcript to the sense core clock gene Period2 (Per2) in mouse and its expression is rhythmic and antiphasic to Per2. To understand the impact of Per2AS-Per2 interaction, we developed a new mathematical model that mechanistically described the mutually repressive relationship between Per2 and Per2AS. This mutual repression can regulate both amplitude and period of circadian oscillation by affecting a negative feedback regulation of Per2. Simulations from this model also fit with experimental observations that could not be fully explained by our previous model. Our revised model can not only serve as a foundation to build more detailed models to better understand the impact of Per2AS-Per2 interaction in the future, but also be used to analyze other sense-antisense RNA pairs that mutually repress each other.

Crispr-SGRU: Prediction of CRISPR/Cas9 Off-Target Activities with Mismatches and Indels Using Stacked BiGRU

CRISPR/Cas9 is a popular genome editing technology, yet its clinical application is hindered by off-target effects. Many deep learning-based methods are available for off-target prediction. However, few can predict off-target activities with insertions or deletions (indels) between single guide RNA and DNA sequence pairs. Additionally, the analysis of off-target data is challenged due to a data imbalance issue. Moreover, the prediction accuracy and interpretability remain to be improved. Here, we introduce a deep learning-based framework, named Crispr-SGRU, to predict off-target activities with mismatches and indels. This model is based on Inception and stacked BiGRU. It adopts a dice loss function to solve the inherent imbalance issue. Experimental results show our model outperforms existing methods for off-target prediction in terms of accuracy and robustness. Finally, we study the interpretability of this model through Deep SHAP and teacher–student-based knowledge distillation, and find it can provide meaningful explanations for sequence patterns regarding off-target activity.

Comparative Long Non-Coding Transcriptome Analysis of Three Contrasting Barley Varieties in Response to Aluminum Stress.

Aluminum toxicity is a major abiotic stress on acidic soils, leading to restricted root growth and reduced plant yield. Long non-coding RNAs are crucial signaling molecules regulating the expression of downstream genes, particularly under abiotic stress conditions. However, the extent to which lncRNAs participate in the response to aluminum (Al) stress in barley remains largely unknown. Here, we conducted RNA sequencing of root samples under aluminum stress and compared the lncRNA transcriptomes of two Tibetan wild barley genotypes, XZ16 (Al-tolerant) and XZ61 (Al-sensitive), as well as the aluminum-tolerant cultivar Dayton. In total, 268 lncRNAs were identified as aluminum-responsive genes on the basis of their differential expression profiles under aluminum treatment. Through target gene prediction analysis, we identified 938 candidate lncRNA-messenger RNA (mRNA) pairs that function in a cis-acting manner. Subsequently, enrichment analysis showed that the genes targeted by aluminum-responsive lncRNAs were involved in diterpenoid biosynthesis, peroxisome function, and starch/sucrose metabolism. Further analysis of genotype differences in the transcriptome led to the identification of 15 aluminum-responsive lncRNAs specifically altered by aluminum stress in XZ16. The RNA sequencing data were further validated by RT-qPCR. The functional roles of lncRNA-mRNA interactions demonstrated that these lncRNAs are involved in the signal transduction of secondary messengers, and a disease resistance protein, such as RPP13-like protein 4, is probably involved in aluminum tolerance in XZ16. The current findings significantly contribute to our understanding of the regulatory roles of lncRNAs in aluminum tolerance and extend our knowledge of their importance in plant responses to aluminum stress.

A-to-I RNA co-editing predicts clinical outcomes and is associated with immune cells infiltration in hepatocellular carcinoma

Aberrant RNA editing has emerged as a pivotal factor in the pathogenesis of hepatocellular carcinoma (HCC), but the impact of RNA co-editing within HCC remains underexplored. We used a multi-step algorithm to construct an RNA co-editing network in HCC, and found that HCC-related RNA editings are predominantly centralized within the network. Furthermore, five pairs of risk RNA co-editing events were significantly correlated with the overall survival in HCC. Based on presence of risk RNA co-editings resulted in the categorization of HCC patients into high-risk and low-risk groups. Disparities in immune cell infiltrations were observed between the two groups, with the high-risk group exhibiting a greater abundance of exhausted T cells. Additionally, seven genes associated with risk RNA co-editing pairs were identified, whose expression effectively differentiates HCC tumor samples from normal ones. Our research offers an innovative perspective on the etiology and potential therapeutics for HCC.

