If bacteria and archaea form species-like populations, the question of how to define those populations has been a long standing debate. In contrast to eukaryotic organisms, horizontal gene transfer in microorganisms can make observing vertical lineages of descent difficult. There is evidence to support the idea that bacteria and archaea form ecological species similar to eukaryotic organisms, that the rate of homologous recombination (i.e. sex) can be used as the defining species barrier, and the idea that microbial species simply do not exist due to speed of evolution and a high rate of horizontal gene transfer. Often the system or organisms analyzed effect how researchers subsequently define species, though until about 10 years ago sequencing technologies and techniques were not adequate to answer many of these questions in situ. The rapid increase in sequencing technologies, such as deep-barcode sequencing, metagenomics and single cell genomics, is allowing researchers to once again approach this topic with higher genetic resolution.
Microorganisms evolve very fast, most are difficult to culture, and the number of cells in a population and community can often be >10^9 cells/ml, all making studying the process of evolution and speciation difficult. While there are varying mechanisms and speeds of evolution, the processes of genetic change is consistent across the tree of life. Often researchers will examine more simple ecosystems and apply the principles learned to more complex systems, and these systems have often been employed to analyze microbial population genetics and patterns of biogeography. Modern molecular tools make examining this question more obtainable than ever. We will employ a variety of molecular methods such as 1) targeted gene studies of high-resolution genetic markers that look beyond the 16S rRNA level to map species-level populations relative to niche space, 2) looking at temporal gene fluctuations to determine if individuals within a predicted species are ecologically interchangeable (i.e. individuals in populations will change in unison over space and time), and 3) in depth analysis of acquired genomes to determine what makes closely related species different or better adapted to a given environmental niche.
In this Research Topic, we welcome Original Research papers, Opinion articles, mini Reviews and Reviews that cover molecular methods used to identify microbial species and algorithms used in microbial species prediction. We hope to continue the advancement towards understanding microbial species to understand the forces that led to genetic divergence and microbial speciation.
Thematic areas include:
• Targeted gene or multi-locus studies that attempt to define species-like populations.
• Temporal influence of species populations.
• Genome comparison studies to determine evolutionary rates and niche-specific adaptations.
• Measurements of horizontal gene transfer rates across populations and lineages in an environment.
• The use of algorithms that apply molecular cut offs that define species.
• Analysis of species-specific transcription patterns.
If bacteria and archaea form species-like populations, the question of how to define those populations has been a long standing debate. In contrast to eukaryotic organisms, horizontal gene transfer in microorganisms can make observing vertical lineages of descent difficult. There is evidence to support the idea that bacteria and archaea form ecological species similar to eukaryotic organisms, that the rate of homologous recombination (i.e. sex) can be used as the defining species barrier, and the idea that microbial species simply do not exist due to speed of evolution and a high rate of horizontal gene transfer. Often the system or organisms analyzed effect how researchers subsequently define species, though until about 10 years ago sequencing technologies and techniques were not adequate to answer many of these questions in situ. The rapid increase in sequencing technologies, such as deep-barcode sequencing, metagenomics and single cell genomics, is allowing researchers to once again approach this topic with higher genetic resolution.
Microorganisms evolve very fast, most are difficult to culture, and the number of cells in a population and community can often be >10^9 cells/ml, all making studying the process of evolution and speciation difficult. While there are varying mechanisms and speeds of evolution, the processes of genetic change is consistent across the tree of life. Often researchers will examine more simple ecosystems and apply the principles learned to more complex systems, and these systems have often been employed to analyze microbial population genetics and patterns of biogeography. Modern molecular tools make examining this question more obtainable than ever. We will employ a variety of molecular methods such as 1) targeted gene studies of high-resolution genetic markers that look beyond the 16S rRNA level to map species-level populations relative to niche space, 2) looking at temporal gene fluctuations to determine if individuals within a predicted species are ecologically interchangeable (i.e. individuals in populations will change in unison over space and time), and 3) in depth analysis of acquired genomes to determine what makes closely related species different or better adapted to a given environmental niche.
In this Research Topic, we welcome Original Research papers, Opinion articles, mini Reviews and Reviews that cover molecular methods used to identify microbial species and algorithms used in microbial species prediction. We hope to continue the advancement towards understanding microbial species to understand the forces that led to genetic divergence and microbial speciation.
Thematic areas include:
• Targeted gene or multi-locus studies that attempt to define species-like populations.
• Temporal influence of species populations.
• Genome comparison studies to determine evolutionary rates and niche-specific adaptations.
• Measurements of horizontal gene transfer rates across populations and lineages in an environment.
• The use of algorithms that apply molecular cut offs that define species.
• Analysis of species-specific transcription patterns.
