Biomedical subjects

James Borneman

Publications and source records attributed to James Borneman.

11 recordsLinked to original sources

PubMed2026

High-efficiency genome-editing, transgene evaluation, and antimicrobial efficacy testing using Citrus medica L. hairy roots.

Huanglongbing (HLB) disease, associated with the fastidious bacterium Candidatus Liberibacter asiaticus (CLas), has a significant impact on citrus production worldwide. Conventional biochemical and genetic evaluation studies to identify potential disease resistance strategies have been mainly hindered due to the inability to culture CLas in a defined medium and the general recalcitrance of Citrus cultivars (grapefruits and oranges) to Agrobacterium-mediated plant transformation. We previously demonstrated the utility of plant hairy roots to co-cultivate CLas. In this study, we developed a hairy root transformation system using citron (Citrus medica L.), which is highly amenable to Rhizobium-mediated hairy root transformation. The explant survival and hairy root transformation efficiencies were up to 100% and 73%, respectively, and transgenic roots can be attained in as little as 30-60 days. We demonstrate the utility of this citron-based hairy root transformation for rapid CRISPR/Cas9-mediated gene editing, transgene evaluation, and antimicrobial efficacy testing. The citron-based hairy root transformation system will significantly help the research community to speed-track the assessment of potential HLB disease resistance strategies.

Citrus↗

PubMed2006

Development of a 9600-clone procedure for oligonucleotide fingerprinting of rRNA genes: utilization to identify soil bacterial rRNA genes that correlate in abundance with the development of avocado root rot.

Oligonucleotide fingerprinting of rRNA genes (OFRG) is an array-based method that generates microbial community profiles through analysis of rRNA gene clone libraries. The original OFRG method allowed 1536 clones to be analyzed per experiment. This report describes a procedure for analyzing 9600 clones per experiment, including a new probe set for bacterial analysis, and improved data processing and statistical analysis tools. The software tools are available at the OFRG website (). Use of the 9600-clone procedure was demonstrated by examining the bacterial rRNA gene compositions of soils subjected to various temperature treatments. These treatments produced a series of soils with a range of abilities to suppress avocado root rot, enabling the identification of bacterial rRNA genes that correlate in abundance with root rot suppressiveness. OFRG analysis of these soils produced 8876 bacterial rRNA gene fingerprints grouped into 5123 clusters, or operational taxonomic units (OTUs). Eleven OTUs exhibited a positive correlation between the number of clones and the percentage of healthy roots. An in silico analysis was performed to examine the relationship between the number of rRNA genes analyzed and the number of correlates (rRNA gene-avocado root rot symptoms) identified. As the number of clones decreased, fewer correlates were identified. To further increase the throughput of the OFRG method, use of a glass slide-fluorescent probe microarray format was also explored.

DNA Fingerprinting↗

PubMed2006

Abundant and diverse fungal microbiota in the murine intestine.

Enteric microbiota play a variety of roles in intestinal health and disease. While bacteria in the intestine have been broadly characterized, little is known about the abundance or diversity of enteric fungi. This study utilized a culture-independent method termed oligonucleotide fingerprinting of rRNA genes (OFRG) to describe the compositions of fungal and bacterial rRNA genes from small and large intestines (tissue and luminal contents) of restricted-flora and specific-pathogen-free mice. OFRG analysis identified rRNA genes from all four major fungal phyla: Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota. The largest assemblages of fungal rRNA sequences were related to the genera Acremonium, Monilinia, Fusarium, Cryptococcus/Filobasidium, Scleroderma, Catenomyces, Spizellomyces, Neocallimastix, Powellomyces, Entophlyctis, Mortierella, and Smittium and the order Mucorales. The majority of bacterial rRNA gene clones were affiliated with the taxa Bacteroidetes, Firmicutes, Acinetobacter, and Lactobacillus. Sequence-selective PCR analyses also detected several of these bacterial and fungal rRNA genes in the mouse chow. Fluorescence in situ hybridization analysis with a fungal small-subunit rRNA probe revealed morphologically diverse microorganisms resident in the mucus biofilm adjacent to the cecal and proximal colonic epithelium. Hybridizing organisms comprised about 2% of the DAPI (4',6-diamidino-2-phenylindole, dihydrochloride)-positive organisms in the mucus biofilm, but their abundance in fecal material may be much lower. These data indicate that diverse fungal taxa are present in the intestinal microbial community. Their abundance suggests that they may play significant roles in enteric microbial functions.

