Dataset Information

Infection by Diverse HIV-1 Subtypes Leads to Different Elevations in HERV-K Transcriptional Levels in Human T Cell Lines.

ABSTRACT: Human endogenous retroviruses (HERVs) make up ~8% of the human genome, and for millions of years, they have been subject to strict biological regulation. Many HERVs do not participate in normal physiological activities in the body. However, in some pathological conditions, they can be abnormally activated. For example, HIV infection can cause abnormal activation of HERVs, and under different infection conditions, HERV expression may be different. We observed significant differences in HERV-K transcription levels among HIV-1 subtype-infected individuals. The transcriptional levels in the HERV-K gag region were significantly increased in HIV-1 B subtype-infected patients, while the transcriptional levels in the HERV-K pol region were significantly increased in CRF01_AE and CRF07_BC subtype-infected patients. In vitro, the transcriptional levels of HEVR-K were increased 5-fold and 15-fold in MT2 cells transfected with two different HIV-1 strains (B and CRF01_AE, respectively). However, there was no significant difference in transcriptional levels among regions of HERV-K. When MT2 cells were infected with different subtypes of HIV-1 Tat proteins (B, CRF01_AE), which is constructed by lentiviruses, and the transcription levels of HERV-K were increased 4-fold and 2-fold, respectively. Thus, different subtypes of HIV-1 have different effects on HERV-K transcription levels, which may be caused by many factors, not only Tat protein.

SUBMITTER: Li X

PROVIDER: S-EPMC8165174 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Infection by Diverse HIV-1 Subtypes Leads to Different Elevations in HERV-K Transcriptional Levels in Human T Cell Lines.

Li Xi X Guo Yaolin Y Li Hanping H Huang Xiaofeng X Pei Zhichao Z Wang Xiaolin X Liu Yongjian Y Jia Lei L Li Tianyi T Bao Zuoyi Z Wang Xiaorui X Han Leilei L Han Jingwan J Li Jingyun J Li Lin L

Frontiers in microbiology 20210517

Human endogenous retroviruses (HERVs) make up ~8% of the human genome, and for millions of years, they have been subject to strict biological regulation. Many HERVs do not participate in normal physiological activities in the body. However, in some pathological conditions, they can be abnormally activated. For example, HIV infection can cause abnormal activation of HERVs, and under different infection conditions, HERV expression may be different. We observed significant differences in HERV-K tra ...[more]

PMID: 34079529

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Dataset Information

Infection by Diverse HIV-1 Subtypes Leads to Different Elevations in HERV-K Transcriptional Levels in Human T Cell Lines.

Publications

Infection by Diverse HIV-1 Subtypes Leads to Different Elevations in HERV-K Transcriptional Levels in Human T Cell Lines.

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