Biology subjects

Geller, A. M.

Publications and source records attributed to Geller, A. M..

3 recordsLinked to original sources

bioRxiv2021

Large-scale discovery of candidate type VI secretion effectors with antibacterial activity

Type VI secretion systems (T6SS) are common bacterial contractile injection systems that inject toxic "effector" proteins into neighboring cells. Effector discovery is generally done manually, and computational approaches used for effector discovery depend on genetic linkage to T6SS genes and/or sequence similarity to known effectors. We bioinformatically investigated T6SS in more than 11,832 genomes of Gram negative bacteria. We found that T6SS encoding bacteria are host-associated and pathogenic, enriched in specific human and plant tissues, while depleted in marine, soil, and engineered environments. Analysis of T6SS cores with C-terminal domains ("evolved" cores) showed "evolved" HCP are rare, overwhelmingly encoded in orphan operons, and are largely restricted to Escherichia. Using the wealth of data generated from our bioinformatic analysis, we developed two algorithms for large-scale discovery of T6SS effector proteins (T6Es). We experimentally validated ten putative antibacterial T6SS effector proteins and one cognate immunity gene from a diverse species. This study provides a systematic genomic perspective of the role of the T6SS in nature, a thorough analysis of T6E evolution and genomic properties, and discovery of a large number of candidate T6Es using new approaches.

microbiology↗

bioRxiv2021

Deeplasmid: Deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC-ROC of over 93%, and it was much more precise than the state-of-the-art methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 Kbp long plasmid, demonstrating Deeplasmids ability to detect novel plasmids. AvailabilityThe software is available with a BSD license: deeplasmid.sourceforge.io. A Docker container is available on DockerHub under: billandreo/deeplasmid. Contactwandreopoulos@lbl.gov alevy@mail.huji.ac.il

bioinformatics↗

bioRxiv2020

The extracellular contractile injection system is enriched in environmental microbes and associates with numerous toxins

Bacteria employ toxin delivery systems to exclude bacterial competitors and to infect host cells. Characterization of these systems and the toxins they secrete is important for understanding microbial interactions and virulence in different ecosystems. The extracellular Contractile Injection System (eCIS) is a toxin delivery particle that evolved from a bacteriophage tail. Four known eCIS systems have been shown to mediate interactions between bacteria and their invertebrate hosts, but the broad ecological function of these systems remains unknown. Here, we identify eCIS loci in 1,249 prokaryotic genomes and reveal a striking enrichment of these loci in environmental microbes and absence from mammalian pathogens. We uncovered 13 toxin genes that associate with eCIS from diverse microbes and show that they can inhibit growth of bacteria, yeast or both. We also found immunity genes that protect bacteria from self-intoxication, supporting an antibacterial role for eCIS. Furthermore, we identified multiple new eCIS core genes including a conserved eCIS transcriptional regulator. Finally, we present our data through eCIStem; an extensive eCIS repository. Our findings define eCIS as a widespread environmental prokaryotic toxin delivery system that likely mediates antagonistic interactions with eukaryotes and prokaryotes. Future understanding of eCIS functions can be leveraged for the development of new biological control systems, antimicrobials, and cell-free protein delivery tools.

microbiology↗