Comprehensive characterization of somatic mutations associated with chimeric RNAs in human cancers.

Chimeric RNAs, which can arise from gene recombination at the DNA level or non-canonical splicing events at the RNA level, have been identified as important roles in human tumors. Dysregulated gene expression caused by somatic mutations and altered splicing patterns of oncogenes or tumor suppressor genes can contribute to the development of tumors. Therefore, investigating the formation mechanism of chimeric RNAs via somatic mutations is critical for understanding tumor pathogenesis. This project is the first to propose studying the association between somatic single nucleotide variants and chimeric RNAs, identifying around 2900 somatic SNVs affecting chimeric RNAs in pan-cancer level. The somatic SNVs on chimeric RNAs were commonly observed in various types of tumor tissues, providing a valuable resource for future study. Additionally, these SNVs show distinct tumor specificity, and those with high frequency had a significant impact on the survival time of patients with tumors. Further research revealed that somatic SNVs associated with chimeric RNA (chiR-SNVs) were typically found within 10 nt of the junction site of chimeric RNAs and had a particularly significant effect on chimeric RNAs from different chromosomes. The enrichment analysis revealed that chiR-SNVs were significantly overrepresented in oncogenes and genes related to RNA binding proteins involved in RNA splicing, which could imply that chiR-SNVs may disrupt the process of RNA splicing and induce the occurrence of chimeric RNAs. This study sheds light on the potential molecular interaction mechanism between somatic SNVs and chimeric RNAs, which opens up a new avenue for researching disease pathway and tumorigenesis development.

InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data.

Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data.

An atlas of cell-type-specific interactome networks across 44 human tumor types.

BACKGROUND: Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors. METHODS: We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions. RESULTS: We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes. CONCLUSIONS: This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.

ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features.

The three-dimensional genome organization influences diverse nuclear processes. Here we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks, random forest, and gradient boosting to predict cohesin-mediated chromatin interaction strength between any two loci in the genome. The predictions of ChIPr correlate well with ChIA-PET data in four cell lines. The standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac, and H3K27me3 but works well with just RAD21 signal. Integrative analysis reveals novel insights into the role of CTCF motif, its orientation, and CTCF binding on cohesin-mediated chromatin interactions.

The effects of coagulation factors on the risk of endometriosis: a Mendelian randomization study.

BACKGROUND: Endometriosis is recognized as a complex gynecological disorder that can cause severe pain and infertility, affecting 6-10% of all reproductive-aged women. Endometriosis is a condition in which endometrial tissue, which normally lines the inside of the uterus, deposits in other tissues. The etiology and pathogenesis of endometriosis remain ambiguous. Despite debates, it is generally agreed that endometriosis is a chronic inflammatory disease, and patients with endometriosis appear to be in a hypercoagulable state. The coagulation system plays important roles in hemostasis and inflammatory responses. Therefore, the purpose of this study is to use publicly available GWAS summary statistics to examine the causal relationship between coagulation factors and the risk of endometriosis. METHODS: To investigate the causal relationship between coagulation factors and the risk of endometriosis, a two-sample Mendelian randomization (MR) analytic framework was used. A series of quality control procedures were followed in order to select eligible instrumental variables that were strongly associated with the exposures (vWF, ADAMTS13, aPTT, FVIII, FXI, FVII, FX, ETP, PAI-1, protein C, and plasmin). Two independent cohorts of European ancestry with endometriosis GWAS summary statistics were used: UK Biobank (4354 cases and 217,500 controls) and FinnGen (8288 cases and 68,969 controls). We conducted MR analyses separately in the UK Biobank and FinnGen, followed by a meta-analysis. The Cochran's Q test, MR-Egger intercept test, and leave-one-out sensitivity analyses were used to assess the heterogeneities, horizontal pleiotropy, and stabilities of SNPs in endometriosis. RESULTS: Our two-sample MR analysis of 11 coagulation factors in the UK Biobank suggested a reliable causal effect of genetically predicted plasma ADAMTS13 level on decreased endometriosis risk. A negative causal effect of ADAMTS13 and a positive causal effect of vWF on endometriosis were observed in the FinnGen. In the meta-analysis, the causal associations remained significant with a strong effect size. The MR analyses also identified potential causal effects of ADAMTS13 and vWF on different sub-phenotypes of endometrioses. CONCLUSIONS: Our MR analysis based on GWAS data from large-scale population studies demonstrated the causal associations between ADAMTS13/vWF and the risk of endometriosis. These findings suggest that these coagulation factors are involved in the development of endometriosis and may represent potential therapeutic targets for the management of this complex disease.

eaQTLdb: An atlas of enhancer activity quantitative trait loci across cancer types.