Animals↗

PubMed2005

Commensal microbiota alter the abundance and TCR responsiveness of splenic naïve CD4+ T lymphocytes.

The epidemiologic risk of certain systemic immunologic diseases is affected by commensal or environmental microbiota, but the cellular basis of the "hygiene hypothesis" is poorly understood. In this study, we demonstrate that composition of the commensal microbiota affects the functional state of the peripheral naïve (CD62L(hi)CD44(lo)) T lymphocyte populations. Restricted flora (RF) mice (stably colonized with excess nonpathogenic Clostridium sp., and changes in other bacterial and fungal taxa) were distinguished after the neonatal period by a progressive deficiency in absolute numbers of naïve CD4+ and CD8+ T lymphocytes. SPF and RF mice had comparable levels of memory CD4+ and CD8+ T cells. This phenotype was attributable to the altered levels of certain commensals and their products, since germ-free mice had normal absolute numbers of splenic CD4+ and CD8+ T cells and their respective naïve and memory subsets. The naïve CD4+ T cell subset was functionally distinguished in RF mice versus SPF mice by TCR hyperresponsiveness, pro-inflammatory cytokine production, and increased activation-induced cell death. Biochemically, these traits were associated with higher basal phosphorylation of the TCR signaling proteins ZAP-70, Lck, and LAT. These findings indicate that enteric microbial products, through unknown cellular circuitry, influence steps in CD4 T cell differentiation moderating basal TCR signaling and immune responsiveness.

Animals↗

PubMed2005

Classification of oligonucleotide fingerprints: application for microbial community and gene expression analyses.

MOTIVATION: Oligonucleotide fingerprinting of ribosomal RNA genes (OFRG) is a procedure that sorts rRNA gene (rDNA) clones into taxonomic groups through a series of hybridization experiments. The hybridization signals are classified into three discrete values 0, 1 and N, where 0 and 1, respectively, specify negative and positive hybridization events and N designates an uncertain assignment. This study examined various approaches for classifying the values including Bayesian classification with normally distributed signal data, Bayesian classification with the exponentially distributed data, and with gamma distributed data, along with tree-based classification. All classification data were clustered using the unweighted pair group method with arithmetic mean. RESULTS: The performance of each classification/clustering procedure was compared with results from known reference data. Comparisons indicated that the approach using the Bayesian classification with normal densities followed by tree clustering out-performed all others. The paper includes a discussion of how this Bayesian approach may be useful for the analysis of gene expression data.

Algorithms↗

PubMed2004

Clustering binary fingerprint vectors with missing values for DNA array data analysis.

Oligonucleotide fingerprinting is a powerful DNA array-based method to characterize cDNA and ribosomal RNA gene (rDNA) libraries and has many applications including gene expression profiling and DNA clone classification. We are especially interested in the latter application. A key step in the method is the cluster analysis of fingerprint data obtained from DNA array hybridization experiments. Most of the existing approaches to clustering use (normalized) real intensity values and thus do not treat positive and negative hybridization signals equally (positive signals are much more emphasized). In this paper, we consider a discrete approach. Fingerprint data are first normalized and binarized using control DNA clones. Because there may exist unresolved (or missing) values in this binarization process, we formulate the clustering of (binary) oligonucleotide fingerprints as a combinatorial optimization problem that attempts to identify clusters and resolve the missing values in the fingerprints simultaneously. We study the computational complexity of this clustering problem and a natural parameterized version and present an efficient greedy algorithm based on MINIMUM CLIQUE PARTITION on graphs. The algorithm takes advantage of some unique properties of the graphs considered here, which allow us to efficiently find the maximum cliques as well as some special maximal cliques. Our preliminary experimental results on simulated and real data demonstrate that the algorithm runs faster and performs better than some popular hierarchical and graph-based clustering methods. The results on real data from DNA clone classification also suggest that this discrete approach is more accurate than clustering methods based on real intensity values in terms of separating clones that have different characteristics with respect to the given oligonucleotide probes.

