Climate change has significantly increased the frequency of our exposure to heat, adversely affecting human health and industries. Heat stress is an environmental stress defined as the exposure of organisms and cells to abnormally high temperatures. To comprehensively explain the mechanisms underlying an organism’s response to heat stress, it is essential to investigate and analyze genes that have been under-represented or less well-known in previous studies. In this study, we analyzed heat stress-responsive genes using a meta-analysis of numerous gene expression datasets from the public database. We obtained 322 human and 242 mouse pairs as the heat exposure and control data. The meta-analysis of these data identified 76 upregulated and 37 downregulated genes common to both humans and mice. We performed enrichment, protein–protein interaction network, and transcription factor target gene analyses for these genes. Furthermore, we conducted an integrated analysis of these genes using publicly available chromatin immunoprecipitation sequencing (ChIP-seq) data for HSF1, HSF2, and PPARGC1A (PGC-1α) as well as gene2pubmed data from the existing literature. The results identified previously overlooked genes, such as ABHD3, ZFAND2A, and USPL1, as commonly upregulated genes. Further functional analysis of these genes can contribute to coping with climate change and potentially lead to technological advancements.

Background: Climate change has significantly increased the frequency of exposure to heat, adversely affecting human health and various industrial sectors. Heat stress is an environmental stress defined as the exposure of organisms and cells to abnormally high temperatures. Heat-stress research has predominantly focused on response systems involving heat shock factors acting as transcription factors and heat shock proteins functioning as molecular chaperones. However, to comprehensively elucidate the mechanisms underlying an organism’s response to heat stress, it is essential to investigate and analyze genes that have been underrepresented, less well-known, or overlooked in previous studies. In this study, we analyzed heat stress-responsive genes using a meta-analysis of numerous gene expression datasets. Results: First, we collected paired heat exposure and control data from public databases. Gene expression data were obtained for 322 human and 242 mouse pairs. The expression ratios (HN-ratios) of the collected pairs were calculated, and the identification of upregulated and downregulated expression profiles was determined according to defined thresholds. The number of upregulated and downregulated genes was calculated as the heat stress - non-treatment score (HN-score), which is the value of: [number of upregulated genes] - [number of downregulated genes] for each gene and was used as the index of analysis. The HN-score comprehensively evaluated gene expression variation, and 76 upregulated and 37 downregulated genes common to human and mouse were identified. We performed enrichment, protein-protein interaction network, and transcription factor target gene analyses. Furthermore, we evaluated the extracted genes through integrated analysis using publicly available ChIP-seq data for HSF1, HSF2, and PPARGC1A (PGC1-α), and gene2pubmed data, which were sourced from previous literature. The results identified previously overlooked genes, such as ABHD3 , ZFAND2A , and USPL1 , as commonly upregulated genes. Conclusions: Based on the findings of this study, further functional analysis of the extracted genes using genome editing and other technologies has the potential to contribute to coping with climate change and potentially lead to new knowledge and technological advances.

Base-editing technologies, particularly cytosine base editors (CBEs), allow precise gene modification without introducing double-strand breaks; however, unintended RNA off-target effects remain a critical concern and are under studied. To address this gap, we developed the Pipeline for CRISPR-induced Transcriptome-wide Unintended RNA Editing (PiCTURE), a standardized computational pipeline for detecting and quantifying transcriptome-wide CBE-induced RNA off-target events. PiCTURE identifies both canonical ACW (W = A or T/U) motif-dependent and non-canonical RNA off-targets, revealing a broader WCW motif that underlies many unanticipated edits. Additionally, we developed two machine learning models based on the DNABERT-2 language model, termed STL and SNL, which outperformed motif-only approaches in terms of accuracy, precision, recall, and F1 score. To demonstrate the practical application of our predictive model for CBE-induced RNA off-target risk, we integrated PiCTURE outputs with the Predicting RNA Off-target compared with Tissue-specific Expression for Caring for Tissue and Organ (PROTECTiO) pipeline and estimated RNA off-target risk for each transcript showing tissue-specific expression. The analysis revealed differences among tissues: while the brain and ovaries exhibited relatively low off-target burden, the colon and lungs displayed relatively high risks. Our study provides a comprehensive framework for RNA off-target profiling, emphasizing the importance of advanced machine learning-based classifiers in CBE safety evaluations and offering valuable insights to inform the development of safer genome-editing therapies.

Motivation: Life science databases include large collections of public transcriptome and large-scale structural data. The reuse and integration of these datasets may facilitate the identification of understudied genes and enable functional annotation across distantly related species, including plants and humans. Results: In this study, we used heat stress-responsive genes in rice as a model to functionally annotate previously understudied genes by integrating publicly available transcriptome data with structural information from the AlphaFold Protein Structure Database. Initially, we conducted a meta-analysis of public heat stress-related transcriptome datasets, identified gene groups, and verified stress-related terms through enrichment analysis. Subsequently, we performed structural alignment and sequence alignment between rice and human proteins, focusing on candidates exhibiting low sequence similarity but high structural similarity (LS–HS conditions). We further incorporated supplemental data from public databases, including shared domain information between rice and human. This approach yielded a unique set of LS–HS candidates, notably those associated with metal homeostasis, such as iron and copper metabolism. Overall, our integrative method provided insights into these genes by leveraging diverse, publicly available datasets. Availability and implementation: The “plant2human workflow” for this analysis is available at https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.1206.8 .

Building on our work at DBCLS BioHackathon 2023 (BH23), where we introduced QPX and promoted pathway modeling with WikiPathways (Pico et al., 2008) using PathVisio (Kutmon et al., 2015), we now focused on creating new pathway diagrams for diverse species and registering them in WikiPathways with functional annotations. In parallel, we deployed WikiPathways node data into Elasticsearch to enable fast and flexible search and integration of pathway information.

Base-editing technologies, particularly cytosine base editors (CBEs), allow precise gene modification without introducing double-strand breaks; however, unintended RNA off-target effects remain a critical concern and are under-studied. To address this gap, we developed PiCTURE, a standardized computational pipeline for detecting and quantifying transcriptome-wide CBE-induced RNA off-target events. PiCTURE identifies both canonical ACW (W = A or T/U) motif-dependent and non-canonical RNA off-targets, revealing a broader WCW motif that underlies many unanticipated edits. Additionally, we developed two machine learning models based on the DNABERT-2 language model, termed STL and SNL, which outperformed motif-only approaches in terms of accuracy, precision, recall, and F1 score. To demonstrate the practical application of our predictive model for CBE-induced RNA off-target risk, we integrated PiCTURE outputs with the PROTECTiO pipeline and estimated RNA off-target risk for each transcript showing tissue-specific expression. The analysis revealed differences among tissues: while the brain and ovaries exhibited relatively low off-target burden, the colon and lungs displayed relatively high risks. Our study provides a comprehensive frame-work for RNA off-target profiling, emphasizing the importance of advanced machine learning-based classifiers in CBE safety evaluations and offering valuable insights for the development of safer genome-editing therapies.