Integrative genomic and transcriptomic analysis in plasmablastic lymphoma identifies disruption of key regulatory pathways

Plasmablastic-lymphoma (PBL) represents a clinically heterogeneous subtype of aggressive-B-cell-non-Hodgkin-lymphoma. Although targeted-sequencing-studies and a single-center whole-exome-sequencing (WES) study in HIV+ patients recently revealed several genes, associated with PBL-pathogenesis, the global mutational-landscape and transcriptional-profile of PBL remain elusive. To inform on disease-associated mutational-drivers, mutational-patterns and perturbed pathways in HIV+ and HIV-PBL we performed WES and transcriptome sequencing (RNA-seq) of 33 PBL-tumors. Integrative analysis of somatic-mutations and gene-expression-profiles were performed to acquire insights into the divergent genotype-phenotype-correlation in EBV+ and EBV-PBL. We describe a significant accumulation of mutations in the Janus-kinase-signal-transducer and transcription-activator (OSMR, STAT3, PIM1, SOCS1) as well as receptor tyrosine-kinase RAS-pathways (ERBB3, NRAS, PDGFRB, NTRK). We provide further evidence of frequent perturbance of nuclear-factor κB (NFκB) signaling (NFKB2, BTK). Induced pathways, identified by RNA-seq closely resemble the mutational-profile regarding alterations accentuated in IL-6/JAK/STAT-signaling, NFκB-activity and MYC-signaling. Moreover, class I-MHC mediated antigen-processing and cell-cycle-regulation were significantly impacted by the EBV-status. An almost exclusive upregulation of PI3K/AKT/MTOR-signaling in EBV+ PBL and a significantly induced expression of NTRK3 in concert with recurrent oncogenic-mutations in EBV- PBL, hints at specific therapeutically targetable-mechanism in PBL-subgroups. Our characterization of a mutational and transcriptomic-landscape in PBL, distinct from DLBCL and MM substantiates the pathobiological-independence of PBL in the spectrum of B-cell-malignancies and thereby refines the taxonomy for aggressive-lymphomas.

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Integrated genomic analysis reveals regulatory pathways and dynamic landscapes of the tRNA transcriptome

Scientific Reports ◽

10.1038/s41598-021-83469-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zefang Sun ◽

Jia Tan ◽

Minqiong Zhao ◽

Qiyao Peng ◽

Mingqing Zhou ◽

...

Keyword(s):

Expression Profiles ◽

Genomic Analysis ◽

Rna Seq ◽

Regulatory Pathways ◽

Cellular Processes ◽

Dynamic Landscapes ◽

Rna Fragments ◽

A Cell ◽

Considerable Impact ◽

New Algorithms

AbstracttRNAs and tRNA-derived RNA fragments (tRFs) play various roles in many cellular processes outside of protein synthesis. However, comprehensive investigations of tRNA/tRF regulation are rare. In this study, we used new algorithms to extensively analyze the publicly available data from 1332 ChIP-Seq and 42 small-RNA-Seq experiments in human cell lines and tissues to investigate the transcriptional and posttranscriptional regulatory mechanisms of tRNAs. We found that histone acetylation, cAMP, and pluripotency pathways play important roles in the regulation of the tRNA gene transcription in a cell-specific manner. Analysis of RNA-Seq data identified 950 high-confidence tRFs, and the results suggested that tRNA pools are dramatically distinct across the samples in terms of expression profiles and tRF composition. The mismatch analysis identified new potential modification sites and specific modification patterns in tRNA families. The results also show that RNA library preparation technologies have a considerable impact on tRNA profiling and need to be optimized in the future.

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LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽

10.1186/s12864-021-07900-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yanan Ren ◽

Ting-You Wang ◽

Leah C. Anderton ◽

Qi Cao ◽

Rendong Yang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Samples ◽

Sequencing Data ◽

Multiple Cancer ◽

Regulatory Pathways ◽

Cancer Transcriptome ◽

Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

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Bulk and single-cell RNA-seq reveal dmrtb1 gene expression profiles during sex change in zig-zag eel (Mastacembelus armatus)

Aquaculture ◽

10.1016/j.aquaculture.2021.737194 ◽

2021 ◽

pp. 737194

Author(s):

Lingzhan Xue ◽

Dan Jia ◽

Luohao Xu ◽

Zhen Huang ◽

Haiping Fan ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sex Change ◽

Rna Seq ◽

Mastacembelus Armatus

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The t(14;18) defines a unique subset of diffuse large B-cell lymphoma with a germinal center B-cell gene expression profile

Blood ◽

10.1182/blood.v99.7.2285 ◽

2002 ◽

Vol 99 (7) ◽

pp. 2285-2290 ◽

Cited By ~ 202

Author(s):

