Precise annotation of Drosophila mitochondrial genomes leads to insights into AT-rich regions.

In the present study, we performed precise annotation of Drosophila melanogaster, D. simulans, D. grimshawi, Bactrocera oleae mitochondrial (mt) genomes using pan RNA-seq analysis. Using PacBio cDNA-seq data from D. simulans, we precisely annotated the Transcription Initiation Sites (TISs) of the mt Heavy and Light strands in Drosophila mt genomes and reported that the polyA(+) and polyA(-) motifs in the CRs are associated with TISs. The discovery of the conserved polyA(+) and polyA(-) motifs provides insights into many polyA and polyT sequences in CRs of insect mt genomes, leading to reveal the mt transcription and its regulation in invertebrates. Notably, we propose that: (1) polyA/polyT motifs in CRs function as signals to initiate mtDNA transcription; (2) the duplication, recombination or mutation of these polyA/polyT sequences formed the AT-rich regions during evolution; and (3) since CRs of many invertebrate species still contain many polyA/polyT sequences, there is a high probability that several TISs and TTSs exist in invertebrate mt genomes.

How the Replication and Transcription Complex Functions in Jumping Transcription of SARS-CoV-2.

Background: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although unprecedented efforts are underway to develop therapeutic strategies against this disease, scientists have acquired only a little knowledge regarding the structures and functions of the CoV replication and transcription complex (RTC). Ascertaining all the RTC components and the arrangement of them is an indispensably step for the eventual determination of its global structure, leading to completely understanding all of its functions at the molecular level. Results: The main results include: 1) hairpins containing the canonical and non-canonical NSP15 cleavage motifs are canonical and non-canonical transcription regulatory sequence (TRS) hairpins; 2) TRS hairpins can be used to identify recombination regions in CoV genomes; 3) RNA methylation participates in the determination of the local RNA structures in CoVs by affecting the formation of base pairing; and 4) The eventual determination of the CoV RTC global structure needs to consider METTL3 in the experimental design. Conclusions: In the present study, we proposed the theoretical arrangement of NSP12-15 and METTL3 in the global RTC structure and constructed a model to answer how the RTC functions in the jumping transcription of CoVs. As the most important finding, TRS hairpins were reported for the first time to interpret NSP15 cleavage, RNA methylation of CoVs and their association at the molecular level. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, providing a crucial basis for future studies.

Full-Length Genome of an Ogataea polymorpha Strain CBS4732 ura3Δ Reveals Large Duplicated Segments in Subtelomeric Regions.

Background: Currently, methylotrophic yeasts (e.g., Pichia pastoris, Ogataea polymorpha, and Candida boindii) are subjects of intense genomics studies in basic research and industrial applications. In the genus Ogataea, most research is focused on three basic O. polymorpha strains-CBS4732, NCYC495, and DL-1. However, the relationship between CBS4732, NCYC495, and DL-1 remains unclear, as the genomic differences between them have not be exactly determined without their high-quality complete genomes. As a nutritionally deficient mutant derived from CBS4732, the O. polymorpha strain CBS4732 ura3Δ (named HU-11) is being used for high-yield production of several important proteins or peptides. HU-11 has the same reference genome as CBS4732 (noted as HU-11/CBS4732), because the only genomic difference between them is a 5-bp insertion. Results: In the present study, we have assembled the full-length genome of O. polymorpha HU-11/CBS4732 using high-depth PacBio and Illumina data. Long terminal repeat retrotransposons (LTR-rts), rDNA, 5' and 3' telomeric, subtelomeric, low complexity and other repeat regions were exactly determined to improve the genome quality. In brief, the main findings include complete rDNAs, complete LTR-rts, three large duplicated segments in subtelomeric regions and three structural variations between the HU-11/CBS4732 and NCYC495 genomes. These findings are very important for the assembly of full-length genomes of yeast and the correction of assembly errors in the published genomes of Ogataea spp. HU-11/CBS4732 is so phylogenetically close to NCYC495 that the syntenic regions cover nearly 100% of their genomes. Moreover, HU-11/CBS4732 and NCYC495 share a nucleotide identity of 99.5% through their whole genomes. CBS4732 and NCYC495 can be regarded as the same strain in basic research and industrial applications. Conclusion: The present study preliminarily revealed the relationship between CBS4732, NCYC495, and DL-1. Our findings provide new opportunities for in-depth understanding of genome evolution in methylotrophic yeasts and lay the foundations for the industrial applications of O. polymorpha CBS4732, NCYC495, DL-1, and their derivative strains. The full-length genome of O. polymorpha HU-11/CBS4732 should be included into the NCBI RefSeq database for future studies of Ogataea spp.

