RESUMEN

Recent technological advances in single-cell RNA sequencing (scRNA-Seq) have enabled scientists to answer novel questions in biology with unparalleled precision. Indeed, in the field of ocular development and regeneration, scRNA-Seq studies have resulted in a number of exciting discoveries that have begun to revolutionize the way we think about these processes. Despite the widespread success of scRNA-Seq, many scientists are wary to perform scRNA-Seq experiments due to the uncertainty of obtaining high-quality viable cell populations that are necessary for the generation of usable data that enable rigorous computational analyses. Here, we describe methodology to reproducibility generate high-quality single-cell suspensions from embryonic zebrafish eyes. These single-cell suspensions served as inputs to the 10× Genomics v3.1 system and yielded high-quality scRNA-Seq data in proof-of-principle studies. In describing methodology to quantitatively assess cell yields, cell viability, and other critical quality control parameters, this protocol can serve as a useful starting point for others in designing their scRNA-Seq experiments in the zebrafish eye and in other developing or regenerating tissues in zebrafish or other model systems.

Asunto(s)

RESUMEN

Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.

Asunto(s)

RESUMEN

Transcriptomics is an extremely important area of molecular biology and is a powerful tool for studying all RNA molecules in an organism. Conventional transcriptomic technologies include microarrays and RNA sequencing, and the rapid development of single-cell sequencing and spatial transcriptomics in recent years has provided an enormous scope for research in this field. This chapter describes the application, significance, and experimental procedures of a variety of transcriptomic technologies in antiviral natural immunity.

Asunto(s)

RESUMEN

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer.

Asunto(s)

RESUMEN

Background: Inflammatory Bowel Diseases (IBDs), encompassing Ulcerative Colitis (UC) and Crohn's Disease (CD), are chronic, recurrent inflammatory conditions of the gastrointestinal tract. The microRNA (miRNA) -mRNA regulatory network is pivotal in the initiation and progression of IBDs. Although individual studies provide valuable insights into miRNA mechanisms in IBDs, they often have limited scope due to constraints in population diversity, sample size, sequencing platform variability, batch effects, and potential researcher bias. Our study aimed to construct comprehensive miRNA-mRNA regulatory networks and determine the cellular sources and functions of key miRNAs in IBD pathogenesis. Methods: To minimize potential bias from individual studies, we utilized a text mining-based approach on published scientific literature from PubMed and PMC databases to identify miRNAs and mRNAs associated with IBDs and their subtypes. We constructed miRNA-mRNA regulatory networks by integrating both predicted and experimentally validated results from DIANA, Targetscan, PicTar, Miranda, miRDB, and miRTarBase (all of which are databases for miRNA target annotation). The functions of miRNAs were determined through gene enrichment analysis of their target mRNAs. Additionally, we used two large-scale single-cell RNA sequencing datasets to identify the cellular sources of miRNAs and the association of their expression levels with clinical status, molecular and functional alternation in CD and UC. Results: Our analysis systematically summarized IBD-related genes using text-mining methodologies. We constructed three comprehensive miRNA-mRNA regulatory networks specific to IBD, CD, and UC. Through cross-analysis with two large-scale scRNA-seq datasets, we determined the cellular sources of the identified miRNAs. Despite originating from different cell types, hsa-miR-142, hsa-miR-145, and hsa-miR-146a were common to both CD and UC. Notably, hsa-miR-145 was identified as myofibroblast-specific in both CD and UC. Furthermore, we found that higher tissue repair and enhanced glucose and lipid metabolism were associated with hsa-miR-145 in myofibroblasts in both CD and UC contexts. Conclusion: This comprehensive approach revealed common and distinct miRNA-mRNA regulatory networks in CD and UC, identified cell-specific miRNA expressions (notably hsa-miR-145 in myofibroblasts), and linked miRNA expression to functional alterations in IBD. These findings not only enhance our understanding of IBD pathogenesis but also offer promising diagnostic biomarkers and therapeutic targets for clinical practice in managing IBDs.