Algorithms↗

PubMed2003

Bacterial rRNA genes associated with soil suppressiveness against the plant-parasitic nematode Heterodera schachtii.

The goal of this study was to identify bacteria involved in soil suppressiveness against the plant-parasitic nematode Heterodera schachtii. Since H. schachtii cysts isolated from the suppressive soil can transfer this beneficial property to nonsuppressive soils, analysis of the cyst-associated microorganisms should lead to the identification of the causal organisms. Our experimental approach was to identify bacterial rRNA genes (rDNA) associated with H. schachtii cysts obtained from soil mixtures with various levels of suppressiveness. We hypothesized that we would be able to identify bacteria involved in the suppressiveness by correlating population shifts with differing levels of suppressiveness. Soil treatments containing different amounts of suppressive and fumigation-induced nonsuppressive soils exhibited various levels of suppressiveness after two nematode generations. The 10%-suppressive-soil treatment contained numbers of eggs per gram of soil similar to those of the 100%-suppressive-soil treatment, indicating that the suppressive factor(s) had been transferred. Bacterial rDNA associated with H. schachtii cysts were identified using a culture-independent method termed oligonucleotide fingerprinting of rRNA genes. Bacteria from five major taxonomic groups (Actinobacteria, Cytophaga-Flexibacter-Bacteroides, alpha-Proteobacteria, beta-Proteobacteria, and gamma-Proteobacteria) were identified. Three bacterial rDNA groups contained clones that were more prevalent in the highly suppressive soil treatments than in the less suppressive treatments, indicating a potential involvement in the H. schachtii suppressiveness. When these three groups were examined with specific PCR analyses performed on H. schachtii cysts that developed in soils treated with three biocidal compounds, only one bacterial rDNA group with moderate to high sequence identity to rDNA from several Rhizobium species and uncultured alpha-proteobacterial clones was consistently associated with the highly suppressive treatments. A quantitative PCR analysis confirmed the association of this Rhizobium-like rDNA group with the H. schachtii suppressiveness.

Animals↗

PubMed2003

Clustering binary fingerprint vectors with missing values for DNA array data analysis.

Oligonucleotide fingerprinting is a powerful DNA array based method to characterize cDNA and ribosomal RNA gene (rDNA) libraries and has many applications including gene expression profiling and DNA clone classification. We are especially interested in the latter application. A key step in the method is the cluster analysis of fingerprint data obtained from DNA array hybridization experiments. Most of the existing approaches to clustering use (normalized) real intensity values and thus do not treat positive and negative hybridization signals equally (positive signals are much more emphasized). In this paper, we consider a discrete approach. Fingerprint data are first normalized and binarized using control DNA clones. Because there may exist unresolved (or missing) values in this binarization process, we formulate the clustering of (binary) oligonucleotide fingerprints as a combinatorial optimization problem that attempts to identify clusters and resolve the missing values in the fingerprints simultaneously. We study the computational complexity of this clustering problem and a natural parameterized version, and present an efficient greedy algorithm based on MINIMUM CLIQUE PARTITION on graphs. The algorithm takes advantage of some unique properties of the graphs considered here, which allow us to efficiently find the maximum cliques as well as some special maximal cliques. Our experimental results on simulated and real data demonstrate that the algorithm runs faster and performs better than some popular hierarchical and graph-based clustering methods. The results on real data from DNA clone classification also suggest that this discrete approach is more accurate than clustering methods based on real intensity values, in terms of separating clones that have different characteristics with respect to the given oligonucleotide probes.

