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viral dark matter metagenomics next generation sequencing transcription proteins

Discovery Of Novel Transcription Proteins From Viral Dark Matter

Abstract ID: 34-SM

Camilla Do 1, Carmen Gu Liu 1, James Chang 1, Paul Nicholls 1, Anthony Maresso 1*

  1. Baylor College of Medicine, Houston, Texas

With an estimated population of 1031 viral-like particles, viruses dominate the planet – outnumbering the entire biosphere. However, <1% have been observed and sequenced due to the constraints of host culturing. Furthermore, the lack of universal gene markers for viral communities makes them harder to study. Advances in technology, particularly metagenomic next generation sequencing, have allowed researchers to study the virome without the need for culturing hosts. Metagenomics refer to the study of all nucleic acid recovered from an environment. In a single metagenome, 60-99% of viral proteins are identified as “unknown.” These viral proteins have no sequence homology or probabilistic similarity to known proteins – making them a reservoir of unknown genetic diversity. Over the years, many scientists have used bacteriophages, or phages, to understand fundamental molecular biology; yet, how various phages “hijack” bacterial transcription activity in favor of their own genome viral remain understudied and unclear. Because transcription is essential for a phage’s life cycle, it is likely there are more viral proteins involved but have not been identified. I hypothesize that phages encode for a myriad of unique protein structures and enzymatic abilities involved in the synthesis of viral RNA, and with viral metagenomics and the development of a functional screen, we can elucidate the biosynthetic capabilities of phages. I will use function-based analyses to identify RNAPs or unknown proteins involved in transcription with a metagenomic library made. To accomplish this, my assays will use phenotypic complementation to screen for viral DNA that is able to rescue genetic mutations on essential genes involved in bacterial transcription. Data from this work will contribute to the field by identifying essential viral genes involved in transcription that can be used as viral markers for community studies or exploited in biotechnology.