Early B Cell Progenitors Deficient for GON4L Fail To Differentiate Due to a Block in Mitotic Cell Division

Barr, Jennifer; Goodfellow, Renee X.; Colgan, Diana F.; Colgan, John

doi:10.4049/jimmunol.1602054

Cited by 8 publications

(6 citation statements)

References 69 publications

(87 reference statements)

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“…Similarly, the GON4L gene is a transcription regulator that plays vital roles in cell division and differentiation. In particular, GON4L gene-based transcription regulation is essential for B cell development and differentiation [ 37 ]. Finally, the SYT11 gene encodes a protein that facilitates calcium signal-dependent membrane trafficking.…”

Section: Resultsmentioning

confidence: 99%

SVFX: a machine learning framework to quantify the pathogenicity of structural variants

et al. 2020

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There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity scores to somatic and germline SVs. In particular, we generate somatic and germline training models, which include genomic, epigenomic, and conservation-based features, for SV call sets in diseased and healthy individuals. We then apply SVFX to SVs in cancer and other diseases; SVFX achieves high accuracy in identifying pathogenic SVs. Predicted pathogenic SVs in cancer cohorts are enriched among known cancer genes and many cancer-related pathways.

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Section: Resultsmentioning

confidence: 99%

SVFX: a machine learning framework to quantify the pathogenicity of structural variants

et al. 2020

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“…Similarly, the GON4L gene is a transcription regulator that plays a vital role in cell division and differentiation. In particular, GON4L gene-based transcription regulation is essential for B cell development and differentiation 32 . Moreover, the SYT11 gene encodes a protein that facilitate calcium-signal dependent membrane trafficking.…”

Section: Case Studies Highlighting High Impact Somatic Deletion and Dmentioning

confidence: 99%

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

Kumar

Harmanci

Vytheeswaran

et al. 2019

Preprint

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A rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a "pathogenicity score" to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

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“…Similar defective hematopoietic phenotypes were also observed in Gon4l/udu mutants. The proliferation and differentiation of pre-pro B cells to early pro-B cells are impaired in Justy mutant mice 3,4 . In zebrafish, udu tu24 mutants exhibited fewer blood cells and defective proliferation and differentiation of erythrocytes were observed in udu sq3 mutants 6,7 .…”

Section: Discussionmentioning

confidence: 99%

“…For mammals, Justy mutant mice, which carry a point mutation within the Gon4l gene, exhibit defects in B cell development 3 . Detailed analysis reveals that a block in the G1/S phase transition results in severely defective differentiation of pre-pro B cells to pro-B cells in these mutant mice 4 . Moreover, a frameshift mutation in this gene causes proportionate dwarfism in Fleckvieh cattles 5 , indicating that the Gon4l protein not only affects cell proliferation/differentiation in specific types of tissues but also can impair the growth of an entire organism.…”

mentioning

confidence: 98%

Newly identified Gon4l/Udu-interacting proteins implicate novel functions

Tsai

Chu

Jiang

2020

Sci Rep

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Mutations of the Gon4l/udu gene in different organisms give rise to diverse phenotypes. Although the effects of Gon4l/Udu in transcriptional regulation have been demonstrated, they cannot solely explain the observed characteristics among species. To further understand the function of Gon4l/Udu, we used yeast two-hybrid (Y2H) screening to identify interacting proteins in zebrafish and mouse systems, confirmed the interactions by co-immunoprecipitation assay, and found four novel Gon4l-interacting proteins: BRCA1 associated protein-1 (Bap1), DNA methyltransferase 1 (Dnmt1), Tho complex 1 (Thoc1, also known as Tho1 or HPR1), and Cryptochrome circadian regulator 3a (Cry3a). Furthermore, all known Gon4l/Udu-interacting proteins-as found in this study, in previous reports, and in online resources-were investigated by Phenotype Enrichment Analysis. The most enriched phenotypes identified include increased embryonic tissue cell apoptosis, embryonic lethality, increased T cell derived lymphoma incidence, decreased cell proliferation, chromosome instability, and abnormal dopamine level, characteristics that largely resemble those observed in reported Gon4l/udu mutant animals. Similar to the expression pattern of udu, those of bap1, dnmt1, thoc1, and cry3a are also found in the brain region and other tissues. Thus, these findings indicate novel mechanisms of Gon4l/ Udu in regulating CpG methylation, histone expression/modification, DNA repair/genomic stability, and RNA binding/processing/export.

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Early B Cell Progenitors Deficient for GON4L Fail To Differentiate Due to a Block in Mitotic Cell Division

Cited by 8 publications

References 69 publications

SVFX: a machine learning framework to quantify the pathogenicity of structural variants

SVFX: a machine learning framework to quantify the pathogenicity of structural variants

SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

Newly identified Gon4l/Udu-interacting proteins implicate novel functions

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