A network-based algorithm for the identification of moonlighting noncoding RNAs and its application in sepsis

Abstract Moonlighting proteins provide more options for cells to execute multiple functions without increasing the genome and transcriptome complexity. Although there have long been calls for computational methods for the prediction of moonlighting proteins, no method has been designed for determining moonlighting long noncoding ribonucleicacidz (RNAs) (mlncRNAs). Previously, we developed an algorithm MoonFinder for the identification of mlncRNAs at the genome level based on the functional annotation and interactome data of lncRNAs and proteins. Here, we update MoonFinder to MoonFinder v2.0 by providing an extensive framework for the detection of protein modules and the establishment of RNA–module associations in human. A novel measure, moonlighting coefficient, was also proposed to assess the confidence of an ncRNA acting in a moonlighting manner. Moreover, we explored the expression characteristics of mlncRNAs in sepsis, in which we found that mlncRNAs tend to be upregulated and differentially expressed. Interestingly, the mlncRNAs are mutually exclusive in terms of coexpression when compared to the other lncRNAs. Overall, MoonFinder v2.0 is dedicated to the prediction of human mlncRNAs and thus bears great promise to serve as a valuable R package for worldwide research communities (https://cran.r-project.org/web/packages/MoonFinder/index.html). Also, our analyses provide the first attempt to characterize mlncRNA expression and coexpression properties in adult sepsis patients, which will facilitate the understanding of the interaction and expression patterns of mlncRNAs.

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Unleash multifunctional role of long noncoding RNAs biomarker panel in breast cancer: a predictor classification model

Epigenomics ◽

10.2217/epi-2019-0291 ◽

2020 ◽

Vol 12 (14) ◽

pp. 1215-1237

Author(s):

Eman A Toraih ◽

Aya El-Wazir ◽

Essam Al Ageeli ◽

Mohammad H Hussein ◽

Mohamed M Eltoukhy ◽

...

Keyword(s):

Breast Cancer ◽

Expression Patterns ◽

Noncoding Rnas ◽

The Other ◽

Classification Model ◽

Gene Expression Patterns ◽

Healthy Controls ◽

Reverse Transcription Pcr ◽

Non Coding Rnas

Aim: We aimed to explore the circulating expression profile of nine lncRNAs (MALAT1, HOTAIR, PVT1, H19, ROR, GAS5, ANRIL, BANCR, MIAT) in breast cancer (BC) patients relative to normal and risky individuals. Methods: Serum relative expressions of the specified long non-coding RNAs were quantified in 155 consecutive women, using quantitative reverse-transcription PCR. Random Forest (RF) and decision tree were also applied. Results: Significant MALAT1 upregulation and GAS5 downregulation could discriminate risky women from healthy controls. Overexpression of the other genes showed good diagnostic performances. Lower GAS5 levels were associated with metastasis and recurrence. RF model revealed a better performance when combining gene expression patterns with risk factors. Conclusion: The studied panel could be utilized as diagnostic/prognostic biomarkers in BC, providing promising epigenetic-based therapeutic targets.

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NoRCE: non-coding RNA sets cis enrichment tool

BMC Bioinformatics ◽

10.1186/s12859-021-04112-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Gulden Olgun ◽

Afshan Nabi ◽

Oznur Tastan

Keyword(s):

Expression Patterns ◽

Target Prediction ◽

Enrichment Analysis ◽

Fruit Fly ◽

Relevant Information ◽

R Package ◽

Data Repository ◽

Biologically Relevant ◽

Gene Sets ◽

Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

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Cloning and mapping of human PKIB and PKIG, and comparison of tissue expression patterns of three members of the protein kinase inhibitor family, including PKIA

Biochemical Journal ◽

10.1042/bj3490403 ◽

2000 ◽

Vol 349 (2) ◽

pp. 403-407 ◽

Cited By ~ 9

Author(s):

Lihua ZHENG ◽

Long YU ◽

Qiang TU ◽

Min ZHANG ◽

Hua HE ◽

...

Keyword(s):

Skeletal Muscle ◽

Protein Kinase ◽

Nuclear Export ◽

Kinase Inhibitor ◽

Expression Patterns ◽

Nuclear Export Signal ◽

Tissue Expression ◽

Protein Kinase Inhibitor ◽

The Other ◽

Dependent Protein

Two novel members of the human cAMP-dependent protein kinase inhibitor (PKI) gene family, PKIB and PKIG, were cloned. The deduced proteins showed 70% and 90% identity with mouse PKIβ and PKIγ respectively. Both the already identified pseudosubstrate site and leucine-rich nuclear export signal motifs were defined from the 11 PKIs of different species. The PKIB and PKIG genes were mapped respectively to chromosome 6q21-22.1, using a radiation hybrid GB4 panel, and to chromosome 20q13.12-13.13, using a Stanford G3 panel. Northern-blot analysis of three PKI isoforms, including the PKIA identified previously, revealed significant differences in their expression patterns. PKIB had two transcripts of 1.9 kb and 1.4 kb. The former transcript was abundant in both placenta and brain and the latter was expressed most abundantly in placenta, highly in brain, heart, liver, pancreas, moderately in kidney, skeletal muscle and colon, and very little in the other eight tissues tested. PKIG was widely expressed as a 1.5-kb transcript with the highest level in heart, hardly detectable in thymus and peripheral blood leucocytes and was moderately expressed in the other tissues, with slightly different levels. However, PKIA was specifically expressed as two transcripts of 3.3 kb and 1.5 kb in heart and skeletal muscle. The distinct expression patterns of the three PKIs suggest that their roles in various tissues are probably different.