Genomic Feature Analysis of Betacoronavirus Provides Insights Into SARS and COVID-19 Pandemics.

In December 2019, the world awoke to a new betacoronavirus strain named severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Betacoronavirus consists of A, B, C and D subgroups. Both SARS-CoV and SARS-CoV-2 belong to betacoronavirus subgroup B. In the present study, we divided betacoronavirus subgroup B into the SARS1 and SARS2 classes by six key insertions and deletions (InDels) in betacoronavirus genomes, and identified a recently detected betacoronavirus strains RmYN02 as a recombinant strain across the SARS1 and SARS2 classes, which has potential to generate a new strain with similar risk as SARS-CoV and SARS-CoV-2. By analyzing genomic features of betacoronavirus, we concluded: (1) the jumping transcription and recombination of CoVs share the same molecular mechanism, which inevitably causes CoV outbreaks; (2) recombination, receptor binding abilities, junction furin cleavage sites (FCSs), first hairpins and ORF8s are main factors contributing to extraordinary transmission, virulence and host adaptability of betacoronavirus; and (3) the strong recombination ability of CoVs integrated other main factors to generate multiple recombinant strains, two of which evolved into SARS-CoV and SARS-CoV-2, resulting in the SARS and COVID-19 pandemics. As the most important genomic features of SARS-CoV and SARS-CoV-2, an enhanced ORF8 and a novel junction FCS, respectively, are indispensable clues for future studies of their origin and evolution. The WIV1 strain without the enhanced ORF8 and the RaTG13 strain without the junction FCS "RRAR" may contribute to, but are not the immediate ancestors of SARS-CoV and SARS-CoV-2, respectively.

A Negative Feedback Model to Explain Regulation of SARS-CoV-2 Replication and Transcription.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a preliminary understanding of the replication and transcription of SARS-CoV-2 has recently emerged, their regulation remains unknown. RESULTS: By comprehensive analysis of genome sequence and protein structure data, we propose a negative feedback model to explain the regulation of CoV replication and transcription, providing a molecular basis of the "leader-to-body fusion" model. The key step leading to the proposal of our model was that the transcription regulatory sequence (TRS) motifs were identified as the cleavage sites of nsp15, a nidoviral RNA uridylate-specific endoribonuclease (NendoU). According to this model, nsp15 regulates the synthesis of subgenomic RNAs (sgRNAs), and genomic RNAs (gRNAs) by cleaving TRSs. The expression level of nsp15 controls the relative proportions of sgRNAs and gRNAs, which in turn change the expression level of nsp15 to reach equilibrium between the CoV replication and transcription. CONCLUSION: The replication and transcription of CoVs are regulated by a negative feedback mechanism that influences the persistence of CoVs in hosts. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, and provide new clues for future studies. One important clue is that nsp15 may be an important and ideal target for the development of drugs (e.g., uridine derivatives) against CoVs.

A negative feedback model to explain regulation of SARS-CoV-2 replication and transcription

BackgroundCoronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a preliminary understanding of the replication and transcription mechanisms of SARS-CoV-2 has recently emerged, their regulation remains unclear. ResultsBased on reanalysis of public data, we propose a negative feedback model to explain the regulation of replication and transcription in--but not limited to--SARS-CoV-2. The key step leading to new discoveries was the identification of the cleavage sites of nsp15--an RNA uridylate-specific endoribonuclease, encoded by CoVs. According to this model, nsp15 regulates the synthesis of subgenomic RNAs (sgRNAs) and genomic RNAs (gRNAs) by cleaving transcription regulatory sequences in the body. The expression level of nsp15 determines the relative proportions of sgRNAs and gRNAs, which in turn change the expression level of nps15 to reach equilibrium between the replication and transcription of CoVs. ConclusionsThe replication and transcription of CoVs are regulated by a negative feedback mechanism that influences the persistence of CoVs in hosts. Our findings enrich fundamental knowledge in the field of gene expression and its regulation, and provide new clues for future studies. One important clue is that nsp15 may be an important and ideal target for the development of drugs (e.g. uridine derivatives) against CoVs.

