L. Safak Yilmaz

DECIPHER, a Search-Based Approach to Chimera Identification for 16S rRNA Sequences

ABSTRACT DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchimes performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (<0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (http://DECIPHER.cee.wisc.edu).

mathFISH, a Web Tool That Uses Thermodynamics-Based Mathematical Models for In Silico Evaluation of Oligonucleotide Probes for Fluorescence In Situ Hybridization†

ABSTRACT Mathematical models of RNA-targeted fluorescence in situ hybridization (FISH) for perfectly matched and mismatched probe/target pairs are organized and automated in web-based mathFISH (http://mathfish.cee.wisc.edu). Offering the users up-to-date knowledge of hybridization thermodynamics within a theoretical framework, mathFISH is expected to maximize the probability of success during oligonucleotide probe design.

Mechanistic Approach to the Problem of Hybridization Efficiency in Fluorescent In Situ Hybridization

ABSTRACT In fluorescent in situ hybridization (FISH), the efficiency of hybridization between the DNA probe and the rRNA has been related to the accessibility of the rRNA when ribosome content and cell permeability are not limiting. Published rRNA accessibility maps show that probe brightness is sensitive to the organism being hybridized and the exact location of the target site and, hence, it is highly unpredictable based on accessibility only. In this study, a model of FISH based on the thermodynamics of nucleic acid hybridization was developed. The model provides a mechanistic approach to calculate the affinity of the probe to the target site, which is defined as the overall Gibbs free energy change (ΔG°overall) for a reaction scheme involving the DNA-rRNA and intramolecular DNA and rRNA interactions that take place during FISH. Probe data sets for the published accessibility maps and experiments targeting localized regions in the 16S rRNA of Escherichia coli were used to demonstrate that ΔG°overall is a strong predictor of hybridization efficiency and superior to conventional estimates based on the dissociation temperature of the DNA/rRNA duplex. The use of the proposed model also allowed the development of mechanistic approaches to increase probe brightness, even in seemingly inaccessible regions of the 16S rRNA. Finally, a threshold ΔG°overall of −13.0 kcal/mol was proposed as a goal in the design of FISH probes to maximize hybridization efficiency without compromising specificity.

Interspecies Systems Biology Uncovers Metabolites Affecting C. elegans Gene Expression and Life History Traits

Diet greatly influences gene expression and physiology. In mammals, elucidating the effects and mechanisms of individual nutrients is challenging due to the complexity of both the animal and its diet. Here, we used an interspecies systems biology approach with Caenorhabditis elegans and two of its bacterial diets, Escherichia coli and Comamonas aquatica, to identify metabolites that affect the animals gene expression and physiology. We identify vitamin B12 as the major dilutable metabolite provided by Comamonas aq. that regulates gene expression, accelerates development, and reduces fertility but does not affect lifespan. We find that vitamin B12 has a dual role in the animal: it affects development and fertility via the methionine/S-Adenosylmethionine (SAM) cycle and breaks down the short-chain fatty acid propionic acid, preventing its toxic buildup. Our interspecies systems biology approach provides a paradigm for understanding complex interactions between diet and physiology.

Automated Design of Probes for rRNA-Targeted Fluorescence In Situ Hybridization Reveals the Advantages of Using Dual Probes for Accurate Identification

ABSTRACT Fluorescence in situ hybridization (FISH) is a common technique for identifying cells in their natural environment and is often used to complement next-generation sequencing approaches as an integral part of the full-cycle rRNA approach. A major challenge in FISH is the design of oligonucleotide probes with high sensitivity and specificity to their target group. The rapidly expanding number of rRNA sequences has increased awareness of the number of potential nontargets for every FISH probe, making the design of new FISH probes challenging using traditional methods. In this study, we conducted a systematic analysis of published probes that revealed that many have insufficient coverage or specificity for their intended target group. Therefore, we developed an improved thermodynamic model of FISH that can be applied at any taxonomic level, used the model to systematically design probes for all recognized genera of bacteria and archaea, and identified potential cross-hybridizations for the selected probes. This analysis resulted in high-specificity probes for 35.6% of the genera when a single probe was used in the absence of competitor probes and for 60.9% when up to two competitor probes were used. Requiring the hybridization of two independent probes for positive identification further increased specificity. In this case, we could design highly specific probe sets for up to 68.5% of the genera without the use of competitor probes and 87.7% when up to two competitor probes were used. The probes designed in this study, as well as tools for designing new probes, are available online (http://DECIPHER.cee.wisc.edu).

