Transcription factors interact with RNA to regulate genes.

Transcription factors (TFs) orchestrate the gene expression programs that define each cell's identity. The canonical TF accomplishes this with two domains, one that binds specific DNA sequences and the other that binds protein coactivators or corepressors. We find that at least half of TFs also bind RNA, doing so through a previously unrecognized domain with sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat. RNA binding contributes to TF function by promoting the dynamic association between DNA, RNA, and TF on chromatin. TF-RNA interactions are a conserved feature important for vertebrate development and disrupted in disease. We propose that the ability to bind DNA, RNA, and protein is a general property of many TFs and is fundamental to their gene regulatory function.

CASPR, an analysis pipeline for single and paired guide RNA CRISPR screens, reveals optimal target selection for long non-coding RNAs.

MOTIVATION: CRISPR-Cas9 loss-of-function (LOF) pooled screening promises to identify which long non-coding RNAs (lncRNAs), amongst the many thousands to have been annotated so far, are capable of mediating cellular functions. The two principal LOF perturbations, CRISPR-inhibition and CRISPR-deletion, employ one and two guide RNAs, respectively. However, no software solution has the versatility to identify hits across both modalities, and the optimal design parameters for such screens remain poorly understood. RESULTS: Here, we present CRISPR Analysis for Single and Paired RNA-guides (CASPR), a user-friendly, end-to-end screen analysis tool. CASPR is compatible with both CRISPRi and CRISPR-del screens, and balances sensitivity and specificity by generating consensus predictions from multiple algorithms. Benchmarking on ground-truth sets of cancer-associated lncRNAs demonstrates CASPR's improved sensitivity with respect to existing methods. Applying CASPR to published screens, we identify two parameters that predict lncRNA hits: expression and annotation quality of the transcription start site. Thus, CASPR is a versatile and complete solution for lncRNA CRISPR screen analysis, and reveals principles for including lncRNAs in screening libraries. AVAILABILITY AND IMPLEMENTATION: https://judithbergada.github.io/CASPR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tumour mutations in long noncoding RNAs enhance cell fitness.

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

Is Evolutionary Conservation a Useful Predictor for Cancer Long Noncoding RNAs? Insights from the Cancer LncRNA Census 3.

Evolutionary conservation is a measure of gene functionality that is widely used to prioritise long noncoding RNAs (lncRNA) in cancer research. Intriguingly, while updating our Cancer LncRNA Census (CLC), we observed an inverse relationship between year of discovery and evolutionary conservation. This observation is specific to cancer over other diseases, implying a sampling bias in the selection of lncRNA candidates and casting doubt on the value of evolutionary metrics for the prioritisation of cancer-related lncRNAs.

Multi-hallmark long noncoding RNA maps reveal non-small cell lung cancer vulnerabilities.

Long noncoding RNAs (lncRNAs) are widely dysregulated in cancer, yet their functional roles in cancer hallmarks remain unclear. We employ pooled CRISPR deletion to perturb 831 lncRNAs detected in KRAS-mutant non-small cell lung cancer (NSCLC) and measure their contribution to proliferation, chemoresistance, and migration across two cell backgrounds. Integrative analysis of these data outperforms conventional "dropout" screens in identifying cancer genes while prioritizing disease-relevant lncRNAs with pleiotropic and background-independent roles. Altogether, 80 high-confidence oncogenic lncRNAs are active in NSCLC, which tend to be amplified and overexpressed in tumors. A follow-up antisense oligonucleotide (ASO) screen shortlisted two candidates, Cancer Hallmarks in Lung LncRNA 1 (CHiLL1) and GCAWKR, whose knockdown consistently suppressed cancer hallmarks in two- and three-dimension tumor models. Molecular phenotyping reveals that CHiLL1 and GCAWKR control cellular-level phenotypes via distinct transcriptional networks. This work reveals a multi-dimensional functional lncRNA landscape underlying NSCLC that contains potential therapeutic vulnerabilities.