Detecting distant-homology protein structures by aligning deep neural-network based contact maps.

Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

Predicting essential proteins from protein-protein interactions using order statistics.

Many computational methods have been proposed to predict essential proteins from protein-protein interaction (PPI) networks. However, it is still challenging to improve the prediction accuracy. In this study, we propose a new method, esPOS (essential proteins Predictor using Order Statistics) to predict essential proteins from PPI networks. Firstly, we refine the networks by using gene expression information and subcellular localization information. Secondly, we design some new features, which combine the protein predicted secondary structure with PPI network. We show that these new features are useful to predict essential proteins. Thirdly, we optimize these features by using a greedy method, and combine the optimized features by order statistic method. Our method achieves the prediction accuracy of 0.76-0.79 on two network datasets. The proposed method is available at https://sourceforge.net/projects/espos/.

NormExpression: An R Package to Normalize Gene Expression Data Using Evaluated Methods.

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets.

Using Pan RNA-Seq Analysis to Reveal the Ubiquitous Existence of 5' and 3' End Small RNAs.

In this study, we used pan RNA-seq analysis to reveal the ubiquitous existence of both 5' and 3' end small RNAs (5' and 3' sRNAs). 5' and 3' sRNAs alone can be used to annotate nuclear non-coding and mitochondrial genes at 1-bp resolution and identify new steady RNAs, which are usually transcribed from functional genes. Then, we provided a simple and cost effective way for the annotation of nuclear non-coding and mitochondrial genes and the identification of new steady RNAs, particularly long non-coding RNAs (lncRNAs). Using 5' and 3' sRNAs, the annotation of human mitochondrial was corrected and a novel ncRNA named non-coding mitochondrial RNA 1 (ncMT1) was reported for the first time in this study. We also found that most of human tRNA genes have downstream lncRNA genes as lncTRS-TGA1-1 and corrected the misunderstanding of them in previous studies. Using 5', 3', and intronic sRNAs, we reported for the first time that enzymatic double-stranded RNA (dsRNA) cleavage and RNA interference (RNAi) might be involved in the RNA degradation and gene expression regulation of U1 snRNA in human. We provided a different perspective on the regulation of gene expression in U1 snRNA. We also provided a novel view on cancer and virus-induced diseases, leading to find diagnostics or therapy targets from the ribonuclease III (RNase III) family and its related pathways. Our findings pave the way toward a rediscovery of dsRNA cleavage and RNAi, challenging classical theories.

Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data.

Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (positive samples) is much less than that of unknown genes which are typically considered to be negative samples. In addition, most methods have not utilized clinical data from patients with a specific disease to predict disease genes. In this study, we propose a disease gene prediction algorithm (called dgSeq) by combining protein-protein interaction (PPI) network, clinical RNA-Seq data, and Online Mendelian Inheritance in Man (OMIN) data. Our dgSeq constructs differential networks based on rewiring information calculated from clinical RNA-Seq data. To select balanced sets of non-disease genes (negative samples), a disease-gene network is also constructed from OMIM data. After features are extracted from the PPI networks and differential networks, the logistic regression classifiers are trained. Our dgSeq obtains AUC values of 0.88, 0.83, and 0.80 for identifying breast cancer genes, thyroid cancer genes, and Alzheimer's disease genes, respectively, which indicates its superiority to other three competing methods. Both gene set enrichment analysis and predicted results demonstrate that dgSeq can effectively predict new disease genes.

Complemented Palindromic Small RNAs First Discovered from SARS Coronavirus.

