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The research done in
Dr. Larry Forney's laboratory centers on the diversity and distribution of
prokaryotes. Both field and laboratory studies are done to
explore the temporal and spatial patterns of community
diversity, as well as factors that influence the dynamics of
inter- and intra-species competition and how environmental
conditions might influence the tempo of adaptive evolution. Most
of these studies are highly interdisciplinary in nature, and
done in collaboration with mathematicians, statisticians,
computer scientists, geologists, environmental engineers,
physicians, and clinical scientists.
Studies of
microbial community ecology heavily rely on the use of
culture-independent methods based on the analysis of terminal
restriction fragment length polymorphisms of 16S rRNA genes, as
well as phylogenetic analyses of 16S rRNA gene sequences. These
methods are used to explore the diversity and ecology of
microorganisms in a wide variety of habitats, and include
studies on: (a) the human vagina and vulva to assess how the
composition of these communities influence susceptibility to
various diseases; (b) geothermal hot springs in southern Oregon
to identify environmental factors that influence the incidence
and distribution of microbial populations on various spatial
scales; (c) microbial biofilms to develop spatial mathematical
models that account for patterns of colonization and succession
in microbial biofilms; (d) dry forests of western Mexico to
measure the b-diversity
of prokaryotes in soils; (e) hypersaline springs and ponds of
Cuatro Cienegas (Mexico) to study the biogeography of
prokaryotes ; (f) chemosynthetic microbial communities found at
"mud volcanoes" of the Mediterranean Sea to ascertain whether
the community structure, function, or both are conserved between
sites; and finally (g) glacial moraines in the Arctic to define
the events of primary succession in eubacterial and
nitrogen-fixing cyanobacterial communities.
Studies are also
done to understand the population genetics and
adaptive evolution in prokaryotic species. Genomic
fingerprinting and phylogenetic analyses of metabolic and
ribosomal genes have shown that bacterial populations in soil
are assemblages of genetically distinct ecotypes, and that
multiple phenotypically distinct strains somehow avoid
competitive exclusion and instead coexist in quite small areas.
These observations have led to laboratory studies designed to
explore how spatial heterogeneity can lead to the apparent
coexistence of distinct genotypes. Other studies explore the
possibility that bacterial mutation rates are regulated in
response to changes in cellular growth rate and stress. This
research is done using reporter strains to monitor the
expression of genes involved in the global control of responses
to nutrient stress, cell signaling, as well as mismatch repair
and the recombination of DNA. As part of these investigations,
researchers determine the frequency of mutations and the fitness
of mutants under defined growth conditions in biofilms and
continuous cultures. One aim of these studies is to assess
whether the modulation of mutation frequencies is a genetically
programmed evolutionary strategy that enhances the ability of
prokaryotes to adapt to changing condition and to exploit
various ecological niches.
Dr. Forney and lab members
From left to right:
Ursel Schuette (graduate student),
Xia Zhou (postdoctoral scientist),
Tema Bassett (former undergraduate student),
Stephen Bent (graduate student),
Maria Schneider (research assistant),
Jacob Pierson (graduate student),
Larry Forney (Professor),
Hyo-Jin Ahn (graduate student),
Mayee Wong (former research assistant),
Mohammed Islam (former postdoctoral scientist)
Many research projects in the laboratory focus on
the analysis of spatial and temporal differences in microbial
community structure. The studies rely heavily on the assessment
of community diversity and composition based on analyses of
terminal restriction fragment length polymorphisms of 16S or 18S
ribosomal RNA (rRNA) genes in samples from habitats under study.
This approach is employed because traditional culture-dependent
methods are tedious, and labor intensive; thus, their use for
the analysis of large numbers of samples is impractical and
costly. Moreover, such methods are further limited due to the
reliance on selective media, and because many bacterial
populations are refractory to cultivation. Consequently, they
provide an incomplete assessment of community structure.
In recent years, culture-independent methods
based on the analysis of 16S and 18S rRNA gene sequences have
been used to overcome these limitations. The analysis of
terminal restriction fragment length polymorphisms of 16S or 18S
ribosomal RNA (rRNA) genes is one such method. This method,
developed by Liu et al. (Appl. Environ. Microbiol. 63:4516-4522)
in my laboratory in 1997, yields a community “profile” that
reflects the kind and relative abundance of the numerically
dominant populations in a community. Similarities and
differences among microbial communities can readily be discerned
based on the profiles of fragments produced. Moreover, the data
can be statistically analyzed to test the significance of
changes that occur within individuals over time, or between
individuals and treatments. Another important advantage of this
method is that sample throughput is high and samples can be
archived for analysis later.
We have undertaken the task of developing a suite
of web-based tools that will facilitate analyses of microbial
community structure based on terminal-restriction fragment
length polymorphisms (T-RFLP). These tools are now available to
the research community on the Web
[http://mica.ibest.uidaho.edu/].
This suite of tools called Microbial Community Analysis, or MiCA,
was developed by students and faculty affiliated with the
Initiative for Bioinformatics and Evolutionary Studies (IBEST)
at the University of Idaho who are members of the Departments of
Computer Science and Biological Sciences. MiCA enables
researchers to perform the following tasks:
(a) in silico PCR amplification and restriction
of 16S rRNA gene sequences found in public database;
(b) automatic retrieval of data and archival
storage in a relational database;
(c) comparison of multiple T-RFLP profiles
obtained from a single sample using different primer-enzyme
combinations;
(d) statistical analysis of T-RFLP data and
clustering of samples based on similarities and differences.
Selected Publications
Read more from Dr. Forney in the current
issue of the BIOTA newsletter.
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