Enhancers are key regulatory elements that exert crucial roles in diverse biological processes, including tumorigenesis and cancer development. Active enhancers could produce transcripts termed enhancer RNAs (eRNAs), which could be used as an index of enhancer activity. Here, we present a versatile data portal, enhancer activity quantitative trait loci database (eaQTLdb; http://www.bioailab.com:3838/eaQTLdb), for exploring the effects of genetic variants on enhancer activity and prioritizing candidate variants across different cancer types. By leveraging the accumulated multiomics data, we systematically identified genetic variants which influence enhancer activity in different cancer types, termed as eaQTLs. We have linked the eaQTLs to hallmarks of cancer and patients' overall survival to illustrate their potential biological roles in cancer development and progression. Notably, eaQTLs associated with the infiltration abundance of 24 different immune cell types were identified and incorporated into eaQTLdb. In addition, we applied colocalization analyses to examine 59 complex diseases and traits to identify eaQTLs colocalized with diseases/traits GWAS signals. Overall, eaQTLdb, incorporating a rich resource for exploration of eaQTLs in different cancer types, will not only benefit users in prioritizing candidate genetic variants and enhancers, but also help researchers decipher the roles of eaQTLs in the dysregulated pathways of cancer and tumor immune microenvironment, opening new diagnostic and therapeutic avenues in precise medicine.

Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture.

Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation. SIGNIFICANCE: Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.

scAPAatlas: an atlas of alternative polyadenylation across cell types in human and mouse.

Alternative polyadenylation (APA) has been widely recognized as a crucial step during the post-transcriptional regulation of eukaryotic genes. Recent studies have demonstrated that APA exerts key regulatory roles in many biological processes and often occurs in a tissue- and cell-type-specific manner. However, to our knowledge, there is no database incorporating information about APA at the cell-type level. Single-cell RNA-seq is a rapidly evolving and powerful tool that enable APA analysis at the cell-type level. Here, we present a comprehensive resource, scAPAatlas (http://www.bioailab.com:3838/scAPAatlas), for exploring APA across different cell types, and interpreting potential biological functions. Based on the curated scRNA-seq data from 24 human and 25 mouse normal tissues, we systematically identified cell-type-specific APA events for different cell types and examined the correlations between APA and gene expression level. We also estimated the crosstalk between cell-type-specific APA events and microRNAs or RNA-binding proteins. A user-friendly web interface has been constructed to support browsing, searching and visualizing multi-layer information of cell-type-specific APA events. Overall, scAPAatlas, incorporating a rich resource for exploration of APA at the cell-type level, will greatly help researchers chart cell type with APA and elucidate the biological functions of APA.

The landscape of RNA polymerase II-associated chromatin interactions in prostate cancer.

Transcriptional dysregulation is a hallmark of prostate cancer (PCa). We mapped the RNA polymerase II-associated (RNA Pol II-associated) chromatin interactions in normal prostate cells and PCa cells. We discovered thousands of enhancer-promoter, enhancer-enhancer, as well as promoter-promoter chromatin interactions. These transcriptional hubs operate within the framework set by structural proteins - CTCF and cohesins - and are regulated by the cooperative action of master transcription factors, such as the androgen receptor (AR) and FOXA1. By combining analyses from metastatic castration-resistant PCa (mCRPC) specimens, we show that AR locus amplification contributes to the transcriptional upregulation of the AR gene by increasing the total number of chromatin interaction modules comprising the AR gene and its distal enhancer. We deconvoluted the transcription control modules of several PCa genes, notably the biomarker KLK3, lineage-restricted genes (KRT8, KRT18, HOXB13, FOXA1, ZBTB16), the drug target EZH2, and the oncogene MYC. By integrating clinical PCa data, we defined a germline-somatic interplay between the PCa risk allele rs684232 and the somatically acquired TMPRSS2-ERG gene fusion in the transcriptional regulation of multiple target genes - VPS53, FAM57A, and GEMIN4. Our studies implicate changes in genome organization as a critical determinant of aberrant transcriptional regulation in PCa.

Genome-wide germline correlates of the epigenetic landscape of prostate cancer.

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor's epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays.

