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Circular RNAs (circRNAs) are circularized, single-stranded RNAs that are covalently linked. With their abundance in tissues and developmental stage-specific expression, circRNAs participate in a variety of physiological and pathological processes. In this review, we discuss the development of circRNAs used as biomarkers and therapeutic targets for cardiovascular diseases (CVDs), focusing on recent discoveries and applications of exosomal circRNAs that highlight opportunities and challenges. Some studies have identified a spectrum of circRNAs that are differentially expressed in CVDs, while other studies further manipulated specific circRNA expression and showed an ameliorated pathogenic state such as ischemic injury, hypertrophy, and cardiac fibrosis. Studies and applications of circRNAs are being rapidly developed. We expect to see clinical use of circRNAs as biomarkers and targets for disease treatment in the near future.

Sorghum (Sorghum bicolor), also known as great millet, is one of the most popular cultivated grass species in the world. Sorghum is frequently consumed as food for humans and animals as well as used for ethanol production. In this study, we conducted de novo transcriptome assembly for sorghum variety Taejin by next-generation sequencing, obtaining 8.748 GB of raw data. The raw data in this study can be available in NCBI SRA database with accession number of SRX1715644. Using the Trinity program, we identified 222,161 transcripts from sorghum variety Taejin. We further predicted coding regions within the assembled transcripts by the TransDecoder program, resulting in a total of 148,531 proteins. We carried out BLASTP against the Swiss-Prot protein sequence database to annotate the functions of the identified proteins. To our knowledge, this is the first transcriptome data for a sorghum variety derived from Korea, and it can be usefully applied to the generation of genetic markers.

The adzuki bean (Vigna angularis), a member of the family Fabaceae, is widely grown in Asia, from East Asia to the Himalayas. The adzuki bean is known as an ingredient that adds sweetness to diverse desserts made in Eastern Asian countries. Libraries prepared from two V. angularis varieties referred to as Taejin Black and Taejin Red were paired-end sequenced using the Illumina HiSeq 2000 system. The raw data in this study can be available in NCBI SRA database with accession numbers of SRR3406660 and SRR3406553. After de novo transcriptome assembly using Trinity, we obtained 324,219 and 280,056 transcripts from Taejin Black and Taejin Red, respectively. We predicted a total of 238,321 proteins and 179,519 proteins for Taejin Black and Taejin Red, respectively, by the TransDecoder program. We carried out BLASTP on the predicted proteins against the Swiss-Prot protein sequence database to predict the putative functions of identified proteins. Taken together, we provide transcriptomes of two adzuki bean varieties by RNA-Seq, which might be usefully applied to generate molecular markers.

Foxtail millet (Setaria italica) belonging to the family Poaceae is an important millet that is widely cultivated in East Asia. Of the cultivated millets, the foxtail millet has the longest history and is one of the main food crops in South India and China. Moreover, foxtail millet is a model plant system for biofuel generation utilizing the C4 photosynthetic pathway. In this study, we carried out de novo transcriptome assembly for the foxtail millet variety Taejin collected from Korea using next-generation sequencing. We obtained a total of 8.676 GB raw data by paired-end sequencing. The raw data in this study can be available in NCBI SRA database with accession number of SRR3406552. The Trinity program was used to de novo assemble 145,332 transcripts. Using the TransDecoder program, we predicted 82,925 putative proteins. BLASTP was performed against the Swiss-Prot protein sequence database to annotate the functions of identified proteins, resulting in 20,555 potentially novel proteins. Taken together, this study provides transcriptome data for the foxtail millet variety Taejin by RNA-Seq.

Sour cherry (Prunus cerasus) in the genus Prunus in the family Rosaceae is one of the most popular stone fruit trees worldwide. Of known sour cherry cultivars, the Schattenmorelle is a famous old sour cherry with a high amount of fruit production. The Schattenmorelle was selected before 1650 and described in the 1800s. This cultivar was named after gardens of the Chateau de Moreille in which the cultivar was initially found. In order to identify new genes and to develop genetic markers for sour cherry, we performed a transcriptome analysis of a sour cherry. We selected the cultivar Schattenmorelle, which is among commercially important cultivars in Europe and North America. We obtained 2.05 GB raw data from the Schattenmorelle (NCBI accession number: SRX1187170). De novo transcriptome assembly using Trinity identified 61,053 transcripts in which N50 was 611 bp. Next, we identified 25,585 protein coding sequences using TransDecoder. The identified proteins were blasted against NCBI's non-redundant database for annotation. Based on blast search, we taxonomically classified the obtained sequences. As a result, we provide the transcriptome of sour cherry cultivar Schattenmorelle using next generation sequencing.

