Mitochondrial sequencing identifies long noncoding RNA features that promote binding to PNPase.

Extranuclear localization of long noncoding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where polynucleotide phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and cross-linked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. lncRNA sequence and structure were evaluated through supervised [classification and regression trees (CART) and support vector machines (SVM)] machine learning algorithms. In HL-1 cells, quantitative PCR of PNPase CLIP knockout mutants (KH and S1) was performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. One hundred twelve (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most abundant. Most noncoding RNAs binding PNPase were lncRNAs, including Malat1. lncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. The binding of lncRNAs to PNPase was decreased through the knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs binding to PNPase and undergoing import into the mitochondrion.NEW & NOTEWORTHY Long noncoding RNAs (lncRNAs) are relatively novel RNAs with increasingly prominent roles in regulating genetic expression, mainly in the nucleus but more recently in regions such as the mitochondrion. This study explores how lncRNAs interact with polynucleotide phosphorylase (PNPase), a protein that regulates RNA import into the mitochondrion. Machine learning identified several RNA structural features that improved lncRNA binding to PNPase, which may be useful in targeting RNA therapeutics to the mitochondrion.

More than the SRY: The Non-Coding Landscape of the Y Chromosome and Its Importance in Human Disease.

Historically, the Y chromosome has presented challenges to classical methodology and philosophy of understanding the differences between males and females. A genetic unsolved puzzle, the Y chromosome was the last chromosome to be fully sequenced. With the advent of the Human Genome Project came a realization that the human genome is more than just genes encoding proteins, and an entire universe of RNA was discovered. This dark matter of biology and the black box surrounding the Y chromosome have collided over the last few years, as increasing numbers of non-coding RNAs have been identified across the length of the Y chromosome, many of which have played significant roles in disease. In this review, we will uncover what is known about the connections between the Y chromosome and the non-coding RNA universe that originates from it, particularly as it relates to long non-coding RNAs, microRNAs and circular RNAs.

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.

Flower lose, a cell fitness marker, predicts COVID-19 prognosis.

Risk stratification of COVID-19 patients is essential for pandemic management. Changes in the cell fitness marker, hFwe-Lose, can precede the host immune response to infection, potentially making such a biomarker an earlier triage tool. Here, we evaluate whether hFwe-Lose gene expression can outperform conventional methods in predicting outcomes (e.g., death and hospitalization) in COVID-19 patients. We performed a post-mortem examination of infected lung tissue in deceased COVID-19 patients to determine hFwe-Lose's biological role in acute lung injury. We then performed an observational study (n = 283) to evaluate whether hFwe-Lose expression (in nasopharyngeal samples) could accurately predict hospitalization or death in COVID-19 patients. In COVID-19 patients with acute lung injury, hFwe-Lose is highly expressed in the lower respiratory tract and is co-localized to areas of cell death. In patients presenting in the early phase of COVID-19 illness, hFwe-Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8-100% and a negative predictive value of 64.1-93.2%. hFwe-Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93-0.97 in predicting hospitalization/death. Specifically, this is significantly higher than the prognostic value of combining biomarkers (serum ferritin, D-dimer, C-reactive protein, and neutrophil-lymphocyte ratio), patient age and comorbidities (AUROC of 0.67-0.92). The cell fitness marker, hFwe-Lose, accurately predicts outcomes in COVID-19 patients. This finding demonstrates how tissue fitness pathways dictate the response to infection and disease and their utility in managing the current COVID-19 pandemic.