SUDAZFLNC - a curated and searchable online database for zebrafish lncRNAs, mRNAs, miRNAs, and circadian expression profiles.

The zebrafish (Danio rerio) has emerged as a model organism for investigating lncRNAs-driven fundamental biological processes, such as circadian rhythms, physiology, metabolism, and various diseases. While state-of-the-art sequencing technologies have identified an increasing number of lncRNAs in zebrafish, their annotations are far from complete. In this study, we collect 28,925 lncRNAs from both the published studies and our own RNA-seq analyses and establish a novel webserver-based database called SUDAZFLNC (https://sudarna.website/). The database, containing 28,925 lncRNAs, 25,432 mRNAs, and 368 miRNAs, provides several crucial features and annotations for the zebrafish RNAs, such as sequence identifiers (IDs), sequence length, hexamer score, coding probabilities, GO and KEGG annotations, and micropeptides. SUDAZFLNC also includes time-course expression profiles of 3288 lncRNAs, 25,432 mRNAs, and 342 miRNAs generated from our RNA-seq experiments, and 149, 4407, and 43 rhythmically expressed lncRNAs, mRNAs, and miRNAs, respectively. Based on the peak expression patterns, we classified these RNAs into morning RNAs, evening RNAs, and night RNAs. Users of the database can access the RNA sequences and their expression profiles by searching the corresponding IDs from the Graphical User Interface (GUI) of the database. The database supports several features to investigate RNA sequences and expression profiles, including BLAST, search of sequence and data, ID conversion, and RNA-RNA interaction prediction. This is the largest curated database of zebrafish RNAs and their expression profiles to date.

Thousands of oscillating LncRNAs in the mouse testis.

The long noncoding RNAs (lncRNAs) are involved in numerous fundamental biological processes, including circadian regulation. Although recent studies have revealed insights into the functions of lncRNAs, how the lncRNAs regulate circadian rhythms still requires a deeper investigation. In this study, we generate two datasets of RNA-seq profiles of the mouse (Mus musculus) testis under light-dark (LD) cycle. The first dataset included 18,613 unannotated transcripts measured at 12 time points, each with duplicate samples, under LD conditions; while the second dataset included 21,414 unannotated transcripts measured at six time points, each with three replicates, under desynchronized and control conditions. We identified 5964 testicular lncRNAs in each dataset by BLASTing these transcripts against the known mouse lncRNAs from the NONCODE database. MetaCycle analyses were performed to identify 519, 475, and 494 rhythmically expressed mouse testicular lncRNAs in the 12-time-point dataset, the six-time-point control dataset, and the six-time-point desynchronized dataset, respectively. A comparison of the expression profiles of the lncRNAs under desynchronized and control conditions revealed that 427 rhythmically expressed lncRNAs from the control condition became arrhythmic under the desynchronized condition, suggesting a possible loss of rhythmicity. In contrast, 446 arrhythmic lncRNAs from the control condition became rhythmic under the desynchronized condition, suggesting a possible gain of rhythmicity. Interestingly, 48 lncRNAs were rhythmically expressed under both desynchronized and control conditions. These oscillating lncRNAs were divided into morning lncRNAs, evening lncRNAs, and night lncRNAs based on their time-course expression patterns. We interrogated the promoter regions of these rhythmically expressed mouse testicular lncRNAs to predict their possible regulation by the E-box, D-box, or RORE promoter motifs. GO and KEGG analyses were performed to identify the possible biological functions of these rhythmically expressed mouse testicular lncRNAs. Further, we conducted conservation analyses of the rhythmically expressed mouse testicular lncRNAs with lncRNAs from humans, rats, and zebrafish, and uncovered three mouse testicular lncRNAs conserved across these four species. Finally, we computationally predicted the conserved lncRNA-encoded peptides and their 3D structures from each of the four species. Taken together, our study revealed thousands of rhythmically expressed lncRNAs in the mouse testis, setting the stage for further computational and experimental validations.

Hundreds of LncRNAs Display Circadian Rhythmicity in Zebrafish Larvae.

Long noncoding RNAs (lncRNAs) have been shown to play crucial roles in various life processes, including circadian rhythms. Although next generation sequencing technologies have facilitated faster profiling of lncRNAs, the resulting datasets require sophisticated computational analyses. In particular, the regulatory roles of lncRNAs in circadian clocks are far from being completely understood. In this study, we conducted RNA-seq-based transcriptome analysis of zebrafish larvae under both constant darkness (DD) and constant light (LL) conditions in a circadian manner, employing state-of-the-art computational approaches to identify approximately 3220 lncRNAs from zebrafish larvae, and then uncovered 269 and 309 lncRNAs displaying circadian rhythmicity under DD and LL conditions, respectively, with 30 of them are coexpressed under both DD and LL conditions. Subsequently, GO, COG, and KEGG pathway enrichment analyses of all these circadianly expressed lncRNAs suggested their potential involvement in numerous biological processes. Comparison of these circadianly expressed zebrafish larval lncRNAs, with rhythmically expressed lncRNAs in the zebrafish pineal gland and zebrafish testis, revealed that nine (DD) and twelve (LL) larval lncRNAs are coexpressed in the zebrafish pineal gland and testis, respectively. Intriguingly, among peptides encoded by these coexpressing circadianly expressed lncRNAs, three peptides (DD) and one peptide (LL) were found to have the known domains from the Protein Data Bank. Further, the conservation analysis of these circadianly expressed zebrafish larval lncRNAs with human and mouse genomes uncovered one lncRNA and four lncRNAs shared by all three species under DD and LL conditions, respectively. We also investigated the conserved lncRNA-encoded peptides and found one peptide under DD condition conserved in these three species and computationally predicted its 3D structure and functions. Our study reveals that hundreds of lncRNAs from zebrafish larvae exhibit circadian rhythmicity and should help set the stage for their further functional studies.

Identification of Rhythmically Expressed LncRNAs in the Zebrafish Pineal Gland and Testis.

Noncoding RNAs have been known to contribute to a variety of fundamental life processes, such as development, metabolism, and circadian rhythms. However, much remains unrevealed in the huge noncoding RNA datasets, which require further bioinformatic analysis and experimental investigation-and in particular, the coding potential of lncRNAs and the functions of lncRNA-encoded peptides have not been comprehensively studied to date. Through integrating the time-course experimentation with state-of-the-art computational techniques, we studied tens of thousands of zebrafish lncRNAs from our own experiments and from a published study including time-series transcriptome analyses of the testis and the pineal gland. Rhythmicity analysis of these data revealed approximately 700 rhythmically expressed lncRNAs from the pineal gland and the testis, and their GO, COG, and KEGG pathway functions were analyzed. Comparative and conservative analyses determined 14 rhythmically expressed lncRNAs shared between both the pineal gland and the testis, and 15 pineal gland lncRNAs as well as 3 testis lncRNAs conserved among zebrafish, mice, and humans. Further, we computationally analyzed the conserved lncRNA-encoded peptides, and revealed three pineal gland and one testis lncRNA-encoded peptides conserved among these three species, which were further investigated for their three-dimensional (3D) structures and potential functions. Our computational findings provided novel annotations and regulatory mechanisms for hundreds of rhythmically expressed pineal gland and testis lncRNAs in zebrafish, and set the stage for their experimental studies in the near future.

Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish.

Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730-6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data.

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.