Mitochondrion-processed TERC regulates senescence without affecting telomerase activities.

Mitochondrial dysfunctions play major roles in ageing. How mitochondrial stresses invoke downstream responses and how specificity of the signaling is achieved, however, remains unclear. We have previously discovered that the RNA component of Telomerase TERC is imported into mitochondria, processed to a shorter form TERC-53, and then exported back to the cytosol. Cytosolic TERC-53 levels respond to mitochondrial functions, but have no direct effect on these functions, suggesting that cytosolic TERC-53 functions downstream of mitochondria as a signal of mitochondrial functions. Here, we show that cytosolic TERC-53 plays a regulatory role on cellular senescence and is involved in cognition decline in 10 months old mice, independent of its telomerase function. Manipulation of cytosolic TERC-53 levels affects cellular senescence and cognition decline in 10 months old mouse hippocampi without affecting telomerase activity, and most importantly, affects cellular senescence in terc-/- cells. These findings uncover a senescence-related regulatory pathway with a non-coding RNA as the signal in mammals.

Mitochondrial Trafficking and Processing of Telomerase RNA TERC.

Mitochondrial dysfunctions play major roles in many diseases. However, how mitochondrial stresses are relayed to downstream responses remains unclear. Here we show that the RNA component of mammalian telomerase TERC is imported into mitochondria, processed to a shorter form TERC-53, and then exported back to the cytosol. We found that the import is regulated by PNPASE, and the processing is controlled by mitochondrion-localized RNASET2. Cytosolic TERC-53 levels respond to changes in mitochondrial functions but have no direct effect on these functions. These findings uncover a mitochondrial RNA trafficking pathway and provide a potential mechanism for mitochondria to relay their functional states to other cellular compartments.

Stress-responsive regulation of long non-coding RNA polyadenylation in Oryza sativa.

Recently, long non-coding RNAs (lncRNAs) have been demonstrated to be involved in many biological processes of plants; however, a systematic study on transcriptional and, in particular, post-transcriptional regulation of stress-responsive lncRNAs in Oryza sativa (rice) is lacking. We sequenced three types of RNA libraries (poly(A)+, poly(A)- and nuclear RNAs) under four abiotic stresses (cold, heat, drought and salt). Based on an integrative bioinformatics approach and ~200 high-throughput data sets, ~170 of which have been published, we revealed over 7000 lncRNAs, nearly half of which were identified for the first time. Notably, we found that the majority of the ~500 poly(A) lncRNAs that were differentially expressed under stress were significantly downregulated, but approximately 25% were found to have upregulated non-poly(A) forms. Moreover, hundreds of lncRNAs with downregulated polyadenylation (DPA) tend to be highly conserved, show significant nuclear retention and are co-expressed with protein-coding genes that function under stress. Remarkably, these DPA lncRNAs are significantly enriched in quantitative trait loci (QTLs) for stress tolerance or development, suggesting their potential important roles in rice growth under various stresses. In particular, we observed substantially accumulated DPA lncRNAs in plants exposed to drought and salt, which is consistent with the severe reduction of RNA 3'-end processing factors under these conditions. Taken together, the results of this study reveal that polyadenylation and subcellular localization of many rice lncRNAs are likely to be regulated at the post-transcriptional level. Our findings strongly suggest that many upregulated/downregulated lncRNAs previously identified by traditional RNA-seq analyses need to be carefully reviewed to assess the influence of post-transcriptional modification.

Recurrently deregulated lncRNAs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) cells often invade the portal venous system and subsequently develop into portal vein tumour thrombosis (PVTT). Long noncoding RNAs (lncRNAs) have been associated with HCC, but a comprehensive analysis of their specific association with HCC metastasis has not been conducted. Here, by analysing 60 clinical samples' RNA-seq data from 20 HCC patients, we have identified and characterized 8,603 candidate lncRNAs. The expression patterns of 917 recurrently deregulated lncRNAs are correlated with clinical data in a TCGA cohort and published liver cancer data. Matched array data from the 60 samples show that copy number variations (CNVs) and alterations in DNA methylation contribute to the observed recurrent deregulation of 235 lncRNAs. Many recurrently deregulated lncRNAs are enriched in co-expressed clusters of genes related to cell adhesion, immune response and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. Thus, we provide a valuable resource of functional lncRNAs and biomarkers associated with HCC tumorigenesis and metastasis.

Comprehensive analysis of long non-coding RNAs highlights their spatio-temporal expression patterns and evolutional conservation in Sus scrofa.

