scholarly journals Distributions of Extracellular Peptidases Across Prokaryotic Genomes Reflect Phylogeny and Habitat

2019 ◽  
Vol 10 ◽  
Author(s):  
Trang T. H. Nguyen ◽  
David D. Myrold ◽  
Ryan S. Mueller
Keyword(s):  
Prokaryotic Genomes ◽  
Extracellular Peptidases

scholarly journals panRGP: a pangenome-based method to predict genomic islands and explore their diversity

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i651-i658 ◽  
Author(s):  
Adelme Bazin ◽  
Guillaume Gautreau ◽  
Claudine Médigue ◽  
David Vallenet ◽  
Alexandra Calteau
Keyword(s):  
Escherichia Coli ◽  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Species Level ◽  
Genomic Islands ◽  
Genome Plasticity ◽  
Reference Dataset ◽  
Prokaryotic Genomes ◽  
Ideal Approach ◽  
Genomic Regions

Abstract Motivation Horizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of genome plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to genomic islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date, no methods are available to identify GIs using hundreds of genomes to explore their diversity. Results We present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on metagenome assembled genomes by redefining the borders of the leuX tRNA hotspot, a well-studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies. Availability and implementation The methods presented in the current work are available through the following software: https://github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https://github.com/axbazin/panrgp_supdata.

scholarly journals Gezielte Zellsortierung in der Einzelzellgenomik

BIOspektrum ◽  
2021 ◽  
Vol 27 (3) ◽  
pp. 274-276
Author(s):  
Morgan S. Sobol ◽  
Anne-Kristin Kaster
Keyword(s):  
Dark Matter ◽  
Single Cell ◽  
Cell Sorting ◽  
Genetic Information ◽  
Fluorescent Labeling ◽  
Single Cell Genomics ◽  
Rare Biosphere ◽  
Prokaryotic Genomes ◽  
Selection Of

AbstractSingle cell genomics (SCG) can provide reliable context for assembled genome fragments on the level of individual prokaryotic genomes and has rapidly emerged as an essential complement to cultivation-based and metagenomics research approaches. Targeted cell sorting approaches, which enable the selection of specific taxa by fluorescent labeling, compatible with subsequent single cell genomics offers an opportunity to access genetic information from rare biosphere members which would have otherwise stayed hidden as microbial dark matter.

Genome features and GC content in prokaryotic genomes in connection with environmental evolution

2013 ◽  
Vol 47 (9) ◽  
pp. 1056-1060 ◽  
Author(s):  
V. V. Suslov ◽  
D. A. Afonnikov ◽  
N. L. Podkolodny ◽  
Yu. L. Orlov
Keyword(s):  
Gc Content ◽  
Environmental Evolution ◽  
Prokaryotic Genomes ◽  
Genome Features

scholarly journals BAGET 2.0: an updated web tool for the effortless retrieval of prokaryotic gene context and sequence

2021 ◽  
Author(s):  
Benjamin Hepp ◽  
Violette Da Cunha ◽  
Florence Lorieux ◽  
Jacques Oberto
Keyword(s):  
Gene Sequence ◽  
Single Gene ◽  
Daily Basis ◽  
Web Tool ◽  
Web Based ◽  
Internet Explorer ◽  
Prokaryotic Genomes ◽  
Exploration Tool ◽  
Genome Browsers ◽  
Archaeal Gene

Abstract Motivation The retrieval of a single gene sequence and context from completely sequenced bacterial and archaeal genomes constitutes an intimidating task for the wet bench biologist. Existing web-based genome browsers are either too complex for routine use or only provide a subset of the available prokaryotic genomes. Results We have developed BAGET 2.0 (Bacterial and Archaeal Gene Exploration Tool), an updated web service granting access in just three mouse clicks to the sequence and synteny of any gene from completely sequenced bacteria and archaea. User-provided annotated genomes can be processed as well. BAGET 2.0 relies on a local database updated on a daily basis. Availability and implementation BAGET 2.0 befits all current browsers such as Chrome, Firefox, Edge, Opera and Safari. Internet Explorer 11 is supported. BAGET 2.0 is freely accessible at https://archaea.i2bc.paris-saclay.fr/baget/

scholarly journals BAC-BROWSER: The Tool for Visualization and Analysis of Prokaryotic Genomes

2018 ◽  
Vol 9 ◽  
Author(s):  
Irina A. Garanina ◽  
Gleb Y. Fisunov ◽  
Vadim M. Govorun
Keyword(s):  
Prokaryotic Genomes

scholarly journals GAAP: Genome-organization-framework-Assisted Assembly Pipeline for prokaryotic genomes