Algorithms↗

PubMed2002

Oligonucleotide fingerprinting of rRNA genes for analysis of fungal community composition.

Thorough assessments of fungal diversity are currently hindered by technological limitations. Here we describe a new method for identifying fungi, oligonucleotide fingerprinting of rRNA genes (OFRG). ORFG sorts arrayed rRNA gene (ribosomal DNA [rDNA]) clones into taxonomic clusters through a series of hybridization experiments, each using a single oligonucleotide probe. A simulated annealing algorithm was used to design an OFRG probe set for fungal rDNA. Analysis of 1,536 fungal rDNA clones derived from soil generated 455 clusters. A pairwise sequence analysis showed that clones with average sequence identities of 99.2% were grouped into the same cluster. To examine the accuracy of the taxonomic identities produced by this OFRG experiment, we determined the nucleotide sequences for 117 clones distributed throughout the tree. For all but two of these clones, the taxonomic identities generated by this OFRG experiment were consistent with those generated by a nucleotide sequence analysis. Eighty-eight percent of the clones were affiliated with Ascomycota, while 12% belonged to BASIDIOMYCOTA: A large fraction of the clones were affiliated with the genera Fusarium (404 clones) and Raciborskiomyces (176 clones). Smaller assemblages of clones had high sequence identities to the Alternaria, Ascobolus, Chaetomium, Cryptococcus, and Rhizoctonia clades.

DNA Fingerprinting↗

PubMed2002

Analysis of bacterial community composition by oligonucleotide fingerprinting of rRNA genes.

One of the first steps in characterizing an ecosystem is to describe the organisms inhabiting it. For microbial studies, experimental limitations have hindered the ability to depict diverse communities. Here we describe oligonucleotide fingerprinting of rRNA genes (OFRG), a method that permits identification of arrayed rRNA genes (rDNA) through a series of hybridization experiments using small DNA probes. To demonstrate this strategy, we examined the bacteria inhabiting two different soils. Analysis of 1,536 rDNA clones revealed 766 clusters grouped into five major taxa: Bacillus, Actinobacteria, Proteobacteria, and two undefined assemblages. Soil-specific taxa were identified and then independently confirmed through cluster-specific PCR of the original soil DNA. Near-species-level resolution was obtained by this analysis as clones with average sequence identities of 97% were grouped in the same cluster. A comparison of these OFRG results with the results obtained in a denaturing gradient gel electrophoresis analysis of the same two soils demonstrated the significance of this methodological advance. OFRG provides a cost-effective means to extensively analyze microbial communities and should have applications in medicine, biotechnology, and ecosystem studies.

Bacteria↗

PubMed1998

A functional myo-inositol catabolism pathway is essential for rhizopine utilization by Sinorhizobium meliloti.

Rhizopine (L-3-O-methyl-scyllo-inosamine) is a symbiosis-specific compound found in alfalfa nodules induced by specific Sinorhizobium meliloti strains. It has been postulated that rhizobial strains able to synthesize and catabolize rhizopine gain a competitive advantage in the rhizosphere. The pathway of rhizopine degradation is analysed here. Since rhizopine is an inositol derivative, it was tested whether inositol catabolism is involved in rhizopine utilization. A genetic locus required for the catabolism of inositol as sole carbon source was cloned from S. meliloti. This locus was delimited by transposon Tn5 mutagenesis and its DNA sequence was determined. Based on DNA similarity studies and enzyme assays, this genetic region was shown to encode an S. meliloti myo-inositol dehydrogenase. Strains that harboured a mutation in the myo-inositol dehydrogenase gene (idhA) did not display myo-inositol dehydrogenase activity, were unable to utilize myo-inositol as sole carbon/energy source, and were unable to catabolize rhizopine. Thus, myo-inositol dehydrogenase activity is essential for rhizopine utilization in S. meliloti.

Amino Acid Sequence↗