Detection and localization of early- and late-stage cancers using platelet RNA.

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

Cancer LncRNA Census 2 (CLC2): an enhanced resource reveals clinical features of cancer lncRNAs.

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs' evolutionary and clinical significance.

Blood platelet RNA enables the detection of multiple sclerosis.

BACKGROUND: In multiple sclerosis (MS), clinical assessment, MRI and cerebrospinal fluid are important in the diagnostic process. However, no blood biomarker has been confirmed as a useful tool in the diagnostic work-up. OBJECTIVES: Blood platelets contain a rich spliced mRNA repertoire that can alter during megakaryocyte development but also during platelet formation and platelet circulation. In this proof of concept study, we evaluate the diagnostic potential of spliced blood platelet RNA for the detection of MS. METHODS: We isolated and sequenced platelet RNA of blood samples obtained from 57 MS patients and 66 age- and gender-matched healthy controls (HCs). 60% was used to develop a particle swarm-optimized (PSO) support vector machine classification algorithm. The remaining 40% served as an independent validation series. RESULTS: In total, 1249 RNAs with differential spliced junction expression levels were identified between platelets of MS patients as compared to HCs, including EPSTI1, IFI6, and RPS6KA3, in line with reported inflammatory signatures in the blood of MS patients. The RNAs were subsequently used as input for a MS classifier, capable of detecting MS with 80% accuracy in the independent validation series. CONCLUSIONS: Spliced platelet RNA may enable the blood-based diagnosis of MS, warranting large-scale validation.

Tumor-Educated Platelet RNA for the Detection and (Pseudo)progression Monitoring of Glioblastoma.

Tumor-educated platelets (TEPs) are potential biomarkers for cancer diagnostics. We employ TEP-derived RNA panels, determined by swarm intelligence, to detect and monitor glioblastoma. We assessed specificity by comparing the spliced RNA profile of TEPs from glioblastoma patients with multiple sclerosis and brain metastasis patients (validation series, n = 157; accuracy, 80%; AUC, 0.81 [95% CI, 0.74-0.89; p < 0.001]). Second, analysis of patients with glioblastoma versus asymptomatic healthy controls in an independent validation series (n = 347) provided a detection accuracy of 95% and AUC of 0.97 (95% CI, 0.95-0.99; p < 0.001). Finally, we developed the digitalSWARM algorithm to improve monitoring of glioblastoma progression and demonstrate that the TEP tumor scores of individual glioblastoma patients represent tumor behavior and could be used to distinguish false positive progression from true progression (validation series, n = 20; accuracy, 85%; AUC, 0.86 [95% CI, 0.70-1.00; p < 0.012]). In conclusion, TEPs have potential as a minimally invasive biosource for blood-based diagnostics and monitoring of glioblastoma patients.

Determination of the presence of 5-methylcytosine in Paramecium tetraurelia.

5-methylcytosine DNA methylation regulates gene expression and developmental programming in a broad range of eukaryotes. However, its presence and potential roles in ciliates, complex single-celled eukaryotes with germline-somatic genome specialization via nuclear dimorphism, are largely uncharted. While canonical cytosine methyltransferases have not been discovered in published ciliate genomes, recent studies performed in the stichotrichous ciliate Oxytricha trifallax suggest de novo cytosine methylation during macronuclear development. In this study, we applied bisulfite genome sequencing, DNA mass spectrometry and antibody-based fluorescence detection to investigate the presence of DNA methylation in Paramecium tetraurelia. While the antibody-based methods suggest cytosine methylation, DNA mass spectrometry and bisulfite sequencing reveal that levels are actually below the limit of detection. Our results suggest that Paramecium does not utilize 5-methylcytosine DNA methylation as an integral part of its epigenetic arsenal.

Correction: Determination of the presence of 5-methylcytosine in Paramecium tetraurelia.

[This corrects the article DOI: 10.1371/journal.pone.0206667.].

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.

Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.