Constructing lncRNA-miRNA-mRNA networks specific to individual cancer patients and finding prognostic biomarkers

BackgroundThe competitive endogenous RNA (ceRNA) hypothesis suggests that microRNAs (miRNAs) mediate a regulatory relation between long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) which share similar miRNA response elements (MREs) to bind to the same miRNA. Since the ceRNA hypothesis was proposed, several studies have been conducted to construct a network of lncRNAs, miRNAs and mRNAs in cancer. However, most cancer-related ceRNA networks are intended for representing a general relation of RNAs in cancer rather than for a patient-specific relation. Due to the heterogeneous nature of cancer, lncRNA-miRNA-mRNA interactions can vary in different patients.ResultsWe have developed a new method for constructing a ceRNA network of lncRNAs, miRNAs and mRNAs, which is specific to an individual cancer patient and for finding prognostic biomarkers consisting of lncRNA-miRNA-mRNA triplets. We tested our method on extensive data sets of three types of cancer (breast cancer, liver cancer, and lung cancer) and obtained potential prognostic lncRNA-miRNA-mRNA triplets for each type of cancer.ConclusionsAnalysis of expression patterns of the RNAs involved in the triplets and survival rates of cancer patients revealed several interesting findings. First, even for the same cancer type, prognostic lncRNA-miRNA-mRNA triplets can be different depending on whether lncRNA and mRNA show opposite or similar expression patterns. Second, prognostic lncRNA-miRNA-mRNA triplets are often more predictive of survival rates than RNA pairs or individual RNAs. Our approach will be useful for constructing patient-specific lncRNA-miRNA-mRNA networks and for finding prognostic biomarkers from the networks.

BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning

Machine Learning (ML) algorithms have been important tools for the extraction of useful knowledge from biological sequences, particularly in healthcare, agriculture, and the environment. However, the categorical and unstructured nature of these sequences requiring usually additional feature engineering steps, before an ML algorithm can be efficiently applied. The addition of these steps to the ML algorithm creates a processing pipeline, known as end-to-end ML. Despite the excellent results obtained by applying end-to-end ML to biotechnology problems, the performance obtained depends on the expertise of the user in the components of the pipeline. In this work, we propose an end-to-end ML-based framework called BioPrediction-RPI, which can identify implicit interactions between sequences, such as pairs of non-coding RNA and proteins, without the need for specialized expertise in end-to-end ML. This framework applies feature engineering to represent each sequence by structural and topological features. These features are divided into feature groups and used to train partial models, whose partial decisions are combined into a final decision, which, provides insights to the user by giving an interpretability report. In our experiments, the developed framework was competitive when compared with various expert-created models. We assessed BioPrediction-RPI with 12 datasets when it presented equal or better performance than all tools in 40% to 100% of cases, depending on the experiment. Finally, BioPrediction-RPI can fine-tune models based on new data and perform at the same level as ML experts, democratizing end-to-end ML and increasing its access to those working in biological sciences.

Molecular mechanism for target RNA recognition and cleavage of Cas13h.

RNA-targeting type VI CRISPR-Cas effectors are widely used in RNA applications. Cas13h is a recently identified subtype of Cas13 ribonuclease, with strong RNA cleavage activity and robust in vivo RNA knockdown efficiency. However, little is known regarding its biochemical properties and working mechanisms. Biochemical characterization of Cas13h1 indicated that it lacks in vitro pre-crRNA processing activity and adopts a central seed. The cleavage activity of Cas13h1 is enhanced by a R(G/A) 5'-PFS, and inhibited by tag:anti-tag RNA pairing. We determined the structures of Cas13h1-crRNA binary complex at 3.1 Å and Cas13h1-crRNA-target RNA ternary complex at 3.0 Å. The ternary complex adopts an elongated architecture, and encodes a nucleotide-binding pocket within Helical-2 domain to recognize the guanosine at the 5'-end of the target RNA. Base pairing between crRNA guide and target RNA disrupts Cas13h1-guide interactions, leading to dramatic movement of HEPN domains. Upon target RNA engagement, Cas13h1 adopts a complicated activation mechanism, including separation of HEPN catalytic residues and destabilization of the active site loop and NTD domain, to get activated. Collectively, these insights expand our understanding into Cas13 effectors.