Despite modest sequence conservation and rapid evolution, long non-coding RNAs (lncRNAs) appear to be conserved in expression pattern and function. However, analysis of lncRNAs across tissues and developmental stages remains largely uncharacterized in mammals. Here, we systematically investigated the lncRNAs of the Guizhou miniature pig (Sus scrofa), which was widely used as biomedical model. We performed RNA sequencing across 9 organs and 3 developmental skeletal muscle, and developed a filtering pipeline to identify 10,813 lncRNAs (9,075 novel). Conservation patterns analysis revealed that 57% of pig lncRNAs showed homology to humans and mice based on genome alignment. 5,455 lncRNAs exhibited typical hallmarks of regulatory molecules, such as high spatio-temporal specificity. Notably, conserved lncRNAs exhibited higher tissue specificity than pig-specific lncRNAs and were significantly enriched in testis and ovary. Weighted co-expression network analysis revealed a set of conserved lncRNAs that are likely involved in postnatal muscle development. Based on the high degree of similarity in the structure, organization, and dynamic expression of pig lncRNAs compared with human and mouse lncRNAs, we propose that these lncRNAs play an important role in organ physiology and development in mammals. Our results provide a resource for studying animal evolution, morphological complexity, breeding, and biomedical research.

Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states.

Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control.

Systematic characterization of novel lncRNAs responding to phosphate starvation in Arabidopsis thaliana.

BACKGROUND: Previously, several long non-coding RNAs (lncRNAs) were characterized as regulators in phosphate (Pi) starvation responses. However, systematic studies of novel lncRNAs involved in the Pi starvation signaling pathways have not been reported. RESULTS: Here, we used a genome-wide sequencing and bioinformatics approach to identify both poly(A) + and poly(A)- lncRNAs that responded to Pi starvation in Arabidopsis thaliana. We sequenced shoot and root transcriptomes of the Arabidopsis seedlings grown under Pi-sufficient and Pi-deficient conditions, and predicted 1212 novel lncRNAs, of which 78 were poly(A)- lncRNAs. By employing strand-specific RNA libraries, we discovered many novel antisense lncRNAs for the first time. We further defined 309 lncRNAs that were differentially expressed between P+ and P- conditions in either shoots or roots. Through Gene Ontology enrichment of the associated protein-coding genes (co-expressed or close on the genome), we found that many lncRNAs were adjacent or co-expressed with the genes involved in several Pi starvation related processes, including cell wall organization and photosynthesis. In total, we identified 104 potential lncRNA targets of PHR1, a key regulator for transcriptional response to Pi starvation. Moreover, we identified 16 candidate lncRNAs as potential targets of miR399, another key regulator of plant Pi homeostasis. CONCLUSIONS: Altogether, our data provide a rich resource of candidate lncRNAs involved in the Pi starvation regulatory network.

Characterization of stress-responsive lncRNAs in Arabidopsis thaliana by integrating expression, epigenetic and structural features.

Recently, in addition to poly(A)+ long non-coding RNAs (lncRNAs), many lncRNAs without poly(A) tails, have been characterized in mammals. However, the non-polyA lncRNAs and their conserved motifs, especially those associated with environmental stresses, have not been fully investigated in plant genomes. We performed poly(A)- RNA-seq for seedlings of Arabidopsis thaliana under four stress conditions, and predicted lncRNA transcripts. We classified the lncRNAs into three confidence levels according to their expression patterns, epigenetic signatures and RNA secondary structures. Then, we further classified the lncRNAs to poly(A)+ and poly(A)- transcripts. Compared with poly(A)+ lncRNAs and coding genes, we found that poly(A)- lncRNAs tend to have shorter transcripts and lower expression levels, and they show significant expression specificity in response to stresses. In addition, their differential expression is significantly enriched in drought condition and depleted in heat condition. Overall, we identified 245 poly(A)+ and 58 poly(A)- lncRNAs that are differentially expressed under various stress stimuli. The differential expression was validated by qRT-PCR, and the signaling pathways involved were supported by specific binding of transcription factors (TFs), phytochrome-interacting factor 4 (PIF4) and PIF5. Moreover, we found many conserved sequence and structural motifs of lncRNAs from different functional groups (e.g. a UUC motif responding to salt and a AU-rich stem-loop responding to cold), indicated that the conserved elements might be responsible for the stress-responsive functions of lncRNAs.