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Biology subjects

Kang, T. H.

Publications and source records attributed to Kang, T. H..

3 recordsLinked to original sources

SF3B1 mutation generates novel peptides from long non-coding RNAs

While mRNA alternative splicing caused by somatic mutation in SF3B1 is well-characterized, long non-coding RNAs (lncRNAs) impacted by this mutation are completely unknown. Here, we report long-read sequencing of full-length transcripts from cell lines with and without SF3B1 mutation. We present ALLR (Annotation of human lncRNAs using Long-Read sequencing), a computational workflow to identify and quantify lncRNAs, and to annotate their potential roles in impacting cellular functions. Harnessing matched short-read RNA sequencing and proteomic data, we demonstrate that SF3B1 mutation generates thousands of novel lncRNAs with diverse levels of alternative splicing. Moreover, SF3B1 mutation-associated lncRNAs produce translatable peptides with putative functions, immunogenicity, and potential to bind proteins with impact on cellular signalling networks. 47 lncRNAs could also be detected in primary chronic lymphocytic leukemia (CLL) samples and are associated with significant risks towards adverse disease prognosis, in conjunction with other known CLL risk factors. Our work demonstrates the utility of long-read sequencing for lncRNA research and provides a new territory of functional RNA molecules that SF3B1 mutation may use to impact cancer.

genomics↗

ACCELERATING ANTIBODY DESIGN WITH ACTIVE LEARNING

Discovering therapeutic antibody starts by screening antibody library of phage-displayed, transgenic mouse or human B cells. The coverage of those kinds of libraries in the entire antibody sequence space is small; thus, the result highly depends on the quality of the library. Exploring sequence space by mutating a template antibody is also impossible to even with the state-of-the-art screening methods because of the cost. Deep learning helped with its pattern recognition nature to predict target binding, which is only applied to HCDR3 because the number of data deep learning needs increases exponentially. We construct a sequence generation model with transfer learning and active learning to leverage deep learning even in data deficiency. With only six thousands data, the generative model finds nine binding antibody sequences at least per antigen with novel HCDR3.

bioengineering↗

Development of a Synthetic Poxvirus-Based SARS-CoV-2 Vaccine

Modified Vaccinia Ankara (MVA) is a highly attenuated poxvirus vector that is widely used to develop vaccines for infectious diseases and cancer. We developed a novel vaccine platform based on a unique three-plasmid system to efficiently generate recombinant MVA vectors from chemically synthesized DNA. In response to the ongoing global pandemic caused by SARS coronavirus-2 (SARS-CoV-2), we used this novel vaccine platform to rapidly produce fully synthetic MVA (sMVA) vectors co-expressing SARS-CoV-2 spike and nucleocapsid antigens, two immunodominant antigens implicated in protective immunity. Mice immunized with these sMVA vectors developed robust SARS-CoV-2 antigen-specific humoral and cellular immune responses, including potent neutralizing antibodies. These results demonstrate the potential of a novel vaccine platform based on synthetic DNA to efficiently generate recombinant MVA vectors and to rapidly develop a multi-antigenic poxvirus-based SARS-CoV-2 vaccine candidate.Competing Interest StatementThe authors have declared no competing interest.View Full Text

immunology↗