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An evolutionary and developmental approach toward understanding of growth variations of two closely related dwarfism of Galium verum

Biologia ◽

10.2478/s11756-014-0479-0 ◽

2014 ◽

Vol 69 (12) ◽

Cited By ~ 1

Author(s):

Keum Jeong ◽

Jae-Hong Pak ◽

Jeong Kim

Keyword(s):

Cell Proliferation ◽

Genetic Factors ◽

Expression Patterns ◽

Mrna Differential Display ◽

Growth Patterns ◽

The Other ◽

Differential Growth ◽

Developmental Approach ◽

Differential Display Method ◽

Low Activity

AbstractGalium L. is one of the largest and most widespread genus of Rubiaceae, consisting of more than 650 species worldwide. Galium verum var. asiaticum (G. verum a.) is a perennial herbaceous and widely distributed in in Korea peninsula. On the other hand, Galium verum var. asiaticum for. pusillum (G. verum a.p.) is endemic to Korea, inhabiting only on high land of Mt. Halla of Jeju. G. verum a.p. appears to be a dwarfism of G. verum a. We wondered what physiological, environmental, or genetic factors rendered those two taxa morphologically differentiated. We found that G. verum a.p. shows a low activity of the cell proliferation and was not associated with responsiveness contents of auxin and gibberellins. In order to search for genetic factors involved, we carried out an mRNA differential display method using the ACPs, and isolated several different expression genes between the two taxa. We chose one of those genes, which encoded RADIALIS-like proteins: GvaRADL1 from G. verum a. and GvapRADL1 from G. verum a.p. We discuss the relevancy of the genetic variations in regard to the differential expression patterns of those genes and the differential growth patterns of the two variants.

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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Bioinformatics ◽

10.1093/bioinformatics/btab257 ◽

2021 ◽

Author(s):

Yixuan Qiu ◽

Jiebiao Wang ◽

Jing Lei ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Supplementary Information ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Correlation Pattern ◽

Tissue Samples ◽

Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

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Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽

10.1182/blood-2020-134929 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 32-33

Author(s):

Tomohiro Aoki ◽

Lauren C. Chong ◽

Katsuyoshi Takata ◽

Katy Milne ◽

Elizabeth Chavez ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

B Cells ◽

Single Cell ◽

Immune Cells ◽

Research Funding ◽

Immune Cell ◽

Expression Patterns ◽

The Other ◽

Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

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Genome-Wide Identification and Comparative Analysis for OPT Family Genes in Panax ginseng and Eleven Flowering Plants

Molecules ◽

10.3390/molecules24010015 ◽

2018 ◽

Vol 24 (1) ◽

pp. 15 ◽

Cited By ~ 2

Author(s):

He Su ◽

Yang Chu ◽

Junqi Bai ◽

Lu Gong ◽

Juan Huang ◽

...

Keyword(s):

Transcription Factors ◽

Panax Ginseng ◽

Expression Patterns ◽

Flowering Plants ◽

Functional Development ◽

Wide Range ◽

Organ Specific ◽

Genome Level ◽

Negative Links ◽

Global Platform

Herb genomics and comparative genomics provide a global platform to explore the genetics and biology of herbs at the genome level. Panax ginseng C.A. Meyer is an important medicinal plant for a variety of bioactive chemical compounds of which the biosynthesis may involve transport of a wide range of substrates mediated by oligopeptide transporters (OPT). However, information about the OPT family in the plant kingdom is still limited. Only 17 and 18 OPT genes have been characterized for Oryza sativa and Arabidopsis thaliana, respectively. Additionally, few comprehensive studies incorporating the phylogeny, gene structure, paralogs evolution, expression profiling, and co-expression network between transcription factors and OPT genes have been reported for ginseng and other species. In the present study, we performed those analyses comprehensively with both online tools and standalone tools. As a result, we identified a total of 268 non-redundant OPT genes from 12 flowering plants of which 37 were from ginseng. These OPT genes were clustered into two distinct clades in which clade-specific motif compositions were considerably conservative. The distribution of OPT paralogs was indicative of segmental duplication and subsequent structural variation. Expression patterns based on two sources of RNA-Sequence datasets suggested that some OPT genes were expressed in both an organ-specific and tissue-specific manner and might be involved in the functional development of plants. Further co-expression analysis of OPT genes and transcription factors indicated 141 positive and 11 negative links, which shows potent regulators for OPT genes. Overall, the data obtained from our study contribute to a better understanding of the complexity of the OPT gene family in ginseng and other flowering plants. This genetic resource will help improve the interpretation on mechanisms of metabolism transportation and signal transduction during plant development for Panax ginseng.

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