BMC Genomics ◽  
2017 ◽  
Vol 18 (S1) ◽  
Author(s):  
Lina Yuan ◽  
Yang Yu ◽  
Yanmin Zhu ◽  
Yulai Li ◽  
Changqing Li ◽  
...  
Keyword(s):  
Genome Organization ◽  
Prokaryotic Genomes ◽  
Assembly Pipeline ◽  
Organization Framework

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

2020 ◽  
Author(s):  
Alexander Martin Geller ◽  
Inbal Pollin ◽  
David Zlotkin ◽  
Aleks Danov ◽  
Nimrod Nachmias ◽  
...  
Keyword(s):  
Control Systems ◽  
Protein Delivery ◽  
Microbial Interactions ◽  
Host Cells ◽  
Injection System ◽  
Prokaryotic Genomes ◽  
Immunity Genes ◽  
Core Genes ◽  
Environmental Microbes

AbstractBacteria 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.

scholarly journals Evolutionary assembly patterns of prokaryotic genomes

2015 ◽  
Author(s):  
Maximilian O. Press ◽  
Christine Queitsch ◽  
Elhanan Borenstein
Keyword(s):  
Evolutionary Constraints ◽  
Historical Contingency ◽  
Phenotypic Characters ◽  
Functional Relationships ◽  
Evolutionary Innovation ◽  
Gene Level ◽  
Prokaryotic Genomes ◽  
Evolutionary Paths ◽  
Evolution Of Proteins ◽  
Sequence Substitution

AbstractEvolutionary innovation must occur in the context of some genomic background, which limits available evolutionary paths. For example, protein evolution by sequence substitution is constrained by epistasis between residues. In prokaryotes, evolutionary innovation frequently happens by macrogenomic events such as horizontal gene transfer (HGT). Previous work has suggested that HGT can be influenced by ancestral genomic content, yet the extent of such gene-level constraints has not yet been systematically characterized. Here, we evaluated the evolutionary impact of such constraints in prokaryotes, using probabilistic ancestral reconstructions from 634 extant prokaryotic genomes and a novel framework for detecting evolutionary constraints on HGT events. We identified 8,228 directional dependencies between genes, and demonstrated that many such dependencies reflect known functional relationships, including, for example, evolutionary dependencies of the photosynthetic enzyme RuBisCO. Modeling all dependencies as a network, we adapted an approach from graph theory to establish chronological precedence in the acquisition of different genomic functions. Specifically, we demonstrated that specific functions tend to be gained sequentially, suggesting that evolution in prokaryotes is governed by functional assembly patterns. Finally, we showed that these dependencies are universal rather than clade-specific and are often sufficient for predicting whether or not a given ancestral genome will acquire specific genes. Combined, our results indicate that evolutionary innovation via HGT is profoundly constrained by epistasis and historical contingency, similar to the evolution of proteins and phenotypic characters, and suggest that the emergence of specific metabolic and pathological phenotypes in prokaryotes can be predictable from current genomes.

scholarly journals Consistent Clustering Pattern of Prokaryotic Genes Based on Base Frequency at the Second Codon Position and its Association with Functional Category Preference

2021 ◽  
Author(s):  
Yan-Ting Jin ◽  
Cong Ma ◽  
Xin Wang ◽  
Shu-Xuan Wang ◽  
Kai-Yue Zhang ◽  
...  
Keyword(s):  
Protein Function ◽  
Codon Position ◽  
Systematic Investigation ◽  
Functional Category ◽  
Gene Clustering ◽  
Base Frequency ◽  
Significant Difference ◽  
Prokaryotic Genomes ◽  
Functional Classes ◽  
Clustering Pattern

AbstractIn 2002, our research group observed a gene clustering pattern based on the base frequency of A versus T at the second codon position in the genome of Vibrio cholera and found that the functional category distribution of genes in the two clusters was different. With the availability of a large number of sequenced genomes, we performed a systematic investigation of A2–T2 distribution and found that 2694 out of 2764 prokaryotic genomes have an optimal clustering number of two, indicating a consistent pattern. Analysis of the functional categories of the coding genes in each cluster in 1483 prokaryotic genomes indicated, that 99.33% of the genomes exhibited a significant difference (p < 0.01) in function distribution between the two clusters. Specifically, functional category P was overrepresented in the small cluster of 98.65% of genomes, whereas categories J, K, and L were overrepresented in the larger cluster of over 98.52% of genomes. Lineage analysis uncovered that these preferences appear consistently across all phyla. Overall, our work revealed an almost universal clustering pattern based on the relative frequency of A2 versus T2 and its role in functional category preference. These findings will promote the understanding of the rationality of theoretical prediction of functional classes of genes from their nucleotide sequences and how protein function is determined by DNA sequence. Graphical abstract

Sign in / Sign up

Export Citation Format

Share Document