A novel immune-related long noncoding RNA (lncRNA) pair model to predict the prognosis of triple-negative breast cancer.

Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) immunotherapy has shown promising effects in metastatic triple-negative breast cancer (TNBC), but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance. We retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model. Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction tool in TNBC patients. Clinical stage and risk score were proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and Multi Experiment Matrix (MEM) databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses. Our study constructed a risk assessment model by irlncRNA pairs regardless of expression levels, which contributed to predicting the efficacy of immunotherapy in TNBC. Furthermore, the lncRNA USP30-AS1 in the model was positively correlated with the expression of PD-L1 and provided a potential therapeutic target for TNBC.

Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT

Off-target effects of CRISPR/Cas9 can lead to suboptimal genome editing outcomes. Numerous deep learning-based approaches have achieved excellent performance for off-target prediction; however, few can predict the off-target activities with both mismatches and indels between single guide RNA (sgRNA) and target DNA sequence pair. In addition, data imbalance is a common pitfall for off-target prediction. Moreover, due to the complexity of genomic contexts, generating an interpretable model also remains challenged. To address these issues, firstly we developed a BERT-based model called CRISPR-BERT for enhancing the prediction of off-target activities with both mismatches and indels. Secondly, we proposed an adaptive batch-wise class balancing strategy to combat the noise exists in imbalanced off-target data. Finally, we applied a visualization approach for investigating the generalizable nucleotide position-dependent patterns of sgRNA-DNA pair for off-target activity. In our comprehensive comparison to existing methods on five mismatches-only datasets and two mismatches-and-indels datasets, CRISPR-BERT achieved the best performance in terms of AUROC and PRAUC. Besides, the visualization analysis demonstrated how implicit knowledge learned by CRISPR-BERT facilitates off-target prediction, which shows potential in model interpretability. Collectively, CRISPR-BERT provides an accurate and interpretable framework for off-target prediction, further contributes to sgRNA optimization in practical use for improved target specificity in CRISPR/Cas9 genome editing. The source code is available at https://github.com/BrokenStringx/CRISPR-BERT.

Simultaneous deep transcriptome and proteome profiling in a single mouse oocyte

Although single-cell multi-omics technologies are undergoing rapid development, simultaneous transcriptome and proteome analysis of a single-cell individual still faces great challenges. Here, we developed a single-cell simultaneous transcriptome and proteome (scSTAP) analysis platform based on microfluidics, high-throughput sequencing, and mass spectrometry technology to achieve deep and joint quantitative analysis of transcriptome and proteome at the single-cell level, providing an important resource for understanding the relationship between transcription and translation in cells. This platform was applied to analyze single mouse oocytes at different meiotic maturation stages, reaching an average quantification depth of 19,948 genes and 2,663 protein groups in single mouse oocytes. In particular, we analyzed the correlation of individual RNA and protein pairs, as well as the meiosis regulatory network with unprecedented depth, and identified 30 transcript-protein pairs as specific oocyte maturational signatures, which could be productive for exploring transcriptional and translational regulatory features during oocyte meiosis.

Alu transposable elements rewire enhancer-promoter network through RNA pairing

A recent study by Liang etal.1 reveals that interacting enhancer RNAs (eRNAs) and promoter-transcribed upstream antisense RNAs (uaRNAs) can identify enhancer-promoter interactions. Complementary sequences within the interacting eRNAs and uaRNAs, predominantly Alu sequences, confer the specificity for eRNA-uaRNA pairing and hence enhancer-promoter recognition.

Quintuply orthogonal pyrrolysyl-tRNA synthetase/tRNAPyl pairs.

Mutually orthogonal aminoacyl transfer RNA synthetase/transfer RNA pairs provide a foundation for encoding non-canonical amino acids into proteins, and encoded non-canonical polymer and macrocycle synthesis. Here we discover quintuply orthogonal pyrrolysyl-tRNA synthetase (PylRS)/pyrrolysyl-tRNA (tRNAPyl) pairs. We discover empirical sequence identity thresholds for mutual orthogonality and use these for agglomerative clustering of PylRS and tRNAPyl sequences; this defines numerous sequence clusters, spanning five classes of PylRS/tRNAPyl pairs (the existing classes +N, A and B, and newly defined classes C and S). Most of the PylRS clusters belong to classes that were unexplored for orthogonal pair generation. By testing pairs from distinct clusters and classes, and pyrrolysyl-tRNAs with unusual structures, we resolve 80% of the pairwise specificities required to make quintuply orthogonal PylRS/tRNAPyl pairs; we control the remaining specificities by engineering and directed evolution. Overall, we create 924 mutually orthogonal PylRS/tRNAPyl pairs, 1,324 triply orthogonal pairs, 128 quadruply orthogonal pairs and 8 quintuply orthogonal pairs. These advances may provide a key foundation for encoded polymer synthesis.