In this study, we report for the first time the existence of complemented palindromic small RNAs (cpsRNAs) and propose that cpsRNAs and palindromic small RNAs (psRNAs) constitute a novel class of small RNAs. The first discovered 19-nt cpsRNA UUAACAAGCUUGUUAAAGA, named SARS-CoV-cpsR-19, was detected from a 22-bp DNA complemented palindrome TCTTTAACAAGCTTGTTAAAGA in the severe acute respiratory syndrome coronavirus (SARS-CoV) genome. The phylogenetic analysis supported that this DNA complemented palindrome originated from bat betacoronavirus. The results of RNA interference (RNAi) experiments showed that one 19-nt segment corresponding to SARS-CoV-cpsR-19 significantly induced cell apoptosis. Using this joint analysis of the molecular function and phylogeny, our results suggested that SARS-CoV-cpsR-19 could play a role in SARS-CoV infection or pathogenesis. The discovery of cpsRNAs has paved a way to find novel markers for pathogen detection and to reveal the mechanisms underlying infection or pathogenesis from a different point of view. Researchers can use cpsRNAs to study the infection or pathogenesis of pathogenic viruses when these viruses are not available. The discovery of psRNAs and cpsRNAs, as a novel class of small RNAs, also inspire researchers to investigate DNA palindromes and DNA complemented palindromes with lengths of psRNAs and cpsRNAs in viral genomes.

A Novel Method for Drug Screen to Regulate G Protein-Coupled Receptors in the Metabolic Network of Alzheimer's Disease.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder and the pathogenesis of AD is poorly understood. G protein-coupled receptors (GPCRs) are involved in numerous key AD pathways and play a key role in the pathology of AD. To fully understand the pathogenesis of AD and design novel drug therapeutics, analyzing the connection between AD and GPCRs is of great importance. In this paper, we firstly build and analyze the AD-related pathway by consulting the KEGG pathway of AD and a mass of literature and collect 25 AD-related GPCRs for drug discovery. Then the ILbind and AutoDock Vina tools are integrated to find out potential drugs related to AD. According to the analysis of DUD-E dataset, we select five drugs, that is, Acarbose (ACR), Carvedilol (CVD), Digoxin (DGX), NADH (NAI), and Telmisartan (TLS), by sorting the ILbind scores (≥0.73). Then depending on their AutoDock Vina scores and pocket position information, the binding patterns of these five drugs are obtained. We analyze the regulation function of GPCRs in the metabolic network of AD based on the drug screen results, which may be helpful for the study of the off-target effect and the side effect of drugs.

Two novel lncRNAs discovered in human mitochondrial DNA using PacBio full-length transcriptome data.

In this study, we established a general framework to use PacBio full-length transcriptome sequencing for the investigation of mitochondrial RNAs. As a result, we produced the first full-length human mitochondrial transcriptome using public PacBio data and characterized the human mitochondrial genome with more comprehensive and accurate information. Other results included determination of the H-strand primary transcript, identification of the ND5/ND6AS/tRNAGluAS transcript, discovery of palindrome small RNAs (psRNAs) and construction of the "mitochondrial cleavage" model, etc. These results reported for the first time in this study fundamentally changed annotations of human mitochondrial genome and enriched knowledge in the field of animal mitochondrial studies. The most important finding was two novel long non-coding RNAs (lncRNAs) of MDL1 and MDL1AS exist ubiquitously in animal mitochondrial genomes.

Using Small RNA-seq Data to Detect siRNA Duplexes Induced by Plant Viruses.

Small interfering RNA (siRNA) duplexes are short (usually 21 to 24 bp) double-stranded RNAs (dsRNAs) with several overhanging nucleotides at both 5'- and 3'-ends. It has been found that siRNA duplexes bind the RNA-induced silencing complex (RISC) and cleave the sense strands with endonucleases. In this study, for the first time, we detected siRNA duplexes induced by plant viruses on a large scale using next-generation sequencing (NGS) data. In addition, we used the detected 21 nucleotide (nt) siRNA duplexes with 2 nt overhangs to construct a dataset for future data mining. The analytical results of the features in the detected siRNA duplexes were consistent with those from previous studies. The investigation of siRNA duplexes is useful for a better understanding of the RNA interference (RNAi) mechanism. It can also help to improve the virus detection based on the small RNA sequencing (sRNA-seq) technologies and to rationally design siRNAs for RNAi experiments.

Two featured series of rRNA-derived RNA fragments (rRFs) constitute a novel class of small RNAs.