Asunto(s)

RESUMEN

Rationale: Understanding the immune mechanisms associated with liver transplantation (LT), particularly the involvement of tissue-resident memory T cells (TRMs), represents a significant challenge. Methods: This study employs a multi-omics approach to analyse liver transplant samples from both human (n = 17) and mouse (n = 16), utilizing single-cell RNA sequencing, bulk RNA sequencing, and immunological techniques. Results: Our findings reveal a comprehensive T cell-centric landscape in LT across human and mouse species, involving 235,116 cells. Notably, we found a substantial increase in CD8+ TRMs within rejected grafts compared to stable ones. The elevated presence of CD8+ TRMs is characterised by a distinct expression profile, featuring upregulation of tissue-residency markers (CD69, CXCR6, CD49A and CD103+/-,), immune checkpoints (PD1, CTLA4, and TIGIT), cytotoxic markers (GZMB and IFNG) and proliferative markers (PCNA and TOP2A) during rejection. Furthermore, there is a high expression of transcription factors such as EOMES and RUNX3. Functional assays and analyses of cellular communication underscore the active role of CD8+ TRMs in interacting with other tissue-resident cells, particularly Kupffer cells, especially during rejection episodes. Conclusions: These insights into the distinctive activation and interaction patterns of CD8+ TRMs suggest their potential utility as biomarkers for graft rejection, paving the way for novel therapeutic strategies aimed at enhancing graft tolerance and improving overall transplant outcomes.

Asunto(s)

RESUMEN

MOTIVATION: Single cell RNA sequencing (scRNA-seq) technique enables the transcriptome profiling of hundreds to ten thousands of cells at the unprecedented individual level and provides new insights to study cell heterogeneity. However, its advantages are hampered by dropout events. To address this problem, we propose a Blockwise Accelerated Non-negative Matrix Factorization framework with Structural network constraints (BANMF-S) to impute those technical zeros. RESULTS: BANMF-S constructs a gene-gene similarity network to integrate prior information from the external PPI network by the Triadic Closure Principle and a cell-cell similarity network to capture the neighborhood structure and temporal information through a Minimum-Spanning Tree. By collaboratively employing these two networks as regularizations, BANMF-S encourages the coherence of similar gene and cell pairs in the latent space, enhancing the potential to recover the underlying features. Besides, BANMF-S adopts a blocklization strategy to solve the traditional NMF problem through distributed Stochastic Gradient Descent method in a parallel way to accelerate the optimization. Numerical experiments on simulations and real datasets verify that BANMF-S can improve the accuracy of downstream clustering and pseudo-trajectory inference, and its performance is superior to seven state-of-the-art algorithms. AVAILABILITY: All data used in this work are downloaded from publicly available data sources, and their corresponding accession numbers or source URLs are provided in Supplementary File Section 5.1 Dataset Information. The source codes are publicly available in Github repository https://github.com/jiayingzhao/BANMF-S.

Asunto(s)

RESUMEN

BACKGROUND: Gene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate developing harmonized data handling and processing pipelines which in turn require considerable computational resources and in-depth bioinformatics expertise. RESULTS: Here, we present Cortexa-a comprehensive web portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform provides SplicePCA-a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All standardized gene expression and alternative splicing datasets can be downloaded for further in-depth downstream analysis without the need for extensive preprocessing. CONCLUSIONS: Cortexa provides a robust and readily available resource for unraveling the complexity of gene expression and alternative splicing regulatory processes in the mouse brain. The data portal is available at https://cortexa-rna.com/.

Asunto(s)

RESUMEN

BACKGROUND: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging. RESULTS: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property. CONCLUSIONS: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .

Asunto(s)

RESUMEN

BACKGROUND AND OBJECTIVES: Spinal cord injury (SCI) results in significant neurological deficits, and microglia play the critical role in regulating the immune microenvironment and neurological recovery. Protein lactylation has been found to modulate the function of immune cells. Therefore, this study aimed to elucidate the effects of glycolysis-derived lactate on microglial function and its potential neuroprotective mechanisms via lactylation after SCI. METHODS: Single-cell RNA sequencing (scRNA-seq) data were obtained from figshare to analyze cellular and molecular alterations within the spinal cord post-SCI, further focusing on the expression of microglia-related genes for cell sub-clustering, trajectory analysis, and glycolysis function analysis. We also evaluated the expression of lactylation-related genes in microglia between day 7 after SCI and sham group. Additionally, we established the mice SCI model and performed the bulk RNA sequencing in a time-dependent manner. The expression of glycolysis- and lactylation-related genes was evaluated, as well as the immune infiltration analysis based on the lactylation-related genes. Then, we investigated the bio-effects of lactate on the inflammation and polarization phenotype of microglia. Finally, adult male C57BL/6 mice were subjected to exercise first to increase lactate level, before SCI surgery, aiming to evaluate the protective effects of lactate-mediated lactylation of microglia-related proteins on SCI. RESULTS: scRNA-seq identified a subcluster of microglia, recombinant chemokine C-X3-C-motif receptor 1+ (CX3CR1+) microglia, which is featured by M1-like phenotype and increased after SCI. KEGG analysis revealed the dysfunctional glycolysis in microglia after SCI surgery, and AUCell analysis suggested that the decreased glycolysis an increased oxidative phosphorylation in CX3CR1+ microglia. Differential gene analysis suggested that several lactylation-related genes (Fabp5, Lgals1, Vim, and Nefl) were downregulated in CX3CR1+ microglia at day 7 after SCI, further validated by the results from bulk RNA sequencing. Immunofluorescence staining indicated the expression of lactate dehydrogenase A (LDHA) in CX3CR1+ microglia also decreased at day 7 after SCI. Cellular experiments demonstrated that the administration of lactate could increase the lactylation level and inhibit the pro-inflammatory phenotype in microglia. Functionally, exercise-mediated lactate production resulted in improved locomotor recovery and decreased inflammatory markers in SCI mice compared to SCI alone. CONCLUSIONS: In the subacute phase of SCI, metabolic remodeling in microglia may be key therapeutic targets to promote nerve regeneration, and lactate contributed to neuroprotection after SCI by influencing microglial lactylation and inflammatory phenotype, which offered a novel approach for therapeutic intervention.

Asunto(s)

RESUMEN

Single-cell RNA sequencing (scRNA-seq) data, susceptible to noise arising from biological variability and technical errors, can distort gene expression analysis and impact cell similarity assessments, particularly in heterogeneous populations. Current methods, including deep learning approaches, often struggle to accurately characterize cell relationships due to this inherent noise. To address these challenges, we introduce scAMF (Single-cell Analysis via Manifold Fitting), a framework designed to enhance clustering accuracy and data visualization in scRNA-seq studies. At the heart of scAMF lies the manifold fitting module, which effectively denoises scRNA-seq data by unfolding their distribution in the ambient space. This unfolding aligns the gene expression vector of each cell more closely with its underlying structure, bringing it spatially closer to other cells of the same cell type. To comprehensively assess the impact of scAMF, we compile a collection of 25 publicly available scRNA-seq datasets spanning various sequencing platforms, species, and organ types, forming an extensive RNA data bank. In our comparative studies, benchmarking scAMF against existing scRNA-seq analysis algorithms in this data bank, we consistently observe that scAMF outperforms in terms of clustering efficiency and data visualization clarity. Further experimental analysis reveals that this enhanced performance stems from scAMF's ability to improve the spatial distribution of the data and capture class-consistent neighborhoods. These findings underscore the promising application potential of manifold fitting as a tool in scRNA-seq analysis, signaling a significant enhancement in the precision and reliability of data interpretation in this critical field of study.

