Biology subjects

Piening, B.

Publications and source records attributed to Piening, B..

3 recordsLinked to original sources

bioRxiv2025

A tailored in vivo CRISPR screen identifies BAP1 as a potent tumor suppressor of soft tissue sarcoma

Undifferentiated pleomorphic sarcoma (UPS) is one of the most common soft tissue sarcomas (STS) in adults. Despite decades of research, therapeutic advancements for STS, including UPS, have remained limited. The genetic complexity of UPS, characterized by the absence of recurrent driver oncogene mutations, has hindered the development of effective targeted therapies beyond conventional chemotherapy and immunotherapy. To address this challenge, we conducted a customized in vivo CRISPR/Cas9 screen in mice to systematically identify potential tumor suppressors involved in UPS development. Our screen revealed BRCA1-associated protein 1 (Bap1) as a potent tumor suppressor in STS. Using total RNA sequencing, multiplex immunohistochemistry, and flow cytometry, we found that Bap1-deficient mouse sarcomas exhibit significant immune suppression. Further analysis indicated that polo-like kinase 1 (Plk1) is essential for the survival of Bap1-deficient sarcomas. Treatment with volasertib, a Plk1 inhibitor, markedly inhibited tumor growth in both syngeneic and spontaneous mouse models of Bap1-loss sarcoma. In conclusion, our findings suggest that PLK1 inhibition, or combined with immunotherapy, may represent a promising targeted therapeutic strategy for tumors lacking BAP1.

cancer biology↗

bioRxiv2025

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this unique, comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8+ T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

immunology↗

bioRxiv2019

MOBN: an interactive database of multi-omics biological networks

It is essential to reveal the associations between different omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics and metabolomics as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their own data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and diseases research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

bioinformatics↗