James Z. Huang ◽

Warren G. Sanger ◽

Timothy C. Greiner ◽

Louis M. Staudt ◽

Dennis D. Weisenburger ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Gene Expression Profile ◽

Expression Profile ◽

Germinal Center ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Germinal Center B Cell ◽

Cell Gene Expression ◽

Cell Gene

Recently we have identified subgroups of de novo primary diffuse large B-cell lymphoma (DLBCL) based on complementary DNA microarray-generated gene expression profiles. To correlate the gene expression profiles with cytogenetic abnormalities in these DLBCLs, we examined the occurrence of the t(14;18)(q32;q21) in the 2 distinctive subgroups of DLBCL: one with the germinal center B-cell gene expression signature and the other with the activated B cell–like gene expression signature. The t(14;18) was detected in 7 of 35 cases (20%). All 7 t(14;18)-positive cases had a germinal center B-cell gene expression profile, representing 35% of the cases in this subgroup, and 6 of these 7 cases had very similar gene expression profiles. The expression of bcl-2 and bcl-6 proteins was not significantly different between the t(14;18)-positive and -negative cases, whereas CD10 was detected only in the group with the germinal center B-cell expression profile, and CD10 was most frequently expressed in the t(14;18)-positive cases. This study supports the validity of subdividing DLBCL into 2 major subgroups by gene expression profiling, with the t(14;18) being an important event in the pathogenesis of a subset of DLBCL arising from germinal center B cells. CD10 protein expression is useful in identifying cases of DLBCL with a germinal center B-cell gene expression profile and is often expressed in cases with the t(14;18).

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A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

10.21203/rs.3.rs-766884/v2 ◽

2021 ◽

Author(s):

Taguchi Y-h. ◽

Turki Turki

Keyword(s):

Gene Expression ◽

Learning Strategies ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Tensor Decomposition ◽

Integrated Analysis ◽

Rna Seq ◽

Prior Learning ◽

Multiple Gene ◽

Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

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Dimethyl fumarate induces ferroptosis and impairs NF-κB/STAT3 signaling in DLBCL

Blood ◽

10.1182/blood.2020009404 ◽

2021 ◽

Author(s):

Anja Schmitt ◽

Wendan Xu ◽

Philip Bucher ◽

Melanie Grimm ◽

Martina Konantz ◽

...

Keyword(s):

Cell Death ◽

B Cell ◽

Expression Profiles ◽

Therapeutic Option ◽

Gene Expression Profiles ◽

B Cell Lymphoma ◽

Dimethyl Fumarate ◽

Specific Gene ◽

Molecular Heterogeneity ◽

Stat3 Signaling

Despite the development of novel targeted drugs, the molecular heterogeneity of diffuse large B-cell lymphoma (DLBCL) still poses a major therapeutic challenge. DLBCL can be classified into at least two major subtypes, i.e. germinal center B-cell-like (GCB) and the aggressive activated B-cell-like (ABC) DLBCL, each characterized by specific gene expression profiles and mutation patterns. Here we demonstrate a broad anti-tumor effect of dimethyl fumarate (DMF) on both DLBCL subtypes, which is mediated by the induction of ferroptosis, a form of cell death driven by the peroxidation of phospholipids. Due to high expression of arachidonate 5-lipoxygenase in concert with low glutathione and glutathione peroxidase 4 levels, DMF induces lipid peroxidation and thus ferroptosis particularly in GCB DLBCL. In ABC DLBCL cells, which are addicted to NF-κB and STAT3 survival signaling, DMF treatment efficiently inhibits the activity of the IKK complex and JAK kinases. Interestingly, the BCL-2 specific BH3 mimetic ABT-199 and an inhibitor of ferroptosis suppressor protein 1 synergize with DMF in inducing cell death in DLBCL. Collectively, our findings identify the clinically approved drug DMF as a promising novel therapeutic option in the treatment of both GCB and ABC DLBCL.

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The characteristics of mesenteric adipose tissue attached to different intestinal segments and their roles in immune regulation

AJP Gastrointestinal and Liver Physiology ◽

10.1152/ajpgi.00256.2021 ◽

2022 ◽

Author(s):

Haowei Zhang ◽

Yujin Ding ◽

Qin Zeng ◽

Dandan Wang ◽

Ganglei Liu ◽

...