Accelerating Cell-Free Biosensors by Entropy-Driven Assembly of Transcription Templates.

Due to its high efficiency and selectivity, cell-free biosynthesis has found broad utility in the fields of bioproduction, environment monitoring, and disease diagnostics. However, the practical application is limited by its low productivity. Here, we introduce the entropy-driven assembly of transcription templates as dynamic amplifying modules to accelerate the cell-free transcription process. The catalytic DNA circuit with high sensitivity and enzyme-free format contributes to the production of large amounts of transcription templates, drastically accelerating the as-designed cell-free transcription system without interference from multiple enzymes. The proposed approach was successfully applied to the ultrasensitive detection of SARS-CoV-2, improving the sensitivity by 3 orders of magnitude. Thanks to the high programmability and diverse light-up RNA pairs, the method can be adapted to multiplexing detection, successfully demonstrated by the analysis of two different sites of the SARS-CoV-2 gene in parallel. Further, the flexibility of the entropy-driven circuit enables a dynamic responding range by tuning the circuit layers, which is beneficial for responding to targets with different concentration ranges. The strategy was also applied to the analysis of clinical samples, providing an alternative for sensitively detecting the current SARS-CoV-2 RNA that quickly mutates.

Identification and validation of an epithelial-mesenchymal transition-related lncRNA pairs prognostic model for gastric cancer.

Gastric cancer (GC) is a common malignancy. A mounting body of evidence has demonstrated the correlation between GC prognosis and epithelial-mesenchymal transition (EMT)-related biomarkers. This research constructed an available model using EMT-related long noncoding RNA (lncRNA) pairs to predict the survival for GC patients. The transcriptome data along with clinical information on GC samples were derived from The Cancer Genome Atlas (TCGA). Differentially expressed EMT-related lncRNAs were acquired and paired. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied to filter lncRNA pairs, and the risk model was built to investigate its effect on the prognosis of GC patients. Then, the areas under the receiver operating characteristic curves (AUCs) were calculated and the cutoff point for distinguishing low- or high-risk GC patients was identified. And the predictive ability of this model was tested in the GSE62254. Furthermore, the model was evaluated from the perspectives of survival time, clinicopathological parameters, infiltration of immunocytes, and functional enrichment analysis. The risk model was built by using the identified twenty EMT-related lncRNA pairs, and it was not necessary to know the specific expression level of each lncRNA. Survival analysis pointed out that GC patients with high risk had poorer outcomes. Additionally, this model could be an independent prognostic variable for GC patients. The accuracy of the model was also verified in the testing set. The new predictive model constructed here is composed of EMT-related lncRNA pairs, with reliable prognostic values, and can be utilized to predict the survival of GC.

Transformer-based anti-noise models for CRISPR-Cas9 off-target activities prediction.

The off-target effect occurring in the CRISPR-Cas9 system has been a challenging problem for the practical application of this gene editing technology. In recent years, various prediction models have been proposed to predict potential off-target activities. However, most of the existing prediction methods do not fully exploit guide RNA (gRNA) and DNA sequence pair information effectively. In addition, available prediction methods usually ignore the noise effect in original off-target datasets. To address these issues, we design a novel coding scheme, which considers the key features of mismatch type, mismatch location and the gRNA-DNA sequence pair information. Furthermore, a transformer-based anti-noise model called CrisprDNT is developed to solve the noise problem that exists in the off-target data. Experimental results of eight existing datasets demonstrate that the method with the inclusion of the anti-noise loss functions is superior to available state-of-the-art prediction methods. CrisprDNT is available at https://github.com/gzrgzx/CrisprDNT.