In this study, we reported two featured series of rRNA-derived RNA fragments (rRFs) from the small RNA sequencing (sRNA-seq) data of Amblyomma testudinarium using the Illunima platform. Two series of rRFs (rRF5 and rRF3) were precisely aligned to the 5' and 3' ends of the 5.8S and 28S rRNA gene. The rRF5 and rRF3 series were significantly more highly expressed than the rRFs located in the body of the rRNA genes. These series contained perfectly aligned reads, the lengths of which varied progressively with 1-bp differences. The rRF5 and rRF3 series in the same expression pattern exist ubiquitously from ticks to human. The cellular experiments showed the RNAi knockdown of one 20-nt rRF3 induced the cell apoptosis and inhibited the cell proliferation. In addition, the RNAi knockdown resulted in a significant decrease of H1299 cells in the G2 phase of the cell cycle. These results indicated the rRF5 and rRF3 series were not random intermediates or products during rRNA degradation, but could constitute a new class of small RNAs that deserves further investigation.

PrAS: Prediction of amidation sites using multiple feature extraction.

Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at https://sourceforge.net/p/praspkg.

Western medication plus Traditional Chinese Medicine preparations in patients with chronic heart failure: a prospective, single-blind, randomized, controlled, and multicenter clinical trial.

OBJECTIVE: To assess the efficacy and safety in patients with chronic heart failure (CHF) of Western medication plus Traditional Chinese Medicine (TCM) preparations. METHODS: This prospective, single-blind, randomized, controlled, and multicenter clinical trial began on September 17, 2008, and was completed on June 25, 2011. A total of 340 inpatients, aged 40-79 years, with exacerbating CHF from 10 hospitals were enrolled and randomly allocated within 24 h of admission. The trial included three intervention periods. During hospitalization, the control group received western medication for CHF and the treatment group received Danhong injection with Shenfu injection or Shenmai injection. After discharge, all patients were treated with Qiliqiangxin capsules and Buyiqiangxin tablets or a placebo for 6 months. After the 6-month intervention, both groups received only continuous western medication. The primary endpoint was all-cause mortality. The efficacy assessments were as follows: B-type natriuretic peptide (BNP), Lee's HF score, the 6-minute walking test (6MWT), left ventricular ejection fraction (LVEF), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). The safety assessments were as follows: blood and urine routine examination, hepatic and renal function, electrolytes in blood and adverse events. RESULTS: Compared with the control group, the treatment group showed a 30.99% reduction in all-cause mortality and an improved survival rate. The treatment group showed greater improvement in 6MWT (P = 0.02) than the control group on discharge, after 12-month follow-up, there was a time-group interaction for MLHFQ (P = 0.03). Incidence rate of adverse events and other relevant safety indexes were not statistically significant between the two groups. CONCLUSION: Western medication plus TCM treatment can increase 6-minute walking distance (improve exercise tolerance) and quality of life with heart failure patients.

Gene expression profiling reveals U1 snRNA regulates cancer gene expression.

U1 small nuclear RNA (U1 snRNA), as one of the most abundant ncRNAs in human cells, plays an important role in splicing of pre-mRNAs. Compared to previous studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by abnormalities of U1 snRNA, this study is to investigate how U1 snRNA over-expression affects the expression of mammal genes on a genome-wide scale. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using microarray experiments, 916 genes or loci were identified significantly Differentially Expressed (DE). These 595 up-regulated DE genes and 321 down-regulated DE genes were analyzed using annotations from GO categories and pathways from the KEGG database. As a result, three of 12 enriched pathways were well-known cancer pathways, while the other nine pathways were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggested that U1 snRNA could regulate cancer gene expression. The microarray data under the GEO Series accession number GSE84304 is available in the NCBI GEO database.

PacBio full-length transcriptome profiling of insect mitochondrial gene expression.

In this study, we sequenced the first full-length insect transcriptome using the Erthesina fullo Thunberg based on the PacBio platform. We constructed the first quantitative transcription map of animal mitochondrial genomes and built a straightforward and concise methodology to investigate mitochondrial gene transcription, RNA processing, mRNA maturation and several other related topics. Most of the results were consistent with the previous studies, while to the best of our knowledge some findings were reported for the first time in this study. The new findings included the high levels of mitochondrial gene expression, the 3' polyadenylation and possible 5' m(7)G caps of rRNAs, the isoform diversity of 12S rRNA, the polycistronic transcripts and natural antisense transcripts of mitochondrial genes et al. These findings could challenge and enrich fundamental concepts of mitochondrial gene transcription and RNA processing, particularly of the rRNA primary (sequence) structure. The methodology constructed in this study can also be used to study gene expression or RNA processing of nuclear genomes.