Modeling formamide denaturation of probe-target hybrids for improved microarray probe design in microbial diagnostics

Application of high-density microarrays to the diagnostic analysis of microbial communities is challenged by the optimization of oligonucleotide probe sensitivity and specificity, as it is generally unfeasible to experimentally test thousands of probes. This study investigated the adjustment of hybridization stringency using formamide with the idea that sensitivity and specificity can be optimized during probe design if the hybridization efficiency of oligonucleotides with target and non-target molecules can be predicted as a function of formamide concentration. Sigmoidal denaturation profiles were obtained using fluorescently labeled and fragmented 16S rRNA gene amplicon of Escherichia coli as the target with increasing concentrations of formamide in the hybridization buffer. A linear free energy model (LFEM) was developed and microarray-specific nearest neighbor rules were derived. The model simulated formamide melting with a denaturant m-value that increased hybridization free energy (ΔG°) by 0.173 kcal/mol per percent of formamide added (v/v). Using the LFEM and specific probe sets, free energy rules were systematically established to predict the stability of single and double mismatches, including bulged and tandem mismatches. The absolute error in predicting the position of experimental denaturation profiles was less than 5% formamide for more than 90 percent of probes, enabling a practical level of accuracy in probe design. The potential of the modeling approach for probe design and optimization is demonstrated using a dataset including the 16S rRNA gene of Rhodobacter sphaeroides as an additional target molecule. The LFEM and thermodynamic databases were incorporated into a computational tool (ProbeMelt) that is freely available at http://DECIPHER.cee.wisc.edu.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

Mathematical tools to optimize the design of oligonucleotide probes and primers

The identification and quantification of specific organisms in mixed microbial communities often relies on the ability to design oligonucleotide probes and primers with high specificity and sensitivity. The design of these oligonucleotides (or “oligos” for short) shares many of the same principles in spite of their widely divergent applications. Three common molecular biology technologies that require oligonucleotide design are polymerase chain reaction (PCR), fluorescence in situ hybridization (FISH), and DNA microarrays. This article reviews techniques and software available for the design and optimization of oligos with the goal of targeting a specific group of organisms within mixed microbial communities. Strategies for enhancing specificity without compromising sensitivity are described, as well as design tools well suited for this purpose.

Exploring the in situ accessibility of small subunit ribosomal RNA of members of the domains Bacteria and Eukarya to oligonucleotide probes

The principle that the small subunit ribosomal RNA (ssu rRNA) is generally accessible to oligonucleotide probes designed to have high thermodynamic affinity was tested with Stenotrophomonas maltophilia, Rhodobacter sphaeroides, Bacillus subtilis, and Saccharomyces cerevisiae. Fluorescein-labeled probes, designed to have ΔG(overall)°=-14±1 and to avoid the potential of nucleobase-specific quenching, were used to target 20 randomly selected sites in each organism. A site was considered accessible if probe brightness was at least 10 times the background signal. With 30-h hybridizations, 71 out of 80 target sites passed the accessibility criterion. Three additional sites were demonstrated to be accessible with either longer hybridizations, which seemed to have a negative effect on some probes, or the addition of formamide to the hybridization buffer. The remaining 6 sites were demonstrated to be accessible by changing the fluorophore to Cy5, slightly modifying probe lengths, using dual-labeled fluorescein probes, or a combination of these approaches. Probe elongations were only needed in 4 probes, indicating a 95% success in correctly predicting ΔG(overall)°, the key parameter for the design of high affinity probes. In addition, 94% of the fluorescein labeled probes yielded bright signals, demonstrating that nucleobase-specific quenching of fluorescein is an important factor affecting probe brightness that can be predicted during probe design. Overall, the results support the principle that with a rational design of probes, it is possible to make most target sites in the ssu rRNA accessible.

A Persistence Detector for Metabolic Network Rewiring in an Animal

Biological systems must possess mechanisms that prevent inappropriate responses to spurious environmental signals. Gene regulatory network circuitries known as coherent type 1 feed-forward loops (FFLs) with AND-logic gates have been proposed to function as a persistence detector because it generates a delay in target activation and prevents target induction unless the input signal is sustained. While such a circuit has been found for the L-arabinose utilization system in E. coli, their existence and relevance multicellular organisms has remained unclear. Here, we identify the first persistence detector in an animal that redirects propionate breakdown to a shunt pathway when flux through the canonical propionate breakdown pathway is perturbed. We propose that this mechanism has evolved to ensure the shunt pathway stays off unless propionate accumulation is persistent because the shunt pathway generates highly toxic acrylate. Our study uniquely connects persistence detector circuitry to a physiological response in an animal.