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.

Metabolic phenotyping of healthy and diseased human RPE cells.

Purpose: Metabolic defects in retinal pigment epithelium (RPE) are underlying many retinal degenerative diseases. This study aims to identify the nutrient requirements of healthy and diseased human RPE cells. Methods: We profiled the utilization of 183 nutrients in human RPE cells: 1) differentiated and dedifferentiated fetal RPE (fRPE), 2) induced pluripotent stem cell derived-RPE (iPSC RPE), 3) Sorsby fundus dystrophy (SFD) patient-derived iPSC RPE and its CRISPR-corrected isogenic SFD (cSFD) iPSC RPE, and 5) ARPE-19 cell lines cultured under different conditions. Results: Differentiated fRPE cells and healthy iPSC RPE cells can utilize 51 and 48 nutrients respectively, including sugars, intermediates from glycolysis and tricarboxylic acid (TCA) cycle, fatty acids, ketone bodies, amino acids, and dipeptides. However, when fRPE cells lose epithelial phenotype through dedifferentiated, they can only utilize 17 nutrients, primarily sugar and glutamine-related amino acids. SFD RPE cells can utilize 37 nutrients; however, Compared to cSFD RPE and healthy iPSC RPE, they are unable to utilize lactate, some TCA cycle intermediates, and short-chain fatty acids. Nonetheless, they show increased utilization of branch-chain amino acids (BCAAs) and BCAA-containing dipeptides. The dedifferentiated ARPE-19 cells in traditional culture media cannot utilize lactate and ketone bodies. In contrast, nicotinamide supplementation promotes differentiation into epithelial phenotype, restoring the ability to use these nutrients. Conclusions: Epithelial phenotype confers metabolic flexibility to the RPE for utilizing various nutrients. SFD RPE cells have reduced metabolic flexibility, relying on the oxidation of BCAAs. Our findings highlight the importance of nutrient availability and utilization in RPE differentiation and diseases.

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.

Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets.

N6-methyladenosine (M6A) in fetal offspring modifies mitochondrial gene expression following gestational nano-TiO2 inhalation exposure.

N6-methyladenosine (m6A) is the most prominent epitranscriptomic modification to RNA in eukaryotes, but it's role in adaptive changes within the gestational environment are poorly understood. We propose that gestational exposure to nano titanium dioxide (TiO2) contributes to cardiac m6A methylation in fetal offspring and influences mitochondrial gene expression. 10-week-old pregnant female FVB/NJ wild-type mice underwent 6 nonconsecutive days of whole-body inhalation exposure beginning on gestational day (GD) 5. Mice were exposed to filtered room air or nano-TiO2 with a target aerosol mass concentration of 12 mg/m3. At GD 15 mice were humanely killed and cardiac RNA and mitochondrial proteins extracted. Immunoprecipitation with m6A antibodies was performed followed by sequencing of immunoprecipitant (m6A) and input (mRNA) on the Illumina NextSeq 2000. Protein extraction, preparation, and LC-MS/MS were used for mitochondrial protein quantification. There were no differences in maternal or fetal pup weights, number of pups, or pup heart weights between exposure and control groups. Transcriptomic sequencing revealed 3648 differentially expressed mRNA in nano-TiO2 exposed mice (Padj ≤ 0.05). Transcripts involved in mitochondrial bioenergetics were significantly downregulated (83 of 85 genes). 921 transcripts revealed significant m6A methylation sites (Padj ≤ 0.10). 311 of the 921 mRNA were identified to have both 1) significantly altered expression and 2) differentially methylated sites. Mitochondrial proteomics revealed decreased expression of ATP Synthase subunits in the exposed group (P ≤ 0.05). The lack of m6A modifications to mitochondrial transcripts suggests a mechanism for decreased transcript stability and reduced protein expression due to gestational nano-TiO2 inhalation exposure.

Manipulation of the miR-378a/mt-ATP6 regulatory axis rescues ATP synthase in the diabetic heart and offers a novel role for lncRNA Kcnq1ot1.

Diabetes mellitus has been linked to an increase in mitochondrial microRNA-378a (miR-378a) content. Enhanced miR-378a content has been associated with a reduction in mitochondrial genome-encoded mt-ATP6 abundance, supporting the hypothesis that miR-378a inhibition may be a therapeutic option for maintaining ATP synthase functionality during diabetes mellitus. Evidence also suggests that long noncoding RNAs (lncRNAs), including lncRNA potassium voltage-gated channel subfamily Q member 1 overlapping transcript 1 (Kcnq1ot1), participate in regulatory axes with microRNAs (miRs). Prediction analyses indicate that Kcnq1ot1 has the potential to bind miR-378a. This study aimed to determine if loss of miR-378a in a genetic mouse model could ameliorate cardiac dysfunction in type 2 diabetes mellitus (T2DM) and to ascertain whether Kcnq1ot1 interacts with miR-378a to impact ATP synthase functionality by preserving mt-ATP6 levels. MiR-378a was significantly higher in patients with T2DM and 25-wk-old Db/Db mouse mitochondria, whereas mt-ATP6 and Kcnq1ot1 levels were significantly reduced when compared with controls. Twenty-five-week-old miR-378a knockout Db/Db mice displayed preserved mt-ATP6 and ATP synthase protein content, ATP synthase activity, and preserved cardiac function, implicating miR-378a as a potential therapeutic target in T2DM. Assessments following overexpression of the 500-bp Kcnq1ot1 fragment in established mouse cardiomyocyte cell line (HL-1) cardiomyocytes overexpressing miR-378a revealed that Kcnq1ot1 may bind and significantly reduce miR-378a levels, and rescue mt-ATP6 and ATP synthase protein content. Together, these data suggest that Kcnq1ot1 and miR-378a may act as constituents in an axis that regulates mt-ATP6 content, and that manipulation of this axis may provide benefit to ATP synthase functionality in type 2 diabetic heart.