Keyword(s):

Gene Expression ◽

Adipose Tissue ◽

Immune Regulation ◽

Expression Profiles ◽

Critical Role ◽

Gene Expression Profiles ◽

Rna Seq ◽

Mesenteric Adipose Tissue ◽

Cell Components ◽

Subsequent Effect

Background: Mesenteric adipose tissue (MAT) plays a critical role in the intestinal physiological ecosystems. Small and large intestines have evidently intrinsic and distinct characteristics. However, whether there exist any mesenteric differences adjacent to the small and large intestines (SMAT and LMAT) has not been properly characterized. We studied the important facets of these differences, such as morphology, gene expression, cell components and immune regulation of MATs, to characterize the mesenteric differences. Methods: The SMAT and LMAT of mice were utilized for comparison of tissue morphology. Paired mesenteric samples were analyzed by RNA-seq to clarify gene expression profiles. MAT partial excision models were constructed to illustrate the immune regulation roles of MATs, and 16S-seq was applied to detect the subsequent effect on microbiota. Results: Our data show that different segments of mesenteries have different morphological structures. SMAT not only has smaller adipocytes but also contains more fat-associated lymphoid clusters than LMAT. The gene expression profile is also discrepant between these two MATs in mice. B-cell markers were abundantly expressed in SMAT, while development-related genes were highly expressed in LMAT. Adipose-derived stem cells of LMAT exhibited higher adipogenic potential and lower proliferation rates than those of SMAT. In addition, SMAT and LMAT play different roles in immune regulation and subsequently affect microbiota components. Finally, our data clarified the described differences between SMAT and LMAT in humans. Conclusions: There were significant differences in cell morphology, gene expression profiles, cell components, biological characteristics, and immune and microbiota regulation roles between regional MATs.

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Comparative Transcriptome Analysis of Different Dendrobium Species Reveals Active Ingredients-Related Genes and Pathways

International Journal of Molecular Sciences ◽

10.3390/ijms21030861 ◽

2020 ◽

Vol 21 (3) ◽

pp. 861 ◽

Cited By ~ 3

Author(s):

Yingdan Yuan ◽

Bo Zhang ◽

Xinggang Tang ◽

Jinchi Zhang ◽

Jie Lin

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Rna Seq ◽

Active Ingredients ◽

Hub Genes ◽

Transcriptome Database ◽

Gene Modules ◽

Functional Investigation ◽

First Time

Dendrobium is widely used in traditional Chinese medicine, which contains many kinds of active ingredients. In recent years, many Dendrobium transcriptomes have been sequenced. Hence, weighted gene co-expression network analysis (WGCNA) was used with the gene expression profiles of active ingredients to identify the modules and genes that may associate with particular species and tissues. Three kinds of Dendrobium species and three tissues were sampled for RNA-seq to generate a high-quality, full-length transcriptome database. Based on significant changes in gene expression, we constructed co-expression networks and revealed 19 gene modules. Among them, four modules with properties correlating to active ingredients regulation and biosynthesis, and several hub genes were selected for further functional investigation. This is the first time the WGCNA method has been used to analyze Dendrobium transcriptome data. Further excavation of the gene module information will help us to further study the role and significance of key genes, key signaling pathways, and regulatory mechanisms between genes on the occurrence and development of medicinal components of Dendrobium.

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Prognostic Significance of in situ Phenotypic Marker Expression in Diffuse Large B-cell Lymphomas

Biomarker Insights ◽

10.1177/117727190700200009 ◽

2007 ◽

Vol 2 ◽

pp. 117727190700200 ◽

Cited By ~ 4

Author(s):

Alexandar Tzankov ◽

Philip Went ◽

Stephan Dirnhofer

Keyword(s):

B Cell ◽

Expression Profiles ◽

Prognostic Significance ◽

Gene Expression Profiles ◽

Small Sample ◽

B Cell Lymphomas ◽

Phenotypic Marker ◽

Small Sample Sizes ◽

Protein Expression Profiles ◽

Large B Cell

Diffuse large B-cell lymphomas (DLBCL) are the most common lymphoid malignancies, and encompass all malignant lymphomas characterized by large neoplastic cells and B-cell derivation. In the last decade, DLBCL has been subjected to intense clinical, phenotypic and molecular studies, and were found to represent a heterogeneous group of tumors. These studies suggested new disease subtypes and variants with distinct clinical characteristics, morphologies, immunophenotypes, genotypes or gene expression profiles, associated with distinct prognoses or unique sensitivities to particular therapy regimens. Unfortunately, the reliability and reproducibility of the molecular results remains unclear due to contradictory reports in the literature resulting from small sample sizes, referral and selection biases, and variable methodologies and cut-off levels used to determine positivity. Here, we review phenotypic studies on the prognostic significance of protein expression profiles in DLBCL and reconsider our own retrospective data on 301 primary DLBCL cases obtained on a previously validated tissue microarray in light of powerful statistical methods of determining optimal cut-off values of phenotypic factors for prediction of outcome.

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