A model based on immune-related lncRNA pairs and its potential prognostic value in immunotherapy for melanoma

A model based on long non-coding RNA (lncRNA) pairs independent of expression quantification was constructed to evaluate prognosis melanoma and response to immunotherapy in melanoma. RNA sequencing data and clinical information were retrieved and downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. We identified differentially expressed immune-related lncRNAs (DEirlncRNAs), matched them, and used least absolute shrinkage and selection operator and Cox regression to construct predictive models. The optimal cutoff value of the model was determined using a receiver operating characteristic curve and used to categorize melanoma cases into high-risk and low-risk groups. The predictive efficacy of the model with respect to prognosis was compared with that of clinical data and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data). Then, we analyzed the correlations of risk score with clinical characteristics, immune cell invasion, anti-tumor, and tumor-promoting activities. Differences in survival, degree of immune cell infiltration, and intensity of anti-tumor and tumor-promoting activities were also evaluated in the high- and low-risk groups. A model based on 21 DEirlncRNA pairs was established. Compared with ESTIMATE score and clinical data, this model could better predict outcomes of melanoma patients. Follow-up analysis of the model's effectiveness showed that patients in the high-risk group had poorer prognosis and were less likely to benefit from immunotherapy compared with those in the low-risk group. Moreover, there were differences in tumor-infiltrating immune cells between the high-risk and low-risk groups. By pairing the DEirlncRNA, we constructed a model to evaluate the prognosis of cutaneous melanoma independent of a specific level of lncRNA expression.

Establishment and preliminary evaluation of a fluorescent recombinase-aided amplification/CRISPR-Cas12a system for rapid detection of Plasmodium falciparum

To establish a fluorescent assay for rapid detection of Plasmodium falciparum based on recombinaseaided amplification (RAA) and CRISPR-Cas12a system,and to preliminarily evaluate the diagnostic efficiency of this system. The 18S ribosomal RNA (rRNA) gene of P. falciparum was selected as the target sequence, and three pairs of RAA primers and CRISPR-derived RNA (crRNA) were designed and synthesized. The optimal combination of RAA primers and crRNA was screened and the reaction conditions of the system were optimized to create a fluorescent RAA/CRISPR-Cas12a system. The plasmid containing 18S rRNA gene of the P. falciparum strain 3D7 was generated, and diluted into concentrations of 1 000, 100, 10, 1 copy/μL for the fluorescent RAA/CRISPR-Cas12a assay, and its sensitivity was evaluated. The genomic DNA from P. vivax, P. malariae, P. ovum, hepatitis B virus, human immunodeficiency virus and Treponema pallidum was employed as templates for the fluorescent RAA/CRISPR-Cas12a assay, and its specificity was evaluated. Fifty malaria clinical samples were subjected to the fluorescent RAA/CRISPR-Cas12a assay and nested PCR assay, and the consistency between two assays was compared. In addition, P. falciparum strain 3D7 was cultured in vitro. Then, the culture was diluted into blood samples with parasite densities of 1 000, 500, 200, 50, 10 parasites/μL with healthy volunteers' O-positive red blood cells for the RAA/CRISPR-Cas12a assay, and the detection efficiency was tested. The Pf-F3/Pf-R3/crRNA2 combination, 2.5 μL as the addition amount of B buffer, 40 min as the RAA reaction time, 37 °C as the reaction temperature of the CRISPR-Cas12a system were employed to establish the fluorescent RAA/CRISPR-Cas12a system. Such a system was effective to detect the plasmid containing 18S rRNA gene of the P. falciparum strain 3D7 at a concentration of 1 copy/μL, and presented fluorescent signals for detection of P. falciparum, but failed to detect P. ovum, P. malariae, P. vivax, T. pallidum, hepatitis B virus or human immunodeficiency virus. The fluorescent RAA/CRISPR-Cas12a system and nested PCR assay showed completely consistent results for detection of 50 malaria clinical samples (kappa = 1.0, P < 0.001). Following 6-day in vitro culture of the P. falciparum strain 3D7, 10 mL cultures were generated and the fluorescent RAA/CRISPR-Cas12a system showed the minimal detection limit of 50 parasites/μL. The fluorescent RAA/CRISPR-Cas12a system is rapid, sensitive and specific for detection of P. falciparum, which shows promising value for rapid detection and risk monitoring of P. falciparum.

Finding miRNA–RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.