deepBase: a database for deeply annotating and mining deep sequencing data

Advances in high-throughput next-generation sequencing technology have reshaped the transcriptomic research landscape. However, exploration of these massive data remains a daunting challenge. In this study, we describe a novel database, deepBase, which we have developed to facilitate the comprehensive annotation and discovery of small RNAs from transcriptomic data. The current release of deepBase contains deep sequencing data from 185 small RNA libraries from diverse tissues and cell lines of seven organisms: human, mouse, chicken, Ciona intestinalis, Drosophila melanogaster, Caenhorhabditis elegans and Arabidopsis thaliana. By analyzing ∼14.6 million unique reads that perfectly mapped to more than 284 million genomic loci, we annotated and identified ∼380 000 unique ncRNA-associated small RNAs (nasRNAs), ∼1.5 million unique promoter-associated small RNAs (pasRNAs), ∼4.0 million unique exon-associated small RNAs (easRNAs) and ∼6 million unique repeat-associated small RNAs (rasRNAs). Furthermore, 2038 miRNA and 1889 snoRNA candidates were predicted by miRDeep and snoSeeker. All of the mapped reads can be grouped into about 1.2 million RNA clusters. For the purpose of comparative analysis, deepBase provides an integrative, interactive and versatile display. A convenient search option, related publications and other useful information are also provided for further investigation. deepBase is available at: http://deepbase.sysu.edu.cn/

Bibliometric analysis of evolutionary trends and hotspots of super-enhancers in cancer

Introduction: In the past decade, super-enhancer (SE) has become a research hotspot with increasing attention on cancer occurrence, development, and prognosis. To illustrate the hotspots of SE in cancer research and its evolutionary tendency, bibliometric analysis was carried out for this topic.Methods: Literature published before Dec 31, 2022, in WOSCC, was systematically classified, and Citespace, bibliometric.com/app, and GraphPad Prism analyzed the data.Results: After screening out inappropriate documents and duplicate data, 911 publications were selected for further bibliometric analysis. The top five research areas were Oncology (257, 28.211%), Cell Biology (210, 23.052%), Biochemistry Molecular Biology (209, 22.942%), Science Technology Other Topics (138, 15.148%), and Genetics Heredity (132, 14.490%). The United States of America (United States) has the highest number of documents (462, 50.71%), followed by China (303, 33.26%). Among the most productive institutions, four of which are from the United States and one from Singapore, the National University of Singapore. Harvard Medical School (7.68%) has the highest percentage of articles. Young, Richard A, with 32 publications, ranks first in the number of articles. The top three authors came from Whitehead Institute for Biomedical Research as a research team. More than two-thirds of the research are supported by the National Institutes of Health of the United States (337, 37.654%) and the United States Department of Health Human Services (337, 37.654%). And “super enhancer” (525), “cell identity” (258), “expression” (223), “cancer” (205), and “transcription factor” (193) account for the top 5 occurrence keywords.Discussion: Since 2013, SE and cancer related publications have shown a rapid growth trend. The United States continues to play a leading role in this field, as the top literature numbers, affiliations, funding agencies, and authors were all from the United States, followed by China and European countries. A high degree of active cooperation is evident among a multitude of countries. The role of SEs in cell identity, gene transcription, expression, and inhibition, as well as the relationship between SEs and TFs, and the selective inhibition of SEs, have received much attention, suggesting that they are hot issues for research

Development of marker-free transgenic Jatropha plants with increased levels of seed oleic acid

<p>Abstract</p> <p>Background</p> <p><it>Jatropha curcas </it>is recognized as a new energy crop due to the presence of the high amount of oil in its seeds that can be converted into biodiesel. The quality and performance of the biodiesel depends on the chemical composition of the fatty acids present in the oil. The fatty acids profile of the oil has a direct impact on ignition quality, heat of combustion and oxidative stability. An ideal biodiesel composition should have more monounsaturated fatty acids and less polyunsaturated acids. Jatropha seed oil contains 30% to 50% polyunsaturated fatty acids (mainly linoleic acid) which negatively impacts the oxidative stability and causes high rate of nitrogen oxides emission.</p> <p>Results</p> <p>The enzyme 1-acyl-2-oleoyl-sn-glycero-3-phosphocholine delta 12-desaturase (FAD2) is the key enzyme responsible for the production of linoleic acid in plants. We identified three putative <it>delta </it><it>12 </it><it>fatty acid desaturase </it>genes in <it>Jatropha </it>(<it>JcFAD2s</it>) through genome-wide analysis and downregulated the expression of one of these genes, <it>JcFAD2-1</it>, in a seed-specific manner by RNA interference technology. The resulting <it>JcFAD2-1 </it>RNA interference transgenic plants showed a dramatic increase of oleic acid (> 78%) and a corresponding reduction in polyunsaturated fatty acids (< 3%) in its seed oil. The control <it>Jatropha </it>had around 37% oleic acid and 41% polyunsaturated fatty acids. This indicates that FAD2-1 is the major enzyme responsible for converting oleic acid to linoleic acid in <it>Jatropha</it>. Due to the changes in the fatty acids profile, the oil of the <it>JcFAD2-1 </it>RNA interference seed was estimated to yield a cetane number as high as 60.2, which is similar to the required cetane number for conventional premium diesel fuels (60) in Europe. The presence of high seed oleic acid did not have a negative impact on other <it>Jatropha </it>agronomic traits based on our preliminary data of the original plants under greenhouse conditions. Further, we developed a marker-free system to generate the transgenic <it>Jatropha </it>that will help reduce public concerns for environmental issues surrounding genetically modified plants.</p> <p>Conclusion</p> <p>In this study we produced seed-specific <it>JcFAD2-1 </it>RNA interference transgenic <it>Jatropha </it>without a selectable marker. We successfully increased the proportion of oleic acid versus linoleic in <it>Jatropha </it>through genetic engineering, enhancing the quality of its oil.</p

Association of lipoprotein(a) and major adverse cardiovascular events in patients with percutaneous coronary intervention.