Prunus mume, belonging to the Prunus genus, is an Asian tree, and its common names are Chinese plum and Japanese plum. P. mume are cultivated for fruit production as well as ornamental purposes. In this study, we conducted de novo transcriptome assembly for two selected P. mume cultivars referred to as Takada and Wallyoung (commercially important cultivars for fruit production and ornamental trees, respectively) by RNA-sequencing. We obtained 9.14 GB and 9.48 GB sequence data from Takada and Wallyoung (NCBI accession numbers: SRX1187101 and SRX1187169), respectively. De novo transcriptome assembly identified 130,989 and 116,941 transcripts for Takada and Wallyoung, respectively. In addition, we identified 96,681 and 91,429 proteins from Takada and Wallyoung, respectively, by TransDecoder program. We performed BLASTP against the NCBI non-redundant (nr) datasets to annotate identified proteins. This study provides transcriptomes and proteomes for two different P. mume cultivars, which might be useful for comparative transcriptome analyses and assist development of genetic markers.

Peach (Prunus persica) is one of the most popular stone fruits worldwide. Next generation sequencing (NGS) has facilitated genome and transcriptome analyses of several stone fruit trees. In this study, we conducted de novo transcriptome analyses of two peach cultivars grown in Korea. Leaves of two cultivars, referred to as Jangtaek and Mibaek, were harvested and used for library preparation. The two prepared libraries were paired-end sequenced by the HiSeq2000 system. We obtained 8.14 GB and 9.62 GB sequence data from Jangtaek and Mibaek (NCBI accession numbers: SRS1056585 and SRS1056587), respectively. The Trinity program was used to assemble two transcriptomes de novo, resulting in 110,477 (Jangtaek) and 136,196 (Mibaek) transcripts. TransDecoder identified possible coding regions in assembled transcripts. The identified proteins were subjected to BLASTP search against NCBI's non-redundant database for functional annotation. This study provides transcriptome data for two peach cultivars, which might be useful for genetic marker development and comparative transcriptome analyses.

Apricot (Prunus armeniaca) belonging to the Prunus species is a popular kind of stone fruit tree. Apricot is native to Armenia and is currently cultivated in many countries with climates adaptable for apricot growth. In general, fresh fruits as well as dried apricot are produced. However, the information associated with genes and genetic markers for apricot is very limited. In this study, we carried out de novo transcriptome assembly for two selected apricot cultivars referred to as Harcot and Ungarische Beste, which are commercially important apricot cultivars in the world, using next generation sequencing. We obtained a total of 9.31 GB and 8.88 GB raw data from Harcot and Ungarische Beste (NCBI accession numbers: SRX1186946 and SRX1186893), respectively. De novo transcriptome assembly using Trinity identified 147,501 and 152,235 transcripts for Harcot and Ungarische Beste, respectively. Next, we identified 113,565 and 126,444 proteins from Harcot and Ungarische Beste using the TransDecoder program. We performed BLASTP against an NCBI non-redundant (nr) dataset to annotate identified proteins. Taken together, we provide transcriptomes of two different apricot cultivars by RNA-Seq.

Plum is a globally grown stone fruit and can be divided into several species. In particular, the Prunus salicina, which is native to China, is widely grown in many fruit orchards in Korea and Japan, as well as the United States and Australia. The transcriptome data for Prunus salicina has not been reported to our knowledge. In this study, we performed de novo transcriptome assembly for two selected P. salicina cultivars referred to as Akihime and Formosa (commercially important plum cultivars in Korea) using next generation sequencing. We obtained a total of 9.04 GB and 8.68 GB raw data from Akihime and Formosa, respectively. De novo transcriptome assembly using Trinity revealed 155,169 and 160,186 transcripts for Akihime and Formosa. Next, we identified 121,278 and 116,544 proteins from Akihime and Formosa using TransDecoder. We performed BLASTP against the NCBI non-redundant (nr) dataset to annotate proteins. Taken together, this is the first transcriptome data for P. salicina to our knowledge.