Asunto(s)

RESUMEN

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

Asunto(s)

RESUMEN

BACKGROUND: Immunotherapy represents a groundbreaking and monumental achievement in the field of cancer therapy, marking a significant advancement in fighting against this devastating disease. Lung cancer has showed consistent clinical improvements in response to immunotherapy treatments, yet, it is undeniable that challenges such as limited response rates acquire resistance, and the unclear fundamental mechanisms were inevitable problems. METHODS: The cellular composition was defined and distinguished through single-cell RNA sequencing (scRNA-seq) analysis of MPR (major pathologic response) and NMPR (non-major pathologic response) samples in GSE207422, including four primary MPR samples and eight primary NMPR samples. RESULTS: We found obvious difference in CD8+ T cell population between MPR and NMPR samples, with high expression of TYMS, RRM2, and BIRC5 in NPMR samples. Meanwhile, the proportion of macrophages and tumor epithelial cells infiltration increased in the NMPR samples. We discovered biomarkers (ACTN4, ATF3, BRD2, CDKN1A, and CHMP4B) in epithelial cells which were potentially represented worse outcomes. CONCLUSIONS: By exploring the difference of tumor microenvironment (TME) in samples with different corresponding degrees of neoadjuvant immunotherapy, this research introduces a number of novel biomarkers for predicting the response of treatment and a theoretical basis for overcoming immunotherapy resistance.

Asunto(s)

RESUMEN

Background: Passion fruit (Passiflora edulis) is loved for its delicious flavor and nutritious juice. Although studies have delved into the cultivation and enhancement of passion fruit varieties, the underlying factors contributing to the fruit's appealing aroma remain unclear. Methods: This study analyzed the full-length transcriptomes of two passion fruit cultivars with different flavor profiles: "Tainong 1" (TN1), known for its superior fruit flavor, and "Guihan 1" (GH1), noted for its strong environmental resilience but lackluster taste. Utilizing PacBio Iso-Seq and Illumina RNA-Seq technologies, we discovered terpene synthase (TPS) genes implicated in fruit ripening that may help explain the flavor disparities. Results: We generated 15,913 isoforms, with N50 lengths of 1,500 and 1,648 bp, and mean lengths of 1,319 and 1,463 bp for TN1 and GH1, respectively. Transcript and isoform lengths ranged from a maximum of 7,779 bp to a minimum of 200 and 209 bp. We identified 14,822 putative coding DNA sequences (CDSs) averaging 1,063 bp, classified 1,007 transcription factors (TFs) into 84 families. Additionally, differential expression analysis of ripening fruit from both cultivars revealed 314 upregulated and 43 downregulated unigenes in TN1 compared to GH1. The top 10 significantly enriched Gene Ontology (GO) terms for the differentially expressed genes (DEGs) indicated that TN1's upregulated genes were primarily involved in nutrient transport, whereas GH1's up-regulated genes were associated with resistance mechanisms. Meanwhile, 17 PeTPS genes were identified in P. edulis and 13 of them were TPS-b members. A comparative analysis when compared PeTPS with AtTPS highlighted an expansion of the PeTPS-b subfamily in P. edulis, suggesting a role in its fruit flavor profile. Conclusion: Our findings explain that the formation of fruit flavor is attributed to the upregulation of essential genes in synthetic pathway, in particular the expansion of TPS-b subfamily involved in terpenoid synthesis. This finding will also provide a foundational genetic basis for understanding the nuanced flavor differences in this species.

Asunto(s)

RESUMEN

R package pathlinkR is designed to aid transcriptomic analyses by streamlining and simplifying the process of analyzing and interpreting differentially expressed genes derived from human RNA-Seq data. It provides an integrated approach to performing pathway enrichment and network-based analyses, while also producing publication-quality figures to summarize these results, allowing users to more efficiently interpret their findings and extract biological meaning from large amounts of data. pathlinkR is available to install from the software repository Bioconductor at https://bioconductor.org/packages/pathlinkR/, with support available through the Bioconductor forums. The code, example, and supporting data is available on the GitHub repository at https://github.com/hancockinformatics/pathlinkR, under the GPL-3.0 license, where users may report problems or make suggestions using GitHub's issue system.