Introduction(#br)The aim of the current study was to evaluate the association between lipoprotein(a) [Lp(a)] and major adverse cardiovascular events (MACEs) in patients with percutaneous coronary intervention (PCI) treatment.(#br)Material and methods(#br)This was a retrospective study. The demographics, prior medical histories, comorbidities and laboratory parameters were collected from the electronic health record. All participants were followed up for 1 year after the indexed PCI. Studied end points were a composite of MACEs including all-cause mortality, non-fatal myocardial infarction (MI), non-fatal ischemic stroke, transient ischemic attack and stent restenosis.(#br)Results(#br)During 1-year follow-up, 87 MACEs occurred. Compared to patients who did not have MACEs, patients who had MACEs were older, more likely to have higher body mass index, diabetes mellitus and left main lesion, and also had higher baseline low density lipoprotein cholesterol (LDL-C) and Lp(a) levels. All patients in both groups were prescribed aspirin and clopidogrel at discharge. Nearly 97.4% and 95.4% of patients in both groups were treated with statins and a higher proportion of patients in the MACE group were treated with ezetimibe (11.5% vs. 3.5%, p < 0.05). In multivariate regression analysis, diabetes mellitus, LDL-C, Lp(a) and glomerular filtration rate were independent risk factors for MACEs; statin use appeared to be a protective factor for MACEs. Patients with increased Lp(a) level had significantly higher incidence of MACEs than the normal Lp(a) level group ( p = 0.001).(#br)Conclusions(#br)Baseline serum Lp(a) can be used to predict MACEs in patients after PCI treatment, which was independent of LDL-C

Prediction of liver cancer prognosis based on immune cell marker genes

IntroductionMonitoring the response after treatment of liver cancer and timely adjusting the treatment strategy are crucial to improve the survival rate of liver cancer. At present, the clinical monitoring of liver cancer after treatment is mainly based on serum markers and imaging. Morphological evaluation has limitations, such as the inability to measure small tumors and the poor repeatability of measurement, which is not applicable to cancer evaluation after immunotherapy or targeted treatment. The determination of serum markers is greatly affected by the environment and cannot accurately evaluate the prognosis. With the development of single cell sequencing technology, a large number of immune cell-specific genes have been identified. Immune cells and microenvironment play an important role in the process of prognosis. We speculate that the expression changes of immune cell-specific genes can indicate the process of prognosis.MethodTherefore, this paper first screened out the immune cell-specific genes related to liver cancer, and then built a deep learning model based on the expression of these genes to predict metastasis and the survival time of liver cancer patients. We verified and compared the model on the data set of 372 patients with liver cancer.ResultThe experiments found that our model is significantly superior to other methods, and can accurately identify whether liver cancer patients have metastasis and predict the survival time of liver cancer patients according to the expression of immune cell-specific genes.DiscussionWe found these immune cell-specific genes participant multiple cancer-related pathways. We fully explored the function of these genes, which would support the development of immunotherapy for liver cancer

SnoRNAs from the filamentous fungus Neurospora crassa: structural, functional and evolutionary insights

<p>Abstract</p> <p>Background</p> <p>SnoRNAs represent an excellent model for studying the structural and functional evolution of small non-coding RNAs involved in the post-transcriptional modification machinery for rRNAs and snRNAs in eukaryotic cells. Identification of snoRNAs from <it>Neurospora crassa</it>, an important model organism playing key roles in the development of modern genetics, biochemistry and molecular biology will provide insights into the evolution of snoRNA genes in the fungus kingdom.</p> <p>Results</p> <p>Fifty five box C/D snoRNAs were identified and predicted to guide 71 2'-O-methylated sites including four sites on snRNAs and three sites on tRNAs. Additionally, twenty box H/ACA snoRNAs, which potentially guide 17 pseudouridylations on rRNAs, were also identified. Although not exhaustive, the study provides the first comprehensive list of two major families of snoRNAs from the filamentous fungus <it>N. crassa</it>. The independently transcribed strategy dominates in the expression of box H/ACA snoRNA genes, whereas most of the box C/D snoRNA genes are intron-encoded. This shows that different genomic organizations and expression modes have been adopted by the two major classes of snoRNA genes in <it>N. crassa </it>. Remarkably, five gene clusters represent an outstanding organization of box C/D snoRNA genes, which are well conserved among yeasts and multicellular fungi, implying their functional importance for the fungus cells. Interestingly, alternative splicing events were found in the expression of two polycistronic snoRNA gene hosts that resemble the UHG-like genes in mammals. Phylogenetic analysis further revealed that the extensive separation and recombination of two functional elements of snoRNA genes has occurred during fungus evolution.</p> <p>Conclusion</p> <p>This is the first genome-wide analysis of the filamentous fungus <it>N. crassa </it>snoRNAs that aids in understanding the differences between unicellular fungi and multicellular fungi. As compared with two yeasts, a more complex pattern of methylation guided by box C/D snoRNAs in multicellular fungus than in unicellular yeasts was revealed, indicating the high diversity of post-transcriptional modification guided by snoRNAs in the fungus kingdom.</p