Emerging evidence has linked arsenic exposure and metabolic homeostasis, but the mechanism is incompletely understood, especially at relatively low concentrations. In this study, we used a mouse model to evaluate the health impacts and metabolic toxicity of arsenic exposure in drinking water at environmentally relevant levels (0.25 and 1.0 ppm). Our results indicated that arsenic damaged intestinal barrier and induced arsenic accumulation, oxidative stress, and pathological changes in the liver and illum. Interestingly, arsenic increased the hepatic triglyceride (TG) and total cholesterol (TC), while reduced serum TG and TC levels. The liver transcriptome found that arsenic exposure caused transcriptome perturbation and promoted hepatic lipid accumulation by regulating the exogenous fatty acids degradation and apolipoproteins related genes. The serum metabolomics identified 74 and 88 differential metabolites in 0.25 and 1.0 ppm, respectively. The KEGG disease and subcellular location analysis indicated that arsenic induced liver and intestinal diseases, and the mitochondrion might be the target organelle for arsenic-induced toxicity. Co-enrichment of transcriptome and metabolome identified 24 metabolites and 9 genes as metabolic toxicity biomarkers. Moreover, 40 male (20 nonalcoholic fatty liver disease (NAFLD) cases and 20 healthy controls) was further selected to validate our findings. Importantly, the significantly changed L-palmitoylcarnitine, 3-hydroxybutyric acid, 2-hydroxycaproic acid and 6 genes of Hadha, Acadl, Aldh3a2, Cpt1a, Cpt2, and Acox1 were found in the NAFLD cases. The results from integrated multi-omics and chemical-protein network analysis indicated that L-palmitoylcarnitine played a critical role in metabolic toxicity by regulating mitochondrial fatty acids β-oxidation genes (Cpt1a, Cpt2). In conclusion, these findings provided new clues for the metabolic toxicity of arsenic exposure at environmentally relevant levels, which involved in the late-life NAFLD development. Our results also contribute to understanding the human responses and phenotypic changes to this hazardous material exposure in the environment.

Industrial areas emit large amounts of extremely high temperatures and aggravate the extreme urban heat island (E-UHI) effect, which has become a non-negligible environmental issue worldwide. Due to the lack of in-depth understanding of the relationship between E-UHI and surface structural synergy, few studies explored the causes and mitigation measures of E-UHI. Therefore, this study accordingly defined the E-UHI effect at a finer scale based on the thermal characteristics and human thermal perceptions, and a multi-indicator’ method based on machine learning was proposed to quantify internal causes and collaborative mitigation measures. The results revealed that the average E-UHI scale shows a quadratic growth in Wu’an. Artificial heat from production space (PS) is the main cause of E-UHI formation. The synergy between green space (GS), PS and shape of industrial land determines the development of E-UHI. Although both the decrease of PS and the increase of GS are ways to mitigate high land surface temperature, when PS is higher than 50%, the cooling effect of GS is not obvious. When GS is less than 2.5%, the adjustment of PS is invalid for improving the thermal environment. For mitigating E-UHI, a factory with high PS needs to increase its shape index. The synergy between the structural indicators is a novel model to solve the shortcoming of adjusting a single factor in mitigating complex E-UHI. The results provide theoretical guidance for mitigating E-UHIs.

Co-based catalyst has excellent chlorine resistance, so it is an effective catalyst to degrade VC at present. A series of high-performance CoxBa1 catalysts were prepared by doping Ba into Co3O4 and changing the Ba/Co mol ratio. Under the condition of space velocity of 15,000 ml·g−1·h−1, the conversion rate of VC reached 90% at 199 °C in Co19Ba1, the temperature is much lower than T90 of pure Co3O4 (309 °C), and the catalysis activity of Co19Ba1 was higher than that of the other alkali metal-doped Co19M1 (M = Sr, Ca). Ba species in CoxBa1 mainly exist in the form of BaCO3. With the addition of BaCO3, the particle size of CoxBa1 decreases, resulting in the increase of active site of CoxBa1. BaCO3 can also make CoxBa1 obtain better low-temperature reducibility and more active components. In addition, the increase of BaCO3 content will also increase the basic sites of CoxBa1, and more basic sites can effectively protect Co3O4 from Cl poisoning. When Ba/Co is 1/19, the content of BaCO3 is the highest, so Co19Ba1 also shows the best activity.

With the rapid accumulation of gene expression data from various technologies, e.g., microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry out dimensional reduction and feature (signature genes) selection in support of making sense out of such high dimensional data. These computational methods significantly facilitate further data analysis and interpretation, such as gene function enrichment analysis, cancer biomarker detection, and drug targeting identification in precision medicine. Although numerous methods have been developed for feature selection in bioinformatics, it is still a challenge to choose the appropriate methods for a specific problem and seek for the most reasonable ranking features. Meanwhile, the paired gene expression data under matched case-control design (MCCD) is becoming increasingly popular, which has often been used in multi-omics integration studies and may increase feature selection efficiency by offsetting similar distributions of confounding features. The appropriate feature selection methods specifically designed for the paired data, which is named as matched-pairs feature selection (MPFS), however, have not been maturely developed in parallel. In this review, we compare the performance of 10 feature-selection methods (eight MPFS methods and two traditional unpaired methods) on two real datasets by applied three classification methods, and analyze the algorithm complexity of these methods through the running of their programs. This review aims to induce and comprehensively present the MPFS in such a way that readers can easily understand its characteristics and get a clue in selecting the appropriate methods for their analyses. Keywords: Matched-pairs feature selection, Matched case-control design, Paired data, Gene expression