Asunto(s)

RESUMEN

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT .

Asunto(s)

RESUMEN

BACKGROUND: Organellar transcriptomes are relatively under-studied systems, with data related to full-length transcripts and posttranscriptional modifications remaining sparse. Direct RNA sequencing presents the possibility of accessing a previously unavailable layer of information pertaining to transcriptomic data, as well as circumventing the biases introduced by second-generation RNA-seq platforms. Direct long-read ONT sequencing allows for the isoform analysis of full-length transcripts and the detection of posttranscriptional modifications. However, there are still relatively few projects employing this method specifically for studying organellar transcriptomes. RESULTS: Candida albicans is a promising model for investigating nucleo-mitochondrial interactions. This work comprises ONT sequencing of the Candida albicans mitochondrial transcriptome along with the development of a dedicated data analysis pipeline. This approach allowed for the detection of complete transcript isoforms and posttranslational RNA modifications, as well as an analysis of C. albicans deletion mutants in genes coding for the 5' and 3' mitochondrial RNA exonucleases CaPET127 and CaDSS1. It also enabled for corrections to previous studies in terms of 3' and 5' transcript ends. A number of intermediate splicing isoforms was also discovered, along with mature and unspliced transcripts and changes in their abundances resulting from disruption of both 5' and 3' exonucleolytic processing. Multiple putative posttranscriptional modification sites have also been detected. CONCLUSIONS: This preliminary work demonstrates the suitability of direct RNA sequencing for studying yeast mitochondrial transcriptomes in general and provides new insights into the workings of the C. albicans mitochondrial transcriptome in particular. It also provides a general roadmap for analyzing mitochondrial transcriptomic data from other organisms.

Asunto(s)

Asunto(s)

RESUMEN

Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.

Asunto(s)

RESUMEN

BACKGROUND: SmithRNAs (Small MITochondrial Highly-transcribed RNAs) are a novel class of small RNA molecules that are encoded in the mitochondrial genome and regulate the expression of nuclear transcripts. Initial evidence for their existence came from the Manila clam Ruditapes philippinarum, where they have been described and whose activity has been biologically validated through RNA injection experiments. Current evidence on the existence of these RNAs in other species is based only on small RNA sequencing. As a preliminary step to characterize smithRNAs across different metazoan lineages, a dedicated, unified, analytical workflow is needed. RESULTS: We propose a novel workflow specifically designed for smithRNAs. Sequence data (from small RNA sequencing) uniquely mapping to the mitochondrial genome are clustered into putative smithRNAs and prefiltered based on their abundance, presence in replicate libraries and 5' and 3' transcription boundary conservation. The surviving sequences are subsequently compared to the untranslated regions of nuclear transcripts based on seed pairing, overall match and thermodynamic stability to identify possible targets. Ample collateral information and graphics are produced to help characterize these molecules in the species of choice and guide the operator through the analysis. The workflow was tested on the original Manila clam data. Under basic settings, the results of the original study are largely replicated. The effect of additional parameter customization (clustering threshold, stringency, minimum number of replicates, seed matching) was further evaluated. CONCLUSIONS: The study of smithRNAs is still in its infancy and no dedicated analytical workflow is currently available. At its core, the SmithHunter workflow builds over the bioinformatic procedure originally applied to identify candidate smithRNAs in the Manila clam. In fact, this is currently the only evidence for smithRNAs that has been biologically validated and, therefore, the elective starting point for characterizing smithRNAs in other species. The original analysis was readapted using current software implementations and some minor issues were solved. Moreover, the workflow was improved by allowing the customization of different analytical parameters, mostly focusing on stringency and the possibility of accounting for a minimal level of genetic differentiation among samples.

Asunto(s)